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WO2024014580A1 - Procédé pour fournir des informations pour un diagnostic pour le cancer fondé sur l'intelligence artificielle en utilisant un biomarqueur exprimé dans un exosome - Google Patents

Procédé pour fournir des informations pour un diagnostic pour le cancer fondé sur l'intelligence artificielle en utilisant un biomarqueur exprimé dans un exosome Download PDF

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Publication number
WO2024014580A1
WO2024014580A1 PCT/KR2022/010342 KR2022010342W WO2024014580A1 WO 2024014580 A1 WO2024014580 A1 WO 2024014580A1 KR 2022010342 W KR2022010342 W KR 2022010342W WO 2024014580 A1 WO2024014580 A1 WO 2024014580A1
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cancer
present
hidden layer
neural network
deep neural
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Korean (ko)
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이왕재
이희원
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Lee Wangjae Bio Laboratory Co Ltd
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Lee Wangjae Bio Laboratory Co Ltd
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    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
    • C12Q1/6883—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material
    • C12Q1/6886—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B40/00—ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding

Definitions

  • the present invention relates to a method for diagnosing cancer by analyzing circulating tumor cells, and more specifically, to a composition, diagnostic kit, and diagnostic method for diagnosing cancer using the gene methylation level of circulating tumor cells.
  • Cancer is one of the most common causes of death worldwide. Approximately 10 million new cases occur each year, accounting for approximately 12% of all deaths, making it the third most common cause of death.
  • cancer markers such as antibodies.
  • the diagnostic market using antibodies has been rapidly increasing since 1980, and is used in the development of highly efficient diagnostic kits and diagnostic techniques due to its excellent sensitivity as it can detect proteins specifically expressed according to diseases or symptoms even in very small amounts. .
  • antibodies that have both specificity and sensitivity to proteins (antigens) expressed by diseases and symptoms.
  • several considerations can be taken to maximize the efficiency of the antibody. First, comparing the primary sequence of the antigen to be generated and selecting one with strong heterogeneity between the animal species of the antigen and the immune animal can maximize the immune response.
  • high-titer antibodies when using the sequence of a human protein, high-titer antibodies can be obtained by selecting an animal with high heterogeneity from the sequence and inducing an immune response.
  • the quality of the antigen may vary depending on the characteristics and three-dimensional structure of the antigen due to post-translational modifications in the sequence, so select the antigen in consideration.
  • special consideration when using a partial peptide as an antigen rather than using the entire protein as an antigen, special consideration must be taken when selecting it.
  • exosomes one of the extracellular vesicles, are nano-sized vesicles with the same composition as the outer cell wall. Exosomes were discovered about 30 years ago and were initially recognized as a mechanism for removing waste from cells. According to research over the past 10 years, it is known to play a functional role in mediating biologically important cell-cell communication and cellular immunity.
  • the present inventors developed a diagnostic method that can diagnose cancer with high accuracy by analyzing the methylation of genes in exosomes and completed the present invention.
  • the present invention includes the steps of isolating an exosome fraction from a biological sample isolated from a subject of interest;
  • the object of the present invention is to provide a method and device for providing information for diagnosing cancer, further comprising calculating the probability of cancer occurring in the subject using a learned deep neural network.
  • a method of providing information for cancer diagnosis is provided.
  • the method includes the steps of isolating an exosome fraction from a biological sample isolated from a subject of interest; And measuring the expression level of the biomarker in the exosome fraction.
  • the biomarkers include phospholipid transport ATPase IB, beta-1,3-galactosyltransferase 6, CUGBP Elav-like family member 6, leptin, glutathione S-transferase P1, and neural pentraxin. 2, P16, and high-affinity choline transporter 1.
  • the method may further include calculating the likelihood of cancer occurring in the subject using a learned deep neural network.
  • the deep neural network includes an input layer into which data is input; first hidden layer (hidden layer 1); second hidden layer (hidden layer 1); third hidden layer (hidden layer 1); fourth hidden layer (hidden layer 1); and an output layer.
  • an information provision device for cancer diagnosis can be provided.
  • the device includes a sample processing unit for isolating an exosome fraction from a biological sample isolated from a target individual; And it may include a measuring unit that measures methylation of nucleotide molecules in the exosome fraction.
  • the device may further include an arithmetic unit that calculates the likelihood of cancer occurring in the individual using a learned deep neural network.
  • Cancer can be diagnosed through the method and device for providing information for cancer diagnosis according to the present invention, and whether a candidate substance can be used as a cancer treatment can be confirmed through a screening method.
  • Figure 1 shows a deep neural network with three hidden layers according to an embodiment of the present invention.
  • Figure 2 shows a deep neural network with four hidden layers according to an embodiment of the present invention.
  • a method of providing information for cancer diagnosis is provided.
  • the method includes the steps of isolating an exosome fraction from a biological sample isolated from a subject of interest; And measuring the expression level of the biomarker in the exosome fraction.
  • the biomarkers include phospholipid transport ATPase IB, beta-1,3-galactosyltransferase 6, CUGBP Elav-like family member 6, leptin, glutathione S-transferase P1, and neural pentraxin. 2, P16, and high-affinity choline transporter 1.
  • the method may further include calculating the likelihood of cancer occurring in the subject using a learned deep neural network.
  • the deep neural network includes an input layer into which data is input; first hidden layer (hidden layer 1); second hidden layer (hidden layer 1); third hidden layer (hidden layer 1); fourth hidden layer (hidden layer 1); and an output layer.
  • an information provision device for cancer diagnosis can be provided.
  • the device includes a sample processing unit for isolating an exosome fraction from a biological sample isolated from a target individual; And it may include a measuring unit that measures methylation of nucleotide molecules in the exosome fraction.
  • the device may further include an arithmetic unit that calculates the likelihood of cancer occurring in the individual using a learned deep neural network.
  • a device may be a general device (or object) connected to a gateway and applied to IoT (Internet of Things). Additionally, in this specification, device may be used interchangeably with 'equipment' or 'apparatus', and 'device', 'equipment', and 'apparatus' may be described with the same expression.
  • a method for providing information for cancer diagnosis is provided.
  • cancer to “tumor” is a disease characterized by rapid and uncontrolled growth of mutant cells, and the cancer includes breast cancer, ovarian cancer, colon cancer, stomach cancer, liver cancer, pancreatic cancer, cervical cancer, thyroid cancer, Parathyroid cancer, lung cancer, non-small cell lung cancer, prostate cancer, gallbladder cancer, biliary tract cancer, non-Hodgkin lymphoma, Hodgkin lymphoma, blood cancer, bladder cancer, kidney cancer, melanoma, colon cancer, bone cancer, skin cancer, head and neck cancer, uterine cancer, rectal cancer, Brain tumor, perianal cancer, fallopian tube carcinoma, endometrial carcinoma, vaginal cancer, vulvar carcinoma, esophageal cancer, small intestine cancer, endocrine cancer, adrenal cancer, soft tissue sarcoma, urethral cancer, penile cancer, ureteral carcinoma, renal cell carcinoma, renal pelvic carcinoma, central It may be a nervous system tumor, spinal cord tumor, brainstem
  • the terms “marker” or “biomarker” are those that can detect changes in a living organism and objectively measure the normal or pathological state of the living organism, the degree of response to a drug, etc.
  • diagnosis refers to determining a subject's susceptibility to a specific disease or condition, determining whether the subject currently has a specific disease or condition, and determining whether the subject currently has a specific disease or condition. Predicting or determining the prognosis of an affected subject, or therametrics (e.g., monitoring the condition of a subject to provide information about treatment efficacy).
  • the term "individual” refers to any living organism that develops or is likely to develop cancer, and specific examples include mammals including mice, monkeys, cows, pigs, mini-pigs, livestock, humans, etc., and farmed fish. It may include, but is not limited to, etc.
  • sample and “biological sample” refer to any material, biological fluid, tissue, or cell derived from an individual that has developed or is suspected of having cancer.
  • the biological sample is whole blood. (whole blood), leukocytes, peripheral blood mononuclear cells, buffy coat, plasma, serum, sputum, tears, mucus ( mucus, nasal washes, nasal aspirate, breath, urine, semen, saliva, peritoneal washings, ascites, Cystic fluid, meningeal fluid, amniotic fluid, glandular fluid, pancreatic fluid, lymph fluid, pleural fluid, nipple aspirate ), bronchial aspirate, synovial fluid, joint aspirate, organ secretions, or cerebrospinal fluid.
  • the biomarkers include phospholipid transport ATPase IB, beta-1,3-galactosyltransferase 6, CUGBP Elav-like family member 6, leptin, glutathione S-transferase P1, and neuronal pentraxin 2.
  • P16, and high-affinity choline transporter 1, and the biomarker may be shown in Table 1 below.
  • the expression level of the biomarker may include an agent that measures the expression level of the protein represented by SEQ ID NO: 1 to 8 or the gene encoding the same, and the exosome is produced in the endosomal compartment of most eukaryotic cells.
  • Extracellular vesicles (EV), and the isolation method may be included without limitation.
  • the term "detection" may be used to measure and compare the presence or absence of a biomarker to be detected, concentration in a biological sample, expression level, etc., and an increase in the level of the biomarker as a result of the detection encodes the biomarker.
  • increasing the number of genes that are activated increasing gene transcription (e.g., by placing genes under the control of a constitutive promoter), increasing translation of genes, knocking out competing genes, or combinations of these and/or other methods. It can be done by a number of methods, and it can be measuring the expression level of exosomal biomarkers.
  • Agents for detecting the biomarkers of the present invention are not particularly limited, but include, for example, a group consisting of antibodies, oligopeptides, ligands, PNA (peptide nucleic acids) and aptamers that specifically bind to the biomarkers. It may include one or more selected types.
  • the “antibody” refers to a substance that specifically binds to an antigen and causes an antigen-antibody reaction.
  • the antibodies of the present invention include polyclonal antibodies, monoclonal antibodies, and recombinant antibodies.
  • the antibody can be easily produced using techniques well known in the art.
  • polyclonal antibodies can be produced by methods well known in the art, which include injecting the protein antigen into an animal and collecting blood from the animal to obtain serum containing the antibody.
  • These polyclonal antibodies can be produced from any animal, such as goats, rabbits, sheep, monkeys, horses, pigs, cows, dogs, etc.
  • monoclonal antibodies can be produced using hybridoma methods or phage antibody library technology well known in the art.
  • Antibodies prepared by the above method can be separated and purified using methods such as gel electrophoresis, dialysis, salt precipitation, ion exchange chromatography, and affinity chromatography. Additionally, antibodies of the invention include intact forms with two full-length light chains and two full-length heavy chains, as well as functional fragments of the antibody molecule.
  • a functional fragment of an antibody molecule refers to a fragment that possesses at least an antigen-binding function, and includes Fab, F(ab'), F(ab')2, and Fv.
  • PNA Peptide Nucleic Acid
  • DNA has a phosphate-ribose sugar backbone
  • PNA has a repeated N-(2-aminoethyl)-glycine backbone linked by peptide bonds, which greatly increases its binding force and stability to DNA or RNA and is used in molecular biology. , is used in diagnostic analysis and antisense therapy.
  • the “aptamer” is an oligonucleic acid or peptide molecule.
  • the agent for measuring the expression level of genes encoding the biomarkers may include one or more selected from the group consisting of primers, probes, and antisense nucleotides that specifically bind to the genes.
  • the “primer” is a fragment that recognizes the target gene sequence and includes forward and reverse primer pairs, but is preferably a primer pair that provides analysis results with specificity and sensitivity. High specificity can be granted when the nucleic acid sequence of the primer is a sequence that is inconsistent with the non-target sequence present in the sample, so that the primer amplifies only the target gene sequence containing the complementary primer binding site and does not cause non-specific amplification. .
  • the “probe” refers to a substance that can specifically bind to a target substance to be detected in a sample, and refers to a substance that can specifically confirm the presence of the target substance in the sample through the binding.
  • the type of probe is not limited as it is a material commonly used in the art, but is preferably PNA (peptide nucleic acid), LNA (locked nucleic acid), peptide, polypeptide, protein, RNA or DNA, and most preferably is PNA.
  • the probe is a biomaterial that is derived from or similar to living organisms or includes those manufactured in vitro, such as enzymes, proteins, antibodies, microorganisms, animal and plant cells and organs, nerve cells, DNA, and It may be RNA, DNA includes cDNA, genomic DNA, and oligonucleotides, RNA includes genomic RNA, mRNA, and oligonucleotides, and examples of proteins may include antibodies, antigens, enzymes, peptides, etc.
  • LNA Locked nucleic acids
  • LNA nucleosides contain the common nucleic acid bases of DNA and RNA and can form base pairs according to the Watson-Crick base pairing rules. However, due to the 'locking' of the molecule due to the methylene bridge, LNA does not form the ideal shape in Watson-Crick bonding.
  • LNA When LNA is included in a DNA or RNA oligonucleotide, the LNA can pair with the complementary nucleotide chain more quickly and increase the stability of the double helix.
  • the "antisense” refers to a sequence of nucleotide bases in which an antisense oligomer hybridizes with a target sequence in RNA by Watson-Crick base pairing, typically allowing the formation of an mRNA and RNA:oligomer heteroduplex within the target sequence. and oligomers having an intersubunit backbone. Oligomers may have exact or approximate sequence complementarity to the target sequence.
  • the method may further include calculating the likelihood of cancer occurring in the individual using a learned deep neural network.
  • the calculation may be to divide the individual into a risk group with a high possibility of developing cancer and a control group.
  • the “control group” may refer to a general population that does not develop cancer, an entire population of subjects that develop cancer, or a population that shows a good prognosis among subjects that develop cancer.
  • the deep neural network is a type of artificial neural network and includes a convolution layer, a pooling layer, a ReLu layer, and a transpose layer that allows deep learning learning.
  • Convolutional layer Unpooling layer, 1x1 convolutional layer, Skip connection, Global Average Pooling (GAP) layer, Fully Connected layer, SVM (support Vector Machine), LSTM (long short term memory)
  • GAP Global Average Pooling
  • SVM support Vector Machine
  • LSTM long short term memory
  • It may be a neural network including one or more layers or elements selected from the group consisting of Atrous Convolution, Atrous Spatial Pyramid Pooling, Separable Convolution, and Bilinear Upsampling, It may further include a batch normalization operation at the front end. For example, as shown in FIG.
  • the deep neural network includes an input layer into which data is input; first hidden layer (hidden layer 1); second hidden layer (hidden layer 1); third hidden layer (hidden layer 1); fourth hidden layer (hidden layer 1); and an output layer.
  • first hidden layer hidden layer 1
  • second hidden layer hidden layer 1
  • third hidden layer hidden layer 1
  • fourth hidden layer hidden layer 1
  • output layer the expression level of the biomarker of the present invention can be input into the input layer.
  • the deep neural network may be learned by a backpropagation learning algorithm, and the learning data may include data on the expression level of the biomarker of the present invention and result data on whether cancer actually occurs.
  • the learning data is data input for learning of a deep neural network, and for learning, information about the error between the predicted value obtained by processing the information by the deep neural network and the actual result is required. Accordingly, the learning data must necessarily include actual results.
  • the learning data may require patient-specific data above a certain standard value to ensure that the weight coefficients of the deep neural network are derived at a sufficiently reliable level. Thereafter, the diagnostic device may perform an operation of generating an initial prediction value that predicts the probability of developing cancer using a deep neural network.
  • the training unit may perform an operation to modify the weight coefficient based on the error between the derived initial prediction value and the actual result value input as learning data.
  • the operation may be an operation using a backpropagation algorithm.
  • each node of the first to third hidden layers may receive a value that is a result of calculation by all nodes and weights of the input layer.
  • each node of the first to fourth hidden layers may receive a result calculated by all nodes and weights of the input layer.
  • each node of the hidden layers can determine whether or not it is activated using an activation function.
  • Each node in the hidden layer is determined to be activated when the corresponding value satisfies a certain standard or deactivated when it does not meet a certain standard, and this is performed based on the activation function. Additionally, in the process of connecting each node of the hidden layer to the positive and negative nodes of the output layer, corresponding weight coefficients may exist. Therefore, if the weights in the deep neural network can be set differently, the weight coefficients applied at the stage between the input layer and the output layer can be set in various ways. For example, a neural network with first to third hidden layers as shown in FIG. 1 may have a maximum of 5,184 weight coefficients, and a neural network with first to fourth hidden layers as shown in FIG. 2 may have a maximum of 46,656 weight coefficients. You can.
  • the training unit modifies the weights starting from the weight coefficients applied at the stage closest to the output layer, and when all inputs of the prepared learning data are completed, the final calculated coefficients are fixed as the weight coefficients of the deep neural network to obtain the learned deep layer.
  • the neural network model can be completed.
  • the data for learning the deep neural network may further include one or more selected from the group consisting of the individual's weight, gender, and age, which are risk factors other than the biomarker.
  • an apparatus for diagnosing cancer can be provided.
  • the device includes a sample processing unit for isolating an exosome fraction from a biological sample isolated from a subject of interest; And it may include a measuring unit that measures methylation of nucleotide molecules in the exosome fraction.
  • the device may further include a calculation unit that calculates the likelihood of cancer occurring in the individual using a learned deep neural network.
  • the device may further include a training unit that receives learning data and performs learning.
  • the learning data may be individually input by the user, or may be input collectively from patient data separately stored in a server computer of a medical institution.
  • the training unit may directly receive data corresponding to learning data (training cohort) or test data (test cohort). Additionally, according to various embodiments, the training unit may receive a user manipulation to change an item of data to be input. In addition, patient data according to various embodiments may be input into the training unit, and the data may be in digital form.
  • the device may further include a storage unit.
  • the storage unit may include, for example, internal memory or external memory.
  • Internal memory includes, for example, volatile memory (e.g., dynamic RAM (DRAM), static RAM (SRAM), or synchronous dynamic RAM (SDRAM), etc.), non-volatile memory (e.g., OTPROM (one time programmable ROM), programmable ROM (PROM), erasable and programmable ROM (EPROM), electrically erasable and programmable ROM (EEPROM), mask ROM, flash ROM, flash memory (such as NAND flash or NOR flash, etc.), hard drive, Or it may include at least one of a solid state drive (SSD).
  • the external memory may be a flash drive, for example, compact flash (CF), secure digital (SD), or Micro-SD. It may further include (micro secure digital), Mini-SD (mini secure digital), xD (extreme digital), MMC (multi-media card), or memory stick, etc. External memory can be stored through various interfaces.
  • the patient data may be extracted by a reader from only data corresponding to risk factors from information collected from other electronic devices or external servers (e.g., related medical institution servers).
  • information transmitted through the communication unit may include information that does not correspond to test results, such as the patient's name, date of birth, and smoking status, and result information for a specific test.
  • the patient data can be input to the training unit to extract only the data items required to perform a cancer diagnosis.
  • the deep neural network can compare the values of positive and negative nodes and calculate the one with the larger value as the final result. For example, the comparison may compare the size of the difference between the values of the positive node and the negative node, or if it is determined that the positive value is greater than or equal to the reference value or the negative value is less than the reference value, the corresponding probability of occurrence of cancer may be calculated.
  • the calculation unit can calculate the most effective risk factors to reduce the probability of cancer occurrence based on the deep neural network.
  • the calculation unit may generate comparison data by changing the value of a specific item selected from each risk factor data at regular intervals. Thereafter, the electronic device can compare the predicted value generated by inputting the raw data into the deep neural network with the predicted value generated by inputting the comparison data into the deep neural network, and if the predicted value (e.g., positive) generated by the raw data is compared. If the predicted values (e.g., negative) generated by the data are calculated differently, data values for each risk factor item for which the two values begin to be calculated differently can be calculated and provided to the user.
  • continuous data can be expressed as mean and standard deviation (SD), and cancer incidence factors can be determined from retrospective data using a model of the training cohort.
  • SD standard deviation
  • the function of displaying data input to the device and results calculated as a result of the operation of a deep neural network can be performed. Additionally, the display unit may calculate data regarding the probability of a result processed by a deep neural network according to various embodiments. For example, according to various embodiments, the deep neural network not only predicts whether cancer will occur, but also provides information about the probability of cancer occurring if it will occur, or the probability of cancer not occurring if it is predicted not to occur. can be calculated together.
  • the display unit may include a panel, a hologram device, or a projector.
  • the panel may be composed of a touch panel and one module.
  • Holographic devices can display three-dimensional images in the air using the interference of light.
  • a projector can display images by projecting light onto a screen.
  • the screen may be located, for example, inside or outside the electronic device.
  • the display unit may further include a control circuit for controlling a panel, a hologram device, or a projector.
  • the device may further include a communication unit.
  • the communication unit can use a network to transmit and receive data with other user electronic devices or other servers, and the type of the network is not particularly limited.
  • the network may be an Internet Protocol (IP) network that provides large data transmission and reception services through the Internet Protocol (IP), or an All IP network that integrates different IP networks.
  • IP Internet Protocol
  • the network includes a wired network, a Wibro (Wireless Broadband) network, a mobile communication network including WCDMA, a mobile communication network including a HSDPA (High Speed Downlink Packet Access) network and an LTE (Long Term Evolution) network, and LTE advanced (LTE-A).
  • the communication unit may support a communication function for receiving methylation information to be input into a deep neural network learned from another electronic device or an external server. Additionally, the communication unit may transmit the information processing results of the deep neural network to another electronic device or external server.
  • the device may further include a control unit, and the control unit may also be called a processor, controller, microcontroller, microprocessor, microcomputer, etc. You can. Meanwhile, the control unit may be implemented by hardware, firmware, software, or a combination thereof.
  • an embodiment of the present invention may be implemented in the form of a module, procedure, function, etc. that performs the functions or operations described above.
  • Software code can be stored in memory and driven by a control unit.
  • the memory may be located inside or outside the user terminal and server, and may exchange data with the control unit through various known means.
  • a screening method for a cancer treatment drug can be provided.
  • the steps include treating a sample isolated from a target individual or a cancer disease animal model with a candidate substance; and confirming the expression level of the biomarker in a sample treated with the candidate substance or a cancer disease animal model. It may be to provide a method of screening a drug for the prevention or treatment of cancer, including: " “Candidate substance” may include without limitation substances that can improve or beneficially change the prognosis when applied to cancer patients, and the candidate substance may include synthetic substances as well as natural substances, but is not limited thereto.
  • the expression level may be the expression level in exosomes of the sample derived from the model.
  • the measurement of the expression level may be performed multiple times.
  • the present invention relates to a method for diagnosing cancer by analyzing circulating tumor cells, and more specifically, to a composition, diagnostic kit, and diagnostic method for diagnosing cancer using the gene methylation level of circulating tumor cells.

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Abstract

La présente invention concerne un procédé permettant de fournir des informations pour un diagnostic dans le cadre d'un cancer, le procédé comprenant les étapes suivantes : isolement d'une fraction d'exosome à partir d'un échantillon biologique isolé chez un individu d'intérêt ; et mesure du niveau d'expression d'un biomarqueur dans la fraction d'exosome.
PCT/KR2022/010342 2022-07-13 2022-07-15 Procédé pour fournir des informations pour un diagnostic pour le cancer fondé sur l'intelligence artificielle en utilisant un biomarqueur exprimé dans un exosome Ceased WO2024014580A1 (fr)

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