CN118510911A - Methods and systems for determining a pregnancy-related status of a subject - Google Patents

Abstract

The present disclosure provides methods and systems for cell-free identification and/or monitoring of pregnancy related conditions. Methods for identifying or monitoring a pregnant subject for the presence or increased risk of a pregnancy-related status may include assaying a cell-free biological sample derived from the pregnant subject to detect a biomarker set, and analyzing the biomarker set using a trained algorithm to determine the presence or increased risk of a pregnancy-related status.

Description

Methods and systems for determining a pregnancy-related status of a subject

Cross-references

This application claims the benefit of U.S. Application No. 63/275,726, filed on November 4, 2021, U.S. Application No. 63/276,809, filed on November 8, 2021, and U.S. Application No. 63/288,044, filed on December 10, 2021, each of which is incorporated herein by reference in its entirety.

Background Art

Every year, approximately 15 million premature births are reported worldwide, and more than 300,000 women die from pregnancy-related complications such as bleeding and hypertensive conditions such as preeclampsia. Preterm births may affect up to approximately 10% of pregnancies, most of which are spontaneous preterm births. Pregnancy-related complications such as premature births are the leading cause of neonatal mortality and complications later in life. In addition, such pregnancy-related complications can have negative health effects on maternal health.

Summary of the invention

Currently, there may be a lack of meaningful, clinically feasible diagnostic screening or testing available for many pregnancy-related complications, such as preterm birth. Therefore, in order to make pregnancy as safe as possible, rapid, accurate methods are needed to identify and monitor pregnancy-related states that are non-invasive and cost-effective to improve maternal and infant health.

The present disclosure provides methods, systems and kits for identifying or monitoring pregnancy-related states by processing acellular biological samples obtained from or derived from a subject. Acellular biological samples (e.g., plasma samples) obtained from a subject can be analyzed to identify pregnancy-related states (which may include, for example, measuring the presence, absence or relative assessment of pregnancy-related states). Such subjects may include subjects with one or more pregnancy-related states and subjects without pregnancy-related states. Pregnancy-related states may include, for example, premature birth, term birth, gestational age, due date (e.g., due date of unborn baby or fetus of the subject), onset of labor, pregnancy-related hypertension disorders (e.g., pre-eclampsia), eclampsia, gestational diabetes, congenital disorders of the subject's fetus, ectopic pregnancy, spontaneous abortion, stillbirth, postpartum complications (e.g., postpartum depression, hemorrhage or excessive bleeding, pulmonary embolism, cardiomyopathy, diabetes, anemia and hypertensive disease), hyperemesis gravidarum (morning sickness), hemorrhage or excessive bleeding during labor, premature rupture of membranes, premature premature rupture of membranes, placenta previa (placenta covering the cervix), intrauterine/fetal growth retardation, ... The invention also provides a method for determining the presence or absence of a fetal malformation, a fetal malformation, a fetal macrosomia (a fetus that is large for gestational age), a neonatal condition (e.g., anemia, apnea, bradycardia and other cardiac defects, bronchopulmonary dysplasia or chronic lung disease, diabetes, gastroschisis, hydrocephalus, hyperbilirubinemia, hypocalcemia, hypoglycemia, intraventricular hemorrhage, jaundice, necrotizing enterocolitis, patent ductus arteriosus, periventricular leukomalacia, persistent pulmonary hypertension, polycythemia, respiratory distress syndrome, retinopathy of prematurity, and transient tachypnea), and a fetal developmental stage or state (e.g., normal fetal organ function or development and abnormal fetal organ function or development). For example, the fetal developmental stage or state may be associated with normal fetal organ function or development and/or abnormal fetal organ function or development of a fetal organ selected from the group consisting of heart, large intestine, small intestine, retina, prefrontal cortex, midbrain, kidney, and esophagus.

In one aspect, the present disclosure provides a method for identifying the presence or increased risk of a pregnancy-associated state in a pregnant subject, comprising assaying a cell-free biological sample derived from the pregnant subject to detect a set of biomarkers, and processing the set of biomarkers using a trained algorithm or against reference values to determine the presence or increased risk of a pregnancy-associated state among a set of at least three different pregnancy-associated states.

In some embodiments, the pregnancy-related condition is selected from preterm birth, term birth, gestational age, due date, onset of labor, pregnancy-related hypertensive disorders, pre-eclampsia, eclampsia, gestational diabetes, a congenital disorder of the fetus of the pregnant subject, ectopic pregnancy, spontaneous abortion, stillbirth, postpartum complications, hyperemesis gravidarum, hemorrhage or excessive bleeding during labor, premature rupture of membranes, preterm premature rupture of membranes, placenta previa, intrauterine/fetal growth restriction, macrosomia, neonatal condition, and fetal development stage or state.

In some embodiments, the pregnancy-related state is a preterm birth subtype, and at least three different pregnancy-related states include at least two different preterm birth subtypes. In some embodiments, the preterm birth subtype is a preterm birth molecular subtype, and at least two different preterm birth subtypes include at least two different preterm birth molecular subtypes. In some embodiments, the preterm birth molecular subtype is selected from the medical history of previous premature birth, the medical history of spontaneous premature birth, the medical history of ethnic-specific premature birth risk, and the medical history of premature premature rupture of membranes (PPROM). In some embodiments, the preterm birth molecular subtype is spontaneous premature birth, and the biomarker set includes genomic sites associated with spontaneous premature birth. In some embodiments, the genomic sites associated with spontaneous premature birth are selected from the genes listed in Table 1, the genes listed in Table 2, the genes listed in Table 3, the genes corresponding to the pathways listed in Table 4, the genes listed in Table 9, the genes listed in Table 10, and the genes listed in Table 11. In some embodiments, spontaneous preterm birth includes delivery at less than 25 weeks, delivery at less than 26 weeks, delivery at less than 27 weeks, delivery at less than 28 weeks, delivery at less than 29 weeks, delivery at less than 30 weeks, delivery at less than 31 weeks, delivery at less than 32 weeks, delivery at less than 33 weeks, delivery at less than 34 weeks, delivery at less than 35 weeks, delivery at less than 36 weeks, delivery at less than 37 weeks, or delivery at less than 38 weeks. In some embodiments, the method further includes identifying a clinical intervention for a pregnant subject based at least in part on the presence or increased risk of a molecular subtype of preterm birth. In some embodiments, the clinical intervention is selected from a plurality of clinical interventions. In some embodiments, the clinical intervention includes medications, supplements, lifestyle advice, cervical cerclage, cervical pessary, or electrical contraction inhibition. In some embodiments, the drug is selected from progesterone, erythromycin, tocolytic drugs, corticosteroids, vaginal flora, and antioxidants.

In some embodiments, the pregnancy-related state is a pre-eclampsia subtype, and at least three different pregnancy-related states include at least two different pre-eclampsia subtypes. In some embodiments, the pre-eclampsia subtype is a pre-eclampsia molecular subtype, and at least two different pre-eclampsia subtypes include at least two different pre-eclampsia molecular subtypes. In some embodiments, the pre-eclampsia molecular subtype is selected from a history of chronic or pre-existing hypertension, the presence or history of gestational hypertension, the presence or history of mild pre-eclampsia, the presence or history of severe pre-eclampsia, the presence or history of eclampsia, and the presence or history of HELLP syndrome. In some embodiments, the pre-eclampsia molecular subtype is pre-eclampsia, and the biomarker set includes genomic sites associated with pre-eclampsia. In some embodiments, the genomic sites associated with pre-eclampsia are selected from the genes listed in Table 5, the genes listed in Table 6, the genes listed in Table 7, and the genes listed in Table 8. In some embodiments, premature pre-eclampsia is included in less than 25 weeks of delivery, less than 26 weeks of delivery, less than 27 weeks of delivery, less than 28 weeks of delivery, less than 29 weeks of delivery, less than 30 weeks of delivery, less than 31 weeks of delivery, less than 32 weeks of delivery, less than 33 weeks of delivery, less than 34 weeks of delivery, less than 35 weeks of delivery, less than 36 weeks of delivery, less than 37 weeks of delivery or less than 38 weeks of delivery. In some embodiments, the method further includes identifying the clinical intervention of the pregnancy object at least in part based on the presence of pre-eclampsia molecular subtypes or the risk of increase. In some embodiments, clinical intervention is selected from a plurality of clinical interventions. In some embodiments, clinical intervention includes medicine, supplements or lifestyle advice. In some embodiments, the medicine is selected from aspirin, progesterone, magnesium sulfate, cholesterol medicine, heartburn medicine, angiotensin II receptor antagonist, calcium channel blocker, diabetes medicine and erectile dysfunction medicine.

In some embodiments, the pregnancy-related state is a gestational diabetes subtype, and at least three different pregnancy-related states include at least two different gestational diabetes subtypes. In some embodiments, the gestational diabetes subtype is a gestational diabetes molecular subtype, and at least two different gestational diabetes subtypes include at least two different gestational diabetes molecular subtypes. In some embodiments, the biomarker set comprises genomic sites associated with gestational diabetes. In some embodiments, the genomic sites associated with gestational diabetes are selected from PDK4, CSH1, PLAC4, TBCEL and FBXO7. In some embodiments, the method further includes identifying a clinical intervention for a pregnant subject based at least in part on the presence or increased risk of a gestational diabetes molecular subtype. In some embodiments, the clinical intervention is selected from a plurality of clinical interventions. In some embodiments, the clinical intervention includes medications, supplements, lifestyle advice, cervical cerclage, cervical pessary or electrical contraction inhibition.

In some embodiments, the biomarker set comprises at least 5 different genomic loci, at least 10 different genomic loci, at least 15 different genomic loci, at least 20 different genomic loci, at least 25 different genomic loci, at least 30 different genomic loci, at least 35 different genomic loci, at least 40 different genomic loci, at least 45 different genomic loci, at least 50 different genomic loci, at least 100 different genomic loci, at least 150 different genomic loci, or at least 200 different genomic loci.

In some embodiments, determining includes using cell-free ribonucleic acid (cfRNA) molecules derived from a cell-free biological sample to generate transcriptomic data, using transcription products derived from a cell-free biological sample to generate transcription product data, using cell-free deoxyribonucleic acid (cfDNA) molecules derived from a cell-free biological sample to generate genomic data and/or methylation data, using proteins derived from a first cell-free biological sample to generate proteomic data, or using metabolites derived from a first cell-free biological sample to generate metabolomic data.

In some embodiments, the cell-free biological sample is selected from cell-free ribonucleic acid (cfRNA), cell-free deoxyribonucleic acid (cfDNA), cell-free fetal DNA (cffDNA), plasma, serum, urine, saliva, amniotic fluid and its derivatives. In some embodiments, the cell-free biological sample is obtained from or derived from a pregnant object using an ethylenediaminetetraacetic acid (EDTA) collection tube, a cell-free RNA collection tube or a cell-free deoxyribonucleic acid (DNA) collection tube. In some embodiments, the method further includes fractionating a whole blood sample of a pregnant object to obtain a cell-free biological sample. In some embodiments, determination includes determination of cell-free ribonucleic acid (cfRNA) or metabolomics determination. In some embodiments, metabolomics determination includes targeted mass spectrometry (MS) or immunoassay. In some embodiments, the cell-free biological sample includes cell-free ribonucleic acid (cfRNA) or urine. In some embodiments, determination includes quantitative polymerase chain reaction (qPCR). In some embodiments, determination includes a home test configured to be performed in a home environment.

In some embodiments, the trained algorithm determines the presence or increased risk of a pregnancy-related state in a pregnant subject with a sensitivity of at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 85%, at least about 90%, or at least about 95%. In some embodiments, the trained algorithm determines the presence or increased risk of a pregnancy-related state in a pregnant subject with a positive predictive value (PPV) of at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 85%, at least about 90%, or at least about 95%. In some embodiments, the trained algorithm determines the presence or increased risk of a pregnancy-related state in a pregnant subject with an area under the curve (AUC) of at least about 0.60, at least about 0.65, at least about 0.70, at least about 0.75, at least about 0.80, at least about 0.85, at least about 0.90, or at least about 0.95.

In some embodiments, the pregnant subject is asymptomatic for the pregnancy-related condition.

In some embodiments, the trained algorithm is trained using a first independent set of training samples associated with the presence or increased risk of a pregnancy-related state and a second independent set of training samples associated with the absence or no increased risk of a pregnancy-related state.

In some embodiments, the method further comprises processing a clinical health dataset of the pregnant subject using the trained algorithm or another trained algorithm to determine the presence or increased risk of a pregnancy-related condition.

In some embodiments, the method further includes subjecting the cell-free biological sample to conditions sufficient to separate, enrich or extract a set of ribonucleic acid (RNA) molecules, deoxyribonucleic acid (DNA) molecules, proteins or metabolites; and wherein the determination includes analyzing a set of RNA molecules, DNA molecules, proteins or metabolites. In some embodiments, the method further includes extracting a set of nucleic acid molecules from the cell-free biological sample, and sequencing the set of nucleic acid molecules to generate a set of sequencing reads. In some embodiments, sequencing is massively parallel sequencing. In some embodiments, sequencing includes nucleic acid amplification. In some embodiments, nucleic acid amplification includes polymerase chain reaction (PCR). In some embodiments, sequencing includes using reverse transcription (RT) and polymerase chain reaction (PCR). In some embodiments, the method further includes using probes, which are configured to selectively enrich a set of nucleic acid molecules corresponding to a grouping of one or more genomic sites. In some embodiments, the probe is a nucleic acid primer. In some embodiments, the probe has sequence complementarity with the nucleic acid sequence of the grouping of one or more genomic sites. In some embodiments, the grouping of one or more genomic loci includes a genomic locus associated with spontaneous preterm birth, wherein the genomic locus is selected from the group consisting of genes listed in Table 1, genes listed in Table 2, genes listed in Table 3, genes corresponding to pathways listed in Table 4, genes listed in Table 9, genes listed in Table 10, and genes listed in Table 11. In some embodiments, the grouping of one or more genomic loci includes a genomic locus associated with preterm preeclampsia, wherein the genomic locus is selected from the group consisting of genes listed in Table 5, genes listed in Table 6, genes listed in Table 7, and genes listed in Table 8. In some embodiments, the grouping of one or more genomic loci includes a genomic locus associated with gestational diabetes, wherein the genomic locus is selected from the group consisting of PDK4, CSH1, PLAC4, TBCEL, and FBXO7. In some embodiments, the grouping of one or more genomic loci comprises at least 5 different genomic loci, at least 10 different genomic loci, at least 15 different genomic loci, at least 20 different genomic loci, at least 25 different genomic loci, at least 30 different genomic loci, at least 35 different genomic loci, at least 40 different genomic loci, at least 45 different genomic loci, at least 50 different genomic loci, at least 100 different genomic loci, at least 150 different genomic loci, or at least 200 different genomic loci.

In some embodiments, the cell-free biological sample is processed without nucleic acid isolation, enrichment, or extraction.

In some embodiments, the method further comprises generating an electronic report including an indication of the determined presence or increased risk of a pregnancy-related condition.

In some embodiments, the method further comprises determining the likelihood of the presence or increased risk of a pregnancy-related condition in the pregnant subject.

In some embodiments, the trained algorithm comprises a trained machine learning algorithm. In some embodiments, the trained machine learning algorithm comprises a deep learning algorithm, a support vector machine (SVM), a neural network, a random forest, a linear regression model, a logistic regression model, or an ANOVA model.

In some embodiments, the method further comprises processing the biomarker set to reduce systematic variation. In some embodiments, reducing systematic variation comprises using residuals from multiple linear regression to correct data residuals, performing ComBat method based on empirical Bayesian method, and performing surrogate variable analysis (SVA) correction. In some embodiments, systematic variation includes sequencing depth of each sample, batch effect of individual processing operations, use of various raw materials, local external temperature of sample collection, BMI of the subject, fetal score, fetal gestational age at sample collection, or a combination thereof.

In some embodiments, the method further comprises monitoring the presence or increased risk of a pregnancy-related state, wherein the monitoring comprises assessing the presence or increased risk of a pregnancy-related state in the pregnant subject at a plurality of time points, wherein the assessment is based at least on the presence or increased risk of a pregnancy-related state determined at each of the plurality of time points. In some embodiments, a difference in the assessment of the presence or increased risk of a pregnancy-related state in the pregnant subject between the plurality of time points indicates one or more clinical indications selected from: (i) diagnosis of the presence or increased risk of a pregnancy-related state in the pregnant subject, (ii) prognosis of the presence or increased risk of a pregnancy-related state in the pregnant subject, and (iii) effectiveness or ineffectiveness of a course of treatment for the presence or increased risk of a pregnancy-related state in the pregnant subject.

In some embodiments, the pregnant subject is in the first trimester, the second trimester, or the third trimester.

In some embodiments, reference values are determined from pregnant subjects and/or non-pregnant subjects. In some embodiments, processing the biomarker set against the reference value comprises determining a difference between the biomarker set and the reference value.

In another aspect, the present disclosure provides a method for identifying the presence or susceptibility of a pregnancy-related state in a subject, comprising determining transcripts and/or metabolites in a cell-free biological sample derived from the subject to detect a biomarker set, and analyzing the biomarker set using a trained algorithm to determine the presence or susceptibility of a pregnancy-related state. In some embodiments, the method comprises determining transcripts in a cell-free biological sample derived from the subject to detect the biomarker set. In some embodiments, the transcripts are determined using nucleic acid sequencing. In some embodiments, the method comprises determining metabolites in a cell-free biological sample derived from the subject to detect the biomarker set. In some embodiments, metabolites are determined using a metabolomics assay.

In another aspect, the present disclosure provides a method for identifying the presence of or susceptibility to a pregnancy-related state in a subject, comprising assaying a cell-free biological sample derived from the subject to detect a set of biomarkers, and analyzing the set of biomarkers using a trained algorithm to determine the presence of or susceptibility to a pregnancy-related state among a set of at least three different pregnancy-related states (e.g., with an accuracy of at least about 80%).

In some embodiments, the pregnancy-related condition is selected from preterm birth, term birth, gestational age, due date, onset of labor, pregnancy-related hypertensive disorders (e.g., preeclampsia), eclampsia, gestational diabetes, congenital disorders of the subject's fetus, ectopic pregnancy, spontaneous abortion, stillbirth, postpartum complications (e.g., postpartum depression, hemorrhage or excessive bleeding, pulmonary embolism, cardiomyopathy, diabetes, anemia, and hypertensive disorders), hyperemesis gravidarum (morning sickness), hemorrhage or excessive bleeding during labor, premature rupture of membranes, premature premature rupture of membranes, placenta previa (placenta covering the cervix), intrauterine/fetal growth restriction, macrosomia (for fetuses The invention also relates to fetal development stages or states (e.g., normal fetal organ function or development and abnormal fetal organ function or development). For example, the fetal development stage or state can be associated with normal fetal organ function or development and/or abnormal fetal organ function or development of a fetal organ selected from the group consisting of heart, large intestine, small intestine, retina, prefrontal cortex, midbrain, kidney, and esophagus.

In some embodiments, the pregnancy-related state is a preterm birth subtype, and at least three different pregnancy-related states include at least two different preterm birth subtypes. In some embodiments, the preterm birth subtype is a preterm birth molecular subtype, and at least two different preterm birth subtypes include at least two different preterm birth molecular subtypes. In some embodiments, different preterm birth molecular subtypes include selected from the following preterm birth molecular subtypes: the presence or history of previous preterm birth, the presence or history of spontaneous preterm birth, the presence or history of late miscarriage, the presence or history of receiving cervical surgery, the presence or history of uterine abnormalities, the presence or history of ethnic-specific preterm birth risk (e.g., in African Americans), and the presence or history of preterm premature rupture of membranes (PPROM).

In some embodiments, the pregnancy-related state is a subtype of pre-eclampsia, and the at least three different pregnancy-related states include at least two different subtypes of eclampsia. In some embodiments, the different molecular subtypes of pre-eclampsia include molecular subtypes of pre-eclampsia selected from the following: the presence or history of chronic or pre-existing hypertension, the presence or history of gestational hypertension, the presence or history of mild pre-eclampsia (e.g., delivery at greater than 34 weeks gestational age), the presence or history of severe pre-eclampsia (e.g., delivery at less than 34 weeks gestational age), the presence or history of eclampsia, and the presence or history of HELLP syndrome.

In some embodiments, the method further includes a clinical intervention to identify an object at least in part based on the presence or susceptibility of pregnancy-related states. In some embodiments, the clinical intervention is selected from a plurality of clinical interventions. In some embodiments, the method further includes determining the likelihood of the susceptibility of the pregnancy-related states of the object, after which the clinical intervention can be provided to the object. In some embodiments, the clinical intervention includes pharmacology, surgery or protocol treatment, to mitigate the severity of the future susceptibility pregnancy-related states of the object, delay or eliminate future susceptibility pregnancy-related states (e.g., aspirin for pre-eclampsia and steroids for premature delivery).

In another aspect, the present disclosure provides a method comprising assaying a cell-free biological sample derived from a subject; identifying the subject as having or at risk of having pre-eclampsia; and administering an antihypertensive drug to the subject after identifying the subject as having or at risk of having pre-eclampsia.

In some embodiments, the cell-free biological sample is collected from a subject within a given gestational age interval for detecting a pregnancy-related condition. In some embodiments, the given gestational age interval is within about 1 day, about 2 days, about 3 days, about 4 days, about 5 days, about 6 days, about 7 days, about 8 days, about 9 days, about 10 days, about 11 days, about 12 days, about 13 days, about 14 days, about 3 weeks, or about 4 weeks of a given gestational age. In some embodiments, the given gestational age is about 0 weeks, about 1 week, about 2 weeks, about 3 weeks, about 4 weeks, about 5 weeks, about 6 weeks, about 7 weeks, about 8 weeks, about 9 weeks, about 10 weeks, about 11 weeks, about 12 weeks, about 13 weeks, about 14 weeks, about 15 weeks, about 16 weeks, about 17 weeks, about 18 weeks, about 19 weeks, about 20 weeks, about 21 weeks, about 22 weeks, about 23 weeks, about 24 weeks, about 25 weeks, about 26 weeks, about 27 weeks, about 28 weeks, about 29 weeks, about 30 weeks, about 31 weeks, about 32 weeks, about 33 weeks, about 34 weeks, about 35 weeks, about 36 weeks, about 37 weeks, about 38 weeks, about 39 weeks, about 40 weeks, about 41 weeks, about 42 weeks, about 43 weeks, about 44 weeks, or about 45 weeks. In some embodiments, pregnancy-related conditions include one or more of: premature birth, onset of labor, pregnancy-related hypertensive disorders, pre-eclampsia, eclampsia, gestational diabetes, congenital disorders of the subject's fetus, ectopic pregnancy, spontaneous abortion, stillbirth, postpartum complications, hyperemesis gravidarum (morning sickness), bleeding or excessive bleeding during labor, premature rupture of membranes, premature premature rupture of membranes, placenta previa (placenta covering the cervix), intrauterine/fetal growth restriction, macrosomia (large fetus for gestational age), neonatal conditions, and abnormal fetal development stages or states. For example, the fetal development stage or state can be associated with normal fetal organ function or development and/or abnormal fetal organ function or development of a fetal organ selected from the group consisting of heart, large intestine, small intestine, retina, prefrontal cortex, midbrain, kidney, and esophagus.

In some embodiments, (a) includes (i) subjecting the cell-free biological sample to conditions sufficient to separate, enrich or extract a set of ribonucleic acid (RNA) molecules, deoxyribonucleic acid (DNA) molecules, transcripts (e.g., messenger RNA, transfer RNA or ribosomal RNA), proteins (e.g., pregnancy-associated proteins corresponding to pregnancy-associated genomic sites or genes), or metabolites, and (ii) analyzing the set of RNA molecules, DNA molecules, proteins or metabolites using a first assay to generate a first data set. In some embodiments, the method further includes extracting a set of nucleic acid molecules from the cell-free biological sample, and sequencing the set of nucleic acid molecules to generate a set of sequencing reads, wherein the first data set includes the set of sequencing reads. In some embodiments, (b) includes (i) subjecting the vaginal or cervical biological sample to conditions sufficient to separate, enrich or extract a microbiome, and (ii) analyzing the microbiome using a second assay to generate a second data set.

In some embodiments, sequencing is large-scale parallel sequencing. In some embodiments, sequencing includes nucleic acid amplification. In some embodiments, nucleic acid amplification includes polymerase chain reaction (PCR). In some embodiments, sequencing includes using simultaneous reverse transcription (RT) and polymerase chain reaction (PCR). In some embodiments, the method further includes using probes, which are configured to selectively enrich for nucleic acid molecule sets corresponding to the groupings of one or more genomic sites. In some embodiments, the probe is a nucleic acid primer. In some embodiments, the probe has sequence complementarity with the nucleic acid sequence of the groupings of one or more genomic sites.

In some embodiments, the grouping of one or more genomic sites includes genomic sites associated with due date. In some embodiments, the grouping of one or more genomic sites includes genomic sites associated with gestational age. In some embodiments, the grouping of one or more genomic sites includes genomic sites associated with premature birth. In some embodiments, the grouping of one or more genomic sites includes genomic sites associated with pre-eclampsia. In some embodiments, the grouping of one or more genomic sites includes genomic sites associated with fetal organ development. In some embodiments, the grouping of one or more genomic sites includes genomic sites associated with gestational diabetes.

In some embodiments, the cell-free biological sample is processed without nucleic acid isolation, enrichment, or extraction.

In some embodiments, the report is presented on a graphical user interface of the user's electronic device.In some embodiments, the user is the subject.

In some embodiments, the method further comprises determining a likelihood of a determination of the presence or susceptibility of a pregnancy-related condition in the subject.

In some embodiments, the trained algorithm comprises a supervised machine learning algorithm. In some embodiments, the supervised machine learning algorithm comprises a deep learning algorithm, a support vector machine (SVM), a neural network, or a random forest. In some embodiments, the trained algorithm comprises a differential expression algorithm. In some embodiments, the differential expression algorithm comprises a random model, a generalized Poisson (GPseq), a mixed Poisson (TSPM), a Poisson log-linear (PoissonSeq), a negative binomial (edgeR, DESeq, baySeq, NBPSeq), a linear model fitted by MAANOVA, or a combination thereof.

In some embodiments, the method further comprises providing the subject with a therapeutic intervention for the presence or susceptibility to a pregnancy-related condition. In some embodiments, the therapeutic intervention comprises hydroxyprogesterone caproate, vaginal progesterone, a natural progesterone IVR product, a prostaglandin F2α receptor antagonist, or a β2-adrenergic receptor agonist.

In some embodiments, the method further comprises monitoring the presence of or susceptibility to a pregnancy-related condition, wherein the monitoring comprises assessing the subject's presence of or susceptibility to a pregnancy-related condition at a plurality of time points, wherein the assessing is based at least on the presence of or susceptibility to the pregnancy-related condition determined in (d) at each of the plurality of time points.

In some embodiments, the difference in the assessment of the presence or susceptibility of a pregnancy-related condition in a subject between multiple time points is indicative of one or more clinical indications selected from: (i) diagnosis of the presence or susceptibility of a pregnancy-related condition in a subject, (ii) prognosis of the presence or susceptibility of a pregnancy-related condition in a subject, and (iii) effectiveness or ineffectiveness of a course of treatment for the presence or susceptibility of a pregnancy-related condition in a subject.

In some embodiments, the method further comprises stratifying preterm birth by determining a preterm birth molecular subtype from a plurality of different preterm birth molecular subtypes using a trained algorithm. In some embodiments, the plurality of different preterm birth molecular subtypes comprises a preterm birth molecular subtype selected from the following: the presence or history of a previous preterm birth, the presence or history of a spontaneous preterm birth, the presence or history of a late miscarriage, the presence or history of undergoing cervical surgery, the presence or history of a uterine abnormality, the presence or history of a race-specific risk of preterm birth (e.g., in African Americans), and the presence or history of preterm premature rupture of membranes (PPROM).

In some embodiments, the method further comprises stratifying pre-eclampsia by determining a pre-eclampsia molecular subtype from a plurality of different pre-eclampsia molecular subtypes using a trained algorithm, the plurality of different pre-eclampsia molecular subtypes comprising a pre-eclampsia molecular subtype selected from the following: chronic/pre-existing hypertension, gestational hypertension, mild pre-eclampsia (e.g., delivery at >34 weeks), severe pre-eclampsia (e.g., delivery at <34 weeks), eclampsia, and a history of HELLP syndrome.

In some embodiments, the method further comprises providing a therapeutic intervention to the subject based at least in part on a risk score indicative of risk for preterm birth. In some embodiments, the therapeutic intervention comprises hydroxyprogesterone caproate, vaginal progesterone, a natural progesterone IVR product, a prostaglandin F2α receptor antagonist, or a β2-adrenergic receptor agonist.

In some embodiments, the method further comprises providing therapeutic intervention to the subject based at least in part on a risk score indicative of risk of pre-eclampsia. In some embodiments, therapeutic intervention comprises antihypertensive drug therapy (such as, but not limited to, hydralazine, labetalol, nifedipine, and sodium nitroprusside), management or prevention of seizures (such as, but not limited to, magnesium sulfate, phenytoin, and diazepam), or prevention of the incidence of pre-eclampsia by low-dose aspirin therapy (e.g., 100 g or less per day).

In some embodiments, the method further comprises monitoring the risk of preterm birth, wherein the monitoring comprises assessing the subject's risk of preterm birth at a plurality of time points, wherein the assessing is based at least on a risk score indicative of the risk of preterm birth determined in (b) at each of the plurality of time points.

In some embodiments, the method further comprises monitoring the risk of pre-eclampsia, wherein the monitoring comprises assessing the subject's risk of pre-eclampsia at a plurality of time points, wherein the assessing is based at least on the risk score indicative of the risk of pre-eclampsia determined in (b) at each of the plurality of time points.

In some embodiments, the method further comprises improving the risk score indicating the risk of pre-eclampsia of the subject by performing one or more subsequent clinical tests on the subject, and processing the results derived from the one or more subsequent clinical tests using the trained algorithm to determine an updated risk score indicating the risk of pre-eclampsia of the subject. In some embodiments, the one or more subsequent clinical tests include ultrasound imaging or blood tests. In some embodiments, the risk score includes the likelihood that the subject will suffer from pre-eclampsia within a predetermined duration.

In some embodiments, the method further comprises improving the risk score of pre-eclampsia of the indicated subject by performing one or more subsequent clinical tests on the subject, and processing the results derived from the one or more subsequent clinical tests using the trained algorithm to determine an updated risk score of pre-eclampsia of the indicated subject. In some embodiments, the one or more subsequent clinical tests comprise ultrasound imaging or blood tests. In some embodiments, the risk score comprises the likelihood that the subject suffers from pre-eclampsia within a predetermined duration.

In some embodiments, the predetermined duration is about 1 hour, about 2 hours, about 4 hours, about 6 hours, about 8 hours, about 10 hours, about 12 hours, about 14 hours, about 16 hours, about 18 hours, about 20 hours, about 22 hours, about 24 hours, about 1.5 days, about 2 days, about 2.5 days, about 3 days, about 3.5 days, about 4 days, about 4.5 days, about 5 days, about 5.5 days, about 6 days, about 6.5 days, about 7 days, about 8 days, about 9 days, about 10 days, about 12 days, about 14 days, about 3 weeks, about 4 weeks, about 5 weeks, about 6 weeks, about 7 weeks, about 8 weeks, about 9 weeks, about 10 weeks, about 11 weeks, about 12 weeks, about 13 weeks, or more than about 13 weeks.

In some embodiments, the method further includes providing a therapeutic intervention for the presence or susceptibility of pregnancy-related states to the subject. In some embodiments, therapeutic intervention includes progesterone treatment, such as hydroxyprogesterone caproate (e.g., 17-α hydroxyprogesterone caproate (17-P), LPCN 1107 from Lipocine, Makena from AMAG Pharmaceuticals), vaginal progesterone or natural progesterone IVR products (e.g., DARE-FRT1 (JNP-0301) from Juniper Pharma); Prostaglandin F2α receptor antagonists (e.g., OBE022 from ObsEva); or β2-adrenergic receptor agonists (e.g., bedoradrine sulfate (MN-221) from MediciNova). Therapeutic intervention can be by, for example, "WHO Recommendations on Interventions to Improve Preterm Birth Outcomes", ISBN9789241508988, World Health Organization, 2015 description, which is incorporated herein by reference as a whole. In some embodiments, the method further comprises monitoring the presence or susceptibility to a pregnancy-related state, wherein the monitoring comprises assessing the presence or susceptibility to a pregnancy-related state in the subject at a plurality of time points, wherein the assessment is based at least on the presence or susceptibility to the pregnancy-related state determined in (d) at each of the plurality of time points. In some embodiments, a difference in the assessment of the presence or susceptibility to a pregnancy-related state in the subject between the plurality of time points indicates one or more clinical indications selected from: (i) diagnosis of the presence or susceptibility to a pregnancy-related state in the subject, (ii) prognosis of the presence or susceptibility to a pregnancy-related state in the subject, and (iii) effectiveness or ineffectiveness of a course of treatment for the presence or susceptibility to a pregnancy-related state in the subject.

In some embodiments, the method further comprises stratifying preterm birth by determining a preterm birth molecular subtype from a plurality of different preterm birth molecular subtypes using a trained algorithm. In some embodiments, the plurality of different preterm birth molecular subtypes comprises a preterm birth molecular subtype selected from the following: the presence or history of a previous preterm birth, the presence or history of a spontaneous preterm birth, the presence or history of a late miscarriage, the presence or history of undergoing cervical surgery, the presence or history of a uterine abnormality, the presence or history of a race-specific risk of preterm birth (e.g., in African Americans), and the presence or history of preterm premature rupture of membranes (PPROM).

In some embodiments, the method further comprises stratifying pre-eclampsia by determining a pre-eclampsia molecular subtype from a plurality of different pre-eclampsia molecular subtypes using a trained algorithm. In some embodiments, the plurality of different pre-eclampsia molecular subtypes include pre-eclampsia molecular subtypes selected from the following: the presence or history of chronic or pre-existing hypertension, the presence or history of gestational hypertension, the presence or history of mild pre-eclampsia (e.g., delivery greater than 34 weeks gestational age), the presence or history of severe pre-eclampsia (delivery less than 34 weeks gestational age), the presence or history of eclampsia, and the presence or history of HELLP syndrome.

In some embodiments, the method further includes analyzing the biomarker set using a trained algorithm. In some embodiments, the health or physiological condition is selected from preterm birth, term birth, gestational age, expected date of delivery, onset of labor, pregnancy-related hypertension, eclampsia, gestational diabetes, congenital conditions of the fetus of the object, ectopic pregnancy, spontaneous abortion, stillbirth, postpartum complications, hyperemesis gravidarum, bleeding or excessive bleeding during delivery, premature rupture of membranes, premature premature rupture of membranes, placenta previa, intrauterine/fetal growth restriction, macrosomia, neonatal conditions, and fetal development stage or state. In some embodiments, the biomarker set includes genomic sites associated with expected date of delivery, gestational age, premature birth, preeclampsia, fetal organ development or gestational diabetes. In some embodiments, the method further includes selecting a therapeutic intervention for the health or physiological condition of a pregnant object or a fetus of a pregnant object based at least in part on a biomarker set. In some embodiments, the therapeutic intervention is selected from a plurality of therapeutic interventions. In some embodiments, the therapeutic intervention is selected at least in part based on a molecular subtype of a health or physiological condition (determined at least in part based on a biomarker set).

In some embodiments, the therapeutic intervention is selected based on the molecular subtype of preterm labor associated with collagen-containing extracellular matrix pathways and comprises a drug selected from the group consisting of collagen modulating therapeutics such as thrombin, Y-27632, TNFα, and indomethacin.

In some embodiments, therapeutic intervention is selected based on the molecular subtype of premature birth associated with the extracellular matrix (ECM) pathway and the inclusion of a therapeutic agent, wherein the therapeutic agent is an oncofetal fibronectin regulator. In some embodiments, the therapeutic agent is a glucocorticoid inhibitor. In some embodiments, the therapeutic agent is dexamethasone. In some embodiments, the therapeutic agent is cycloheximide.

In some embodiments, the therapeutic intervention is selected based on a molecular subtype of preterm birth associated with the endoplasmic reticulum (ER) luminal pathway (associated with ER stress induced by oxidative stress in decidual cells) and comprises a drug selected from the ER stress inhibitors 4-phenylbutyric acid (4-PBA) and tauroursodeoxycholic acid (TUDCA).

In some embodiments, therapeutic intervention is selected based on molecular subtypes of preterm birth associated with inflammatory pathways and comprises a drug selected from a non-specific NF-κB inhibitor, a TLR4 antagonist, a TNF-α biologic, a CTHE (a novel anti-inflammatory drug): a p38 MAPK inhibitor (SKF-86002, SB202190, and SB239063), an IKK complex inhibitor (NBNI, parthenolide, and TPCA-1), or a TAK1 inhibitor (5z-7-oxa-zearalenol (OxZnl)).

In some embodiments, therapeutic intervention is selected based on the molecular subtype of premature birth associated with the insulin growth factor transport pathway and a therapeutic agent selected from metformin, insulin-like growth factor 1 (IGF-1), insulin-like growth factor binding protein-3 (IGFBP-3), and glucose transporter (GLUT3, GLUT8 and GLUT9) regulators. In some embodiments, the therapeutic agent is metformin. In some embodiments, the therapeutic agent is IGF-1. In some embodiments, the therapeutic agent is IGFBP-3. In some embodiments, the therapeutic agent is a regulator of GLUT3. In some embodiments, the therapeutic agent is a regulator of GLUT8. In some embodiments, the therapeutic agent is a regulator of GLUT9.

In some embodiments, therapeutic intervention is selected based on the metabolism of amino acids and derivatives associated with preterm molecular subtypes and includes a therapeutic agent, wherein the therapeutic agent is an endogenous metabolic modulator (EMM).

In some embodiments, health or physiological condition include pre-eclampsia. In some embodiments, therapeutic intervention for pre-eclampsia includes medicine, supplement or lifestyle advice. In some embodiments, medicine is selected from aspirin, progesterone, magnesium sulfate, cholesterol medicine (such as pravastatin), heartburn medicine (such as esomeprazole), angiotensin II receptor antagonist (such as losartan), calcium channel blocker (such as nifedipine), diabetes medicine (such as inositol, metformin, glucovance and liraglutide) and erectile dysfunction medicine (such as sildenafil citrate). In some embodiments, supplement is selected from calcium, vitamin D, vitamin B3 and DHA. In some embodiments, lifestyle advice is selected from exercise, nutritional counseling, meditation, stress relief, weight loss or weight maintenance and improving sleep quality. In some embodiments, the therapeutic intervention for pre-eclampsia is selected from the therapeutic prevention (e.g., treatment or prevention) "WHO recommendations: Prevention and treatment of pre-eclampsia and eclampsia" as disclosed below, World Health Organization, ISBN 9789241548335, World Health Organization, 2011, which is incorporated herein by reference in its entirety. In some embodiments, the therapeutic intervention for pre-eclampsia is selected from the therapeutic prevention (e.g., treatment or prevention) "Summary of recommendations: Prevention and treatment of pre-eclampsia and eclampsia" as disclosed below, World Health Organization, WHO reference number WHO/RHR/11.30, World Health Organization, 2011, which is incorporated herein by reference in its entirety. In some embodiments, the therapeutic intervention for pre-eclampsia is selected from the therapeutic prevention (e.g., treatment or prevention) as disclosed in “WHO recommendations: Drug treatment for severe hypertension in pregnancy”, World Health Organization, ISBN 9789241550437, World Health Organization, 2018, which is incorporated herein by reference in its entirety.

In some embodiments, therapeutic intervention is selected based on the molecular subtype of pre-eclampsia associated with early placentation steps responsible for modulation of vasodilatory mediators and inhibition of vascular remodeling, platelet aggregation and platelet adhesion, selected from direct-acting vasodilators (hydralazine, minoxidil, nitrates, nitroprusside); calcium channel blockers (verapamil, diltiazem); nifedipine, amlodipine); antagonists of the renin-angiotensin-aldosterone system (angiotensin receptor blockers, angiotensin converting enzyme inhibitors); beta-2 receptor agonists (salbutamol, terbutaline); postsynaptic alpha-1 receptor antagonists (prazosin, benzylamine, phentolamine); centrally acting alpha-2 receptor agonists (clonidine, alpha-methyldopa); centrally acting alpha-2 receptor agonists (clonidine, alpha-methyldopa); centrally acting alpha-2 receptor agonists (clonidine, alpha-methyldopa).

In some embodiments, therapeutic intervention is selected based on a pre-eclampsia molecular subtype associated with a PE molecular subtype associated with a keratinocyte endothelial pathway and includes a drug selected from a proton pump inhibitor (PPI): omeprazole, esomeprazole, pantoprazole, rabeprazole, or lansoprazole.

In some embodiments, health or physiological conditions include premature birth. In some embodiments, therapeutic interventions for premature birth include drugs, supplements, lifestyle advice, cervical cerclage, cervical pessary or electrical contraction inhibition. In some embodiments, the drug is selected from progesterone, erythromycin, contraction inhibition drugs (such as indomethacin), corticosteroids, vaginal flora (such as clindamycin and metronidazole) and antioxidants (such as N-acetylcysteine). In some embodiments, supplements are selected from calcium, vitamin D and probiotics (such as lactobacilli). In some embodiments, lifestyle advice is selected from exercise, nutritional counseling, meditation, stress relief, weight loss or weight maintenance and improving sleep quality. In some embodiments, therapeutic interventions for premature birth are selected from therapeutic interventions (e.g., treatment or prevention) "WHO Recommendations on Interventions to Improve Preterm Birth Outcomes" ISBN 9789241508988, World Health Organization, 2015, which is incorporated herein by reference in its entirety.

In some embodiments, health or physiological conditions include gestational diabetes (GDM). In some embodiments, therapeutic interventions for GDM include drugs, supplements or lifestyle advice. In some embodiments, drugs are selected from insulin and diabetes drugs (such as inositol, metformin, glucovance and liraglutide). In some embodiments, supplements are selected from vitamin D, choline, probiotics and DHA. In some embodiments, lifestyle advice is selected from exercise, nutritional counseling, meditation, stress relief, weight loss or maintenance and improving sleep quality. In some embodiments, therapeutic interventions for gestational diabetes (GDM) are selected from therapeutic interventions (e.g., treatment or prevention) disclosed below "Diagnostic criteria and classification of hyperglycaemia first detected in pregnancy" WHO reference number WHO/NMH/MND/13.2, World Health Organization, 2013, which is incorporated herein by reference in its entirety.

In some embodiments, the therapeutic intervention is selected based on a gestational diabetes mellitus (GDM) molecular subtype associated with a GDM molecular subtype associated with placental involution, placental insufficiency, placental failure, placental dysfunction, premature aging, calcification, and comprises a drug selected from sildenafil citrate, tempol (superoxide dismutase dismutase), resveratrol, melatonin, sofalcone, statins, metformin, or [Leu27] insulin-like growth factor-II (IGF-II).

In some embodiments, the therapeutic intervention is selected based on a gestational diabetes mellitus (GDM) molecular subtype associated with a GDM molecular subtype associated with mediated hyperglycemic memory and comprises a drug selected from pravastatin, aminoguanidine, rosiglitazone, grape seed proanthocyanidin extract (GSPE), hesperidin, epalrestat, pyridoxine, telmisartan, metformin, and pioglitazone.

In some embodiments, therapeutic intervention is selected based on a gestational diabetes mellitus (GDM) molecular subtype associated with a GDM molecular subtype associated with the adaptive immune system and antigen-specific pathways and comprises a drug selected from azathioprine, mycophenolate mofetil, otelixizumab, teplizumab, GAD65, DiaPep277, anti-CD20 mAb, rapamycin/IL-2, sulfonylureas, metformin, TZD, dipeptidyl peptidase-4 inhibitor, sodium-glucose co-transporter 2 inhibitor, diacerein, salicylic acid, or GLP-1RA.

Another aspect of the present disclosure provides a non-transitory computer-readable medium comprising machine-executable code that, when executed by one or more computer processors, implements any of the methods described above or elsewhere herein.

Another aspect of the present disclosure provides a system comprising one or more computer processors and a computer memory coupled thereto. The computer memory comprises machine executable code which, when executed by the one or more computer processors, implements any method described above or elsewhere herein.

Additional aspects and advantages of the present disclosure will become apparent to those skilled in the art from the following detailed description, wherein only illustrative embodiments of the present disclosure are shown and described. As will be appreciated, the present disclosure is capable of other and different embodiments, and its several details are capable of modification in various obvious respects, all without departing from the present disclosure. Therefore, the drawings and description should be regarded as illustrative in nature, and not restrictive.

Incorporation by reference

All publications, patents, and patent applications mentioned in this specification are incorporated herein by reference to the same extent as if each individual publication, patent, or patent application was specifically and individually indicated to be incorporated by reference. If publications and patents or patent applications incorporated by reference contradict the disclosure contained in the specification, the specification is intended to supersede and/or take precedence over any such contradictory material.

BRIEF DESCRIPTION OF THE DRAWINGS

The novel features of the present invention are particularly set forth in the appended claims. A better understanding of the features and advantages of the present invention will be obtained by reference to the following detailed description which sets forth illustrative embodiments in which the principles of the present invention are utilized, and to the accompanying drawings (also referred to herein as "Figures"), in which:

FIG. 1 illustrates a computer system programmed or otherwise configured to implement the methods provided herein.

Figures 2A-2C show examples of receiver operating characteristic (ROC) curves for a prediction model using differentially expressed genes identified by gene discovery (Figure 2A), where the model was validated by leave-one-out cross-validation (LOOCV), including cross-validation at site 1 with an average area under the curve (AUC) of 0.77 for the ROC curve (Figure 2B) and testing at sites 2, 3, and 4 with an average AUC of 0.79 (Figure 2C).

FIG. 2D shows examples of pathways identified in the top ranked preterm birth marker genes via gene set enrichment analysis for Gene Ontology and collagen extracellular matrix-containing pathways in samples from mothers who delivered preterm.

FIG3 shows examples of pathways identified in late miscarriage or early preterm birth via gene set enrichment analysis for Gene Ontology and gene sets associated with basement membrane and endoplasmic reticulum lumen in samples from mothers who delivered before 25 weeks gestational age.

FIG4 shows the distribution of gestational age at sample collection and gestational age at delivery for the 242 collected samples.

FIG5A shows the receiver operating characteristic (ROC) curve for sPTB (delivery <35 weeks GA).

Figure 5B shows the probability assigned to each sample by the sPTB model (delivery <35 weeks GA). A value of 0 corresponds to a 0% probability that the sample is classified as an sPTB case, and a value of 1 corresponds to a 100% probability that the sample is classified as an sPTB case. Samples are separated by each true label (orange for cases and blue for controls).

FIG5C shows the receiver operating characteristic (ROC) curve for sPTB (delivery <25 weeks GA).

Figure 5D shows the probability assigned to each sample by the sPTB model (delivery <25 weeks GA). A value of 0 corresponds to a 0% probability that the sample is classified as an sPTB case, and a value of 1 corresponds to a 100% probability that the sample is classified as an sPTB case. Samples are separated by each true label (orange for cases and blue for controls).

Figure 6 shows the demographic and clinical data of the preeclampsia observational cohort, which included 2701 healthy participants and 335 participants diagnosed with preeclampsia.

Figure 7 shows demographic and clinical data for the cohort with a cutoff value for subjects diagnosed with pre-eclampsia who delivered before 38 weeks of gestational age, including 2690 healthy participants and 199 participants who were diagnosed with pre-eclampsia and delivered before 38 weeks of gestational age.

Figure 8 shows demographic and clinical data for the cohort with a cutoff value for subjects diagnosed with pre-eclampsia who delivered before 37 weeks of gestational age, including 2780 healthy participants and 109 participants who were diagnosed with pre-eclampsia and delivered before 37 weeks of gestational age.

Figure 9A shows an example of systematic variance in cell-free RNA (cfRNA) next generation sequencing (NGS) data associated with seasonal changes in pregnancy-associated genes and placenta-associated genes moving in the opposite direction to immune cell genes.

FIG9B shows an example of a high correlation between the local external temperature predicted by genetic modeling and the actual temperature recorded by a weather station close to the blood collection site.

Figure 10A shows an example of systematic variance in cfRNA NGS data associated with blood draw/collection time.

FIG. 10B shows a quantile-quantile (QQ) plot of differential expression between the morning group and the afternoon group associated with the circadian clock.

FIG. 11A shows the gene discovery rate in log2 counts per million (CPM) read space in replicated cross validation by Sure Independence Screening.

Figure 11B shows the gene discovery rate in log2 raw count read space in repeated cross validation by determining independence filtering.

FIG. 12A shows features found by correction in multiple spaces and thresholding according to the Akaike Information Criterion (AIC) for preterm pre-eclampsia cases delivered at less than 38 weeks from downsampled counts with clinical factors.

12B shows an example of features found by correction in multiple spaces and by thresholding of AIC for preterm pre-eclampsia cases delivered at less than 37 weeks with body mass index (BMI) and blood pressure (BP) as the only clinical factors.

FIG. 13 shows an example of an area under the curve (AUC) with an average ROC curve value of 0.83 for a model predicting cases of preterm preeclampsia who deliver at a gestational age of less than 37 weeks.

Figure 14 shows quantile-quantile (QQ) plots of Spearman and DESeq2 differential gene expression analysis of differentially expressed genes in spontaneous preterm birth cases delivered before gestational age of 35 weeks. The QQ plot of Spearman ranked differential gene expression is on the left, and the QQ plot of DESeq2 differential gene expression is on the right.

Figure 15 shows a quantile-quantile (QQ) plot of the DESeq2 differential gene expression analysis of differentially expressed genes in spontaneous preterm birth cases delivered before gestational age of 37 weeks. The QQ plot of the Spearman ranked differential gene expression is on the left, and the QQ plot of the DESeq2 differential gene expression is on the right.

DETAILED DESCRIPTION

Although various embodiments of the present invention have been shown and described herein, it will be apparent to those skilled in the art that these embodiments are provided as examples only. Without departing from the present invention, those skilled in the art may conceive of many variations, changes and substitutions. It should be understood that various alternatives to the embodiments of the present invention described herein may be employed.

As used in this specification and claims, the singular forms "a", "an", and "the" include plural referents unless the context clearly dictates otherwise. For example, the term "nucleic acid" includes a plurality of nucleic acids, including mixtures thereof.

As used herein, the term "subject" generally refers to an entity or medium with testable or detectable genetic information. The object can be a person, an individual, or a patient. The object can be a vertebrate, such as a mammal. Non-limiting examples of mammals include humans, apes, farm animals, sports animals, rodents, and pets. The object can be a pregnant female object. The object can be a woman with a fetus (or multiple births) or suspected of having a fetus (or multiple births). The object can be a person who is pregnant or suspected of being pregnant. The object can show symptoms indicating the health or physiological state or condition of the object, such as the pregnancy-related health or physiological state or condition of the object. Alternatively, the object can be asymptomatic in terms of such health or physiological state or illness.

As used herein, the term "pregnancy-related state" generally refers to any health, physiological and/or biochemical state or condition of a subject who is pregnant or suspected of being pregnant, or of a fetus (or multiple fetuses) of the subject. Examples of pregnancy-related states include, but are not limited to, preterm birth, term birth, gestational age, due date, onset of labor, pregnancy-related hypertensive disorders (e.g., preeclampsia), eclampsia, gestational diabetes, congenital disorders of a subject's fetus, ectopic pregnancy, spontaneous abortion, stillbirth, postpartum complications (e.g., postpartum depression, hemorrhage or excessive bleeding, pulmonary embolism, cardiomyopathy, diabetes, anemia, and hypertensive disorders), hyperemesis gravidarum (morning sickness), hemorrhage or excessive bleeding during labor, premature rupture of membranes, premature premature rupture of membranes, placenta previa (placenta covering the cervix), intrauterine/fetal growth restriction, macrosomia (for fetuses with The invention relates to a fetus that is large for its age, a neonatal condition (e.g., anemia, apnea, bradycardia and other cardiac defects, bronchopulmonary dysplasia or chronic lung disease, diabetes, gastroschisis, hydrocephalus, hyperbilirubinemia, hypocalcemia, hypoglycemia, intraventricular hemorrhage, jaundice, necrotizing enterocolitis, patent ductus arteriosus, periventricular leukomalacia, persistent pulmonary hypertension, polycythemia, respiratory distress syndrome, retinopathy of prematurity, and transient tachypnea), and a fetal developmental stage or state (e.g., normal fetal organ function or development and abnormal fetal organ function or development). For example, the fetal developmental stage or state can be associated with normal fetal organ function or development and/or abnormal fetal organ function or development of a fetal organ selected from the group consisting of heart, large intestine, small intestine, retina, prefrontal cortex, midbrain, kidney, and esophagus. In some cases, the pregnancy-related condition is not related to the health or physiological state or condition of the subject's fetus (or fetuses).

As used herein, the term "sample" generally refers to a biological sample obtained from or derived from one or more objects. A biological sample can be a cell-free biological sample or a substantially cell-free biological sample, or can be processed or fractionated to produce a cell-free biological sample. For example, a cell-free biological sample can include cell-free ribonucleic acid (cfRNA), cell-free deoxyribonucleic acid (cfDNA), cell-free fetal DNA (cffDNA), plasma, serum, urine, saliva, amniotic fluid and derivatives thereof. A cell-free biological sample can be obtained from or derived from an object using ethylenediaminetetraacetic acid (EDTA) collection tubes, cell-free RNA collection tubes (e.g., Streck) or cell-free DNA collection tubes (e.g., Streck). A cell-free biological sample can be derived from a whole blood sample by fractionation. A biological sample or its derivatives can contain cells. For example, a biological sample can be a blood sample or its derivatives (e.g., blood collected by a collection tube or dripping blood), a vaginal sample (e.g., a vaginal swab) or a cervical sample (e.g., a cervical swab).

As used herein, the term "nucleic acid" generally refers to a polymeric form of nucleotides of any length, deoxyribonucleotides (dNTPs) or ribonucleotides (rNTPs), or analogs thereof. Nucleic acids can have any three-dimensional structure and can perform any known or unknown function. Non-limiting examples of nucleic acids include deoxyribonucleic acid (DNA), ribonucleic acid (RNA), coding or non-coding regions of genes or gene fragments, loci defined by linkage analysis, exons, introns, messenger RNA (mRNA), transfer RNA, ribosomal RNA, short interfering RNA (siRNA), short hairpin RNA (shRNA), microRNA (miRNA), ribozymes, cDNA, recombinant nucleic acids, branched nucleic acids, plasmids, vectors, isolated DNA of any sequence, isolated RNA of any sequence, nucleic acid probes and primers. Nucleic acids can include one or more modified nucleotides, such as methylated nucleotides and nucleotide analogs. If present, the nucleotide structure can be modified before or after nucleic acid assembly. The nucleotide sequence of nucleic acids can be interrupted by non-nucleotide components. Nucleic acids can be further modified after polymerization, such as by conjugating or combining with a reporter agent.

As used herein, the term "target nucleic acid" generally refers to a nucleic acid molecule in a population of starting nucleic acid molecules having a nucleotide sequence, the presence, amount and/or sequence of which, or a change in one or more of these, is desired to be determined. The target nucleic acid can be any type of nucleic acid, including DNA, RNA and analogs thereof. As used herein, "target ribonucleic acid (RNA)" generally refers to a target nucleic acid, i.e., RNA. As used herein, "target deoxyribonucleic acid (DNA)" generally refers to a target nucleic acid, i.e., DNA.

As used herein, the term "amplification" generally refers to increasing the size or quantity of a nucleic acid molecule. A nucleic acid molecule can be single-stranded or double-stranded. Amplification can include generating one or more copies or "amplification products" of a nucleic acid molecule. Amplification can be performed, for example, by extension (e.g., primer extension) or connection. Amplification can include performing a primer extension reaction to generate a strand complementary to a single-stranded nucleic acid molecule, and in some cases generating one or more copies of the strand and/or single-stranded nucleic acid molecule. The term "DNA amplification" generally refers to generating one or more copies of a DNA molecule or "amplified DNA product". The term "reverse transcription amplification" generally refers to generating deoxyribonucleic acid (DNA) from a ribonucleic acid (RNA) template by the action of a reverse transcriptase.

Every year, about 15 million premature births are reported worldwide. Premature births can affect up to about 10% of pregnancies, most of which are spontaneous premature births. At present, there may be a lack of meaningful, clinically feasible diagnostic screening or testing that can be used for many pregnancy-related complications (such as premature birth). However, pregnancy-related complications such as premature birth are the main causes of neonatal death and complications in later life. In addition, such pregnancy-related complications can cause negative health effects on maternal health. Therefore, in order to make pregnancy as safe as possible, it is necessary to identify and monitor the state related to pregnancy quickly and accurately, and these methods are non-invasive and cost-effective, to improve maternal and child health.

Current prenatal examinations may be difficult to reach and incomplete. For cases where pregnancy progresses without pregnancy-related complications, pregnant subjects can use limited pregnancy monitoring methods, such as molecular testing, ultrasound imaging, and using the last menstrual period to estimate gestational age and/or expected date of delivery. However, such monitoring methods may be complex, expensive, and unreliable. For example, molecular testing cannot predict gestational age, ultrasound imaging is expensive and is best performed in early pregnancy, and using the last menstrual period to estimate gestational age and/or expected date of delivery may be unreliable. In addition, for cases where pregnancy progresses and is accompanied by pregnancy-related complications (such as spontaneous preterm birth risk), the clinical utility of molecular testing, ultrasound imaging, and demographic factors may be limited. For example, molecular testing may have a limited BMI (body mass index) range, a limited gestational age and/or expected date of delivery range (about 2 weeks), and a low positive predictive value (PPV); ultrasound imaging may be expensive and have low PPV and specificity; using demographic factors to predict the risk of pregnancy-related complications may be unreliable. Therefore, there is an urgent need for accurate and affordable non-invasive diagnostic methods for detecting and monitoring pregnancy-related states (e.g., estimating gestational age, due date and/or onset of labor, and predicting pregnancy-related complications such as preterm birth) to achieve clinically feasible outcomes.

The present disclosure provides methods, systems and kits for identifying or monitoring pregnancy-related states by processing acellular biological samples obtained from or derived from an object (e.g., a pregnant female object). Acellular biological samples (e.g., plasma samples) obtained from an object can be analyzed to identify pregnancy-related states (which can include, for example, measuring the presence, absence or quantitative assessment (e.g., risk) of pregnancy-related states). Such objects can include objects with one or more pregnancy-related states and objects without pregnancy-related states. Pregnancy-related states can include, for example, premature birth, full-term birth, gestational age, expected date of delivery, onset of labor, pregnancy-related hypertension disorders (e.g., pre-eclampsia), eclampsia, gestational diabetes, congenital disorders of the fetus of the object, ectopic pregnancy, spontaneous abortion, stillbirth, postpartum complications (e.g., postpartum depression, hemorrhage or excessive bleeding, pulmonary embolism, cardiomyopathy, diabetes, anemia and hypertensive disease), hyperemesis gravidarum (morning sickness), hemorrhage or excessive bleeding during delivery, premature rupture of membranes, premature premature rupture of membranes, placenta previa (placenta covering the cervix), intrauterine/fetal growth restriction and macrosomia (large fetus for gestational age). In some embodiments, pregnancy-related states are not related to the health of the fetus. In some embodiments, pregnancy-related states include neonatal conditions (e.g., anemia, apnea, bradycardia and other heart defects, bronchopulmonary dysplasia or chronic lung disease, diabetes, gastroschisis, hydrocephalus, hyperbilirubinemia, hypocalcemia, hypoglycemia, intraventricular hemorrhage, jaundice, necrotizing enterocolitis, patent ductus arteriosus, periventricular leukomalacia, persistent pulmonary hypertension, polycythemia, respiratory distress syndrome, retinopathy of prematurity, and transient tachypnea) and fetal development stage or state (e.g., normal fetal organ function or development and abnormal fetal organ function or development). For example, the fetal development stage or state can be related to normal fetal organ function or development and/or abnormal fetal organ function or development of a fetal organ, and the fetal organ is selected from the heart, large intestine, small intestine, retina, prefrontal cortex, midbrain, kidney, and esophagus.

Assays for cell-free biological samples

The cell-free biological sample can be obtained from or derived from a human subject (e.g., a pregnant female subject). The cell-free biological sample can be stored under various storage conditions before processing, such as different temperatures (e.g., at room temperature, under refrigerated or frozen conditions, at 25°C, at 4°C, at -18°C, -20°C, or -80°C) or different suspensions (e.g., EDTA collection tubes, cell-free RNA collection tubes, or cell-free DNA collection tubes).

The cell-free biological sample can be obtained from a subject having a pregnancy-related condition (e.g., a pregnancy-related complication), a subject suspected of having a pregnancy-related condition (e.g., a pregnancy-related complication), or a subject not having or not suspected of having a pregnancy-related condition (e.g., a pregnancy-related complication). Pregnancy-related conditions can include pregnancy-related complications such as premature birth, pregnancy-related hypertensive disorders (e.g., preeclampsia), eclampsia, gestational diabetes, congenital disorders of the subject's fetus, ectopic pregnancy, spontaneous abortion, stillbirth, postpartum complications (e.g., postpartum depression, hemorrhage or excessive bleeding, pulmonary embolism, cardiomyopathy, diabetes, anemia, and hypertensive disorders), hyperemesis gravidarum (morning sickness), hemorrhage or excessive bleeding during labor, premature rupture of membranes, premature premature rupture of membranes, placenta previa (placenta covering the cervix), intrauterine/fetal growth restriction, macrosomia (for gestational age), larger fetus), neonatal conditions (e.g., anemia, apnea, bradycardia and other cardiac defects, bronchopulmonary dysplasia or chronic lung disease, diabetes mellitus, gastroschisis, hydrocephalus, hyperbilirubinemia, hypocalcemia, hypoglycemia, intraventricular hemorrhage, jaundice, necrotizing enterocolitis, patent ductus arteriosus, periventricular leukomalacia, persistent pulmonary hypertension, polycythemia, respiratory distress syndrome, retinopathy of prematurity, and transient tachypnea), and abnormal fetal developmental stage or state (e.g., abnormal fetal organ function or development). Pregnancy-related states may include: full-term birth, normal fetal development stage or state (e.g., normal fetal organ function or development), or the absence of pregnancy-related complications (e.g., premature birth, pregnancy-related hypertensive disorders (e.g., preeclampsia), eclampsia, gestational diabetes, congenital disorders of the subject's fetus, ectopic pregnancy, spontaneous abortion, stillbirth, postpartum complications (e.g., postpartum depression, hemorrhage or excessive bleeding, pulmonary embolism, cardiomyopathy, diabetes, anemia, and hypertensive disorders), hyperemesis gravidarum (morning sickness), hemorrhage or excessive bleeding during labor, premature rupture of membranes, premature premature rupture of membranes, placenta previa (placenta covering the cervix), uterine congestion, Endonatal/fetal growth restriction, macrosomia (a fetus that is large for gestational age), neonatal conditions (e.g., anemia, apnea, bradycardia and other cardiac defects, bronchopulmonary dysplasia or chronic lung disease, diabetes, gastroschisis, hydrocephalus, hyperbilirubinemia, hypocalcemia, hypoglycemia, intraventricular hemorrhage, jaundice, necrotizing enterocolitis, patent ductus arteriosus, periventricular leukomalacia, persistent pulmonary hypertension, polycythemia, respiratory distress syndrome, retinopathy of prematurity, and transient tachypnea), and abnormal fetal developmental stage or state (e.g., abnormal fetal organ function or development). Pregnancy-related status can include a quantitative assessment of pregnancy, such as gestational age (e.g., measured in days, weeks, or months) or due date (e.g., expressed as a predicted or estimated calendar date or range of calendar dates). Pregnancy-related status can include quantitative assessments of pregnancy-related complications, such as pregnancy-related complications (e.g., preterm labor, onset of labor, pregnancy-related hypertensive disorders (e.g., preeclampsia), eclampsia, gestational diabetes, congenital disorders of the subject's fetus, ectopic pregnancy, spontaneous abortion, stillbirth, postpartum complications (e.g., postpartum depression, hemorrhage or excessive bleeding, pulmonary embolism, cardiomyopathy, diabetes, anemia, and hypertensive disorders), hyperemesis gravidarum (morning sickness), hemorrhage or excessive bleeding during labor, premature rupture of membranes, premature premature rupture of membranes, placenta previa (placenta covering the cervix), intrauterine fetal growth restriction, macrosomia (a fetus that is large for gestational age), neonatal conditions (e.g., anemia, apnea, bradycardia and other cardiac defects, bronchopulmonary dysplasia or chronic lung disease, diabetes, gastroschisis, hydrocephalus, hyperbilirubinemia, hypocalcemia, hypoglycemia, intraventricular hemorrhage, jaundice, necrotizing enterocolitis, patent ductus arteriosus, periventricular leukomalacia, persistent pulmonary hypertension, polycythemia, respiratory distress syndrome, retinopathy of prematurity, and transient tachypnea), and abnormal fetal developmental stage or state (e.g., abnormal The likelihood, susceptibility, or risk (e.g., expressed as a probability, relative probability, odds ratio, or risk score or risk index) of a future labor onset (e.g., within about 1 hour, about 2 hours, about 4 hours, about 6 hours, about 8 hours, about 10 hours, about 12 hours, about 14 hours, about 16 hours, about 18 hours, about 20 hours, about 22 hours, about 24 hours, about 1.5 days, about 2 days, about 2.5 days, about 3 days, about 3.5 days, about 4 days, about 4 For example, the fetal development stage or state can be associated with normal fetal organ function or development and/or abnormal fetal organ function or development of a fetal organ selected from the group consisting of heart, large intestine, small intestine, retina, prefrontal cortex, midbrain, kidney, and esophagus.

Cell-free biological samples can be collected before and/or after treating an object with pregnancy-related complications. Cell-free biological samples can be obtained from an object during treatment or a treatment regimen. Multiple cell-free biological samples can be obtained from an object to monitor the effect of treatment over time. Cell-free biological samples can be taken from an object known or suspected to have a pregnancy-related state (e.g., pregnancy-related complications), which cannot obtain a clear positive or negative diagnosis through clinical trials. Samples can be taken from an object suspected of having pregnancy-related complications. Cell-free biological samples can be taken from an object experiencing unexplained symptoms (such as fatigue, nausea, weight loss, pain, weakness or bleeding). Cell-free biological samples can be taken from an object with explained symptoms. Cell-free biological samples can be taken from an object with a risk of pregnancy-related complications due to the following factors: such as family history, age, hypertension or prehypertension, diabetes or prediabetes, overweight or obesity, environmental exposure, lifestyle risk factors (e.g., smoking, drinking or drug abuse) or the presence of other risk factors.

The cell-free biological sample may contain one or more analytes capable of being assayed, such as cell-free ribonucleic acid (cfRNA) molecules suitable for assaying to generate transcriptomic data, using transcripts derived from the cell-free biological sample (e.g., messenger RNA, transfer RNA, or ribosomal RNA) to generate transcript data, cell-free deoxyribonucleic acid (cfDNA) molecules suitable for assaying to generate genomic data and/or methylation data, proteins suitable for assaying to generate proteomic data (e.g., pregnancy-associated proteins corresponding to pregnancy-associated genomic sites or genes), metabolites suitable for assaying to generate metabolomic data, or mixtures or combinations thereof. One or more such analytes (e.g., cfRNA molecules, cfDNA molecules, proteins, or metabolites) may be isolated or extracted from one or more cell-free biological samples of a subject for downstream assaying using one or more suitable assays.

After obtaining a cell-free biological sample from a subject, the cell-free biological sample can be processed to generate a data set indicating a pregnancy-related state of the subject. For example, the presence, absence or quantitative assessment of nucleic acid molecules in a cell-free biological sample grouped at a pregnancy-related state-associated genomic site (e.g., a quantitative measurement of RNA transcripts or DNA in a pregnancy-related state-associated genomic site), proteomic data including quantitative measurements of proteins in a data set of grouped proteins associated with a pregnancy-related state (e.g., corresponding to a pregnancy-associated genomic site or gene), and/or metabolomic data including quantitative measurements of grouped metabolites associated with a pregnancy-associated state can indicate a pregnancy-associated state. Processing the cell-free biological sample obtained from the subject can include (i) subjecting the cell-free biological sample to conditions sufficient to separate, enrich or extract a plurality of nucleic acid molecules, proteins (e.g., pregnancy-associated proteins corresponding to a pregnancy-associated genomic site or gene) and/or metabolites, and (ii) determining a plurality of nucleic acid molecules, proteins and/or metabolites to generate a data set.

In some embodiments, multiple nucleic acid molecules are extracted from acellular biological samples and sequenced to generate multiple sequencing readings. Nucleic acid molecules may include ribonucleic acid (RNA) or deoxyribonucleic acid (DNA). Nucleic acid molecules (e.g., RNA or DNA) can be extracted from acellular biological samples by a variety of methods, such as the FastDNA kit scheme of MP Biomedicals, the QIAamp DNA acellular biological mini kit of Qiagen or the acellular biological DNA separation kit scheme of Norgen Biotek. The extraction method can extract all RNA or DNA molecules from the sample. Alternatively, the extraction method can selectively extract a portion of RNA or DNA molecules from the sample. The RNA molecules extracted from the sample can be converted into DNA molecules by reverse transcription (RT).

Sequencing can be performed by any suitable sequencing method, such as massively parallel sequencing (MPS), paired-end sequencing, high-throughput sequencing, next-generation sequencing (NGS), shotgun sequencing, single-molecule sequencing, nanopore sequencing, semiconductor sequencing, pyrosequencing, synthesis sequencing (SBS), ligation sequencing, hybridization sequencing, and RNA-Seq (Illumina).

Sequencing can include nucleic acid amplification (e.g., of RNA or DNA molecules). In some embodiments, nucleic acid amplification is polymerase chain reaction (PCR). Appropriate rounds of PCR (e.g., PCR, qPCR, reverse transcriptase PCR, digital PCR, etc.) can be performed to fully amplify the initial amount of nucleic acid (e.g., RNA or DNA) to the input amount required for subsequent sequencing. In some cases, PCR can be used for the global amplification of target nucleic acids. This can include the use of a joint sequence, which can first be connected to different molecules, and then PCR amplification using universal primers. PCR can be performed using any of many commercial kits, for example, provided by Life Technologies, Affymetrix, Promega, Qiagen, etc. In other cases, only certain target nucleic acids in the nucleic acid group can be amplified. Specific primers, which can be used in conjunction with joint connections, can be used to selectively amplify some targets for downstream sequencing. PCR can include targeted amplification of one or more genomic sites, such as genomic sites associated with pregnancy-related states. Sequencing may include the use of simultaneous reverse transcription (RT) and polymerase chain reaction (PCR), such as the OneStep RT-PCR kit protocol from Qiagen, NEB, Thermo Fisher Scientific, or Bio-Rad.

The RNA or DNA molecules separated or extracted from the cell-free biological sample can, for example, be marked with identifiable labels to allow multiple reuse of multiple samples. Any number of RNA or DNA samples can be multiple reused. For example, multiple reactions can contain RNA or DNA from at least about 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100 or more than 100 initial cell-free biological samples. For example, multiple cell-free biological samples can be marked with sample barcodes so that each DNA molecule can be traced back to the sample (and object) of the DNA molecule origin. Such labels can be attached to RNA or DNA molecules by connection or by carrying out pcr amplification with primers.

After sequencing the nucleic acid molecules, the sequence reads can be subjected to suitable bioinformatics processing to generate data indicating the presence, absence, or relative assessment of a pregnancy-related state. For example, the sequence reads can be compared to one or more reference genomes (e.g., genomes of one or more species such as the human genome). The sequence reads compared can be quantified at one or more genomic sites to generate a data set indicating a pregnancy-related state. For example, quantifying sequences corresponding to multiple genomic sites associated with a pregnancy-related state can generate a data set indicating a pregnancy-related state.

Cell-free biological samples can be processed without any nucleic acid extraction. For example, a pregnancy-related state can be identified or monitored in a subject by using a probe configured to selectively enrich for nucleic acid (e.g., RNA or DNA) molecules corresponding to a plurality of pregnancy-related state-associated genomic sites. The probe can be a nucleic acid primer. The probe can have sequence complementarity with one or more nucleic acid sequences from a plurality of pregnancy-related state-associated genomic sites or genomic regions. The plurality of pregnancy-related state-associated genomic loci or genomic regions may include at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 11, at least 12, at least 13, at least 14, at least 15, at least 16, at least 17, at least 18, at least 19, at least 20, at least about 25, at least about 30, at least about 35, at least about 40, at least about 45, at least about 50, at least about 55, at least about 60, at least about 65, at least about 70, at least about 75, at least about 80, at least about 85, at least about 90, at least about 95, at least about 100 or more different pregnancy-related state-associated genomic loci or genomic regions. The plurality of pregnancy-related status-associated genomic loci or genomic regions may include one or more (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, about 25, about 30, about 35, about 40, about 45, about 50, about 55, about 60, about 65, about 70, about 75, about 80 or more) members selected from ACTB, ADAM12, ALPP, ANXA 3. APLF, ARG1, AVPR1A, CAMP, CAPN6, CD180, CGA, CGB, CLCN3, CPVL, CSH1, CSH2, CSHL1, CYP3A7, DAPP1, DCX, DEFA4, DGCR14, ELANE, ENAH, EPB42, FABP1, FAM212B-AS1, FGA, FGB, FRMD4B, FRZB, FSTL3, GH2 ,G NAZ, HAL, HSD17B1, HSD3B1, HSPB8, Immune, ITIH2, KLF9, KNG1, KRT8, LGALS14, LTF, LYPLAL1, MAP3K7CL, MEF2C, MMD, MMP8, MOB1B, NFATC2, OTC, P2RY12, PAPPA, PGLYRP1, PKHD1L1, PKHD1L1, PLAC1, PLAC4, POLE2, PPBP, PSG1, PSG4, PSG7, PTGER3, RAB11A, RAB27B, RAP1GAP, RGS18, RPL23AP7, S100A8, S100A9, S100P, SERPINA7, SLC2A2, SLC38A4, SLC4A1, TBC1D15, VCAN, VGLL1, B3GNT2, COL24A1, CXCL8, and PTGS2. Genomic loci or genomic regions associated with pregnancy-related states can be associated with gestational age, preterm birth, due date, onset of labor, or other pregnancy-related states or complications, such as the genomic loci described by, for example, Ngo et al. (“Noninvasive blood tests for fetal development predict gestational age and preterm delivery”, Science, 360(6393), pp. 1133-1136, June 8, 2018), which are hereby incorporated by reference in their entirety.

The probes can be nucleic acid molecules (e.g., RNA or DNA) having sequence complementarity with nucleic acid sequences (e.g., RNA or DNA) of one or more genomic loci (e.g., genomic loci associated with a pregnancy-related state). These nucleic acid molecules can be primers or enrichment sequences. Assays of cell-free biological samples using probes that are selective for one or more genomic loci (e.g., genomic loci associated with a pregnancy-related state) can include the use of array hybridization (e.g., microarray-based), polymerase chain reaction (PCR), or nucleic acid sequencing (e.g., RNA sequencing or DNA sequencing). In some embodiments, DNA or RNA can be assayed by one or more of isothermal DNA/RNA amplification methods (e.g., loop-mediated isothermal amplification (LAMP), helicase-dependent amplification (HDA), rolling circle amplification (RCA), recombinase polymerase amplification (RPA)), immunoassays, electrochemical assays, surface enhanced Raman spectroscopy (SERS), quantum dot (QD)-based assays, molecular inversion probes, droplet digital PCR (ddPCR), CRISPR/Cas-based detection (e.g., CRISPR typing PCR (ctPCR), specific high sensitivity enzymatic reporter gene unlocking (SHERLOCK), DNA endonuclease-targeted CRISPR trans-reporter (DETECTR), and CRISPR-mediated analog multiple event recording device (CAMERA)), and laser transmission spectroscopy (LTS).

The determination reading can be quantified on one or more genomic sites (e.g., genomic sites associated with pregnancy-related states) to generate data indicating pregnancy-related states. For example, array hybridization or polymerase chain reaction (PCR) corresponds to quantification of multiple genomic sites (e.g., genomic sites associated with pregnancy-related states) to generate data indicating pregnancy-related states. The determination reading can include quantitative PCR (qPCR) values, digital PCR (dPCR) values, digital droplet PCR (ddPCR) values, fluorescence values, etc., or their standardized values. The determination can be a home test configured to be performed in a home environment.

In some embodiments, multiple assays are used to process acellular biological samples of an object. For example, a first assay can be used to process a first acellular biological sample obtained from or derived from an object to generate a first data set; and based at least in part on the first data set, a second assay different from the first assay can be used to process a second acellular biological sample obtained from or derived from an object to generate a second data set indicating a pregnancy-related state. The first assay can be used to screen or process acellular biological samples of a set of objects, while a second or subsequent assay can be used to screen or process acellular biological samples of a smaller subset of the set of objects. The first assay can have low cost and/or high sensitivity for detecting one or more pregnancy-related states (e.g., pregnancy-related complications), which is suitable for screening or processing acellular biological samples of a relatively large set of objects. The second assay can have a higher cost and/or higher specificity for detecting one or more pregnancy-related states (e.g., pregnancy-related complications), which is suitable for screening or processing acellular biological samples of a relatively small set of objects (e.g., a subset of objects screened using the first assay). The second assay can generate a second data set with greater specificity (e.g., for one or more pregnancy-related states such as pregnancy-related complications) than the first data set generated using the first assay. For example, one or more cell-free biological samples may be processed using a cfRNA assay on a large set of subjects, and then a metabolomics assay may be used on a smaller subset of subjects, or vice versa. The smaller subset of subjects may be selected based at least in part on the results of the first assay.

Alternatively, a cell-free biological sample of a subject can be processed simultaneously using multiple assays. For example, a first assay can be used to process a first cell-free biological sample obtained from or derived from a subject to generate a first data set indicating a pregnancy-related state; and a second assay different from the first assay can be used to process a second cell-free biological sample obtained from or derived from a subject to generate a second data set indicating a pregnancy-related state. Any or all of the first and second data sets can then be analyzed to assess the pregnancy-related state of the subject. For example, a single diagnostic indicator or diagnostic score can be generated based on a combination of the first and second data sets. As another example, a separate diagnostic indicator or diagnostic score can be generated based on the first and second data sets.

Cell-free biological samples can be processed to identify sets of biomarker RNA transcripts that indicate corresponding biomarker proteins (e.g., pregnancy-associated proteins corresponding to pregnancy-associated genomic sites or genes), pathways, and/or metabolite sets. For example, a given biomarker RNA transcript can be expected to be translated into a corresponding given biomarker protein or a gene regulator of a corresponding given biomarker protein. Therefore, identifying the presence or absence of a given biomarker RNA transcript in a biological sample can indicate the presence or absence of a corresponding biomarker protein. As another example, a given biomarker RNA transcript can be expected to be associated with a corresponding given pathway. Therefore, identifying the presence or absence of a given biomarker RNA transcript in a biological sample can indicate the presence or absence of a corresponding pathway activity. As another example, a given biomarker RNA transcript can be expected to be associated with a corresponding given biomarker metabolite. Therefore, identifying the presence or absence of a given biomarker RNA transcript in a biological sample can indicate the presence or absence of a corresponding biomarker metabolite. In some embodiments, the corresponding biomarker proteins, pathways and/or metabolite sets include pregnancy-related state-associated proteins (e.g., corresponding to pregnancy-associated genomic sites or genes), pathways and/or metabolites. In some embodiments, the corresponding biomarker proteins, pathways and/or metabolite sets include placental proteins, pathways and/or metabolites. For example, identifying the presence or absence of a PAPPA gene can indicate the presence or absence of a PAPPA protein analog.

Cell-free biological samples can be processed using metabolomics assays. For example, metabolomics assays can be used to identify each of the quantitative measurements (e.g., indicating the presence, absence, or relative amount) of multiple pregnancy-related state-associated metabolites in the cell-free biological samples of the object. Metabolomics assays can be configured to process the cell-free biological samples of the object, such as blood samples or urine samples (or derivatives thereof). The quantitative measurements (e.g., indicating the presence, absence, or relative amount) of pregnancy-related state-associated metabolites in the cell-free biological samples can indicate one or more pregnancy-related states. The metabolites in the cell-free biological samples can be produced (e.g., as an end product or by-product) as a result of one or more metabolic pathways corresponding to the genes associated with pregnancy-related states. Determining one or more metabolites of the cell-free biological samples can include separating or extracting metabolites from the cell-free biological samples. Metabolomics assays can be used to generate a data set, which indicates each of the quantitative measurements (e.g., indicating the presence, absence, or relative amount) of multiple pregnancy-related state-associated metabolites in the cell-free biological samples of the object.

Metabolomics assays can analyze a wide variety of metabolites in cell-free biological samples, such as small molecules, lipids, amino acids, peptides, nucleotides, hormones and other signaling molecules, cytokines, minerals and elements, polyphenols, fatty acids, dicarboxylic acids, alcohols and polyols, alkanes and alkenes, keto acids, glycolipids, carbohydrates, hydroxy acids, purines, prostaglandins, catecholamines, acyl phosphates, phospholipids, cyclic amines, aminoketones, nucleosides, glycerolipids, aromatic acids, retinoids, amino alcohols, pterins, steroids, carnitines, leukotrienes, indoles, porphyrins, phosphate sugars, CoA derivatives, glucuronides, ketones, phosphate sugars, inorganic ions and gases, sphingolipids, bile acids, phosphoalcohols, amino acid phosphates, aldehydes, quinones, pyrimidines, pyridoxal, tricarboxylic acids, acylglycines, cobalamin derivatives, lipoamides, biotin, and polyamines.

Metabolomic assays can include, for example, one or more of the following: mass spectrometry (MS), targeted MS, gas chromatography (GC), high performance liquid chromatography (HPLC), capillary electrophoresis (CE), nuclear magnetic resonance (NMR) spectroscopy, ion mobility spectrometry, Raman spectroscopy, electrochemical assays, or immunoassays.

Cell-free biological samples can be processed using methylation-specific assays. For example, methylation-specific assays can be used to identify the quantitative measurement of methylation of each of multiple pregnancy-related state-associated genomic sites in the cell-free biological sample of the object (e.g., indicating the presence, absence, or relative amount). Methylation-specific assays can be configured to process the cell-free biological sample of the object, such as a blood sample or a urine sample (or its derivatives). The quantitative measurement of methylation of the genomic sites associated with pregnancy-related states in the cell-free biological sample (e.g., indicating the presence, absence, or relative amount) can indicate one or more pregnancy-related states. Methylation-specific assays can be used to generate a data set, which indicates the quantitative measurement of methylation of each of multiple pregnancy-related state-associated genomic sites in the cell-free biological sample of the object (e.g., indicating the presence, absence, or relative amount).

Methylation-specific assays can include, for example, one or more of the following: methylation-aware sequencing (e.g., using bisulfite treatment), pyrosequencing, methylation-sensitive single-strand conformation analysis (MS-SSCA), high-resolution melting curve analysis (HRM), methylation-sensitive single nucleotide primer extension (MS-SnuPE), base-specific cleavage/MALDI-TOF, microarray-based methylation assays, methylation-specific PCR, targeted bisulfite sequencing, oxidative bisulfite sequencing, mass spectrometry-based bisulfite sequencing, or reduced representation bisulfite sequencing (RRBS).

Cell-free biological samples can be processed using proteomic assays. For example, proteomic assays can be used to identify a quantitative measure (e.g., indicating presence, absence, or relative amount) of each of a plurality of pregnancy-related state-associated proteins (e.g., corresponding to pregnancy-associated genomic sites or genes) or polypeptides in a cell-free biological sample of an object. Proteomic assays can be configured to process a cell-free biological sample of an object, such as a blood sample or a urine sample (or a derivative thereof). A quantitative measure (e.g., indicating presence, absence, or relative amount) of a pregnancy-related state-associated protein (e.g., corresponding to a pregnancy-associated genomic site or gene) or polypeptide in a cell-free biological sample can indicate one or more pregnancy-related states. A protein or polypeptide in a cell-free biological sample can be produced as a result of one or more biochemical pathways corresponding to a pregnancy-associated state-associated gene (e.g., as an end product, intermediate, or by-product). Determining one or more proteins or polypeptides of a cell-free biological sample can include separating or extracting proteins or polypeptides from a cell-free biological sample. Proteomic assays can be used to generate a data set, which indicates a quantitative measure (e.g., indicating presence, absence, or relative amount) of each of a plurality of pregnancy-associated state-associated proteins or polypeptides in a cell-free biological sample of an object.

Proteomic assays can analyze a variety of proteins (e.g., pregnancy-associated proteins corresponding to pregnancy-associated genomic sites or genes) or polypeptides in a cell-free biological sample, such as proteins produced under different cellular conditions (e.g., development, cell differentiation, or cell cycle). Proteomic assays can include, for example, one or more of the following: antibody-based immunoassays, Edman degradation assays, mass spectrometry-based assays (e.g., matrix-assisted laser desorption/ionization (MALDI) and electrospray ionization (ESI)), top-down proteomic assays, bottom-up proteomic assays, mass spectrometry immunoassays (MSIA), stable isotope standards-captured using anti-peptide antibodies (SISCAPA) assays, two-dimensional differential fluorescence gel electrophoresis (2-DDIGE) assays, quantitative proteomic assays, protein microarray assays, or reversed-phase protein microarray assays. Proteomic assays can detect post-translational modifications (e.g., phosphorylation, ubiquitination, methylation, acetylation, glycosylation, oxidation, and nitrosylation) of proteins or polypeptides. Proteomic assays can identify or quantify one or more proteins or polypeptides from databases (eg, Human Protein Atlas, Peptide Atlas, and UniProt).

Reagent test kit

The present disclosure provides a kit for identifying or monitoring a pregnancy-related state of an object. The kit includes a probe for identifying a quantitative measure (e.g., indicating the presence, absence, or relative amount) of a sequence at each of a plurality of pregnancy-related state-associated genomic sites in a cell-free biological sample of an object. The quantitative measure (e.g., indicating the presence, absence, or relative amount) of a sequence at each of a plurality of pregnancy-related state-associated genomic sites in a cell-free biological sample can indicate one or more pregnancy-related states. The probe can be selective for a sequence at a plurality of pregnancy-related state-associated genomic sites in a cell-free biological sample. The kit can include instructions for processing a cell-free biological sample using a probe to generate a data set, and the data set indicates a quantitative measure (e.g., indicating the presence, absence, or relative amount) of a sequence at each of a plurality of pregnancy-related state-associated genomic sites in a cell-free biological sample of an object.

The probe in the test kit can be selective for sequences at multiple pregnancy-related state-associated genomic sites in acellular biological samples. The probe in the test kit can be configured to selectively enrich nucleic acid (e.g., RNA or DNA) molecules corresponding to multiple pregnancy-related state-associated genomic sites. The probe in the test kit can be a nucleic acid primer. The probe in the test kit can have sequence complementarity with one or more nucleic acid sequences from multiple pregnancy-related state-associated genomic sites or genomic regions. The multiple pregnancy-related state-associated genomic sites or genomic regions can include at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 11, at least 12, at least 13, at least 14, at least 15, at least 16, at least 17, at least 18, at least 19, at least 20 or more different pregnancy-related state-associated genomic sites or genomic regions.

The instructions in the kit may include instructions for measuring acellular biological samples using probes that are selective for sequences at multiple pregnancy-related state-associated genomic sites in acellular biological samples. These probes may be nucleic acid molecules (e.g., RNA or DNA) having sequence complementarity with one or more nucleic acid sequences (e.g., RNA or DNA) from multiple pregnancy-related state-associated genomic sites. These nucleic acid molecules may be primers or enrichment sequences. The instructions for measuring acellular biological samples may include instructions for performing array hybridization, polymerase chain reaction (PCR) or nucleic acid sequencing (e.g., DNA sequencing or RNA sequencing) to process acellular biological samples to generate a data set, and the data set indicates a quantitative measure (e.g., indicating presence, absence or relative amount) of a sequence at each of a plurality of pregnancy-related state-associated genomic sites in acellular biological samples. The quantitative measure (e.g., indicating presence, absence or relative amount) of a sequence at each of a plurality of pregnancy-related state-associated genomic sites in acellular biological samples may indicate one or more pregnancy-related states.

The instructions in the kit may include instructions for measuring and interpreting assay readings, which may be quantified at one or more of a plurality of pregnancy-associated state-associated genomic sites to generate a data set indicating a quantitative measure (e.g., indicating presence, absence, or relative amount) of a sequence at each of a plurality of pregnancy-associated state-associated genomic sites in a cell-free biological sample. For example, quantification of an array hybridization or polymerase chain reaction (PCR) corresponding to a plurality of pregnancy-associated state-associated genomic sites may generate a data set indicating a quantitative measure (e.g., indicating presence, absence, or relative amount) of a sequence at each of a plurality of pregnancy-associated state-associated genomic sites in a cell-free biological sample. Assay readings may include quantitative PCR (qPCR) values, digital PCR (dPCR) values, digital droplet PCR (ddPCR) values, fluorescence values, etc., or standardized values thereof.

The kit may include a metabolomics assay for identifying a quantitative measure (e.g., indicating presence, absence, or relative amount) of each of a plurality of pregnancy-related state-associated metabolites in a cell-free biological sample of the subject. A quantitative measure (e.g., indicating presence, absence, or relative amount) of a pregnancy-related state-associated metabolite in a cell-free biological sample may indicate one or more pregnancy-related states. Metabolites in a cell-free biological sample may be produced (e.g., as an end product or byproduct) as a result of one or more metabolic pathways corresponding to genes associated with a pregnancy-related state. The kit may include instructions for separating or extracting metabolites from a cell-free biological sample and/or for generating a data set using a metabolomics assay, which data sets indicate a quantitative measure (e.g., indicating presence, absence, or relative amount) of each of a plurality of pregnancy-related state-associated metabolites in a cell-free biological sample of the subject.

Trained Algorithm

After processing one or more cell-free biological samples derived from a subject using one or more assays to generate one or more data sets indicative of a pregnancy-related state or a pregnancy-related complication, one or more of the data sets (e.g., at each of a plurality of pregnancy-related state-associated genomic sites) can be processed using a trained algorithm to determine a pregnancy-associated state. For example, a trained algorithm can be used to determine a quantitative measure of a sequence at each of a plurality of pregnancy-associated state-associated genomic sites in the cell-free biological sample. The trained algorithm can be configured to identify a pregnancy-associated status for at least about 25, at least about 50, at least about 100, at least about 150, at least about 200, at least about 250, at least about 300, at least about 350, at least about 400, at least about 450, at least about 500, or more than about 500 independent samples with an accuracy of at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 85%, at least about 90%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, at least about 99%, or more than 99%.

The trained algorithm may include a supervised machine learning algorithm. The trained algorithm may include a classification and regression tree (CART) algorithm. The supervised machine learning algorithm may include, for example, a random forest, a support vector machine (SVM), a neural network, or a deep learning algorithm. The trained algorithm may include a differential expression algorithm. The differential expression algorithm may include a random model, a generalized Poisson (GPseq), a mixed Poisson (TSPM), a Poisson log-linear (PoissonSeq), a negative binomial (edgeR, DESeq, baySeq, NBPSeq), a linear model of MAANOVA fitting, or a combination thereof. The trained algorithm may include an unsupervised machine learning algorithm.

The trained algorithm can be configured to accept multiple input variables and generate one or more output values based on the multiple input variables. The multiple input variables can include one or more data sets indicating pregnancy-related states. For example, the input variables can include a number of sequences corresponding to or aligned with each of the genomic sites associated with the multiple pregnancy-related states. The multiple input variables can also include clinical health data of the subject.

The trained algorithm may include a classifier so that each of one or more output values includes one of a fixed number of possible values (e.g., linear classifier, logistic regression classifier, etc.), indicating the classification of the acellular biological sample by the classifier. The trained algorithm may include a binary classifier so that each of one or more output values includes one of two values (e.g., {0,1}, {positive, negative} or {high risk, low risk}), indicating the classification of the acellular biological sample by the classifier. The trained algorithm may be another type of classifier so that each of one or more output values includes more than two values (e.g., {0,1,2}, {positive, negative or uncertain}, or {high risk, medium risk or low risk}), indicating the classification of the acellular biological sample by the classifier. The output value may include a descriptive label, a numerical value or a combination thereof. Some output values may include a descriptive label. Such a descriptive label may provide an identification or indication of a disease or condition state of an object, and may include, for example, positive, negative, high risk, medium risk, low risk or uncertainty. Such descriptive labels can provide identification of treatment for pregnancy-related states of the object, and can include, for example, therapeutic intervention, duration of therapeutic intervention and/or dosage of therapeutic intervention suitable for treating pregnancy-related conditions. Such descriptive labels can provide identification suitable for secondary clinical trials performed on the object, and can include, for example, imaging detection, blood test, computed tomography (CT) scan, magnetic resonance imaging (MRI) scan, ultrasound scan, chest X-ray, positron emission tomography (PET) scan, PET-CT scan, cell-free biological cytology, amniocentesis, non-invasive prenatal testing (NIPT) or any combination thereof. For example, such descriptive labels can provide the prognosis of pregnancy-related states of the object. As another example, such descriptive labels can provide a relative assessment of the pregnancy-related states of the object (e.g., estimated gestational age expressed in days, weeks or months). Some descriptive labels can be mapped to numerical values, for example, by mapping "positive" to 1 and "negative" to 0.

Some output values may include numerical values, such as binary, integer, or continuous values. Such binary output values may include, for example, {0, 1}, {positive, negative}, or {high risk, low risk}. Such integer output values may include, for example, {0, 1, 2}. Such continuous output values may include, for example, probability values that are at least 0 and not more than 1. Such continuous output values may include, for example, unstandardized probability values that are at least 0. Such continuous output values may indicate a prognosis of a pregnancy-related state of a subject. Some numerical values may be mapped to descriptive labels, for example, by mapping 1 to "positive" and 0 to "negative".

Some output values can be assigned based on one or more cutoff values. For example, if the sample indicates that the probability that the subject has a pregnancy-related condition (e.g., a pregnancy-related complication) is at least 50%, then the binary classification of the sample can assign an output value of "positive" or 1. For example, if the sample indicates that the probability that the subject has a pregnancy-related condition (e.g., a pregnancy-related complication) is less than 50%, then the binary classification of the sample can assign an output value of "negative" or 0. In this case, a single cutoff value of 50% is used to classify the sample into one of two possible binary output values. Examples of single cutoff values can include about 1%, about 2%, about 5%, about 10%, about 15%, about 20%, about 25%, about 30%, about 35%, about 40%, about 45%, about 50%, about 55%, about 60%, about 65%, about 70%, about 75%, about 80%, about 85%, about 90%, about 91%, about 92%, about 93%, about 94%, about 95%, about 96%, about 97%, about 98%, and about 99%.

As another example, a sample classification can be assigned an output value of "positive" or 1 if the sample indicates that the subject has a probability of having a pregnancy-related condition (e.g., a pregnancy-related complication) of at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 85%, at least about 90%, at least about 91%, at least about 92%, at least about 93%, at least about 94%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, at least about 99%, or more. A sample classification can be assigned an output value of "positive" or 1 if the sample indicates that the subject has a probability of having a pregnancy-related condition (e.g., a pregnancy-related complication) of greater than about 50%, greater than about 55%, greater than about 60%, greater than about 65%, greater than about 70%, greater than about 75%, greater than about 80%, greater than about 85%, greater than about 90%, greater than about 91%, greater than about 92%, greater than about 93%, greater than about 94%, greater than about 95%, greater than about 96%, greater than about 97%, greater than about 98%, or greater than about 99%.

A sample classification can be assigned an output value of "negative" or 0 if the sample indicates that the subject has a pregnancy-related condition (e.g., a pregnancy-related complication) with a probability of less than about 50%, less than about 45%, less than about 40%, less than about 35%, less than about 30%, less than about 25%, less than about 20%, less than about 15%, less than about 10%, less than about 9%, less than about 8%, less than about 7%, less than about 6%, less than about 5%, less than about 4%, less than about 3%, less than about 2%, or less than about 1%. A sample classification can be assigned an output value of "negative" or 0 if the sample indicates that the subject has a pregnancy-related condition (e.g., a pregnancy-related complication) with a probability of no greater than about 50%, no greater than about 45%, no greater than about 40%, no greater than about 35%, no greater than about 30%, no greater than about 25%, no greater than about 20%, no greater than about 15%, no greater than about 10%, no greater than about 9%, no greater than about 8%, no greater than about 7%, no greater than about 6%, no greater than about 5%, no greater than about 4%, no greater than about 3%, no greater than about 2%, or no greater than about 1%.

If the sample is not classified as "positive", "negative", 1 or 0, the classification of the sample can be assigned an output value of "uncertain" or 2. In this case, two cutoff value sets are used to classify the sample into one of three possible output values. Examples of cutoff value sets can include {1%, 99%}, {2%, 98%}, {5%, 95%}, {10%, 90%}, {15%, 85%}, {20%, 80%}, {25%, 75%}, {30%, 70%}, {35%, 65%}, {40%, 60%} and {45%, 55%}. Similarly, a set of n cutoff values can be used to classify the sample into one of n+1 possible output values, where n is any positive integer.

The trained algorithm can be trained with multiple independent training samples. Each of the independent training samples can include a cell-free biological sample from the object, an associated data set obtained by measuring the cell-free biological sample (as described elsewhere herein), and one or more known output values corresponding to the cell-free biological sample (e.g., a clinical diagnosis, prognosis, absence or efficacy of a pregnancy-related state of the object). Independent training samples can include cell-free biological samples and associated data sets and outputs obtained from or derived from multiple different objects. Independent training samples can include cell-free biological samples and associated data sets and outputs obtained from the same object at multiple different time points (e.g., regularly such as weekly, biweekly or monthly). Independent training samples can be associated with the presence of pregnancy-related states (e.g., training samples include cell-free biological samples and associated data sets and outputs obtained from or derived from multiple objects known to have pregnancy-related states). Independent training samples can be associated with the absence of pregnancy-related states (e.g., training samples include cell-free biological samples and associated data sets and outputs obtained from or derived from multiple objects known to have no pregnancy-related state diagnosis or have received pregnancy-related state negative test results).

The trained algorithm can be trained with at least about 5, at least about 10, at least about 15, at least about 20, at least about 25, at least about 30, at least about 35, at least about 40, at least about 45, at least about 50, at least about 100, at least about 150, at least about 200, at least about 250, at least about 300, at least about 350, at least about 400, at least about 450, or at least about 500 independent training samples. The independent training samples can include cell-free biological samples associated with the presence of a pregnancy-associated state and/or cell-free biological samples associated with the absence of a pregnancy-associated state. The trained algorithm can be trained with no more than about 500, no more than about 450, no more than about 400, no more than about 350, no more than about 300, no more than about 250, no more than about 200, no more than about 150, no more than about 100, or no more than about 50 independent training samples associated with the presence of a pregnancy-related state. In some embodiments, the cell-free biological sample is independent of the sample used to train the trained algorithm.

The trained algorithm may be trained with a first number of independent training samples associated with the presence of a pregnancy-related state and a second number of independent training samples associated with the absence of the pregnancy-related state. The first number of independent training samples associated with the presence of the pregnancy-related state may be no more than the second number of independent training samples associated with the absence of the pregnancy-related state. The first number of independent training samples associated with the presence of the pregnancy-related state may be equal to the second number of independent training samples associated with the absence of the pregnancy-related state. The first number of independent training samples associated with the presence of the pregnancy-related state may be greater than the second number of independent training samples associated with the absence of the pregnancy-related state.

The trained algorithm can be configured to generate a training image with at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 81%, at least about 82%, at least about 83%, at least about 84%, at least about 85%, at least about 86%, at least about 87%, at least about 88%, at least about 89%, at least about 90%, at least about 91%, at least about 92%, at least about 93%, at least about 94%, at least about 95%, at least about The accuracy of identifying a pregnancy-associated state by the trained algorithm can be calculated as the percentage of independent test samples (e.g., subjects known to have a pregnancy-associated state or subjects with a negative clinical trial result for a pregnancy-associated state) that are correctly identified or classified as having or not having a pregnancy-associated state.

The trained algorithm can be configured to identify a pregnancy-related state with a positive predictive value (PPV) of at least about 5%, at least about 10%, at least about 15%, at least about 20%, at least about 25%, at least about 30%, at least about 35%, at least about 40%, at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 81%, at least about 82%, at least about 83%, at least about 84%, at least about 85%, at least about 86%, at least about 87%, at least about 88%, at least about 89%, at least about 90%, at least about 91%, at least about 92%, at least about 93%, at least about 94%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, at least about 99%, or more. The PPV for identifying a pregnancy-associated state using a trained algorithm can be calculated as the percentage of cell-free biological samples identified or classified as having a pregnancy-associated state that corresponds to subjects who actually have the pregnancy-associated state.

The trained algorithm can be configured to identify a pregnancy-related state with a negative predictive value (NPV) of at least about 5%, at least about 10%, at least about 15%, at least about 20%, at least about 25%, at least about 30%, at least about 35%, at least about 40%, at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 81%, at least about 82%, at least about 83%, at least about 84%, at least about 85%, at least about 86%, at least about 87%, at least about 88%, at least about 89%, at least about 90%, at least about 91%, at least about 92%, at least about 93%, at least about 94%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, at least about 99%, or more. The NPV for identifying a pregnancy-associated state using a trained algorithm can be calculated as the percentage of cell-free biological samples identified or classified as not having the pregnancy-associated state corresponding to subjects who truly do not have the pregnancy-associated state.

The trained algorithm can be configured to identify pregnancy-related states with a clinical sensitivity of at least about 5%, at least about 10%, at least about 15%, at least about 20%, at least about 25%, at least about 30%, at least about 35%, at least about 40%, at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 81%, at least about 82%, at least about 83%, at least about 84%, at least about 85%, at least about 86%, at least about 87%, at least about 88%, at least about 89%, at least about 90%, at least about 91%, at least about 92%, at least about 93%, at least about 94%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, at least about 99%, at least about 100%, at least about 101%, at least about 102%, at least about 103%, at least about 104%, at least about 105%, at least about 106%, at least about 107%, at least about 108%, at least about 109%, at least about 110%, at least about 111%, at least about 112%, at least about 113%, at least about 114%, at least about 115%, at least about 116%, at least about 117%, at least about 118%, at least about 119%, The clinical sensitivity of using the trained algorithm to identify a pregnancy-associated state can be calculated as the percentage of independently tested samples associated with the presence of a pregnancy-associated state (e.g., a subject known to have a pregnancy-associated state) that are correctly identified or classified as having the pregnancy-associated state.

The trained algorithm can be configured to identify pregnancy-related states with a clinical specificity of at least about 5%, at least about 10%, at least about 15%, at least about 20%, at least about 25%, at least about 30%, at least about 35%, at least about 40%, at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 81%, at least about 82%, at least about 83%, at least about 84%, at least about 85%, at least about 86%, at least about 87%, at least about 88%, at least about 89%, at least about 90%, at least about 91%, at least about 92%, at least about 93%, at least about 94%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, at least about 99%, at least about 100%, at least about 101%, at least about 102%, at least about 103%, at least about 104%, at least about 105%, at least about 106%, at least about 107%, at least about 108%, at least about 109%, at least about 110%, at least about 111%, at least about 112%, at least about 113%, at least about 114%, at least about 115%, at least about 116%, at least about 117%, at least about 118%, at least about 119%, %, at least about 99%, at least about 99.1%, at least about 99.2%, at least about 99.3%, at least about 99.4%, at least about 99.5%, at least about 99.6%, at least about 99.7%, at least about 99.8%, at least about 99.9%, at least about 99.99%, at least about 99.999%, or more. The clinical specificity of identifying a pregnancy-associated state using a trained algorithm can be calculated as the percentage of independent test samples (e.g., subjects with a negative clinical trial result for a pregnancy-associated state) associated with the absence of a pregnancy-associated state that are correctly identified or classified as not having a pregnancy-associated state.

The trained algorithm is configured to identify a pregnancy-related state with an area under the curve (AUC) of at least about 0.50, at least about 0.55, at least about 0.60, at least about 0.65, at least about 0.70, at least about 0.75, at least about 0.80, at least about 0.81, at least about 0.82, at least about 0.83, at least about 0.84, at least about 0.85, at least about 0.86, at least about 0.87, at least about 0.88, at least about 0.89, at least about 0.90, at least about 0.91, at least about 0.92, at least about 0.93, at least about 0.94, at least about 0.95, at least about 0.96, at least about 0.97, at least about 0.98, at least about 0.99, or more. The AUC can be calculated as the integral of a receiver operating characteristic (ROC) curve (eg, the area under the ROC curve) associated with a trained algorithm that classifies a cell-free biological sample as having or not having a pregnancy-related state.

The trained algorithm can be adjusted or tuned to improve one or more of performance, accuracy, PPV, NPV, clinical sensitivity, clinical specificity, or AUC in identifying pregnancy-related states. The trained algorithm can be adjusted or tuned by adjusting parameters of the trained algorithm (e.g., a set of cutoff values for classifying acellular biological samples described elsewhere herein, or weights of a neural network). The trained algorithm can be adjusted or tuned continuously during the training process or after the training process is completed.

After initially training the trained algorithm, a subset of the inputs can be identified as the most influential or important to be included for high-quality classification. For example, a subset of multiple pregnancy-related state-associated genomic sites can be identified as the most influential or important to be included for high-quality classification or identification of pregnancy-related states (or subtypes of pregnancy-related states). Multiple pregnancy-related state-associated genomic sites or subsets thereof can be ranked based on classification metrics that indicate the influence or importance of each genomic site for high-quality classification or identification of pregnancy-related states (or subtypes of pregnancy-related states). In some cases, such metrics can be used to significantly reduce the number of input variables (e.g., predictor variables) that can be used to train the trained algorithm to a desired performance level (e.g., based on a desired minimum accuracy, PPV, NPV, clinical sensitivity, clinical specificity, AUC, or a combination thereof). For example, if training a trained algorithm with a plurality of variables including dozens or hundreds of input variables in the trained algorithm results in a classification accuracy greater than 99%, then training the trained algorithm with only a selected subset of no more than about 5, no more than about 10, no more than 15, no more than 20, no more than 25, no more than 30, no more than 35, no more than 40, no more than 45, no more than 50, or no more than about 100 of such most influential or important input variables of the plurality of input variables may produce a reduced but still acceptable classification accuracy (e.g., For example, at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 81%, at least about 82%, at least about 83%, at least about 84%, at least about 85%, at least about 86%, at least about 87%, at least about 88%, at least about 89%, at least about 90%, at least about 91%, at least about 92%, at least about 93%, at least about 94%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, or at least about 99%). The subset can be selected by sorting the entire plurality of input variables and selecting a predetermined number (e.g., no more than about 5, no more than about 10, no more than about 15, no more than about 20, no more than about 25, no more than about 30, no more than about 35, no more than about 40, no more than about 45, no more than about 50, or no more than about 100) of input variables with the best classification indicators.

Identify or monitor pregnancy-related conditions

After processing the dataset using the trained algorithm, a pregnancy-associated state or pregnancy-associated complication can be identified or monitored in a subject. The identification can be based at least in part on quantitative measures of sequence reads of a dataset of grouped genomic sites associated with a pregnancy-associated state (e.g., quantitative measures of RNA transcripts or DNA at genomic sites associated with a pregnancy-associated state), proteomic data including quantitative measures of proteins of a dataset of grouped proteins associated with a pregnancy-associated state, and/or metabolomic data including quantitative measures of grouped metabolites associated with a pregnancy-associated state.

Pregnancy-associated states can be identified in subjects with an accuracy of at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 81%, at least about 82%, at least about 83%, at least about 84%, at least about 85%, at least about 86%, at least about 87%, at least about 88%, at least about 89%, at least about 90%, at least about 91%, at least about 92%, at least about 93%, at least about 94%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, at least about 99% or more. The accuracy of identifying pregnancy-associated states by a trained algorithm can be calculated as the percentage of independently tested samples (e.g., subjects known to have a pregnancy-associated state or subjects with a negative clinical trial result for a pregnancy-associated state) that are correctly identified or classified as having or not having a pregnancy-associated state.

A pregnancy-related state can be identified in a subject with a positive predictive value (PPV) of at least about 5%, at least about 10%, at least about 15%, at least about 20%, at least about 25%, at least about 30%, at least about 35%, at least about 40%, at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 81%, at least about 82%, at least about 83%, at least about 84%, at least about 85%, at least about 86%, at least about 87%, at least about 88%, at least about 89%, at least about 90%, at least about 91%, at least about 92%, at least about 93%, at least about 94%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, at least about 99%, or more. The PPV for identifying a pregnancy-associated state using a trained algorithm can be calculated as the percentage of cell-free biological samples identified or classified as having a pregnancy-associated state that corresponds to subjects who actually have the pregnancy-associated state.

A pregnancy-related state can be identified in a subject with a negative predictive value (NPV) of at least about 5%, at least about 10%, at least about 15%, at least about 20%, at least about 25%, at least about 30%, at least about 35%, at least about 40%, at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 81%, at least about 82%, at least about 83%, at least about 84%, at least about 85%, at least about 86%, at least about 87%, at least about 88%, at least about 89%, at least about 90%, at least about 91%, at least about 92%, at least about 93%, at least about 94%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, at least about 99%, or more. The NPV for identifying a pregnancy-associated state using a trained algorithm can be calculated as the percentage of cell-free biological samples identified or classified as not having the pregnancy-associated state corresponding to subjects who truly do not have the pregnancy-associated state.

A pregnancy-related state can be identified in a subject with a clinical sensitivity of at least about 5%, at least about 10%, at least about 15%, at least about 20%, at least about 25%, at least about 30%, at least about 35%, at least about 40%, at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 81%, at least about 82%, at least about 83%, at least about 84%, at least about 85%, at least about 86%, at least about 87%, at least about 88%. %, at least about 89%, at least about 90%, at least about 91%, at least about 92%, at least about 93%, at least about 94%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, at least about 99%, at least about 99.1%, at least about 99.2%, at least about 99.3%, at least about 99.4%, at least about 99.5%, at least about 99.6%, at least about 99.7%, at least about 99.8%, at least about 99.9%, at least about 99.99%, at least about 99.999%, or more. The clinical sensitivity of using the trained algorithm to identify a pregnancy-associated state can be calculated as the percentage of independently tested samples associated with the presence of a pregnancy-associated state (e.g., a subject known to have a pregnancy-associated state) that are correctly identified or classified as having the pregnancy-associated state.

A pregnancy-related state can be identified in a subject with a clinical specificity of at least about 5%, at least about 10%, at least about 15%, at least about 20%, at least about 25%, at least about 30%, at least about 35%, at least about 40%, at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 81%, at least about 82%, at least about 83%, at least about 84%, at least about 85%, at least about 86%, at least about 87%, at least about 88%. %, at least about 89%, at least about 90%, at least about 91%, at least about 92%, at least about 93%, at least about 94%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, at least about 99%, at least about 99.1%, at least about 99.2%, at least about 99.3%, at least about 99.4%, at least about 99.5%, at least about 99.6%, at least about 99.7%, at least about 99.8%, at least about 99.9%, at least about 99.99%, at least about 99.999%, or more. The clinical specificity of identifying a pregnancy-associated state using a trained algorithm can be calculated as the percentage of independent test samples associated with the absence of a pregnancy-associated state (e.g., subjects with a negative clinical trial result for a pregnancy-associated state) that are correctly identified or classified as not having a pregnancy-associated state.

In one aspect, the present disclosure provides a method for determining that a subject is at risk for preterm birth, comprising assaying a cell-free biological sample derived from the subject to generate a data set indicative of risk for preterm birth with a specificity of at least 80%, and using a trained algorithm trained on a sample independent of the cell-free biological sample to determine that the subject is at risk for preterm birth with an accuracy of at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 81%, at least about 82%, at least about 83%, at least about 84%, at least about 85%, at least about 86%, at least about 87%, at least about 88%, at least about 89%, at least about 90%, at least about 91%, at least about 92%, at least about 93%, at least about 94%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, at least about 99% or more.

After identifying the pregnancy-related state in the object, the subtype of the pregnancy-related state can be further identified (e.g., selected from a plurality of subtypes of the pregnancy-related state). The subtype of the pregnancy-related state can be determined at least in part based on the following: the quantitative measurement of the sequence readings of the data set of the grouping of the pregnancy-related state associated genomic sites (e.g., the quantitative measurement of the RNA transcript or DNA at the pregnancy-related state associated genomic sites), the proteomic data including the quantitative measurement of the protein of the data set of the grouping of the pregnancy-related state associated protein, and/or the metabolomic data including the quantitative measurement of the grouping of the pregnancy-related state associated metabolites. For example, the object can be identified as being at risk of a preterm birth subtype (e.g., selected from a plurality of preterm birth subtypes). After the object is identified as being at risk of a preterm birth subtype, the clinical intervention of the object can be selected based at least in part on the risk of the preterm birth subtype in which the object is identified. In some embodiments, the clinical intervention is selected from a variety of clinical interventions (e.g., clinically indicated for different subtypes of preterm birth).

In some embodiments, the trained algorithm can determine that a subject is at a risk of preterm birth of at least about 5%, at least about 10%, at least about 15%, at least about 20%, at least about 25%, at least about 30%, at least about 35%, at least about 40%, at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 81%, at least about 82%, at least about 83%, at least about 84%, at least about 85%, at least about 86%, at least about 87%, at least about 88%, at least about 89%, at least about 90%, at least about 91%, at least about 92%, at least about 93%, at least about 94%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, at least about 99%, or more.

The trained algorithm can determine that a subject is at risk for preterm birth with an accuracy of at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 81%, at least about 82%, at least about 83%, at least about 84%, at least about 85%, at least about 86%, at least about 87%, at least about 88%, at least about 89%, at least about 90%, at least about 91%, at least about 92%. , at least about 93%, at least about 94%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, at least about 99%, at least about 99.1%, at least about 99.2%, at least about 99.3%, at least about 99.4%, at least about 99.5%, at least about 99.6%, at least about 99.7%, at least about 99.8%, at least about 99.9%, at least about 99.99%, at least about 99.999%, or more.

When the subject is identified as having a pregnancy-related condition, therapeutic intervention may be optionally provided to the subject (e.g., an appropriate treatment regimen is prescribed to treat the subject's pregnancy-related condition). Therapeutic intervention may include prescription of an effective dose of a drug, further testing or evaluation of the pregnancy-related condition, further monitoring of the pregnancy-related condition, induction of labor or inhibition of labor, or a combination thereof. If the subject is currently receiving a course of treatment for a pregnancy-related condition, therapeutic intervention may include a subsequent different course of treatment (e.g., to increase efficacy due to the ineffectiveness of the current course of treatment).

The therapeutic intervention may include recommending that the subject undergo a secondary clinical trial to confirm the diagnosis of the pregnancy-related condition. The secondary clinical trial may include imaging tests, blood tests, computed tomography (CT) scans, magnetic resonance imaging (MRI) scans, ultrasound scans, chest x-rays, positron emission tomography (PET) scans, PET-CT scans, cell-free biocytology, amniocentesis, non-invasive prenatal testing (NIPT), or any combination thereof.

The following items can be evaluated over a period of time: quantitative measures of sequence reads of a data set of grouped genomic sites associated with pregnancy-related states (e.g., quantitative measures of RNA transcripts or DNA at genomic sites associated with pregnancy-related states), proteomic data including quantitative measures of proteins in a data set of grouped proteins associated with pregnancy-related states, and/or metabolomic data including quantitative measures of grouped metabolites associated with pregnancy-related states, to monitor patients (e.g., subjects with pregnancy-related states or being treated for pregnancy-related states). In such cases, the quantitative measures of the patient data set can change during treatment. For example, the quantitative measures of the data set of patients with a reduced risk of pregnancy-related states due to effective treatment can be shifted toward the profile or distribution of healthy subjects (e.g., subjects without pregnancy-related complications). Conversely, for example, the quantitative measures of the data set of patients with an increased risk of pregnancy-related states due to ineffective treatment can be shifted toward the profile or distribution of subjects with a higher risk of pregnancy-related states or a more advanced pregnancy-related state.

The pregnancy-related state of the subject can be monitored by monitoring the course of treatment for the pregnancy-related state of the subject. Monitoring can include evaluating the pregnancy-related state of the subject at two or more time points. The evaluation can be based at least on quantitative measurements of sequence reads of a data set of grouped genomic sites associated with the pregnancy-related state (e.g., quantitative measurements of RNA transcripts or DNA at genomic sites associated with the pregnancy-related state), proteomic data including quantitative measurements of proteins of a data set of grouped proteins associated with the pregnancy-related state, and/or metabolomic data including quantitative measurements of grouped metabolites associated with the pregnancy-related state.

In some embodiments, a quantitative measure of sequence reads in a data set of grouped genomic loci associated with a pregnancy-associated state (e.g., a quantitative measure of RNA transcripts or DNA at a genomic loci associated with a pregnancy-associated state), proteomic data comprising quantitative measures of proteins in a data set of grouped proteins associated with a pregnancy-associated state, and/or a difference in metabolomic data comprising quantitative measures of grouped metabolites associated with a pregnancy-associated state determined between two or more time points can indicate one or more clinical indications, such as (i) diagnosis of a pregnancy-associated state in a subject, (ii) prognosis of a pregnancy-associated state in a subject, (iii) increased risk of a pregnancy-associated state in a subject, (iv) decreased risk of a pregnancy-associated state in a subject, (v) effectiveness of a course of treatment for treating a pregnancy-associated state in a subject, and (vi) ineffectiveness of a course of treatment for treating a pregnancy-associated state in a subject.

In some embodiments, a quantitative measure of sequence reads of a data set of grouped genomic sites associated with a pregnancy-associated state (e.g., a quantitative measure of RNA transcripts or DNA at a genomic site associated with a pregnancy-associated state), a proteomic data comprising a quantitative measure of proteins of a data set of grouped proteins associated with a pregnancy-associated state, and/or a difference in metabolomic data comprising a quantitative measure of grouped metabolites associated with a pregnancy-associated state determined between two or more time points can indicate a diagnosis of a pregnancy-associated state in a subject. For example, if a pregnancy-associated state is not detected in a subject at an earlier time point, but is detected in a subject at a later time point, the difference indicates a diagnosis of a pregnancy-associated state in the subject. A clinical action or decision can be made based on this indication of a diagnosis of a pregnancy-associated state in a subject, for example, prescribing a new therapeutic intervention for a subject. A clinical action or decision can include recommending a subject to undergo a secondary clinical trial to confirm the diagnosis of a pregnancy-associated state. The secondary clinical trial may include imaging tests, blood tests, computed tomography (CT) scans, magnetic resonance imaging (MRI) scans, ultrasound scans, chest X-rays, positron emission tomography (PET) scans, PET-CT scans, cell-free biocytology, amniocentesis, non-invasive prenatal testing (NIPT), or any combination thereof.

In some embodiments, a quantitative measure of sequence reads in a data set of grouped genomic loci associated with a pregnancy-associated state (e.g., a quantitative measure of RNA transcripts or DNA at a pregnancy-associated state genomic loci), a proteomic data comprising quantitative measures of proteins in a data set of grouped proteins associated with a pregnancy-associated state, and/or a difference in metabolomic data comprising quantitative measures of grouped metabolites associated with a pregnancy-associated state determined between two or more time points can indicate a prognosis for a pregnancy-associated state in a subject.

In some embodiments, a difference in a quantitative measure of sequence reads of a data set of grouped genomic sites associated with a pregnancy-associated state (e.g., a quantitative measure of RNA transcripts or DNA at a genomic site associated with a pregnancy-associated state), a proteomic data comprising a quantitative measure of a protein of a data set of grouped proteins associated with a pregnancy-associated state, and/or a metabolomic data comprising a quantitative measure of a grouped metabolite associated with a pregnancy-associated state determined between two or more time points can indicate that the subject has an increased risk of a pregnancy-associated state. For example, if a pregnancy-associated state is detected in the subject at an earlier time point and a later time point, and the difference is a negative difference (e.g., a quantitative measure of sequence reads of a data set of grouped genomic sites associated with a pregnancy-associated state (e.g., a quantitative measure of RNA transcripts or DNA at a genomic site associated with a pregnancy-associated state), a proteomic data comprising a quantitative measure of a protein of a data set of grouped proteins associated with a pregnancy-associated state, and/or a metabolomic data comprising a quantitative measure of a grouped metabolite associated with a pregnancy-associated state increases from an earlier time point to a later time point), then the difference can indicate that the subject has an increased risk of a pregnancy-associated state. A clinical action or decision can be made based on this indication of an increased risk of a pregnancy-related condition, such as prescribing a new therapeutic intervention or switching a therapeutic intervention prescription for the subject (e.g., ending the current treatment and prescribing a new treatment prescription). The clinical action or decision may include recommending that the subject undergo a secondary clinical trial to confirm the increased risk of a pregnancy-related condition. The secondary clinical trial may include imaging tests, blood tests, computed tomography (CT) scans, magnetic resonance imaging (MRI) scans, ultrasound scans, chest x-rays, positron emission tomography (PET) scans, PET-CT scans, cell-free biocytology, amniocentesis, non-invasive prenatal testing (NIPT), or any combination thereof.

In some embodiments, a difference in a quantitative measure of sequence reads of a data set of grouped genomic sites associated with a pregnancy-associated state (e.g., a quantitative measure of RNA transcripts or DNA at a genomic site associated with a pregnancy-associated state), a proteomic data comprising a quantitative measure of a protein of a data set of grouped proteins associated with a pregnancy-associated state, and/or a metabolomic data comprising a quantitative measure of a grouped metabolite associated with a pregnancy-associated state determined between two or more time points can indicate that the subject has a reduced risk of a pregnancy-associated state. For example, if a pregnancy-associated state is detected in the subject at an earlier time point and a later time point, and the difference is a positive difference (e.g., a quantitative measure of sequence reads of a data set of grouped genomic sites associated with a pregnancy-associated state (e.g., a quantitative measure of RNA transcripts or DNA at a genomic site associated with a pregnancy-associated state), a proteomic data comprising a quantitative measure of a protein of a data set of grouped proteins associated with a pregnancy-associated state, and/or a metabolomic data comprising a quantitative measure of a grouped metabolite associated with a pregnancy-associated state decreases from an earlier time point to a later time point), then the difference can indicate that the subject has a reduced risk of a pregnancy-associated state. A clinical action or decision (e.g., to continue or terminate a current therapeutic intervention) can be made based on this indication of a reduced risk of a pregnancy-related condition in the subject. The clinical action or decision can include recommending that the subject undergo a secondary clinical trial to confirm the reduced risk of a pregnancy-related condition. The secondary clinical trial can include imaging tests, blood tests, computed tomography (CT) scans, magnetic resonance imaging (MRI) scans, ultrasound scans, chest x-rays, positron emission tomography (PET) scans, PET-CT scans, cell-free biocytology, amniocentesis, non-invasive prenatal testing (NIPT), or any combination thereof.

In some embodiments, a quantitative measure of sequence reads in a data set of grouped genomic sites associated with a pregnancy-associated state (e.g., a quantitative measure of RNA transcripts or DNA at a genomic site associated with a pregnancy-associated state), a proteomic data comprising a quantitative measure of proteins in a data set of grouped proteins associated with a pregnancy-associated state, and/or a difference in metabolomic data comprising a quantitative measure of grouped metabolites associated with a pregnancy-associated state determined between two or more time points can indicate the effectiveness of a course of treatment for treating a pregnancy-associated state in a subject. For example, if a pregnancy-associated state is detected in a subject at an earlier time point, but not detected in a subject at a later time point, the difference can indicate the effectiveness of the course of treatment for treating the pregnancy-associated state in the subject. A clinical action or decision can be made based on this indication of the effectiveness of the course of treatment for treating the pregnancy-associated state in a subject, for example, to continue or terminate a current therapeutic intervention for the subject. The clinical action or decision can include recommending that the subject undergo a secondary clinical trial to confirm the effectiveness of the course of treatment for treating the pregnancy-associated state. The secondary clinical trial may include imaging tests, blood tests, computed tomography (CT) scans, magnetic resonance imaging (MRI) scans, ultrasound scans, chest X-rays, positron emission tomography (PET) scans, PET-CT scans, cell-free biocytology, amniocentesis, non-invasive prenatal testing (NIPT), or any combination thereof.

In some embodiments, a difference in quantitative measures of sequence reads in a data set of grouped genomic loci associated with a pregnancy-associated state (e.g., quantitative measures of RNA transcripts or DNA at genomic loci associated with a pregnancy-associated state), proteomic data comprising quantitative measures of proteins in a data set of grouped proteins associated with a pregnancy-associated state, and/or metabolomic data comprising quantitative measures of grouped metabolites associated with a pregnancy-associated state determined between two or more time points may indicate that a course of therapy for treating a subject's pregnancy-associated condition is ineffective. For example, if a pregnancy-associated state is detected in a subject at an earlier time point and a later time point, and if the difference is a negative difference or a zero difference (e.g., a quantitative measure of sequence reads (e.g., a quantitative measure of RNA transcripts or DNA at a pregnancy-associated state-associated genomic site) of a data set of grouped genomic sites associated with a pregnancy-associated state, a proteomic data comprising quantitative measures of proteins of a data set of grouped proteins associated with a pregnancy-associated state, and/or a metabolomic data comprising quantitative measures of grouped metabolites associated with a pregnancy-associated state increases or remains at a constant level from the earlier time point to the later time point), and if an effective treatment was indicated at the earlier time point, then the difference can indicate ineffectiveness of the course of treatment for treating the pregnancy-associated state in the subject. A clinical action or decision can be made based on this indication of ineffectiveness of the course of treatment for treating the pregnancy-associated state in the subject, such as, for example, ending the current therapeutic intervention and/or switching to (e.g., prescribing) a different new therapeutic intervention for the subject. The clinical action or decision can include recommending that the subject undergo a secondary clinical trial to confirm the ineffectiveness of the course of treatment for treating the pregnancy-associated state. The secondary clinical trial may include imaging tests, blood tests, computed tomography (CT) scans, magnetic resonance imaging (MRI) scans, ultrasound scans, chest X-rays, positron emission tomography (PET) scans, PET-CT scans, cell-free biocytology, amniocentesis, non-invasive prenatal testing (NIPT), or any combination thereof.

In another aspect, the present disclosure provides a computer-implemented method for predicting a subject's risk of preterm birth, comprising: (a) receiving clinical health data of the subject, wherein the clinical health data comprises a plurality of quantitative or categorical measurements of the subject; (b) processing the subject's clinical health data using a trained algorithm to determine a risk score indicating the subject's risk of preterm birth; and (c) electronically outputting a report of the risk score indicating the subject's risk of preterm birth.

In some embodiments, for example, clinical health data includes one or more quantitative measurements of a subject, such as age, weight, height, body mass index (BMI), blood pressure, heart rate, blood glucose level, number of previous pregnancies, and number of previous births. As another example, clinical health data may include one or more categorical measurements, such as race, ethnicity, medication or other clinical treatment history, smoking history, drinking history, daily activities or fitness levels, genetic test results, blood test results, imaging results, and fetal screening results.

In some embodiments, a computer or mobile device application is used to perform a computer-implemented method for predicting the risk of premature birth of an object. For example, an object can use a computer or mobile device application to input its own clinical health data, including quantitative and/or categorical measurements. Then, the computer or mobile device application can use a trained algorithm to process the clinical health data to determine a risk score for the risk of premature birth of an object. Then, the computer or mobile device application can display a report on the risk score for the risk of premature birth of an object.

In some embodiments, the risk score indicating the risk of premature birth for the subject can be refined by performing one or more subsequent clinical tests on the subject. For example, a physician can refer the subject for one or more subsequent clinical tests (e.g., ultrasound imaging or blood tests) based on the initial risk score. Next, the computer or mobile device application can process the results from the one or more subsequent clinical tests using the trained algorithm to determine an updated risk score indicating the risk of premature birth for the subject.

In some embodiments, the risk score includes the likelihood of a subject having a preterm birth within a predetermined duration. For example, the predetermined duration can be about 1 hour, about 2 hours, about 4 hours, about 6 hours, about 8 hours, about 10 hours, about 12 hours, about 14 hours, about 16 hours, about 18 hours, about 20 hours, about 22 hours, about 24 hours, about 1.5 days, about 2 days, about 2.5 days, about 3 days, about 3.5 days, about 4 days, about 4.5 days, about 5 days, about 5.5 days, about 6 days, about 6.5 days, about 7 days, about 8 days, about 9 days, about 10 days, about 12 days, about 14 days, about 3 weeks, about 4 weeks, about 5 weeks, about 6 weeks, about 7 weeks, about 8 weeks, about 9 weeks, about 10 weeks, about 11 weeks, about 12 weeks, about 13 weeks, or more than about 13 weeks.

Output reports on pregnancy-related status

After identifying the pregnancy-related state of the subject or monitoring the increased risk of the pregnancy-related state, a report indicating (e.g., identifying or providing an indication) the pregnancy-related state of the subject can be output electronically. The subject may not exhibit a pregnancy-related state (e.g., no symptoms of a pregnancy-related state, such as pregnancy-related complications). The report can be presented on a graphical user interface (GUI) of the user's electronic device. The user can be a subject, a caregiver, a doctor, a nurse, or other health care worker.

The report may include one or more clinical indications, such as (i) diagnosis of a subject's pregnancy-related condition, (ii) prognosis of a subject's pregnancy-related condition, (iii) increased risk of a subject's pregnancy-related condition, (iv) reduced risk of a subject's pregnancy-related condition, (v) effectiveness of a course of treatment for a subject's pregnancy-related condition, and (vi) ineffectiveness of a course of treatment for a subject's pregnancy-related condition. The report may include one or more clinical actions or decisions made based on these one or more clinical indications. Such clinical actions or decisions may be directed to therapeutic intervention, induction of labor or inhibition of labor, or further clinical evaluation or testing of a subject's pregnancy-related condition.

For example, the clinical indication of the diagnosis of the pregnancy-related state of the object can be accompanied by the clinical action of prescribing a new therapeutic intervention prescription for the object. As another example, the clinical indication of the increased risk of the pregnancy-related state of the object can be accompanied by the clinical action of prescribing a new therapeutic intervention prescription or switching therapeutic intervention (e.g., ending the current treatment and prescribing a new treatment prescription) for the object. As another example, the clinical indication of the reduced risk of the pregnancy-related state of the object can be accompanied by the clinical action of continuing or ending the current therapeutic intervention of the object. As another example, the clinical indication of the effectiveness of the course of treatment for the pregnancy-related state of the object can be accompanied by the clinical action of continuing or ending the current therapeutic intervention of the object. As another example, the clinical indication of the invalidity of the pregnancy-related state of the object of the treatment course can be accompanied by ending the current therapeutic intervention and/or switching to (e.g., prescribing) different new therapeutic interventions for the object.

Computer Systems

The present disclosure provides computer systems programmed to implement the methods of the present disclosure. Figure 1 shows a computer system 101 that is programmed or otherwise configured to, for example, (i) train and test a trained algorithm, (ii) use the trained algorithm to process data to determine a subject's pregnancy-related state, (iii) determine a quantitative measure indicative of a subject's pregnancy-related state, (iv) identify or monitor a subject's pregnancy-related state, and (v) electronically output a report indicative of a subject's pregnancy-related state.

Computer system 101 can coordinate various aspects of the analysis, calculation, and generation of the present disclosure, such as (i) training and testing a trained algorithm, (ii) using a trained algorithm to process data to determine a subject's pregnancy-related state, (iii) determining a quantitative measure indicative of a subject's pregnancy-related state, (iv) identifying or monitoring a subject's pregnancy-related state, and (v) electronically outputting a report indicative of a subject's pregnancy-related state. Computer system 101 can be a user's electronic device or a computer system remotely located relative to the electronic device. The electronic device can be a mobile electronic device.

Computer system 101 includes a central processing unit (CPU, also referred to herein as a "processor" and "computer processor") 105, which may be a single-core or multi-core processor, or may be multiple processors for parallel processing. Computer system 101 also includes a memory or storage unit 110 (e.g., random access memory, read-only memory, flash memory), an electronic storage unit 115 (e.g., a hard disk), a communication interface 120 (e.g., a network adapter) for communicating with one or more other systems, and peripherals 125, such as cache, other memory, data storage devices, and/or electronic display adapters. Memory 110, storage unit 115, interface 120, and peripherals 125 communicate with CPU 105 via a communication bus (solid lines) such as a motherboard. Storage unit 115 may be a data storage unit (or data repository) for storing data. By means of communication interface 120, computer system 101 may be operatively coupled to a computer network ("network") 130. The network 130 may be the Internet, the internet and/or an extranet, or an intranet and/or an extranet in communication with the Internet.

In some cases, network 130 is a telecommunications and/or data network. Network 130 may include one or more computer servers that can enable distributed computing, such as cloud computing. For example, one or more computer servers may enable cloud computing on network 130 (“cloud”) to perform analysis, calculations, and generate various aspects of the present disclosure, such as (i) training and testing trained algorithms, (ii) using trained algorithms to process data to determine a pregnancy-related state of a subject, (iii) determining a quantitative measure indicating a pregnancy-related state of a subject, (iv) identifying or monitoring a pregnancy-related state of a subject, and (v) electronically outputting a report indicating a pregnancy-related state of a subject. Such cloud computing may be provided by cloud computing platforms, such as Amazon Web Services (AWS), Microsoft Azure, Google Cloud Platform, and IBM Cloud. In some cases, with the aid of computer system 101, network 130 may implement a peer-to-peer network that may enable devices coupled to computer system 101 to act as clients or servers.

The CPU 105 may include one or more computer processors and/or one or more graphics processing units (GPUs). The CPU 105 may execute a series of machine-readable instructions, which may be embodied in a program or software. The instructions may be stored in a storage location, such as a memory 110. The instructions may be directed to the CPU 105, which may then program or otherwise configure the CPU 105 to implement the methods of the present disclosure. Examples of operations performed by the CPU 105 may include fetching, decoding, executing, and writing back.

CPU 105 may be part of a circuit, such as an integrated circuit. One or more other components of system 101 may be included in the circuit. In some cases, the circuit is an application specific integrated circuit (ASIC).

Storage unit 115 may store files, such as drivers, libraries, and saved programs. Storage unit 115 may store user data, such as user preferences and user programs. In some cases, computer system 101 may include one or more additional data storage units external to computer system 101, such as located on a remote server that communicates with computer system 101 via an intranet or the Internet.

Computer system 101 can communicate with one or more remote computer systems via network 130. For example, computer system 101 can communicate with a user's remote computer system. Examples of remote computer systems include personal computers (e.g., portable PCs), tablet personal computers or tablet computers (e.g., iPad, GalaxyTab), phones, smartphones (e.g. iPhone, Android-enabled devices, ) or a personal digital assistant. Users can access the computer system 101 through the network 130.

The methods described herein may be implemented by machine (e.g., computer processor) executable code stored in an electronic storage location of computer system 101 (e.g., on memory 110 or electronic storage unit 115). Machine executable or machine readable code may be provided in the form of software. During use, the code may be executed by processor 105. In some cases, the code may be retrieved from storage unit 115 and stored on memory 110 so that it is accessible to processor 105 at any time. In some cases, electronic storage unit 115 may be excluded and machine executable instructions are stored on memory 110.

The code may be pre-compiled and configured for use with a machine having a processor suitable for executing the code, or may be compiled at run time. The code may be provided in a programming language which may be selected to enable the code to be executed in a pre-compiled or compiled manner.

Various aspects of the systems and methods provided herein, such as computer system 101, can be embodied in programming. Various aspects of the technology can be considered as "products" or "articles", usually in the form of machine (or processor) executable code and/or related data or embodied in a machine-readable medium. The machine executable code can be stored on an electronic storage unit, such as a memory (e.g., read-only memory, random access memory, flash memory) or a hard disk. "Storage" type media can include any or all tangible memories of a computer, processor, etc., or its related modules, such as various semiconductor memories, tape drives, disk drives, etc., which can provide non-temporary storage for software programming at any time. All or part of the software can sometimes communicate through the Internet or various other telecommunications networks. For example, such communications can, for example, enable software to be loaded from one computer or processor to another computer or processor, for example, from a management server or host to a computer platform of an application server. Therefore, another type of medium that can carry software elements includes light, electricity, and electromagnetic waves, such as those used on physical interfaces between local devices through wired and optical landline networks and through various air links. Physical elements that carry such waves, such as wired or wireless links, optical links, etc., can also be considered media that carry the software. As used herein, unless limited to non-transitory, tangible "storage" media, terms such as computer or machine "readable media" refer to any medium that participates in providing instructions to a processor for execution.

Thus, machine-readable media, such as computer executable code, may take a variety of forms, including but not limited to tangible storage media, carrier media, or physical transmission media. Non-volatile storage media include, for example, optical or magnetic disks (such as any storage device in any computer, etc.), such as may be used to implement a database, etc., as shown in the accompanying drawings. Volatile storage media include dynamic memory, such as the main memory of such a computer platform. Tangible transmission media include coaxial cables; copper wire and optical fiber, including the wires that make up a bus within a computer system. Carrier transmission media may take the form of an electrical or electromagnetic signal, or an acoustic or light wave, such as those generated during radio frequency (RF) and infrared (IR) data communications. Thus, common forms of computer-readable media include, for example: a floppy disk, a diskette, a hard disk, magnetic tape, any other magnetic medium, a CD-ROM, a DVD or DVD-ROM, any other optical medium, a punched card tape, any other physical storage medium having a pattern of holes, a RAM, a ROM, a PROM and an EPROM, a FLASH-EPROM, any other memory chip or cartridge, a carrier wave that transmits data or instructions, a cable or link that transmits such a carrier wave, or any other medium from which a computer can read programming code and/or data. Many of these forms of computer-readable media can participate in conveying one or more sequences of one or more instructions to a processor for execution.

Computer system 101 may include or communicate with an electronic display 135 that includes a user interface (UI) 140 for providing, for example, (i) a visual display indicating training and testing of a trained algorithm, (ii) a visual display of data indicating a pregnancy-related state of a subject, (iii) a quantitative measure of a pregnancy-related state of a subject, (iv) an identification of a subject as having a pregnancy-related state, or (v) an electronic report indicating a pregnancy-related state of a subject. Examples of UIs include, but are not limited to, graphical user interfaces (GUIs) and web-based user interfaces.

The methods and systems of the present disclosure may be implemented by one or more algorithms. The algorithms may be implemented by way of software when executed by the central processing unit 205. The algorithms may, for example, (i) train and test the trained algorithms, (ii) use the trained algorithms to process data to determine a subject's pregnancy-related state, (iii) determine a quantitative measure indicative of a subject's pregnancy-related state, (iv) identify or monitor a subject's pregnancy-related state, and (v) electronically output a report indicative of a subject's pregnancy-related state.

Example

Example 1: Early prediction of spontaneous preterm birth and late miscarriage in a high-risk population using molecular subtyping and cfRNA profiling

Using the systems and methods of the present disclosure, early molecular markers for preterm birth (PTB) and stillbirth are identified in maternal blood samples from a population at increased risk for PTB due to clinical history.

The study design was conducted as follows. Blood samples from 229 women were collected between 12-24 weeks of gestation (IQR 18.9-20.9) and before delivery. In the UK, samples were collected across 4 independent sites, and 51% of the cohort was considered to be at high risk, defined by at least one of the following: previous sPTB (16-36), previous cervical surgery, or cervical length less than 25 mm. 80% (n = 183) were term births, and 20% (n = 46) had sPTB (GA less than 35 weeks). 30% of sPTB were delivered at a GA of less than 25 weeks. 80% (n = 183) were term births, and 20% (n = 46) had sPTB (GA less than 35 weeks). 30% of sPTB were delivered at a GA of less than 25 weeks. All samples were processed using a unified experimental and computational NGS pipeline for cell-free (cfRNA) sequencing. Twin pregnancy and preeclampsia cases were excluded.

Plasma obtained from all 229 maternal blood samples was subjected to cfRNA profiling. 41 genes (COL3A1, HSD17B1, GPC4, CDR1-AS, COL5A1, COL6A1, COL1A1, EFHD1, LENG8, DCN, CYB5R2, ELN, ANTXR1, CH507-24F1.2, EIF4A1, ABI3BP, LAPTM5, SLC38A10, CCDC80, C1S, VPS45, COL1A2, C7, FN1, COL14A1, ARPC4-TTLL3, LINC01002, PSG4, TMTC2, STAG3L5P, AMT, TAP1, CSH1, MMP2, PLXNA3, LGMNP1, LUM, MYO18B, DAPK2, GCM1L, GALS14, FNDC 10, FBN2, CAPN13, TNFRSF25, MYH11, POGLUT1, GH2, DNAH1, DES, NUP210) were identified as being associated with an increased risk of sPTB (FDR<0.1). Based on these transcripts, a logistic regression classifier model was developed to predict PTB, obtaining AUC=0.72, as shown in Figures 2A-2C. The model was validated by leave-one-out cross validation (LOOCV). Other insights into PTB in pathophysiology are presented in Figure 2D. Pathway analysis of transcripts associated with sPTB revealed an enrichment of genes related to collagen and extracellular matrix in those individuals who ultimately had sPTB at less than 35 weeks of GA.

Additional analysis was performed on 14 cases and 166 term controls for very late miscarriage/early preterm birth (GA at delivery less than 25 weeks). A logistic regression classifier was developed based on 11 genes (AC011043.1, IGFBP2, SH3GL3, AMT, GTF2IP4, GYPB, PAPPA, CH17-472G23.2, OMA1, ACADSB, ACER3) to predict the risk of very late miscarriage/early preterm birth with enhanced performance in LOOCV with AUC=0.76. Simultaneous enrichment was associated with sPTB with GA less than 25 weeks.

Pathway analysis of the six transcript sets revealed enrichment of gene sets associated with the basement membrane and endoplasmic reticulum lumen in samples from maternal subjects who delivered before 25 weeks GA, as well as enrichment of genes in the insulin-like growth factor transport and uptake and amino acid metabolism pathways, providing additional insights in the pathophysiology of very late miscarriage/early preterm birth, as shown in Figure 3.

In pregnancies at increased risk for pregnancy complications, analysis of cfRNA in maternal plasma provides a non-invasive window into maternal-fetal health. Our data show that elevated expression of a subset of transcripts is potentially involved in at least two molecular subtypes and two different underlining mechanisms of preterm birth. In the first molecular subtype, the collagen-containing extracellular matrix pathway can be associated with cervical remodeling, which is associated with a shorter cervix, and an inadequate cervix can form the basis of the underlying biology for high-risk PTB deliveries. In the second molecular subtype, for the endoplasmic reticulum luminal pathway, endoplasmic reticulum stress induced by oxidative stress in decidual cells may be associated with a possible mechanism of early pregnancy loss. In addition, the basement membrane pathway may indicate abruption of membranes from the uterus during miscarriage.

Based on these results of molecular subtyping of PTB, specific treatments can be selected based on the specific molecular subtype and administered to the maternal subject to modulate the outcome of preterm birth. For example, collagen modulating therapeutics or cervical cerclage can be applied to stabilize the cervix. Late miscarriage cases can be prevented by administering therapeutics to reduce oxidative stress and/or modulate the expression levels of proteins associated with endoplasmic reticulum stress.

Example 2: Early prediction of preeclampsia using molecular subtyping and cfRNA profiling

Using the systems and methods of the present disclosure, early molecular markers of pre-eclampsia (PE) are identified in maternal blood samples from a population at increased risk for pre-eclampsia (PE) due to clinical history.

In addition, the cohort of subjects includes a control subject set that delivered after gestational age 37 weeks. Some control subjects were classified as healthy controls, and some control subjects had a history of chronic hypertension without pre-eclampsia. The case subject set was diagnosed with pre-eclampsia and delivered before gestational age 37 weeks. The case subject set was diagnosed with de novo pre-eclampsia, and the case subject set suffered from pre-eclampsia and had a history of chronic hypertension.

Differential expression analysis of the cohort data set is as follows. Biomarker discovery was performed using cell-free RNA to identify early diagnostic markers for pre-eclampsia. To estimate the impact of chronic hypertension, two separate differential expression analyses were performed to estimate the impact of chronic hypertension. The first analysis was performed on the pre-eclampsia case set and the healthy control set; in addition, a second analysis was performed in which a control subject set with chronic hypertension was added, thereby having more control subjects in total.

The top-ranked differentially expressed gene sets for PE in the identified cohort were used for comparisons including and excluding chronic hypertension. Overlap was observed in the top-ranked genes from both analyses, suggesting a signal associated with pre-eclampsia rather than chronic hypertension.

Additional analysis of highly significant genes associated with a higher risk of PE suggests at least two separate pathways with different underlying biology. In the first molecular subtype, enrichment of low-expressed placenta-specific genes (such as PAPA2 and FABP1) indicates significant changes in the early placenta, which may be associated with pre-eclampsia. Moreover, high-dose aspirin administered in early pregnancy before 13 weeks can reduce the risk of very early onset of PE (<32 weeks), but may not reduce PE that develops later. In the second molecular subtype, pathways associated with keratinocyte endothelium may be associated with vascular inflammation, endothelial dysfunction, and arterial hypertension. In addition, the skin maintains a complex capillary countercurrent system that controls body temperature, skin perfusion, and significant systemic blood pressure. Therefore, a single case of pre-eclampsia may be associated with the underlying biology of the mother's skin, capillary, and/or arterial dysfunction.

Based on these results of the molecular subtyping of PE, specific treatment can be selected and applied to maternal subjects to adjust the results or risks of developing pre-eclampsia. For example, a compound similar to aspirin can be used to treat an object with a molecular subtype of placenta formation, which is related to the regulation of the cyclooxygenase pathway or the pathway responsible for regulating vasodilatory mediators and inhibiting vascular remodeling, platelet aggregation and platelet adhesion. As another example, a compound of blood pressure management compounds or a compound targeting the proton pump inhibitor (PPI) mechanism can be used to treat an object of a molecular subtype of PE associated with the keratinocyte endothelial pathway. Blocking PPI can lead to reduced secretion of sFlt-1 and soluble endoglin (sENG) and endothelial dysfunction, vasodilation, BP reduction, and antioxidant and anti-inflammatory properties. The use of esomeprazole (another proton pump inhibitor, also used for gastric reflux) can be evaluated in Phase II clinical studies to treat premature PE (PIE trace) in maternal subjects.

Example 3: Early prediction of gestational diabetes mellitus (GDM) using molecular subtyping and cfRNA profiling

Using the systems and methods of the present disclosure, early molecular markers of diabetes mellitus (GDM) are identified in maternal blood samples from a population at increased risk for GDM due to clinical history.

In addition, the cohort of subjects includes a control subject set. Some control subjects are classified as healthy controls with negative oral glucose tolerance test (OGTT) tests. Based on the OGTT test, the first case subject set is diagnosed with gestational GDM, the second case subject set is diagnosed with chronic type 2 diabetes, and the third case subject set has an impaired glucose state.

Differential expression analysis of the cohort data set was performed as follows. Biomarker discovery was performed using cell-free RNA to identify early diagnostic markers for GDM. To estimate the effect of chronic type 2 diabetes, two separate differential expression analyses were performed to estimate the effect. The first analysis was performed on the set of pregnant GDM cases and the set of healthy controls; in addition, a second analysis was performed in which a set of control subjects with chronic type 2 diabetes was added, thereby totaling a larger number of control subjects.

The top differentially expressed genes for GDM in the cohort were identified for both comparisons including chronic type 2 diabetes and excluding chronic type 2 diabetes. An overlap of the top genes from both analyses was observed, indicating a signal associated with GDM, rather than chronic type 2 diabetes.

Additional analysis of highly significant genes associated with a higher risk of GDM indicated at least three separate pathways with different underlying biology. In the first molecular subtype, enrichment of low-expressed placenta-specific genes (PDK4, CSH1, and PLAC4) indicated significant changes in the form of placental degeneration, placental insufficiency, placental failure, placental dysfunction, premature aging, calcification, and impaired placental function under gestational diabetes. In the second molecular subtype, one gene, TBCEL (tubulin-specific chaperone cofactor E-like), can be associated with mediated hyperglycemic memory, which can be common to type 1 and type 2 diabetes. In the third molecular subtype, the FBXO7 gene is involved in the adaptive immune system and antigen-specific immune response effective pathways. GDM is characterized not only by increased insulin resistance and glucose intolerance, but also by a state of mild systemic inflammation and immune system dysregulation that induces an imbalance between type 1 and type 2 helper T cells.

Based on these results for the molecular subtyping of GDM, specific treatment can be selected and applied to the maternal object to adjust the results or risks of developing GDM. For example, the object of the molecular subtype with significant changes indicating placental dysfunction or degradation can be treated with potential candidate drug target effectors to improve the blood flow of the uterus and placenta, antioxidants, heme oxygenase induction, HIF inhibition, induction of cholesterol synthesis pathways, and increase the availability of insulin-like growth factor II. As another example, for the object of the second molecular subtype associated with the mediated hyperglycemic memory, the early aggressive treatment of this glucose imbalance can be applied to the object with diabetes. Hyperglycemia can be accompanied by the formation of advanced glycation end products (AGE). Another treatment method can be to try to reduce AGE formation, AGE receptor (RAGE) expression and oxidative stress generation. Different drugs (such as metformin and pioglitazone) can be applied to block AGE formation. ACE inhibitors and AT-1 blockers are compounds used to control blood pressure; however, they can also reduce the formation of AGE. Telmisartan downregulates RAGE mRNA levels and subsequently inhibits superoxide generation, while gliclazide can be used to eliminate "memory". In addition, GLP1 receptor agonists can be administered to reduce inflammation, postprandial hyperlipidemia and coagulation, resulting in beneficial effects on atherosclerotic thrombosis. Aldose reductase inhibitors such as epalrestat can be administered to protect against diabetic peripheral neuropathy by alleviating oxidative stress and inhibiting polyol pathways. As another example, pregnancy is a significant metabolic and immune challenge for subjects with a third molecular subtype involving adaptive immune systems and antigen-specific pathways; in addition, GDM superimposes enhanced levels of low-grade systemic inflammation and uncompensated insulin resistance, which can be further associated with the disorder of potential immune responses, which can be common for type 1 and type 2. Immunomodulators can be administered to treat diabetes, such as: azathioprine, mycophenolate mofetil, oxazolidinone, tilizumab (which can minimize cytokine release and prevent progressive destruction of β-cells).

Example 4: Predictive RNA profiles for early and very early spontaneous preterm birth in high-risk groups and pathway analysis using the Reactome database

Using the systems and methods of the present disclosure, early molecular markers of preterm birth (PTB) and very early spontaneous preterm birth (sPTB) were identified in maternal blood samples from a population with a high risk clinical history.

High-risk pregnancies were defined by at least one of the following: previous sPTB or late miscarriage (12 to 37 weeks' gestation), previous destructive cervical surgery, or incidental cervical length <25 mm on transvaginal ultrasound scan. Low-risk controls were recruited from routine antenatal or ultrasound clinics without sPTB risk factors and who were otherwise well at enrollment.

Blood samples from women with singleton pregnancies recruited from four tertiary antenatal clinics were collected at 12 to 24 weeks of gestation (242 blood samples, one sample per pregnancy). For sPTB cases, samples were collected an average of 9.4 weeks before delivery. The distribution of the 242 collected samples with gestational age at the time of blood sample collection and gestational age at delivery is shown in Figure 4. Of the 242 pregnancies, 194 were term births (≥37 0/7GA), and 48 were premature births (early preterm births, <35 0/7) that were spontaneously delivered before 35 weeks of gestation. A subgroup of 16 pregnancies delivered before 25 weeks of gestation (very early preterm births, <25 0/7).

To identify candidate genes that can predict the risk of early sPTB (<35 0/7), differential expression analysis was performed between all early deliveries (<35 0/7) and controls (≥37 0/7). The results were validated using leave-one-out cross validation (LOOCV), resulting in a list of 25 differentially expressed genes listed in Table 1, which were used to build a logistic regression classifier to predict the risk of preterm birth.

Table 1: Differentially expressed genes in early sPTB identified by LOOCV and their corresponding cross-fold identification frequencies

The model achieved validated LOOCV performance with an area under the curve (AUC) of 0.80 (95% CI 0.72-0.87), sensitivity = 0.76 and specificity = 0.72 (N = 46 early sPTB cases and N = 183 term controls), as shown in Figure 5A. The model also scored each sample with a risk probability of preterm delivery as shown in Figure 5B.

The same approach was used to identify molecular markers specific for very early sPTB (<25 weeks). Differential expression analysis was performed between 16 very early PTB cases and 226 controls, and these results were validated using LOOCV. A list of 65 differentially expressed genes was generated (Table 2), which was used to build a regularized logistic regression classifier to predict very early sPTB with cross-validation.

Table 2: Very early sPTB differentially expressed genes identified by LOOCV and the corresponding cross-fold identification frequencies

The model achieved a validated LOOCV performance of AUC=0.74 (95%CI 0.64-0.83). Several genes were found to be related genes involved in preeclampsia toxemia (PET). In order to reduce the crosstalk of cfRNA features on the two complications, modeling was performed by excluding all PET samples and samples with low-quality sequencing indicators, resulting in a reduction to 14 cases of very early preterm birth (<25 0/7). The model showed improved LOOCV performance, AUC=0.76 (95%CI 0.63-0.87) [sensitivity=0.64, specificity=0.80] for 14 very early sPTB cases (<25 0/7) and 193 samples (≥25 0/7) delivered at 25 weeks or after 25 weeks, as shown in Figure 5C. The model was based on the set of 39 differentially expressed genes listed in Table 3, from a core set where three genes (AC011043.1, IGFBP2, and SH3GL3) were identified at >95% cross-validation compromise, and 13 genes overlapped with the differentially expressed genes found when trained with PET samples. The model probabilities showed significant differences between cases and controls, although a longer tail of high sPTB probabilities was observed for a subset of control samples, as shown in Figure 5D.

Table 3: Very early sPTB differentially expressed genes identified by LOOCV after excluding PET samples and the corresponding cross-fold identification frequencies

Analysis of biological pathways driving sPTB predictive genes was performed using the Reactome database and the top two pathways for each preterm birth model listed in Table 4 .

Table 4. Pathway analysis of differentially expressed genes in sPTB. Pathway analysis for genes found in early sPTB predictors (<35 0/7) and very early sPTB predictors (<25 0/7).

The early preterm birth model (<35 0/7) is enriched for genes involved in extracellular matrix (ECM) degradation and remodeling. The data are consistent with the observation that mediators of cell-cell adhesion such as crude fibromodulin (COL14A1) and elastin (ELN) are among the top genes in this model. Early detection of ECM pathways from a blood draw could be used to identify individuals at risk for premature cervical remodeling. Such a screening test could be implemented at a similar window to ultrasound to measure cervical length in women at high risk.

In contrast, similar analyses performed on genes obtained in a very early preterm birth model (<25 0/7 weeks) revealed that pathways related to insulin-like growth factor transport and amino acid metabolism pathways were observed to be differentially expressed in very early sPTB. Insulin binding protein is highly expressed in the fetus and decidua basalis and is a key regulator of the bioavailability of IGF-1 and, therefore, of fetal growth. For example, IGFBP1 has been associated with intrauterine growth restriction and impaired placentation and is elevated in cord blood from extremely premature infants. Detection of insulin growth factor cfRNA is reasonable as a dominant signal in pregnancies leading to very early sPTB and has potential suggestive significance with respect to downstream events.

Example 5: Prospective Cohort Subjects for Preeclampsia Case Study and cfRNA NGS Data Correction

Using the systems and methods of the present disclosure, a prospective observational study of a cell-free RNA platform using direct-to-participant recruitment via targeted social media was conducted from July 2020 to April 2022. The IRB-approved study was open to subjects aged 18 to 45 with singleton pregnancies in the United States. Participants signed informed consent, provided a record release form, completed a brief questionnaire, and submitted a blood sample via mobile phlebotomy scheduled via a web-based platform.

Participants submitted blood samples between 17 and 22 weeks of gestational age. Medical records were received for more than 85% of participants. The cohort was geographically and ethnically diverse, representing 1,220 zip codes across 30 states. All samples were processed using a unified experimental and computational NGS pipeline for cell-free RNA (cfRNA) sequencing.

3036 samples had complete medical records and passed the cfRNA assay quality indicators. Figure 6 shows demographic and clinical data indicators for the pre-eclampsia observational study, including 2701 healthy participants and 335 participants diagnosed with pre-eclampsia.

Figure 6 shows the distribution of demographic and clinical factors in the cohort associated with the risk of developing preeclampsia, which were collected based on the U.S. Preventive Services Task Force (USPSTF) guidelines and recommendations. Various preeclampsia risk factors were recorded for the cohort, including: parity; race; chronic hypertension (chtn); diabetes status excluding gestational diabetes (diabetic_not_gdm); maternal age; body mass index (bmi); previous diagnosis of preeclampsia (pm_pe); U.S. Preventive Services Task Force (USPSTF) risk level (www.uspreventiveservicestaskforce.org/uspstf/recommendation/preeclampsia-screening); and artificial in vitro fertilization (IVF).

For defining the different subtypes of pre-eclampsia, the different cut-off values of the medically prescribed childbirth (delivery-gestational age) for the patient diagnosed as pre-eclampsia were used. Fig. 7 is presented in 2889 samples of the same queue for the demography of the pre-eclampsia of childbirth less than 38 weeks, including 2690 healthy participants and 199 participants diagnosed as pre-eclampsia and giving birth before 38 weeks of gestational age.

Figure 8 shows the demographics for pre-eclampsia delivered at less than 37 weeks of gestation for 2889 samples from the same cohort, including 2780 healthy participants and 109 participants diagnosed with pre-eclampsia and delivered before 37 weeks of gestational age.

Measurements of cell-free RNA (cfRNA) levels in plasma quantified by NGS techniques can be affected by systematic variation due to sample processing techniques, which can compromise the accuracy of the measurement process and contribute to biasing the sex estimates of associations under study. In datasets characterized by hundreds to thousands of features, quantifying the contribution of systematic sources of variation is challenging.

Several sources of systematic variation were identified in the cfRNA dataset of 3036 samples, and several statistical correction methods were applied. These correction techniques included several methods: 1) using residuals from multiple linear regression to correct data residuals, 2) performing the ComBat method based on an empirical Bayesian approach that can only correct one covariate at the time; and 3) developing a surrogate variable analysis (SVA) to remove pre-identified sources of variability as well as unknown sources of variability. Compared with the other two techniques, the correction method of the data using residuals from multiple linear regression to correct the effects of unwanted covariates demonstrated better correction for the full dataset.

Different sources of variation were identified through analysis of a cfDNA dataset of 3036 samples and successfully corrected by implementing different approaches. First, technical variations attributable to NGS-specific methods (e.g., sequencing depth of each sample; batch effects of individual processing operations; or various raw materials) were identified and corrected.

In addition, two external sources of variation associated with the time of blood collection were identified and corrected by multiple linear regression to correct for data residuals. FIG. 9A shows an example of systematic variation in NGS data associated with seasonal changes in pregnancy-associated genes and placenta-associated genes moving in the opposite direction of immune cell genes. This gene set was analyzed and determined to be highly correlated with the outside temperature recorded by the weather station closest to the blood collection site at the time of the day of blood collection. FIG. 9B shows an example of a high correlation between the local outside temperature predicted by genetic modeling and the actual temperature recorded by a weather station close to the blood collection site.

Additional analyses were performed to identify cfRNA variation in blood draw/collection time associated with the circadian clock, as shown in FIG10A . Samples were grouped into morning (6am to 10am, n=121), noon (10am to 2pm, n=303), and afternoon (2pm to 6pm, n=113) by the time of blood draw. Differential gene expression (DGE) analysis was performed to elucidate the effect of the time of day. Quasi-likelihood negative binomial generalized log-linear models were fitted to count data using the edgeR package (v.3.38.1), and DGEs were found in all 3 possible pairwise comparisons using the empirical Bayesian quasi-likelihood F-test (edgeR). When DGEs were sought between the morning and afternoon groups, 5729 genes (i.e., 43% of all genes analyzed) were determined to be significantly differentially expressed ( FIG10B ). For the morning group vs. the noon group, 4278 genes (32% of all analyzed genes) were determined to be DEGs; and for the noon group vs. the afternoon group, 15 genes (0.1% of all analyzed genes) were determined to be DGEs. These three sets have very high overlap, ranging from 73% to 87% (p < 10-30). In addition, among the genes reported to have the highest separation, they are related to circadian rhythms (e.g., PER1, PER3, DDIT4, FKBP5, RBM3, SOCS1, BTG1, and ARHGEF10L).

In addition, similar techniques were used to efficiently regress biological variation associated with subject BMI, fetal fraction, and gestational age at blood collection to increase the power of gene discovery.

Example 6: Early prediction of preeclampsia and preeclampsia molecular subtypes from a 3036-person prospective trial using cfRNA profiling

Using the systems and methods of the present disclosure, a prospective cohort of 3036 cases described in Example 5 was used to identify early differentially expressed molecular markers of pre-eclampsia in maternal blood samples. All samples were processed for cell-free (cfRNA) sequencing using a unified experimental and computational NGS pipeline. Twin pregnancies, spontaneous preterm births, and/or preterm births based on non-pre-eclampsia diagnoses were excluded.

The study design was carried out as follows. Two methods were used. In the first method, the cohort with pre-eclampsia cases was grouped according to the severity of the pre-eclampsia diagnosis, and medically induced delivery was used to reduce the risk of composite adverse maternal outcomes in women. Severe pre-term pre-eclampsia cases from the cohort were grouped according to delivery at less than 38 weeks or 37 weeks of gestational age, as shown in Figures 8 and 9, respectively. In the second method, when performing feature discovery of pre-term pre-eclampsia cases, the main clinical factors based on USPSTF observed were added as candidate features, as shown in Figure 7.

One approach to feature discovery is to determine independence screening, which ensures that features are orthogonal to each other to capture the maximum variation in the data. Using this approach for the entire data set, the preterm pre-eclampsia signal was analyzed for deliveries at less than 38 weeks gestational age. Figures 11A and 11B show examples of the discovery rate of genes associated with a high risk of developing preterm pre-eclampsia across repeated cross validation.

Table 5 provides a list of differentially expressed genes found by determining independence screening as predicting molecular subtypes of preterm pre-eclampsia with medically prescribed delivery at less than 38 weeks. Similarly, Table 6 provides a list of differentially expressed genes found by determining independence screening as indicating molecular subtypes of preterm pre-eclampsia with medically prescribed delivery at less than 37 weeks gestational age.

Table 5. Differentially expressed genes predicted for preterm preeclampsia at delivery less than 38 weeks found by ascertainment independence screening

Table 6. Differentially expressed genes predicted for preterm preeclampsia at delivery less than 37 weeks found by ascertainment independence screening

PAPPA2 KRT18 SELENOP TOMM5 PAPPA ATP5E APOB KRT8 GID4 VAT1L KISS1

The alternative method for feature discovery is to screen differentially expressed genes in the count space corrected for multiple variables.Example is corrected by BMI or total count, or corrected by individual genes (such as KRT7 or SVEP1).If feature passes through the p-value threshold of multiple corrections, then they are retained, and can be displayed as using Aikake information criterion (AIC<-2) to add value to the model.Figure 12 A-Figure 12 B depicts the exemplary results of this type of modeling for all gene markers and clinical factors found for the premature pre-eclampsia cases less than 38 weeks or less than 37 weeks of childbirth respectively.Table 7 and Table 8 provide the list of all gene markers found for the premature pre-eclampsia cases less than 38 weeks or less than 37 weeks of childbirth respectively by this method.

Table 7. Differentially expressed genes in preterm preeclampsia born at less than 38 weeks identified by multi-space calibration

PAPPA2 SVEP1 KRT7 VSIG4 APOB FGB FCGBP CD163 TCH AXL ETV5 LYVE1 IGSF21 MACROD2 FGG Q1C PPP1R14A APOE THRB APOH EFHD1 TMEM176B FABP1 FPR3 KISS1 ALB NEK11 FGB BCOR AP1B1 FGA

Table 8. Preterm preeclampsia genes identified by multi-spatial calibration for delivery at less than 37 weeks

Modeling based on these features enabled early prediction of preterm pre-eclampsia, yielding AUCs as high as 0.86 (for preterm pre-eclampsia cases delivered at less than 36 weeks). Figure 13 shows an example of an area under the curve (AUC) for a model predicting PE cases delivered at less than 37 weeks with an ROC mean of 0.83.

Example 7: Early prediction of spontaneous preterm birth in a prospective cohort of 3036 people using molecular subtyping and cfRNA profiling

Using the systems and methods of the present disclosure, early differentially expressed molecular markers of preterm birth (PTB) were identified in maternal blood samples from a prospective cohort of 3036 participants described in Example 5. All samples were processed for cell-free (cfRNA) sequencing using a unified experimental and computational NGS pipeline. Twin pregnancies, cases of preterm birth with medically induced labor based on a diagnosis of preeclampsia, and/or other medically induced labor were excluded.

To identify differentially expressed gene markers for spontaneous preterm birth, two types of differential gene expression analyses were performed for two different molecular subtypes of spontaneous preterm birth, defined as birth at less than 35 weeks or birth at less than 37 weeks.

First, Spearman and DESeq2 differential gene expression analysis was performed for 55 spontaneous preterm birth cases delivered before 35 weeks of gestational age and 2899 term birth control cases delivered after 37 weeks of gestational age. Figure 14 shows the quantile-quantile (QQ) graph of differential gene expression signals of differentially expressed genes in premature birth cases delivered before 35 weeks of gestational age. Table 9 shows the set of the top 5 differentially expressed genes analyzed by Spearman ranking for predicting spontaneous preterm birth cases delivered before 35 weeks of gestation.

Table 9. Set of top 5 differentially expressed genes predicting spontaneous preterm birth before 35 weeks of delivery using Spearman rank analysis

index Gene P-value 1 ZNF812P 0.01418108 2 FPR3 0.020841643 3 LILRB5 0.031239504 4 CLEC9A 0.039139074 5 ETV5 0.043737094

Table 10 shows the set of top 43 differentially expressed genes predicting spontaneous preterm birth cases before 35 weeks of gestation by DESeq2 differential gene expression analysis.

Table 10. Set of top 43 differentially expressed genes predicting spontaneous preterm birth cases before 35 weeks of delivery using DESeq2 differential gene expression

index Gene P-value 1 LYVE1 1.66E-12 2 TCH 3.24E-08 3 CD163 1.68E-07 4 DUSP27 1.91E-07 5 VSIG4 4.21E-07 6 MRC1 2.41E-06 7 GPR82 4.81E-06 8 GPR34 5.36E-05 9 MSR1 5.74E-05 10 RNASE1 7.74E-05 11 TREM2 7.86E-05 12 FPR3 1.31E-04 13 FCGBP 1.44E-04 14 MERTK 1.71E-04 15 STAB1 2.13E-04 16 LILRB5 2.29E-04 17 CADM1 3.60E-04 18 SELENOP 4.75E-04 19 NCKAP5 0.00155276 20 Q1C 0.00160502 twenty one Q1Q 0.00182701 twenty two DBNDD2 0.00187367 twenty three MMP2 0.00287475 twenty four AXL 0.00317878 25 GATM 0.00387293 26 FOLR2 0.00412276 27 ZNF812P 0.00530928 28 ETV5 0.00546725 29 MTCO1P12 0.00637143 30 CLEC9A 0.00696217 31 MMP14 0.00759331 32 CD209 0.0080383 33 ZFHX3 0.00868013 34 EPS8 0.00933179 35 SLCO2B1 0.01086637 36 OLFML3 0.01562314 37 CD276 0.0170661 38 CABLES1 0.01958957 39 SDC3 0.01977068 40 MAF 0.02108906 41 RGL1 0.03271522 42 SPRED1 0.04326907 43 UNC13C 0.04952278

A similar analysis of differential gene expression using DESeq2 was performed on 135 spontaneous preterm birth cases that were spontaneously born before 37 weeks of gestational age and 2899 term control cases that were delivered after 37 weeks of gestational age. Figure 15 shows a quantile-quantile (QQ) plot of differential gene expression signals for differentially expressed genes in preterm birth cases delivered before 37 weeks of gestational age.

Table 11 shows the set of top 12 differentially expressed genes predicting spontaneous preterm birth cases before 37 weeks of gestation by DESeq2 differential gene expression analysis.

Table 11. Set of top 12 differentially expressed genes predicting spontaneous preterm birth cases before 37 weeks of delivery using DESeq2 differential gene expression

index Gene P-value 1 TCH 2.15E-06 2 LYVE1 3.15E-06 3 GPR82 2.65E-05 4 CD163 1.45E-04 5 SAA2 0.001162 6 DUSP27 0.001376 7 CSRP3 0.00141 8 VSIG4 0.002136 9 GPR34 0.009453 10 MRC1 0.015219 11 FPR3 0.025791 12 ADGRG7 0.036084

Although preferred embodiments of the present invention have been shown and described herein, it is obvious to those skilled in the art that such embodiments are provided only as examples. This does not mean that the present invention is limited by the specific examples provided in the specification. Although the present invention has been described with reference to the foregoing description, the description and illustration of the embodiments herein are not meant to be interpreted in a limiting sense. Without departing from the present invention, those skilled in the art will conceive of many changes, modifications and substitutions. In addition, it should be understood that all aspects of the present invention are not limited to the specific description, configuration or relative proportions set forth herein, which depend on various conditions and variables. It should be understood that in the implementation of the present invention, various alternatives of the embodiments of the present invention described herein may be adopted. Therefore, it is contemplated that the present invention should also cover any such alternatives, modifications, variations or equivalents. The following claims are intended to define the scope of the present invention, and thus cover methods and structures within the scope of these claims and their equivalents.

Claims (72)

1. A method for identifying the presence or increased risk of a pregnancy-related state in a pregnant subject, the method comprising assaying a cell-free biological sample derived from the pregnant subject to detect a set of biomarkers, and processing the set of biomarkers using a trained algorithm or processing the set of biomarkers against reference values to determine the presence or increased risk of the pregnancy-related state in a set of at least three different pregnancy-related states. 2. The method of claim 1, wherein the pregnancy-related condition is selected from preterm birth, term birth, gestational age, due date, onset of labor, pregnancy-related hypertensive disorders, pre-eclampsia, eclampsia, gestational diabetes, congenital disorders of the fetus of the pregnant subject, ectopic pregnancy, spontaneous abortion, stillbirth, postpartum complications, hyperemesis gravidarum, hemorrhage or excessive bleeding during labor, premature rupture of membranes, preterm premature rupture of membranes, placenta previa, intrauterine/fetal growth restriction, macrosomia, neonatal condition, and fetal development stage or state. 3. The method of claim 1, wherein the pregnancy-related condition is a subtype of preterm birth, and wherein the at least three different pregnancy-related conditions include at least two different subtypes of preterm birth. 4. The method of claim 3, wherein the preterm birth subtype is a preterm birth molecular subtype, and wherein the at least two different preterm birth subtypes comprise at least two different preterm birth molecular subtypes. 5. The method of claim 4, wherein the molecular subtype of preterm birth is selected from a history of previous preterm birth, spontaneous preterm birth, ethnic-specific risk of preterm birth, and preterm premature rupture of membranes (PPROM). 6. The method of claim 5, wherein the preterm birth molecular subtype is spontaneous preterm birth, and wherein the biomarker set comprises genomic loci associated with spontaneous preterm birth. 7. The method of claim 6, wherein the genomic loci associated with spontaneous preterm birth are selected from the group consisting of genes listed in Table 1, genes listed in Table 2, genes listed in Table 3, genes corresponding to pathways listed in Table 4, genes listed in Table 9, genes listed in Table 10, and genes listed in Table 11. 8. The method of claim 6, wherein the spontaneous preterm birth comprises delivering at less than 25 weeks, delivering at less than 26 weeks, delivering at less than 27 weeks, delivering at less than 28 weeks, delivering at less than 29 weeks, delivering at less than 30 weeks, delivering at less than 31 weeks, delivering at less than 32 weeks, delivering at less than 33 weeks, delivering at less than 34 weeks, delivering at less than 35 weeks, delivering at less than 36 weeks, delivering at less than 37 weeks, or delivering at less than 38 weeks. 9. The method of claim 4, further comprising identifying a clinical intervention for the pregnant subject based at least in part on the presence or increased risk of the preterm birth molecular subtype. 10. The method of claim 9, wherein the clinical intervention is selected from a plurality of clinical interventions. 11. The method of claim 9, wherein the clinical intervention comprises medication, supplementation, lifestyle advice, cervical cerclage, cervical pessary, or electrical contraction inhibition. 12. The method of claim 11, wherein the drug is selected from the group consisting of progesterone, erythromycin, tocolytic drugs, corticosteroids, vaginal flora, and antioxidants. 13. The method of claim 1, wherein the pregnancy-related condition is a subtype of pre-eclampsia, and wherein the at least three different pregnancy-related conditions include at least two different subtypes of pre-eclampsia. 14. The method of claim 13, wherein the pre-eclampsia subtype is a pre-eclampsia molecular subtype, and wherein the at least two different pre-eclampsia subtypes comprise at least two different pre-eclampsia molecular subtypes. 15. The method of claim 14, wherein the pre-eclampsia molecular subtype is selected from a history of chronic or pre-existing hypertension, the presence or history of gestational hypertension, the presence or history of mild pre-eclampsia, the presence or history of severe pre-eclampsia, the presence or history of eclampsia, and the presence or history of HELLP syndrome. 16. The method of claim 15, wherein the pre-eclampsia molecular subtype is pre-term pre-eclampsia, and wherein the biomarker set comprises genomic loci associated with pre-term pre-eclampsia. 17. The method of claim 16, wherein the genomic loci associated with preterm pre-eclampsia are selected from the genes listed in Table 5, the genes listed in Table 6, the genes listed in Table 7, and the genes listed in Table 8. 18. The method of claim 16, wherein the preterm pre-eclampsia comprises delivery at less than 25 weeks, delivery at less than 26 weeks, delivery at less than 27 weeks, delivery at less than 28 weeks, delivery at less than 29 weeks, delivery at less than 30 weeks, delivery at less than 31 weeks, delivery at less than 32 weeks, delivery at less than 33 weeks, delivery at less than 34 weeks, delivery at less than 35 weeks, delivery at less than 36 weeks, delivery at less than 37 weeks, or delivery at less than 38 weeks. 19. The method of claim 14, further comprising identifying a clinical intervention for the pregnant subject based at least in part on the presence or increased risk of the pre-eclampsia molecular subtype. 20. The method of claim 19, wherein the clinical intervention is selected from a plurality of clinical interventions. 21. The method of claim 19, wherein the clinical intervention comprises medication, supplementation, or lifestyle advice. 22. The method of claim 21, wherein the drug is selected from the group consisting of aspirin, progesterone, magnesium sulfate, cholesterol medications, heartburn medications, angiotensin II receptor antagonists, calcium channel blockers, diabetes medications, and erectile dysfunction medications. 23. The method of claim 1, wherein the pregnancy-related condition is a subtype of gestational diabetes, and wherein the at least three different pregnancy-related conditions include at least two different subtypes of gestational diabetes. 24. The method of claim 23, wherein the gestational diabetes subtype is a gestational diabetes molecular subtype, and wherein the at least two different gestational diabetes subtypes comprise at least two different gestational diabetes molecular subtypes. 25. The method of claim 23, wherein the biomarker set comprises genomic loci associated with gestational diabetes. 26. The method of claim 25, wherein the genomic loci associated with gestational diabetes are selected from PDK4, CSH1, PLAC4, TBCEL, and FBXO7. 27. The method of claim 24, further comprising identifying a clinical intervention for the pregnant subject based at least in part on the presence or increased risk of the gestational diabetes molecular subtype. 28. The method of claim 27, wherein the clinical intervention is selected from a plurality of clinical interventions. 29. The method of claim 27, wherein the clinical intervention comprises medication, supplementation, lifestyle advice, cervical cerclage, cervical pessary, or electrical contraction inhibition. 30. The method of claim 1, wherein the biomarker set comprises at least 5 different genomic loci, at least 10 different genomic loci, at least 15 different genomic loci, at least 20 different genomic loci, at least 25 different genomic loci, at least 30 different genomic loci, at least 35 different genomic loci, at least 40 different genomic loci, at least 45 different genomic loci, at least 50 different genomic loci, at least 100 different genomic loci, at least 150 different genomic loci, or at least 200 different genomic loci. 31. The method of claim 1, wherein the determining comprises generating transcriptomic data using cell-free ribonucleic acid (cfRNA) molecules derived from the cell-free biological sample, generating transcript data using transcription products derived from the cell-free biological sample, generating genomic data and/or methylation data using cell-free deoxyribonucleic acid (cfDNA) molecules derived from the cell-free biological sample, generating proteomic data using proteins derived from the first cell-free biological sample, or generating metabolomics data using metabolites derived from the first cell-free biological sample. 32. The method of claim 1, wherein the cell-free biological sample is selected from the group consisting of cell-free ribonucleic acid (cfRNA), cell-free deoxyribonucleic acid (cfDNA), cell-free fetal DNA (cffDNA), plasma, serum, urine, saliva, amniotic fluid, and derivatives thereof. 33. The method of claim 1, wherein the cell-free biological sample is obtained or derived from the pregnant subject using an ethylenediaminetetraacetic acid (EDTA) collection tube, a cell-free RNA collection tube, or a cell-free deoxyribonucleic acid (DNA) collection tube. 34. The method of claim 1, further comprising fractionating a whole blood sample from the pregnant subject to obtain the cell-free biological sample. 35. The method of claim 1, wherein the assay comprises a cell-free RNA (cfRNA) assay or a metabolomics assay. 36. The method of claim 35, wherein the metabolomics assay comprises targeted mass spectrometry (MS) or immunoassay. 37. The method of claim 1, wherein the cell-free biological sample comprises cell-free RNA (cfRNA) or urine. 38. The method of claim 1, wherein the assay comprises quantitative polymerase chain reaction (qPCR). 39. The method of claim 1, wherein the assay comprises a home test configured to be performed in a home environment. 40. The method of claim 1, wherein the trained algorithm determines the presence or increased risk of the pregnancy-related condition in the pregnant subject with a sensitivity of at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 85%, at least about 90%, or at least about 95%. 41. The method of claim 1, wherein the trained algorithm determines the presence or increased risk of the pregnancy-related condition in the pregnant subject with a positive predictive value (PPV) of at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 85%, at least about 90%, or at least about 95%. 42. The method of claim 1, wherein the trained algorithm determines the presence or increased risk of the pregnancy-related condition in the pregnant subject with an area under the curve (AUC) of at least about 0.60, at least about 0.65, at least about 0.70, at least about 0.75, at least about 0.80, at least about 0.85, at least about 0.90, or at least about 0.95. 43. The method of claim 1, wherein the pregnant subject is asymptomatic for the pregnancy-related condition. 44. The method of claim 1, wherein the trained algorithm is trained using a first independent set of training samples associated with the presence or increased risk of the pregnancy-related state and a second independent set of training samples associated with the absence or no increased risk of the pregnancy-related state. 45. The method of claim 1, further comprising processing a clinical health dataset of the pregnant subject using the trained algorithm or another trained algorithm to determine the presence or increased risk of the pregnancy-related condition. 46. The method of claim 1, further comprising subjecting the cell-free biological sample to conditions sufficient to separate, enrich or extract a collection of ribonucleic acid (RNA) molecules, deoxyribonucleic acid (DNA) molecules, proteins or metabolites; and wherein the assay comprises analyzing the collection of RNA molecules, DNA molecules, proteins or metabolites. 47. The method of claim 46, further comprising extracting a set of nucleic acid molecules from the cell-free biological sample, and sequencing the set of nucleic acid molecules to generate a set of sequencing reads. 48. The method of claim 47, wherein the sequencing comprises massively parallel sequencing. 49. The method of claim 47, wherein the sequencing comprises nucleic acid amplification. 50. The method of claim 49, wherein the nucleic acid amplification comprises polymerase chain reaction (PCR). 51. The method of claim 50, wherein the sequencing comprises using reverse transcription (RT) and polymerase chain reaction (PCR). 52. The method of claim 46, further comprising using a probe configured to selectively enrich the set of nucleic acid molecules corresponding to a grouping of one or more genomic sites. 53. The method of claim 52, wherein the probe is a nucleic acid primer. 54. The method of claim 52, wherein the probes have sequence complementarity with nucleic acid sequences of the groupings of the one or more genomic loci. 55. The method of claim 52, wherein the grouping of the one or more genomic loci comprises genomic loci associated with spontaneous preterm birth, wherein the genomic loci are selected from the group consisting of genes listed in Table 1, genes listed in Table 2, genes listed in Table 3, genes corresponding to pathways listed in Table 4, genes listed in Table 9, genes listed in Table 10, and genes listed in Table 11. 56. A method as claimed in claim 52, wherein the grouping of the one or more genomic loci includes genomic loci associated with preterm pre-eclampsia, wherein the genomic loci are selected from the genes listed in Table 5, the genes listed in Table 6, the genes listed in Table 7, and the genes listed in Table 8. 57. The method of claim 52, wherein the grouping of the one or more genomic loci comprises genomic loci associated with gestational diabetes, wherein the genomic loci are selected from PDK4, CSH1, PLAC4, TBCEL, and FBXO7. 58. The method of claim 52, wherein the grouping of the one or more genomic loci comprises at least 5 different genomic loci, at least 10 different genomic loci, at least 15 different genomic loci, at least 20 different genomic loci, at least 25 different genomic loci, at least 30 different genomic loci, at least 35 different genomic loci, at least 40 different genomic loci, at least 45 different genomic loci, at least 50 different genomic loci, at least 100 different genomic loci, at least 150 different genomic loci, or at least 200 different genomic loci. 59. The method of claim 1, wherein the cell-free biological sample is processed without nucleic acid isolation, enrichment or extraction. 60. The method of claim 1, further comprising generating an electronic report including an indication of the determined presence or increased risk of the pregnancy-related condition. 61. The method of claim 1, further comprising determining the determined likelihood of the presence or increased risk of the pregnancy-related condition in the pregnant subject. 62. The method of claim 1, wherein the trained algorithm comprises a trained machine learning algorithm. 63. The method of claim 62, wherein the trained machine learning algorithm comprises a deep learning algorithm, a support vector machine (SVM), a neural network, a random forest, a linear regression model, a logistic regression model, or an ANOVA model. 64. The method of claim 1, further comprising processing the biomarker set to reduce systematic variation. 65. The method of claim 64, wherein reducing the systematic variation comprises correcting data residuals using residuals from multiple linear regression, performing a ComBat method based on an empirical Bayesian approach, and performing a surrogate variable analysis (SVA) correction. 66. A method as claimed in claim 64, wherein the systematic variation includes the sequencing depth of each sample, batch effects of individual processing operations, the use of various raw materials, the local external temperature at sample collection, the BMI of the subject, fetal fraction, fetal gestational age at sample collection, or a combination thereof. 67. The method of claim 1, further comprising monitoring the presence or increased risk of the pregnancy-related condition, wherein the monitoring comprises assessing the pregnant subject for the presence or increased risk of the pregnancy-related condition at a plurality of time points, wherein the assessing is based at least on the presence or increased risk of the pregnancy-related condition determined at each of the plurality of time points. 68. The method of claim 67, wherein the difference in the assessment of the presence or increased risk of the pregnancy-associated condition for the pregnant subject between the multiple time points is indicative of one or more clinical indications selected from: (i) diagnosis of the presence or increased risk of the pregnancy-associated condition for the pregnant subject, (ii) prognosis of the presence or increased risk of the pregnancy-associated condition for the pregnant subject, and (iii) effectiveness or ineffectiveness of a course of treatment for the presence or increased risk of the pregnancy-associated condition for the pregnant subject. 69. The method of claim 1, wherein the pregnant subject is in the first trimester, the second trimester, or the third trimester. 70. The method of claim 1, wherein the reference value is determined from a pregnant subject and/or a non-pregnant subject. 71. The method of claim 1, wherein processing the biomarker set against the reference value comprises determining a difference between the biomarker set and the reference value. 72. The method of claim 1, wherein the pregnant subject is in the first trimester, the second trimester, or the third trimester.