WO2012012510A2

WO2012012510A2 - Gene expression profiling for the identification of lung cancer

Info

Publication number: WO2012012510A2
Application number: PCT/US2011/044648
Authority: WO
Inventors: Karl Wassmann; Danute M. Bankaitis-Davis; Kathleen Storm; Lisa Siconolfi
Original assignee: DxTerity Diagnostics
Current assignee: DxTerity Diagnostics
Priority date: 2010-07-21
Filing date: 2011-07-20
Publication date: 2012-01-26
Anticipated expiration: 2013-01-21
Also published as: WO2012012510A3; EP2596131A4; EP2596131A2

Abstract

The invention provides gene models useful in the screening of at-risk patients for lung cancers including smokers with and without chronic obstructive pulmonary disease (COPD).

Description

Gene Expression Profiling for the Identification of Lung Cancer

RELATED APPLICATIONS

This application claims the benefit of USSN 61/366,331 , filed July 21 , 2010, which is incorporated herein by reference in its entirety.

FIELD OF THE INVENTION

The present invention relates generally to the identification of biological markers associated with the identification of lung cancer. More specifically, the present invention relates to the use of gene expression data in the screening of at-risk patients for lung cancers including smokers with and without chronic obstructive pulmonary disease (COPD).

BACKGROUND OF THE INVENTION

Lung cancer is the leading cause of cancer deaths among both men and women. It is a fast growing and highly fatal disease. Nearly 60% of people diagnosed with lung cancer die within one year of diagnosis. Nearly 75% die within 2 years. There are two major types of lung cancer: small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC). If lung cancer has characteristics of both types it is called a mixed small/large cell carcinoma.

Approximately 85% of lung cancers are NSCLC. There are 3 sub-types of NSCLC, which differ in size, shape, and biochemical make-up. Approximately 35-50% of all lung cancers are squamous cell carcinomas. This lung cancer is linked to smoking and is typically found near the bronchus. Adenocarcinomas (e.g., bronchioloalveolar carcinoma) account for approximately 40% of all lung cancers, and is usually found in the outer region of the lung. Large-cell undifferentiated carcinoma accounts for approximately 10-15% of all lung cancers. Large-cell undifferentiated carcinoma can appear in any part of the lung, and grows and spreads very quickly, resulting in poor prognosis.

SCLC accounts for approximately 15% of all lung cancers. SCLC often starts in the bronchi near the center of the chest and tends to spread widely through the body, quickly. The cancer cells can multiply quickly, from large tumors, and spread to lymph nodes and other organs such as the brain, adrenal glands, and liver. Thus, surgery is rarely an option, and is never used as the sole treatment modality. In addition to the SCLC and NSCLC, other types of tumors can occur in the lungs. For example, carcinoid tumors of the lung account for fewer than 5% of lung tumors. Most are slow growin typical carcinoid tumors, which are generally cured by surgery. Cancers intermediate between the benign carcinoid tumors and SCLC are known as atypical carcinoid tumors. Other types of lung tumors include adenoid cystic carcinomas, hamartomas, lymphomas, sarcomas, and mesothelioma (tumor of the pleura (the layer of cells that line the outer surface of the lung)), which is associated with asbestos exposure.

The most important risk factor for lung cancer is smoking, including cigarette, cigar, pipe, marijuana, and hookah smoke. Despite popular belief, there is no evidence that smoking low tar or "light" cigarettes reduces the risk of lung cancer. Mentholated cigarettes may increase the risk of developing lung cancer. Additionally, non-smokers are at risk for lung cancer due to second hand smoke. Other risk factors include age (increased risk in the elderly population, nearly 70% of people diagnosed are over age 65); genetic predisposition; exposure to high levels of arsenic in drinking water, asbestos fibers, and/or long term radon contamination (each more pronounced in smokers); cancer causing agents in the workplace (e.g., radioactive ores, inhaled chemicals or minerals (e.g., arsenic, berrylium, vinyl chloride, nickel chromates, coal products, mustard gas, chloromethyl ethers, fuels such as gasoline, and diesel exhaust)); prior radiation therapy to the lungs; personal and family history of lung cancer; a diet low in fruits and vegetables (more pronounced in smokers); and air pollution.

Frequently, lung cancer remains asymptomatic until it reaches an advanced stage and spreads beyond the lungs. Once symptoms do start presenting, they include persistent cough; chest pain, often aggravated by deep breathing, coughing, or laughing; hoarseness; weight loss and loss of appetite; bloody or rust colored sputum; shortness of breath; recurring infections (e.g., bronchitis); new onset of wheezing; severe shoulder pain and/or Horner syndrome; and paraneoplastic syndromes (problems with distant organs due to hormone producing lung cancer). Further complicating lung cancer detection, is that these symptoms are similarchronic obstructive pulmonary disease (COPD), which is frequent among smokers or ex-smokers.

Since individuals with lung cancer can be-asymptomatic while the disease progresses and metastasizes, screenings are essential to detect lung cancer at the earliest stage possible, expecially for high risk individuals such as thoses with COPD. Diagnosis for lung cancer is typically done through a combination of a medical history to check for risk factors and symptoms, physical exam to look for signs of lung cancer, imaging tests to look for tumors in the lungs or other organs, (e.g., chest X-ray, CT scan, MRI, PET, and bone scans), blood counts and blood chemistry, and invasive procedures that assist the physician to image the inside of the lungs and sample tissues/cells to determine whether a tumor is benign or malignant, and to determine the type of lung cancer (e.g., sputum cytology-microscopic examination of cells in coughed up phlegm; CT guided needle biopsy, bronchoscopy- viewing the inside of the bronchi through a flexible lighted tube; endobronchial ultrasound;

endoscopic esophageal ultrasound; mediastinoscopy, mediastinotomy; thoracentesis; and thorascopy).

Because lung cancer spreads beyond the lungs before causing any symptoms, an effective screening program could save thousands of lives. To date, there is no lung cancer test that has been shown to prevent people from dying from this disease. Studies show that commonly used screening methods such as chest x-rays and sputum cytology are incapable of detecting lung cancer early enough to improve a person's chance for a cure. For this reason, lung cancer screening is not a routine practice for the general population, or even for people at increased risk, such as smokers and those with COPD. Even with the screening procedures currently available, it is nearly impossible to detect or verify a diagnosis of lung cancer in a non-invasive manner, and without causing the patient pain and discomfort. Thus, a need exists for better ways to diagnose lung cancer.

SUMMARY OF THE INVENTION

The invention is in based in part upon the identification of gene expression profiles (Precision Profiles™) associated with lung cancer. These genes are referred to herein as lung cancer associated genes or lung cancer associated constituents. More particularly, the invention is based upon the surprising discovery that the methods provided by the invention are capable of detecting discriminating between patients with lung cancer from smokers with and without chronic obstructive pulmonary disease (COPD),a s well as heathy normal individuals.

In various aspects the invention provides methods of evaluating the presence or absence (e.g., diagnosing or prognosing) of lung cancer, based on a sample from the subject, the sample providing a source of RNAs, and determining a quantitative measure of the amount of CDK2, CXCR3, CD22, CDK6, ERCC2, ABCC5, CDHl , CDKNIB, CDKN2A, CREB3, ICAM1 , MMP8, NCAM1 , SOCS 1 , STK4, TNFRSF10B, and TNFRSF1B and arriving at a measure of each constituent. In various embodiments the method further includes determining a quantitative measure of the amount of (a) CCND2 and TOPORS or (b) IGF2BP2 and ST 14

The methods of the invention further include comparing the quantitative measure of the constituent in the subject derived sample to a reference value or a baseline value, e.g. baseline data set. The reference value is for example an index value. Comparison of the subject measurements to a reference value allows for the present or absence of lung cancer to be determined.

The measure of the constituent is increased or decreased in the subject compared to the expression of the constituent in the reference, e.g. , normal reference sample or baseline value. The measure is increased or decreased 10%, 25%, 50%> compared to the reference level. Alternately, the measure is increased or decreased 1, 2, 5 or more fold compared to the reference level.

In various aspects of the invention the methods are carried out wherein the measurement conditions are substantially repeatable, particularly within a degree of repeatability of better than ten percent, five percent or more particularly within a degree of repeatability of better than three percent, and/or wherein efficiencies of amplification for all constituents are substantially similar, more particularly wherein the efficiency of

amplification is within ten percent, more particularly wherein the efficiency of amplification for all constituents is within five percent, and still more particularly wherein the efficiency of amplification for all constituents is within three percent or less.

In addition, the one or more different subjects may have in common with the subject at least one of age group, gender, ethnicity, geographic location, nutritional history, medical condition, clinical indicator, medication, physical activity, body mass, and environmental exposure. A clinical indicator may be used to assess lung cancer or a condition related to lung cancer of the one or more different subjects, and may also include interpreting the calibrated profile data set in the context of at least one other clinical indicator, wherein the at least one other clinical indicator includes blood chemistry, X-ray or other radiological or metabolic imaging technique, molecular markers in the blood, other chemical assays, and physical findings.

The constituents are selected so as to distinguish from a normal reference subject and a lung cancer-diagnosed subject. Alternatively the constituents are selected so as to distinguish from subjects who are smokers with and without COPD and a lung cancer- diagnosed subjects.

Preferably, the constituents are selected so as to distinguish, e.g., classify between a normal and a lung cancer-diagnosed subject with at least 75%, 80%, 85%, 90%, 95%, 97%, 98%, 99% or greater accuracy. By "accuracy" is meant that the method has the ability to distinguish, e.g., classify, between subjects having lung cancer or conditions associated with lung cancer, and those that do not. Accuracy is determined for example by comparing the results of the Gene Precision Profiling^™ to standard accepted clinical methods of diagnosing lung cancer, e.g., one or more symptoms of lung cancer such chest pain, often aggravated by deep breathing; coughing, or laughing; hoarseness; weight loss and loss of appetite; bloody or rust colored sputum; shortness of breath; recurring infections (e.g., bronchitis); new onset of wheezing; severe shoulder pain and/or Horner syndrome due to damage caused by cancer of the upper lungs to a nerve that passes from the upper chest into the neck; and parneoplastic syndromes (e.g., hypercalcemia, causing urinary frequency, constipation, weakness, dizziness, confusion, and other CNS problems; hypertrophic osteoarthropathy; blood clots; and gynecomastia); bone pain; neurologic changes; jaundice; and masses near the surface of the body due to cancer spreading to the skin or lymph nodes.

The sample is any sample derived from a subject which contains RNA. For example, the sample is blood, a blood fraction, body fluid, a population of cells or tissue from the subject, a lung cell, or a rare circulating tumor cell or circulating endothelial cell found in the blood.

Also included in the invention are kits for the detection of lung cancer in a subject, containing at least one reagent for the detection or quantification of any constituent measured according to the methods of the invention and instructions for using the kit.

Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Although methods and materials similar or equivalent to those described herein can be used in the practice or testing of the present invention, suitable methods and materials are described below. All publications, patent applications, patents, and other references mentioned herein are incorporated by reference in their entirety. In case of conflict, the present specification, including definitions, will control. In addition, the materials, methods, and examples are illustrative only and not intended to be limiting.

Other features and advantages of the invention will be apparent from the following detailed description and claims. BRIEF DESCRIPTION OF THE DRAWINGS

Figure 1 a chart showing patients characteristics of the patient training and validation sets. Figure 2 is is a chart showing that the patients having primary lung cancer were assigned to training and validation groups by type of lung cancer.

Figure 3 is a chart showing the coefficients and the p-values of each of the 24 genes candidate model developed on lung cancer resection cases.

Figure 4 is a ROC curve showing that the 24 gene model discriminates resction positive from resection negative cases.

Figure 5 is a chart showing smoker patient population characteristics were well matched across training and validation sets.

Figure 6 is is a chart showing that the smoker patient population having primary lung cancer were assigned to training and validation groups by type of lung cancer.

Figure 7 is a chart showing the mean delta CT values for the 19 gens in the model for all cohorts. As shown in the table there is a trend for decreases expression of the prime genes for lung cancer patients

Figure 8 is a chart showing the coefficients and the p-values of each of the 19 genes of the model. As shown in the figure the 19-gene model provides significant discrimination between cases and controls.

Figure 9 are ROC curves showing that the 19-gene model validates in an independent dataset the predication of lung cancer cases compared with smoking controls with and without COPD

Figure 10 shows that the 19 model in both the training and validation datatsets demonstrates a high correct classification rates.

Figure 11 is a chart showing coefficients and p-values of the 19 gene 4 component model on the combined training and validation dataset.

Figure 12 is a ROC curve showing that the 19 gene model stongly discriminated between individuals with lung cancer versus smokers with and without COPD.

Figure 13 shows that the 19 gene model from combined training and validation set demonstrate high correct classification rates

Figure 14 shows that the 19 gene models has higher correct classification rates for female smokers than for male smokers

Figure 15 is a graphical representation of the 19 gene model, capable of distinguishing between subjects afflicted with lung cancer, and non-lung cancer subjects (smokers, COPD), with a discrimination line overlaid onto the graph as an example of the Index Function evaluated at a particular logit value. Values below the line represent subjects predicted to be in the non-lung cancer population. Values above the line represent subjects predicted to be in the lung cancer population.

Figure 16 is a chart showing coefficients and p-values of a 19 gene 4 component model developed on the training set females excluding never smokers. As shown ion the figure this 19 gene mofel provides significant discrimination between cases and controls on the combined training and validation dataset.

Figure 17 is a ROC curve showing that the 19 gene model stongly discriminated between individuals with lung cancer versus smokers with and without COPD. As shown in the figure the female only 19-gene model was more satble (smaller fall off in the validation data) than the model developed on males only. Moreover, the model feveloped for females had a better correct classification rate on males in the validation data than the model developed for males.

Figure 18 is a chart showing the coefficients and the p-values of each of the 19 genes of the model. As shown in the figure the 19-gene model developed on females only provides significant discrimination between cases and controls.

Figure 19 shows that the 19-gene model (female only) from combined training and validation set demonstrate high correct classification rates.

Figure 20 is a ROC curve showing that the 19-gene model (females only) stongly discriminated between individuals with lung cancer versus smokers with and without COPD.

Figure 21 shows that the 19-gene model (female only) from combined training and validation set demonstrate high correct classification rates

Figure 22 shows the 19 gene model (female only) correct classification rates for female smokers and male smokers.

DETAILED DESCRIPTION

Definitions

The following terms shall have the meanings indicated unless the context otherwise requires:

"Accuracy" refers to the degree of conformity of a measured or calculated quantity (a test reported value) to its actual (or true) value. Clinical accuracy relates to the proportion of true outcomes (true positives (TP) or true negatives (TN)) versus misclassified outcomes (false positives (FP) or false negatives (FN)), and may be stated as a sensitivity, specificity, positive predictive values (PPV) or negative predictive values (NPV), or as a likelihood, odds ratio, among other measures.

"Algorithm" is a set of rules for describing a biological condition. The rule set may be defined exclusively algebraically but may also include alternative or multiple decision points requiring domain- specific knowledge, expert interpretation or other clinical indicators.

An "agent" is a "composition" or a "stimulus", as those terms are defined herein, or a combination of a composition and a stimulus.

"Amplification" in the context of a quantitative RT-PCR assay is a function of the number of DNA replications that are required to provide a quantitative determination of its concentration. "Amplification" here refers to a degree of sensitivity and specificity of a quantitative assay technique. Accordingly, amplification provides a measurement of concentrations of constituents that is evaluated under conditions wherein the efficiency of amplification and therefore the degree of sensitivity and reproducibility for measuring all constituents is substantially similar.

A "baseline profile data set" is a set of values associated with constituents of a Gene Expression Panel (Precision Profile^™) resulting from evaluation of a biological sample (or population or set of samples) under a desired biological condition that is used for

mathematically normative purposes. The desired biological condition may be, for example, the condition of a subject (or population or set of subjects) before exposure to an agent or in the presence of an untreated disease or in the absence of a disease. Alternatively, or in addition, the desired biological condition may be health of a subject or a population or set of subjects. Alternatively, or in addition, the desired biological condition may be that associated with a population or set of subjects selected on the basis of at least one of age group, gender, ethnicity, geographic location, nutritional history, medical condition, clinical indicator, medication, physical activity, body mass, and environmental exposure.

A "biological condition" of a subject is the condition of the subject in a pertinent realm that is under observation, and such realm may include any aspect of the subject capable of being monitored for change in condition, such as health; disease including cancer; trauma; aging; infection; tissue degeneration; developmental steps; physical fitness; obesity, and mood. As can be seen, a condition in this context may be chronic or acute or simply transient. Moreover, a targeted biological condition may be manifest throughout the organism or population of cells or may be restricted to a specific organ (such as skin, heart, eye or blood), but in either case, the condition may be monitored directly by a sample of the affected population of cells or indirectly by a sample derived elsewhere from the subject. The term "biological condition" includes a "physiological condition".

"Body fluid" of a subject includes blood, urine, spinal fluid, lymph, mucosal secretions, prostatic fluid, semen, haemolymph or any other body fluid known in the art for a subject.

"Calibrated profile data set" is a function of a member of a first profile data set and a corresponding member of a baseline profile data set for a given constituent in a panel.

A "circulating endothelial cell" ("CEC") is an endothelial cell from the inner wall of blood vessels which sheds into the bloodstream under certain circumstances, including inflammation, and contributes to the formation of new vasculature associated with cancer pathogenesis. CECs may be useful as a marker of tumor progression and/or response to antiangio genie therapy.

A "circulating tumor cell" ("CTC") is a tumor cell of epithelial origin which is shed from the primary tumor upon metastasis, and enters the circulation. The number of circulating tumor cells in peripheral blood is associated with prognosis in patients with metastatic cancer. These cells can be separated and quantified using immunologic methods that detect epithelial cells.

A "clinical indicator" is any physiological datum used alone or in conjunction with other data in evaluating the physiological condition of a collection of cells or of an organism. This term includes pre-clinical indicators.

"Clinical parameters" encompasses all non-sample or non-Precision Profiles^™ of a subject's health status or other characteristics, such as, without limitation, age (AGE), ethnicity (RACE), gender (SEX), and family history of cancer.

"Chronic obstructive pulmonary disease " (COPD) refers to a group of lung diseases that block airflow and make it increasingly difficult to breathe. Emphysema and chronic bronchitis are the two main conditions that make up COPD, but COPD can also refer to damage caused by chronic asthmatic bronchitis. In all cases, damage to the airways eventually interferes with the exchange of oxygen and carbon dioxide in the lungs. A COPD diagnosis is confirmed by a test called spirometry, which measures how deeply a person can breathe and how fast air can move into and out of the lungs. Such a diagnosis should be considered in any patient who has symptoms of cough, sputum production, or dyspnea (difficult or labored breathing), and/or a history of exposure to risk factors for the disease

A "composition" includes a chemical compound, a nutraceutical, a pharmaceutical, a homeopathic formulation, an allopathic formulation, a naturopathic formulation, a combination of compounds, a toxin, a food, a food supplement, a mineral, and a complex mixture of substances, in any physical state or in a combination of physical states.

To "derive" a profile data set from a sample includes determining a set of values associated with constituents of a Gene Expression Panel (Precision Profile^™) either (i) by direct measurement of such constituents in a biological sample.

"Distinct RNA or protein constituent" in a panel of constituents is a distinct expressed product of a gene, whether RNA or protein. An "expression" product of a gene includes the gene product whether RNA or protein resulting from translation of the messenger RNA.

"FN" is false negative, which for a disease state test means classifying a disease subject incorrectly as non-disease or normal.

" " is false positive, which for a disease state test means classifying a normal subject incorrectly as having disease.

A "formula," "algorithm," or "model" is any mathematical equation, algorithmic, analytical or programmed process, statistical technique, or comparison, that takes one or more continuous or categorical inputs (herein called "parameters") and calculates an output value, sometimes referred to as an "index" or "index value." Non-limiting examples of "formulas" include comparisons to reference values or profiles, sums, ratios, and regression operators, such as coefficients or exponents, value transformations and normalizations (including, without limitation, those normalization schemes based on clinical parameters, such as gender, age, or ethnicity), rules and guidelines, statistical classification models, and neural networks trained on historical populations. Of particular use in combining constituents of a Gene Expression Panel (Precision Profile^™) are linear and non-linear equations and statistical significance and classification analyses to determine the relationship between levels of constituents of a Gene Expression Panel (Precision Profile^™) detected in a subject sample and the subject's risk of lung cancer. In panel and combination construction, of particular interest are structural and synactic statistical classification algorithms, and methods of risk index construction, utilizing pattern recognition features, including, without limitation, such established techniques such as cross-correlation, Principal Components Analysis (PCA), factor rotation, Logistic Regression Analysis (LogReg), Kolmogorov Smirnoff tests (KS), Linear Discriminant Analysis (LDA), Eigengene Linear Discriminant Analysis (ELD A), Support Vector Machines (SVM), Random Forest (RF), Recursive Partitioning Tree (RPART), as well as other related decision tree classification techniques (CART, LART, LARTree, FlexTree, amongst others), Shrunken Centroids (SC), StepAIC, K-means, Kth-Nearest Neighbor, Boosting, Decision Trees, Neural Networks, Bayesian Networks, Support Vector Machines, and Hidden Markov Models, among others. Other techniques may be used in survival and time to event hazard analysis, including Cox, Weibull, Kaplan-Meier and Greenwood models well known to those of skill in the art. Many of these techniques are useful either combined with a consituentes of a Gene Expression Panel (Precision Profile^™) selection technique, such as forward selection, backwards selection, or stepwise selection, complete enumeration of all potential panels of a given size, genetic algorithms, voting and committee methods, or they may themselves include biomarker selection methodologies in their own technique. These may be coupled with information criteria, such as Akaike's Information Criterion (AIC) or Bayes Information Criterion (BIC), in order to quantify the tradeoff between additional biomarkers and model improvement, and to aid in minimizing overfit. The resulting predictive models may be validated in other clinical studies, or cross-validated within the study they were originally trained in, using such techniques as Bootstrap, Leave-One-Out (LOO) and 10-Fold cross- validation (10-Fold CV). At various steps, false discovery rates (FDR) may be estimated by value permutation according to techniques known in the art.

A "Gene Expression Panel" (Precision Profile^™) is an experimentally verified set of constituents, each constituent being a distinct expressed product of a gene, whether RNA or protein, wherein constituents of the set are selected so that their measurement provides a measurement of a targeted biological condition.

A "Gene Expression Profile" is a set of values associated with constituents of a Gene

Expression Panel (Precision Profile^™) resulting from evaluation of a biological sample (or population or set of samples).

A "Gene Expression Profile Inflammation Index" is the value of an index function that provides a mapping from an instance of a Gene Expression Profile into a single-valued measure of inflammatory condition.

A Gene Expression Profile Cancer Index" is the value of an index function that provides a mapping from an instance of a Gene Expression Profile into a single-valued measure of a cancerous condition.

The "health" of a subject includes mental, emotional, physical, spiritual, allopathic, naturopathic and homeopathic condition of the subject.

"Index" is an arithmetically or mathematically derived numerical characteristic developed for aid in simplifying or disclosing or informing the analysis of more complex quantitative information. A disease or population index may be determined by the application of a specific algorithm to a plurality of subjects or samples with a common biological condition.

"Inflammation" is used herein in the general medical sense of the word and may be an acute or chronic; simple or suppurative; localized or disseminated; cellular and tissue response initiated or sustained by any number of chemical, physical or biological agents or combination of agents.

"Inflammatory state" is used to indicate the relative biological condition of a subject resulting from inflammation, or characterizing the degree of inflammation.

A "large number" of data sets based on a common panel of genes is a number of data sets sufficiently large to permit a statistically significant conclusion to be drawn with respect to an instance of a data set based on the same panel.

"Lung cancer" is the growth of abnormal cells in the lungs, capable of invading and destroying other lung cells, and includes Stage 1, Stage 2 and Stage 3 lung cancer, small cell lung cancer, non-small cell lung cancer (squamous cell carcinoma, adenocarcinoma {e.g., bronchioloalveolar carcinoma and large-cell undifferentiated carcinoma), carcinoid tumors (typical and atypical), lymphomas of the lung, adenoid cystic carcinomas, hamartomas, lymphomas, sarcomas, and mesothelia.

"Negative predictive value" or "NPV" is calculated by TN/(TN + FN) or the true negative fraction of all negative test results. It also is inherently impacted by the prevalence of the disease and pre -test probability of the population intended to be tested.

See, e.g., O'Marcaigh AS, Jacobson RM, "Estimating the Predictive Value of a Diagnostic Test, How to Prevent Misleading or Confusing Results," Clin. Ped. 1993, 32(8): 485-491, which discusses specificity, sensitivity, and positive and negative predictive values of a test, e.g., a clinical diagnostic test. Often, for binary disease state classification approaches using a continuous diagnostic test measurement, the sensitivity and specificity is summarized by

Receiver Operating Characteristics (ROC) curves according to Pepe et al., "Limitations of the Odds Ratio in Gauging the Performance of a Diagnostic, Prognostic, or Screening Marker," Am. J. Epidemiol 2004, 159 (9): 882-890, and summarized by the Area Under the Curve (AUC) or c-statistic, an indicator that allows representation of the sensitivity and specificity of a test, assay, or method over the entire range of test (or assay) cut points with just a single value. See also, e.g., Shultz, "Clinical Interpretation of Laboratory Procedures," chapter 14 in Teitz, Fundamentals of Clinical Chemistry, Burtis and Ashwood (eds.), 4^th edition 1996, W.B. Saunders Company, pages 192-199; and Zweig et al., "ROC Curve Analysis: An Example Showing the Relationships Among Serum Lipid and Apolipoprotein Concentrations in Identifying Subjects with Coronory Artery Disease," Clin. Chem., 1992, 38(8): 1425-1428. An alternative approach using likelihood functions, BIC, odds ratios, information theory, predictive values, calibration (including goodness-of-fit), and reclassification measurements is summarized according to Cook, "Use and Misuse of the Receiver Operating Characteristic Curve in Risk Prediction," Circulation 2007, 115: 928-935.

A normal" subject is a subject who is generally in good health, has not been diagnosed with lung cancer, is asymptomatic for lung cancer, and lacks the traditional laboratory risk factors for lung cancer.

A normative" condition of a subject to whom a composition is to be administered means the condition of a subject before administration, even if the subject happens to be suffering from a disease.

A "panel" of genes is a set of genes including at least two constituents.

A "population of cells" refers to any group of cells wherein there is an underlying commonality or relationship between the members in the population of cells, including a group of cells taken from an organism or from a culture of cells or from a biopsy, for example.

"Positive predictive value" or "PPV" is calculated by TP/(TP+FP) or the true positive fraction of all positive test results. It is inherently impacted by the prevalence of the disease and pre-test probability of the population intended to be tested.

"Risk" in the context of the present invention, relates to the probability that an event will occur over a specific time period, and can mean a subject's "absolute" risk or "relative" risk. Absolute risk can be measured with reference to either actual observation post- measurement for the relevant time cohort, or with reference to index values developed from statistically valid historical cohorts that have been followed for the relevant time period. Relative risk refers to the ratio of absolute risks of a subject compared either to the absolute risks of lower risk cohorts, across population divisions (such as tertiles, quartiles, quintiles, or deciles, etc.) or an average population risk, which can vary by how clinical risk factors are assessed. Odds ratios, the proportion of positive events to negative events for a given test result, are also commonly used (odds are according to the formula p/(l-p) where p is the probability of event and (1- p) is the probability of no event) to no-conversion.

"Risk evaluation," or "evaluation of risk" in the context of the present invention encompasses making a prediction of the probability, odds, or likelihood that an event or disease state may occur, and/or the rate of occurrence of the event or conversion from one disease state to another, i.e., from a normal condition to cancer or from cancer remission to cancer, or from primary cancer occurrence to occurrence of a cancer metastasis. Risk evaluation can also comprise prediction of future clinical parameters, traditional laboratory risk factor values, or other indices of cancer results, either in absolute or relative terms in reference to a previously measured population. Such differing use may require different consituentes of a Gene Expression Panel (Precision Profile^™) combinations and

individualized panels, mathematical algorithms, and/or cut-off points, but be subject to the same aforementioned measurements of accuracy and performance for the respective intended use.

A "sample" from a subject may include a single cell or multiple cells or fragments of cells or an aliquot of body fluid, taken from the subject, by means including venipuncture, excretion, ejaculation, massage, biopsy, needle aspirate, lavage sample, scraping, surgical incision or intervention or other means known in the art. The sample is blood, urine, spinal fluid, lymph, mucosal secretions, prostatic fluid, semen, haemolymph or any other body fluid known in the art for a subject. The sample is also a tissue sample. The sample is or contains a circulating endothelial cell or a circulating tumor cell.

"Sensitivity" is calculated by TP/(TP+FN) or the true positive fraction of disease subjects.

"Specificity" is calculated by TN/(TN+FP) or the true negative fraction of non-disease or normal subjects.

By "statistically significant", it is meant that the alteration is greater than what might be expected to happen by chance alone (which could be a "false positive"). Statistical significance can be determined by any method known in the art. Commonly used measures of significance include the /?-value, which presents the probability of obtaining a result at least as extreme as a given data point, assuming the data point was the result of chance alone. A result is often considered highly significant at a /?-value of 0.05 or less and statistically significant at a /?-value of 0.10 or less. Such /^-values depend significantly on the power of the study performed.

A "set" or "population" of samples or subjects refers to a defined or selected group of samples or subjects wherein there is an underlying commonality or relationship between the members included in the set or population of samples or subjects.

A "Signature Profile" is an experimentally verified subset of a Gene Expression Profile selected to discriminate a biological condition, agent or physiological mechanism of action. A "Signature Panel" is a subset of a Gene Expression Panel (Precision Profile^™), the constituents of which are selected to permit discrimination of a biological condition, agent or physiological mechanism of action.

A "subject" is a cell, tissue, or organism, human or non-human, whether in vivo, ex vivo or in vitro, under observation. As used herein, reference to evaluating the biological condition of a subject based on a sample from the subject, includes using blood or other tissue sample from a human subject to evaluate the human subject's condition; it also includes, for example, using a blood sample itself as the subject to evaluate, for example, the effect of therapy or an agent upon the sample.

A "stimulus" includes (i) a monitored physical interaction with a subject, for example ultraviolet A or B, or light therapy for seasonal affective disorder, or treatment of psoriasis with psoralen or treatment of cancer with embedded radioactive seeds, other radiation exposure, and (ii) any monitored physical, mental, emotional, or spiritual activity or inactivity of a subject.

"Therapy" includes all interventions whether biological, chemical, physical, metaphysical, or combination of the foregoing, intended to sustain or alter the monitored biological condition of a subject.

"77V" is true negative, which for a disease state test means classifying a non-disease or normal subject correctly.

" TP" is true positive, which for a disease state test means correctly classifying a disease subject.

The PCT patent application publication number WO 01/25473, published April 12, 2001, entitled "Systems and Methods for Characterizing a Biological Condition or Agent Using Calibrated Gene Expression Profiles," filed for an invention by inventors herein, and which is herein incorporated by reference, discloses the use of Gene Expression Panels

(Precision Profiles^™) for the evaluation of (i) biological condition (including with respect to health and disease) and (ii) the effect of one or more agents on biological condition

(including with respect to health, toxicity, therapeutic treatment and drug interaction).

It has been discovered that valuable and unexpected results may be achieved when the quantitative measurement of constituents is performed under repeatable conditions (within a degree of repeatability of measurement of better than twenty percent, preferably ten percent or better, more preferably five percent or better, and more preferably three percent or better).

For the purposes of this description and the following claims, a degree of repeatability of measurement of better than twenty percent may be used as providing measurement conditions that are "substantially repeatable". In particular, it is desirable that each time a measurement is obtained corresponding to the level of expression of a constituent in a particular sample, substantially the same measurement should result for substantially the same level of expression. In this manner, expression levels for a constituent in a Gene Expression Panel (Precision Profile™) may be meaningfully compared from sample to sample. Even if the expression level measurements for a particular constituent are inaccurate (for example, say, 30% too low), the criterion of repeatability means that all measurements for this constituent, if skewed, will nevertheless be skewed systematically, and therefore measurements of expression level of the constituent may be compared meaningfully. In this fashion valuable information may be obtained and compared concerning expression of the constituent under varied circumstances.

In addition to the criterion of repeatability, it is desirable that a second criterion also be satisfied, namely that quantitative measurement of constituents is performed under conditions wherein efficiencies of amplification for all constituents are substantially similar as defined herein. When both of these criteria are satisfied, then measurement of the expression level of one constituent may be meaningfully compared with measurement of the expression level of another constituent in a given sample and from sample to sample.

The present invention provides Gene Expression Panels for the evaluation or characterization of lung cancer and conditions related to lung cancer in a subject.

Specifically, the Gene Expression Panel is capable of discriminating patients with lung cancer from smokers with or without chronic obstructive pulmonary disease (COPD). The genes in the Gene Expression Panel include: CDK2, CXCR3, CD22, CDK6, ERCC2, ABCC5, CDHl , CDKNIB, CDKN2A, CREB3, ICAMl , MMP8, NCAMl, SOCS l , STK4, TNFRSF10B, and TNFRSF1B. In some embodiments, the Gene Expression panel further includes CCND2 and TOPORS or IGF2BP2 and ST 14

The evaluation or characterization of lung cancer is defined to be diagnosing lung cancer, assessing the presence or absence of lung cancer, assessing the risk of developing lung cancer or assessing the prognosis of a subject with lung cancer, assessing the recurrence of lung cancer or assessing the presence or absence of a metastasis. Lung cancer and conditions related to lung cancer is evaluated by determining the level of expression (e.g., a quantitative measure) of an effective number (e.g. , one or more) of constituents of a Gene Expression Panel (Precision Profile™) disclosed herein (i.e. , CDK2, CXCR3, CD22, CDK6, ERCC2, ABCC5, CDHl, CDKNIB, CDKN2A, CREB3, ICAMl, MMP8, NCAMl, SOCSl, STK4, TNFRSFIOB, TNFRSF1B, CCND2, TOPORS, IGF2BP2 and ST14). By an effective number is meant the number of constituents that need to be measured in order to discriminate between a normal subject and a subject having lung cancer. In one embodiment, CDK2, CXCR3, CD22, CDK6, ERCC2, CCND2, ABCC5, CDH1 , CDK 1B, CDK 2A, CREB3, ICAM1 , MMP8, NCAM1 , SOCS 1 , STK4, TNFRSF10B, TNFRSF1B, and TOPORS are measured . In an alternative embodiment, CDK2, CXCR3, CD22, CDK6, ERCC2, ABCC5, CDH1 , CDK 1B, CDKN2A, CREB3, ICAM1 , IGF2BP2, MMP8, NCAM1 , SOCS 1 , ST14, STK4, TNFRSF10B, and TNFRSF1B are measured.

Preferably the constituents are selected as to discriminate between a normal subject and a subject having lung cancer with at least 75% accuracy, more preferably 80%, 85%>, 90%, 95%, 97%, 98%, 99% or greater accuracy.

The level of expression is determined by any means known in the art, such as for example quantitative PCR. The measurement is obtained under conditions that are substantially repeatable. Optionally, the qualitative measure of the constituent is compared to a reference or baseline level or value (e.g. a baseline profile set). In one embodiment, the reference or baseline level is a level of expression of one or more constituents in one or more subjects known not to be suffering from lung cancer (e.g., normal, healthy individual(s)). Alternatively, the reference or baseline level is derived from the level of expression of one or more constituents in one or more subjects known to be suffering from lung cancer. In another embodiment, the reference or baseline level is a level of expression of one or more constituents in one or more subjects known to be suffering from COPD. In yet another embodiment, the reference or baseline level is a level of expression of one or more constituents in one or more subjects known to be smokers.

A reference or baseline level or value as used herein can be used interchangeably and is meant to be relative to a number or value derived from population studies, including without limitation, such subjects having similar age range, subjects in the same or similar ethnic group, sex.. Such reference values can be derived from statistical analyses and/or risk prediction data of populations obtained from mathematical algorithms and computed indices of lung cancer. Reference indices can also be constructed and used using algorithms and other methods of statistical and structural classification.

In one embodiment of the present invention, the reference or baseline value is the amount of expression of a cancer associated gene in a control sample derived from one or more subjects who are both asymptomatic and lack traditional laboratory risk factors for lung cancer. In another embodiment of the present invention, the reference or baseline value is the level of cancer associated genes in a control sample derived from one or more subjects who are not at risk or at low risk for developing lung cancer.

In a further embodiment, such subjects are monitored and/or periodically retested for a diagnostically relevant period of time ("longitudinal studies") following such test to verify continued absence from lung cancer (disease or event free survival). Such period of time may be one year, two years, two to five years, five years, five to ten years, ten years, or ten or more years from the initial testing date for determination of the reference or baseline value. Furthermore, retrospective measurement of cancer associated genes in properly banked historical subject samples may be used in establishing these reference or baseline values, thus shortening the study time required, presuming the subjects have been appropriately followed during the intervening period through the intended horizon of the product claim.

In another embodiment, the reference or baseline value is an index value or a baseline value. An index value or baseline value is a composite sample of an effective amount of cancer associated genes from one or more subjects who do not have cancer.

For example, where the reference or baseline level is comprised of the amounts of cancer associated genes derived from one or more subjects who have not been diagnosed with lung cancer, or are not known to be suffereing from lung cancer, a change (e.g. , increase or decrease) in the expression level of a cancer associated gene in the patient-derived sample as compared to the expression level of such gene in the reference or baseline level indicates that the subject is suffering from or is at risk of developing lung cancer. In contrast, when the methods are applied prophylacticly, a similar level of expression in the patient-derived sample of a lung cancer associated gene compared to such gene in the baseline level indicates that the subject is not suffering from or is at risk of developing lung cancer.

Where the reference or baseline level is comprised of the amounts of cancer associated genes derived from one or more subjects who have been diagnosed with lung cancer, or are known to be suffereing from lung cancer, a similarity in the expression pattern in the patient-derived sample of a lung cancer gene compared to the lung cancer baseline level indicates that the subject is suffering from or is at risk of developing lung cancer.

Expression of a lung cancer gene also allows for the course of treatment of lung cancer to be monitored. In this method, a biological sample is provided from a subject undergoing treatment, e.g., if desired, biological samples are obtained from the subject at various time points before, during, or after treatment. Expression of a lung cancer gene is then determined and compared to a reference or baseline profile. The baseline profile may be taken or derived from one or more individuals who have been exposed to the treatment. Alternatively, the baseline level may be taken or derived from one or more individuals who have not been exposed to the treatment. For example, samples may be collected from subjects who have received initial treatment for lung cancer and subsequent treatment for lung cancer to monitor the progress of the treatment.

A Gene Expression Panel (Precision Profile^™) is selected in a manner so that quantitative measurement of RNA or protein constituents in the Panel constitutes a measurement of a biological condition of a subject. In one kind of arrangement, a calibrated profile data set is employed. Each member of the calibrated profile data set is a function of (i) a measure of a distinct constituent of a Gene Expression Panel (Precision Profile^™) and (ii) a baseline quantity.

Additional embodiments relate to the use of an index or algorithm resulting from quantitative measurement of constituents, and optionally in addition, derived from either expert analysis or computational biology (a) in the analysis of complex data sets; (b) to control or normalize the influence of uninformative or otherwise minor variances in gene expression values between samples or subjects; (c) to simplify the characterization of a complex data set for comparison to other complex data sets, databases or indices or algorithms derived from complex data sets; (d) to monitor a biological condition of a subject; (e) for measurement of therapeutic efficacy of natural or synthetic compositions or stimuli that may be formulated individually or in combinations or mixtures for a range of targeted biological conditions; (f) for predictions of toxicological effects and dose effectiveness of a composition or mixture of compositions for an individual or for a population or set of individuals or for a population of cells; (g) for determination of how two or more different agents administered in a single treatment might interact so as to detect any of synergistic, additive, negative, neutral of toxic activity (h) for performing pre-clinical and clinical trials by providing new criteria for pre-selecting subjects according to informative profile data sets for revealing disease status and conducting preliminary dosage studies for these patients prior to conducting Phase 1 or 2 trials.

The subject

The methods disclosed herein may be applied to cells of humans, mammals or other organisms without the need for undue experimentation by one of ordinary skill in the art because all cells transcribe RNA and it is known in the art how to extract RNA from all types of cells.

A subject can include those who have not been previously diagnosed as having lung cancer or a condition related to lung cancer. Alternatively, a subject can also include those who have already been diagnosed as having lung cancer or a condition related to lung cancer. Diagnosis of lung cancer is made, for example, from any one or combination of the following procedures: a medical history, physical exam, blood counts and blood chemistry, and screening and tissue sampling procedures such as sputum cytology, CT guided needle biopsy, bronchoscopy, endobronchial ultrasound, endoscopic esophageal ultrasound,

mediastinoscopy, mediastinotomy, thoracentesis, and thorascopy.

A subject can also include those who are suffering from, or at risk of developing lung cancer or a condition related to lung cancer, such as those who exhibit known risk factors for lung cancer or conditions related to lung cancer. Known risk factors for lung cancer include, but are not limited to: smoking, including cigarette, cigar, pipe, marijuana, and hookah smoke; second hand smoke; age (increased risk in the elderly population over age 65);

genetic predisposition; exposure to high levels of arsenic in drinking water, asbestos fibers, and/or long term radon contamination (each more pronounced in smokers); cancer causing agents in the workplace (e.g., radioactive ores, inhaled chemicals or minerals (e.g., arsenic, berrylium, vinyl chloride, nickel chromates, coal products, mustard gas, chloromethyl ethers, fuels such as gasoline, and diesel exhaust)); prior radiation therapy to the lungs; personal and family history of lung cancer; diet low in fruits and vegetables (more pronounced in smokers); and air pollution.

Inflammation and Cancer

Evidence has shown that cancer in adults arises frequently in the setting of chronic inflammation. Epidemiological and experimental studies provide stong support for the concept that inflammation facilitates malignant growth. Inflammatory components have been shown to 1) induce DNA damage, which contributes to genetic instability (e.g., cell mutation) and transformed cell proliferation (Balkwill and Mantovani, Lancet 357:539-545 (2001)); 2) promote angiogenesis, thereby enhancing tumor growth and invasiveness (Coussens L.M. and Z. Werb, Nature 429:860-867 (2002)); and 3) impair myelopoiesis and hemopoiesis, which cause immune dysfunction and inhibit immune surveillance (Kusmartsev and

Gabrilovic, Cancer Immunol. Immunother. 51 :293-298 (2002); Serafini et al., Cancer Immunol. Immunther. 53:64-72 (2004)).

Studies suggest that inflammation promotes malignancy via proinflammatory cytokines, including but not limited to IL-Ιβ, which enhance immune suppression through the induction of myeloid suppressor cells, and that these cells down regulate immune

surveillance and allow the outgrowth and proliferation of malignant cells by inhibiting the activation and/or function of tumor-specific lymphocytes. (Bunt et al., J. Immunol. 176: 284- 290 (2006). Such studies are consistent with findings that myeloid suppressor cells are found in many cancer patients, including lung and breast cancer, and that chronic inflammation in some of these malignancies may enhance malignant growth (Coussens L.M. and Z. Werb, 2002).

Additionally, many cancers express an extensive repertoire of chemokines and chemokine receptors, and may be characterized by dis-regulated production of chemokines and abnormal chemokine receptor signaling and expression. Tumor-associated chemokines are thought to play several roles in the biology of primary and metastatic cancer such as: control of leukocyte infiltration into the tumor, manipulation of the tumor immune response, regulation of angiogenesis, autocrine or paracrine growth and survival factors, and control of the movement of the cancer cells. Thus, these activities likely contribute to growth within/outside the tumor microenvironment and to stimulate anti-tumor host responses.

As tumors progress, it is common to observe immune deficits not only within cells in the tumor microenvironment but also frequently in the systemic circulation. Whole blood contains representative populations of all the mature cells of the immune system as well as secretory proteins associated with cellular communications. The earliest observable changes of cellular immune activity are altered levels of gene expression within the various immune cell types. Immune responses are now understood to be a rich, highly complex tapestry of cell-cell signaling events driven by associated pathways and cascades— all involving modified activities of gene transcription. This highly interrelated system of cell response is immediately activated upon any immune challenge, including the events surrounding host response to lung cancer and treatment. Modified gene expression precedes the release of cytokines and other immunologically important signaling elements.

Design of assays

Typically, a sample is run through a panel in replicates of three for each target gene

(assay); that is, a sample is divided into aliquots and for each aliquot the concentrations of each constituent in a Gene Expression Panel (Precision Profile^™) is measured. From over thousands of constituent assays, with each assay conducted in triplicate, an average coefficient of variation was found (standard deviation/average)* 100, of less than 2 percent among the normalized ACt measurements for each assay (where normalized quantitation of the target mRNA is determined by the difference in threshold cycles between the internal control (e.g., an endogenous marker such as 18S rRNA, or an exogenous marker) and the gene of interest. This is a measure called "intra-assay variability". Assays have also been conducted on different occasions using the same sample material. This is a measure of "inter-assay variability". Preferably, the average coefficient of variation of intra- assay variability or inter-assay variability is less than 20%, more preferably less than 10%, more preferably less than 5%, more preferably less than 4%, more preferably less than 3%, more preferably less than 2%, and even more preferably less than 1%.

It has been determined that it is valuable to use the quadruplicate or triplicate test results to identify and eliminate data points that are statistical "outliers"; such data points are those that differ by a percentage greater, for example, than 3% of the average of all three or four values. Moreover, if more than one data point in a set of three or four is excluded by this procedure, then all data for the relevant constituent is discarded.

Measurement of Gene Expression for a Constituent in the Panel

For measuring the amount of a particular RNA in a sample, methods known to one of ordinary skill in the art were used to extract and quantify transcribed RNA from a sample with respect to a constituent of a Gene Expression Panel (Precision Profile^™). (See detailed protocols below. Also see PCT application publication number WO 98/24935 herein incorporated by reference for RNA analysis protocols). Briefly, RNA is extracted from a sample such as any tissue, body fluid, cell (e.g., circulating tumor cell) or culture medium in which a population of cells of a subject might be growing. For example, cells may be lysed and RNA eluted in a suitable solution in which to conduct a DNAse reaction. Subsequent to RNA extraction, first strand synthesis may be performed using a reverse transcriptase. Gene amplification, more specifically quantitative PCR assays, can then be conducted and the gene of interest calibrated against an internal marker such as 18S rRNA (Hirayama et al., Blood 92, 1998: 46-52). Any other endogenous marker can be used, such as 28S-25S rRNA and 5S rRNA. Samples are measured in multiple replicates, for example, 3 replicates. In an embodiment of the invention, quantitative PCR is performed using amplification, reporting agents and instruments such as those supplied commercially by Applied Biosystems (Foster City, CA). Given a defined efficiency of amplification of target transcripts, the point (e.g., cycle number) that signal from amplified target template is detectable may be directly related to the amount of specific message transcript in the measured sample. Similarly, other quantifiable signals such as fluorescence, enzyme activity, disintegrations per minute, absorbance, etc., when correlated to a known concentration of target templates (e.g., a reference standard curve) or normalized to a standard with limited variability can be used to quantify the number of target templates in an unknown sample. Although not limited to amplification methods, quantitative gene expression techniques may utilize amplification of the target transcript. Alternatively or in combination with amplification of the target transcript, quantitation of the reporter signal for an internal marker generated by the exponential increase of amplified product may also be used.

Amplification of the target template may be accomplished by isothermic gene amplification strategies or by gene amplification by thermal cycling such as PCR.

It is desirable to obtain a definable and reproducible correlation between the amplified target or reporter signal, i.e., internal marker, and the concentration of starting templates. It has been discovered that this objective can be achieved by careful attention to, for example, consistent primer-template ratios and a strict adherence to a narrow permissible level of experimental amplification efficiencies (for example 80.0 to 100% +/- 5% relative efficiency, typically 90.0 to 100% +/- 5% relative efficiency, more typically 95.0 to 100% +/- 2 %, and most typically 98 to 100% +/- 1 % relative efficiency). In determining gene expression levels with regard to a single Gene Expression Profile, it is necessary that all constituents of the panels, including endogenous controls, maintain similar amplification efficiencies, as defined herein, to permit accurate and precise relative measurements for each constituent.

Amplification efficiencies are regarded as being "substantially similar", for the purposes of this description and the following claims, if they differ by no more than approximately 10%, preferably by less than approximately 5%, more preferably by less than approximately 3%, and more preferably by less than approximately 1%. Measurement conditions are regarded as being "substantially repeatable, for the purposes of this description and the following claims, if they differ by no more than approximately +/- 10% coefficient of variation (CV), preferably by less than approximately +/- 5% CV, more preferably +/- 2% CV. These constraints should be observed over the entire range of concentration levels to be measured associated with the relevant biological condition. While it is thus necessary for various embodiments herein to satisfy criteria that measurements are achieved under measurement conditions that are substantially repeatable and wherein specificity and efficiencies of amplification for all constituents are substantially similar, nevertheless, it is within the scope of the present invention as claimed herein to achieve such measurement conditions by adjusting assay results that do not satisfy these criteria directly, in such a manner as to compensate for errors, so that the criteria are satisfied after suitable adjustment of assay results.

In practice, tests are run to assure that these conditions are satisfied. For example, the design of all primer-probe sets are done in house, experimentation is performed to determine which set gives the best performance. Even though primer-probe design can be enhanced using computer techniques known in the art, and notwithstanding common practice, it has been found that experimental validation is still useful. Moreover, in the course of

experimental validation, the selected primer-probe combination is associated with a set of features:

The reverse primer should be complementary to the coding DNA strand. In one embodiment, the primer should be located across an intron-exon junction, with not more than four bases of the three-prime end of the reverse primer complementary to the proximal exon. (If more than four bases are complementary, then it would tend to competitively amplify genomic DNA.)

In an embodiment of the invention, the primer probe set should amplify cDNA of less than 110 bases in length and should not amplify, or generate fluorescent signal from, genomic DNA or transcripts or cDNA from related but biologically irrelevant loci.

A suitable target of the selected primer probe is first strand cDNA, which in one embodiment may be prepared from whole blood as follows:

(a) Use of whole blood for ex vivo assessment of a biological condition

Human blood is obtained by venipuncture and prepared for assay. The aliquots of heparinized, whole blood are mixed with additional test therapeutic compounds and held at 37°C in an atmosphere of 5% C0₂ for 30 minutes. Cells are lysed and nucleic acids, e.g., RNA, are extracted by various standard means.

Nucleic acids, RNA and or DNA, are purified from cells, tissues or fluids of the test population of cells. RNA is preferentially obtained from the nucleic acid mix using a variety of standard procedures (or RNA Isolation Strategies, pp. 55-104, in RNA Methodologies, A laboratory guide for isolation and characterization, 2nd edition, 1998, Robert E. Farrell, Jr., Ed., Academic Press), in the present using a filter-based RNA isolation system from Ambion (RNAqueous™, Phenol-free Total RNA Isolation Kit, Catalog #1912, version 9908; Austin, Texas).

(b) Amplification strategies.

Specific RNAs are amplified using message specific primers or random primers. The specific primers are synthesized from data obtained from public databases (e.g., Unigene, National Center for Biotechnology Information, National Library of Medicine, Bethesda, MD), including information from genomic and cDNA libraries obtained from humans and other animals. Primers are chosen to preferentially amplify from specific RNAs obtained from the test or indicator samples (see, for example, RT PCR, Chapter 15 in RNA Methodologies, A Laboratory Guide for Isolation and Characterization, 2nd edition, 1998, Robert E. Farrell, Jr., Ed., Academic Press; or Chapter 22 pp.143-151, RNA Isolation and Characterization Protocols, Methods in Molecular Biology, Volume 86, 1998, R. Rapley and D. L. Manning Eds., Human Press, or Chapter 14 Statistical refinement of primer design parameters; or Chapter 5, pp.55-72, PCR Applications: protocols for functional genomics, M.A.Innis, D.H. Gelfand and J.J. Sninsky, Eds., 1999, Academic Press). Amplifications are carried out in either isothermic conditions or using a thermal cycler (for example, a ABI 9600 or 9700 or 7900 obtained from Applied Biosystems, Foster City, CA; see Nucleic acid detection methods, pp. 1-24, in Molecular Methods for Virus Detection, D.L.Wiedbrauk and D.H., Farkas, Eds., 1995, Academic Press). Amplified nucleic acids are detected using fluorescent-tagged detection oligonucleotide probes (see, for example, TaqmanTM PCR Reagent Kit, Protocol, part number 402823, Revision A, 1996, Applied Biosystems, Foster City CA) that are identified and synthesized from publicly known databases as described for the amplification primers.

For example, without limitation, amplified cDNA is detected and quantified using detection systems such as the ABI Prism^® 7900 Sequence Detection System (Applied Biosystems (Foster City, CA)), the Cepheid SmartCycler^® and Cepheid GeneXpert^® Systems, the Fluidigm BioMark^™ System, and the Roche LightCycler^® 480 Real-Time PCR System. Amounts of specific RNAs contained in the test sample can be related to the relative quantity of fluorescence observed (see for example, Advances in Quantitative PCR Technology: 5 ' Nuclease Assays, Y.S. Lie and C.J. Petropolus, Current Opinion in Biotechnology, 1998, 9:43-48, or Rapid Thermal Cycling and PCR Kinetics, pp. 211-229, chapter 14 in PCR applications: protocols for functional genomics, M.A. Innis, D.H. Gelfand and J.J. Sninsky, Eds., 1999, Academic Press). Examples of the procedure used with several of the above- mentioned detection systems are described below. In some embodiments, these procedures can be used for both whole blood RNA and RNA extracted from cultured cells (e.g., without limitation, CTCs, and CECs). In some embodiments, any tissue, body fluid, or cell(s) (e.g., circulating tumor cells (CTCs) or circulating endothelial cells (CECs)) may be used for ex vivo assessment of a biological condition affected by an agent. Methods herein may also be applied using proteins where sensitive quantitative techniques, such as an Enzyme Linked Immunosorbent Assay (ELISA) or mass spectroscopy, are available and well-known in the art for measuring the amount of a protein constituent (see WO 98/24935 herein incorporated by reference). An example of a procedure for the synthesis of first strand cDNA for use in PCR amplification is as follows:

Materials

1. Applied Biosystems TAQMAN Reverse Transcription Reagents Kit (P/N 808- 0234). Kit Components: 10X TaqMan RT Buffer, 25 mM Magnesium chloride, deoxyNTPs mixture, Random Hexamers, RNase Inhibitor, MultiScribe Reverse Transcriptase (50 U/mL) (2) RNase / DNase free water (DEPC Treated Water from Ambion (P/N 9915G), or equivalent).

Methods

1. Place RNase Inhibitor and MultiScribe Reverse Transcriptase on ice immediately. All other reagents can be thawed at room temperature and then placed on ice.

2. Remove RNA samples from -80oC freezer and thaw at room temperature and then place immediately on ice.

3. Prepare the following cocktail of Reverse Transcriptase Reagents for each 100 mL RT reaction (for multiple samples, prepare extra cocktail to allow for pipetting error):

1 reaction (mL) 1 IX, e.g. 10 samples

1 OX RT Buffer 10.0 110.0

25 mM MgCl₂ 22.0 242.0

dNTPs 20.0 220.0

Random Hexamers 5.0 55.0

RNAse Inhibitor 2.0 22.0

Reverse Transcriptase 2.5 27.5

Water 18.5 203.5

Total: 80.0 880.0 (80 per sample)

4. Bring each RNA sample to a total volume of 20 μΐ_^ in a 1.5 mL

microcentrifuge tube (for example, remove 10 μΐ_^ RNA and dilute to 20 μΐ_^ with RNase / DNase free water, for whole blood RNA use 20 μΐ_^ total RNA) and add 80 μΐ_^ RT reaction mix from step 5,2,3. Mix by pipetting up and down.

5. Incubate sample at room temperature for 10 minutes.

6. Incubate sample at 37°C for 1 hour.

7. Incubate sample at 90°C for 10 minutes.

8. Quick spin samples in microcentrifuge.

9. Place sample on ice if doing PCR immediately, otherwise store sample at - 20°C for future use. 10. PCR QC should be run on all RT samples using 18S and β-actin.

Following the synthesis of first strand cDNA, one particular embodiment of the approach for amplification of first strand cDNA by PCR, followed by detection and quantification of constituents of a Gene Expression Panel (Precision Profile™) is performed using the ABI Prism® 7900 Sequence Detection System as follows:

Materials

1. 20X Primer/Probe Mix for each gene of interest.

2. 20X Primer/Probe Mix for 18S endogenous control.

3. 2X Taqman Universal PCR Master Mix .

4. cDNA transcribed from RNA extracted from cells.

5. Applied Biosystems 96-Well Optical Reaction Plates.

6. Applied Biosystems Optical Caps, or optical-clear film.

7. Applied Biosystem Prism® 7700 or 7900 Sequence Detector.

Methods

1. Make stocks of each Primer/Probe mix containing the Primer/Probe for the gene of interest, Primer/Probe for 18S endogenous control, and 2X PCR Master Mix as follows. Make sufficient excess to allow for pipetting error e.g., approximately 10% excess. The following example illustrates a typical set up for one gene with quadruplicate samples testing two conditions (2 plates).

IX (1 well) (μΐ,)

2X Master Mix 7.5

20X 18S Primer/Probe Mix 0.75

20X Gene of interest Primer/Probe Mix 0.75

Total 9.0

2. Make stocks of cDNA targets by diluting 95μΕ of cDNA into 2000μΕ of water. The amount of cDNA is adjusted to give Ct values between 10 and 18, typically between 12 and 16.

3. Pipette 9 μΐ_^ of Primer/Probe mix into the appropriate wells of an Applied Biosystems 384-Well Optical Reaction Plate.

4. Pipette Ι ΟμΙ_^ of cDNA stock solution into each well of the Applied

Biosystems 384-Well Optical Reaction Plate.

5. Seal the plate with Applied Biosystems Optical Caps, or optical-clear film.

6. Analyze the plate on the ABI Prism® 7900 Sequence Detector. In another embodiment of the invention, the use of the primer probe with the first strand cDNA as described above to permit measurement of constituents of a Gene Expression Panel (Precision Profile^™) is performed using a QPCR assay on Cepheid SmartCycler^® and GeneXpert^® Instruments as follows:

I. To run a QPCR assay in duplicate on the Cepheid SmartCycler^® instrument containing three target genes and one reference gene, the following procedure should be followed. A. With 20X Primer/Probe Stocks.

Materials

1. SmartMix™-HM lyophilized Master Mix.

2. Molecular grade water.

3. 20X Primer/Probe Mix for the 18S endogenous control gene. The endogenous control gene will be dual labeled with VIC-MGB or equivalent.

4. 20X Primer/Probe Mix for each for target gene one, dual labeled with FAM- BHQ1 or equivalent.

5. 20X Primer/Probe Mix for each for target gene two, dual labeled with Texas Red- BHQ2 or equivalent.

6. 20X Primer/Probe Mix for each for target gene three, dual labeled with Alexa 647-BHQ3 or equivalent.

7. Tris buffer, pH 9.0

8. cDNA transcribed from RNA extracted from sample.

9. SmartCycler^® 25 μΕ tube.

10. Cepheid SmartCycler® instrument.

Methods

1. For each cDNA sample to be investigated, add the following to a sterile 650

tube.

SmartMix™-HM lyophilized Master Mix 1 bead

20X 18S Primer/Probe Mix 2.5 μΕ

20Χ Target Gene 1 Primer/Probe Mix 2.5 μΕ

20X Target Gene 2 Primer/Probe Mix 2.5 μΕ

20X Target Gene 3 Primer/Probe Mix 2.5 μΕ

Tris Buffer, pH 9.0 2.5 μΕ

Sterile Water 34.5 μΕ

Total 47 μΕ Vortex the mixture for 1 second three times to completely mix the reagents.

Briefly centrifuge the tube after vortexing.

2. Dilute the cDNA sample so that a 3 μΐ_^ addition to the reagent mixture above will give an 18S reference gene CT value between 12 and 16.

3. Add 3 of the prepared cDNA sample to the reagent mixture bringing the total volume to 50 μί. Vortex the mixture for 1 second three times to completely mix the reagents. Briefly centrifuge the tube after vortexing.

4. Add 25 of the mixture to each of two SmartCycler® tubes, cap the tube and spin for 5 seconds in a microcentrifuge having an adapter for SmartCycler® tubes.

5. Remove the two SmartCycler® tubes from the microcentrifuge and inspect for air bubbles. If bubbles are present, re-spin, otherwise, load the tubes into the

SmartCycler® instrument.

6. Run the appropriate QPCR protocol on the SmartCycler®, export the data and analyze the results.

B. With Lyophilized SmartBeads™.

Materials

1. SmartMix™-HM lyophilized Master Mix.

2. Molecular grade water.

3. SmartBeads™ containing the 18S endogenous control gene dual labeled with

VIC-MGB or equivalent, and the three target genes, one dual labeled with FAM- BHQ1 or equivalent, one dual labeled with Texas Red-BHQ2 or equivalent and one dual labeled with Alexa 647-BHQ3 or equivalent.

4. Tris buffer, pH 9.0

5. cDNA transcribed from RNA extracted from sample.

6. SmartCycler® 25 μΙ_, tube.

7. Cepheid SmartCycler® instrument.

Methods

1. For each cDNA sample to be investigated, add the following to a sterile 650 μΙ_, tube.

SmartMix™-HM lyophilized Master Mix 1 bead

SmartBead™ containing four primer/probe sets 1 bead

Tris Buffer, pH 9.0 2.5 μΐ,

Sterile Water 44.5 μΐ,

Total 47 μΐ, Vortex the mixture for 1 second three times to completely mix the reagents.

Briefly centrifuge the tube after vortexing.

5. Remove the two SmartCycler®tubes from the microcentrifuge and inspect for air bubbles. If bubbles are present, re-spin, otherwise, load the tubes into the SmartCycler® instrument.

To run a QPCR assay on the Cepheid GeneXpert® instrument containing three target genes and one reference gene, the following procedure should be followed. Note that to do duplicates, two self contained cartridges need to be loaded and run on the GeneXpert® instrument.

Materials

1. Cepheid GeneXpert® self contained cartridge preloaded with a lyophilized

SmartMix™-HM master mix bead and a lyophilized SmartBead™ containing four primer/probe sets.

2. Molecular grade water, containing Tris buffer, pH 9.0.

3. Extraction and purification reagents.

4. Clinical sample (whole blood, RNA, etc.)

5. Cepheid GeneXpert® instrument.

Methods

1. Remove appropriate GeneXpert® self contained cartridge from packaging.

2. Fill appropriate chamber of self contained cartridge with molecular grade water with Tris buffer, pH 9.0.

3. Fill appropriate chambers of self contained cartridge with extraction and

purification reagents. 4. Load aliquot of clinical sample into appropriate chamber of self contained cartridge.

5. Seal cartridge and load into GeneXpert^® instrument.

6. Run the appropriate extraction and amplification protocol on the GeneXpert^® and analyze the resultant data.

In yet another embodiment of the invention, the use of the primer probe with the first strand cDNA as described above to permit measurement of constituents of a Gene Expression Panel (Precision Profile^™) is performed using a QPCR assay on the Roche LightCycler^® 480 Real-Time PCR System as follows:

Materials

1. 20X Primer/Probe stock for the 18S endogenous control gene. The endogenous control gene may be dual labeled with either VIC-MGB or VIC-TAMRA.

2. 20X Primer/Probe stock for each target gene, dual labeled with either FAM- TAMRA or FAM-BHQ1.

3. 2X LightCycler^® 490 Probes Master (master mix).

4. IX cDNA sample stocks transcribed from RNA extracted from samples.

5. IX TE buffer, pH 8.0.

6. LightCycler^® 480 384-well plates.

7. Source MDx 24 gene Precision Profile^™ 96-well intermediate plates.

8. RNase/DNase free 96-well plate.

9. 1.5 mL microcentrifuge tubes .

10. Beckman/Coulter Biomek^® 3000 Laboratory Automation Workstation.

11. Velocity 11 Bravo™ Liquid Handling Platform.

12. LightCycler^® 480 Real-Time PCR System.

Methods

1. Remove a Source MDx 24 gene Precision Profile™ 96-well intermediate plate from the freezer, thaw and spin in a plate centrifuge.

2. Dilute four (4) IX cDNA sample stocks in separate 1.5 mL microcentrifuge tubes with the total final volume for each of 540 μί.

3. Transfer the 4 diluted cDNA samples to an empty RNase/DNase free 96-well plate using the Biomek^® 3000 Laboratory Automation Workstation.

4. Transfer the cDNA samples from the cDNA plate created in step 3 to the thawed and centrifuged Source MDx 24 gene Precision Profile™ 96-well intermediate plate using Biomek^® 3000 Laboratory Automation Workstation. Seal the plate with a foil seal and spin in a plate centrifuge.

5. Transfer the contents of the cDNA- loaded Source MDx 24 gene Precision

Profile™ 96-well intermediate plate to a new LightCycler® 480 384-well plate using the Bravo™ Liquid Handling Platform. Seal the 384-well plate with a LightCycler^® 480 optical sealing foil and spin in a plate centrifuge for 1 minute at 2000 rpm.

6. Place the sealed in a dark 4°C refrigerator for a minimum of 4 minutes.

7. Load the plate into the LightCycler^® 480 Real-Time PCR System and start the LightCycler® 480 software. Chose the appropriate run parameters and start the run.

8. At the conclusion of the run, analyze the data and export the resulting CP values to the database.

In some instances, target gene FAM measurements may be beyond the detection limit of the particular platform instrument used to detect and quantify constituents of a Gene Expression Panel (Precision Profile^™). To address the issue of "undetermined" gene expression measures as lack of expression for a particular gene, the detection limit may be reset and the "undetermined" constituents may be "flagged". For example without limitation, the ABI Prism^® 7900HT Sequence Detection System reports target gene FAM measurements that are beyond the detection limit of the instrument (>40 cycles) as "undetermined".

Detection Limit Reset is performed when at least 1 of 3 target gene FAM C_T replicates are not detected after 40 cycles and are designated as "undetermined". "Undetermined" target gene FAM C_T replicates are re-set to 40 and flagged. C_T normalization (Δ C_T) and relative expression calculations that have used re-set FAM C_T values are also flagged.

Baseline profile data sets

The analyses of samples from single individuals and from large groups of individuals provide a library of profile data sets relating to a particular panel or series of panels. These profile data sets may be stored as records in a library for use as baseline profile data sets. As the term "baseline" suggests, the stored baseline profile data sets serve as comparators for providing a calibrated profile data set that is informative about a biological condition or agent. Baseline profile data sets may be stored in libraries and classified in a number of cross-referential ways. One form of classification may rely on the characteristics of the panels from which the data sets are derived. Another form of classification may be by particular biological condition, e.g., lung cancer. The concept of a biological condition encompasses any state in which a cell or population of cells may be found at any one time. This state may reflect geography of samples, sex of subjects or any other discriminator.

Some of the discriminators may overlap. The libraries may also be accessed for records associated with a single subject or particular clinical trial. The classification of baseline profile data sets may further be annotated with medical information about a particular subject, a medical condition, and/or a particular agent.

The choice of a baseline profile data set for creating a calibrated profile data set is related to the biological condition to be evaluated, monitored, or predicted, as well as, the intended use of the calibrated panel, e.g., as to monitor drug development, quality control or other uses. It may be desirable to access baseline profile data sets from the same subject for whom a first profile data set is obtained or from different subject at varying times, exposures to stimuli, drugs or complex compounds; or may be derived from like or dissimilar populations or sets of subjects. The baseline profile data set may be normal, healthy baseline.

The profile data set may arise from the same subject for which the first data set is obtained, where the sample is taken at a separate or similar time, a different or similar site or in a different or similar biological condition. For example, a sample may be taken before stimulation or after stimulation with an exogenous compound or substance, such as before or after therapeutic treatment. Alternatively the sample is taken before or include before or after a surgical procedure for lung cancer. The profile data set obtained from the unstimulated sample may serve as a baseline profile data set for the sample taken after stimulation. The baseline data set may also be derived from a library containing profile data sets of a population or set of subjects having some defining characteristic or biological condition. The baseline profile data set may also correspond to some ex vivo or in vitro properties associated with an in vitro cell culture. The resultant calibrated profile data sets may then be stored as a record in a database or library along with or separate from the baseline profile data base and optionally the first profile data set al. though the first profile data set would normally become incorporated into a baseline profile data set under suitable classification criteria. The remarkable consistency of Gene Expression Profiles associated with a given biological condition makes it valuable to store profile data, which can be used, among other things for normative reference purposes. The normative reference can serve to indicate the degree to which a subject conforms to a given biological condition (healthy or diseased) and, alternatively or in addition, to provide a target for clinical intervention. Calibrated data

Given the repeatability achieved in measurement of gene expression, described above in connection with "Gene Expression Panels" (Precision Profiles^™) and "gene amplification", it was concluded that where differences occur in measurement under such conditions, the differences are attributable to differences in biological condition. Thus, it has been found that calibrated profile data sets are highly reproducible in samples taken from the same individual under the same conditions. Similarly, it has been found that calibrated profile data sets are reproducible in samples that are repeatedly tested. Also found have been repeated instances wherein calibrated profile data sets obtained when samples from a subject are exposed ex vivo to a compound are comparable to calibrated profile data from a sample that has been exposed to a sample in vivo.

Calculation of calibrated profile data sets and computational aids

The calibrated profile data set may be expressed in a spreadsheet or represented graphically for example, in a bar chart or tabular form but may also be expressed in a three dimensional representation. The function relating the baseline and profile data may be a ratio expressed as a logarithm. The constituent may be itemized on the x-axis and the logarithmic scale may be on the y-axis. Members of a calibrated data set may be expressed as a positive value representing a relative enhancement of gene expression or as a negative value representing a relative reduction in gene expression with respect to the baseline.

Each member of the calibrated profile data set should be reproducible within a range with respect to similar samples taken from the subject under similar conditions. For example, the calibrated profile data sets may be reproducible within 20%, and typically within 10%. In accordance with embodiments of the invention, a pattern of increasing, decreasing and no change in relative gene expression from each of a plurality of gene loci examined in the Gene Expression Panel (Precision Profile^™) may be used to prepare a calibrated profile set that is informative with regards to a biological condition, biological efficacy of an agent treatment conditions or for comparison to populations or sets of subjects or samples, or for comparison to populations of cells. Patterns of this nature may be used to identify likely candidates for a drug trial, used alone or in combination with other clinical indicators to be diagnostic or prognostic with respect to a biological condition or may be used to guide the development of a pharmaceutical or nutraceutical through manufacture, testing and marketing.

The numerical data obtained from quantitative gene expression and numerical data from calibrated gene expression relative to a baseline profile data set may be stored in databases or digital storage mediums and may be retrieved for purposes including managing patient health care or for conducting clinical trials or for characterizing a drug. The data may be transferred in physical or wireless networks via the World Wide Web, email, or internet access site for example or by hard copy so as to be collected and pooled from distant geographic sites.

The method also includes producing a calibrated profile data set for the panel, wherein each member of the calibrated profile data set is a function of a corresponding member of the first profile data set and a corresponding member of a baseline profile data set for the panel, and wherein the baseline profile data set is related to the lung cancer or conditions related to lung cancer to be evaluated, with the calibrated profile data set being a comparison between the first profile data set and the baseline profile data set, thereby providing evaluation of lung cancer or conditions related to lung cancer of the subject.

In yet other embodiments, the function is a mathematical function and is other than a simple difference, including a second function of the ratio of the corresponding member of first profile data set to the corresponding member of the baseline profile data set, or a logarithmic function. In such embodiments, the first sample is obtained and the first profile data set quantified at a first location, and the calibrated profile data set is produced using a network to access a database stored on a digital storage medium in a second location, wherein the database may be updated to reflect the first profile data set quantified from the sample. Additionally, using a network may include accessing a global computer network.

In an embodiment of the present invention, a descriptive record is stored in a single database or multiple databases where the stored data includes the raw gene expression data (first profile data set) prior to transformation by use of a baseline profile data set, as well as a record of the baseline profile data set used to generate the calibrated profile data set including for example, annotations regarding whether the baseline profile data set is derived from a particular Signature Panel and any other annotation that facilitates interpretation and use of the data.

Because the data is in a universal format, data handling may readily be done with a computer. The data is organized so as to provide an output optionally corresponding to a graphical representation of a calibrated data set.

The above described data storage on a computer may provide the information in a form that can be accessed by a user. Accordingly, the user may load the information onto a second access site including downloading the information. However, access may be restricted to users having a password or other security device so as to protect the medical records contained within. A feature of this embodiment of the invention is the ability of a user to add new or annotated records to the data set so the records become part of the biological information.

The graphical representation of calibrated profile data sets pertaining to a product such as a drug provides an opportunity for standardizing a product by means of the calibrated profile, more particularly a signature profile. The profile may be used as a feature with which to demonstrate relative efficacy, differences in mechanisms of actions, etc. compared to other drugs approved for similar or different uses.

The various embodiments of the invention may be also implemented as a computer program product for use with a computer system. The product may include program code for deriving a first profile data set and for producing calibrated profiles. Such implementation may include a series of computer instructions fixed either on a tangible medium, such as a computer readable medium (for example, a diskette, CD-ROM, ROM, or fixed disk), or transmittable to a computer system via a modem or other interface device, such as a communications adapter coupled to a network. The network coupling may be for example, over optical or wired communications lines or via wireless techniques (for example, microwave, infrared or other transmission techniques) or some combination of these. The series of computer instructions preferably embodies all or part of the functionality previously described herein with respect to the system. Those skilled in the art should appreciate that such computer instructions can be written in a number of programming languages for use with many computer architectures or operating systems. Furthermore, such instructions may be stored in any memory device, such as semiconductor, magnetic, optical or other memory devices, and may be transmitted using any communications technology, such as optical, infrared, microwave, or other transmission technologies. It is expected that such a computer program product may be distributed as a removable medium with accompanying printed or electronic documentation (for example, shrink wrapped software), preloaded with a computer system (for example, on system ROM or fixed disk), or distributed from a server or electronic bulletin board over a network (for example, the Internet or World Wide Web). In addition, a computer system is further provided including derivative modules for deriving a first data set and a calibration profile data set.

The calibration profile data sets in graphical or tabular form, the associated databases, and the calculated index or derived algorithm, together with information extracted from the panels, the databases, the data sets or the indices or algorithms are commodities that can be sold together or separately for a variety of purposes as described in WO 01/25473. In other embodiments, a clinical indicator may be used to assess the lung cancer or conditions related to lung cancer of the relevant set of subjects by interpreting the calibrated profile data set in the context of at least one other clinical indicator, wherein the at least one other clinical indicator is selected from the group consisting of blood chemistry, X-ray or other radiological or metabolic imaging technique, molecular markers in the blood, other chemical assays, and physical findings.

Index construction

In combination, (i) the remarkable consistency of Gene Expression Profiles with respect to a biological condition across a population or set of subject or samples, or across a population of cells and (ii) the use of procedures that provide substantially reproducible measurement of constituents in a Gene Expression Panel (Precision Profile^™) giving rise to a Gene Expression Profile, under measurement conditions wherein specificity and efficiencies of amplification for all constituents of the panel are substantially similar, make possible the use of an index that characterizes a Gene Expression Profile, and which therefore provides a measurement of a biological condition.

An index may be constructed using an index function that maps values in a Gene Expression Profile into a single value that is pertinent to the biological condition at hand. The values in a Gene Expression Profile are the amounts of each constituent of the Gene Expression Panel (Precision Profile^™). These constituent amounts form a profile data set, and the index function generates a single value— the index— from the members of the profile data set.

The index function may conveniently be constructed as a linear sum of terms, each term being what is referred to herein as a "contribution function" of a member of the profile data set. For example, the contribution function may be a constant times a power of a member of the profile data set. So the index function would have the form

/ =∑OMi^P(i) ,

where I is the index, Mi is the value of the member i of the profile data set, Ci is a constant, and P(i) is a power to which Mi is raised, the sum being formed for all integral values of i up to the number of members in the data set. We thus have a linear polynomial expression. The role of the coefficient Ci for a particular gene expression specifies whether a higher ACt value for this gene either increases (a positive Ci) or decreases (a lower value) the likelihood of lung cancer, the ACt values of all other genes in the expression being held constant. The values Ci and P(i) may be determined in a number of ways, so that the index / is informative of the pertinent biological condition. One way is to apply statistical techniques, such as latent class modeling, to the profile data sets to correlate clinical data or

experimentally derived data, or other data pertinent to the biological condition. In this connection, for example, may be employed the software from Statistical Innovations, Belmont, Massachusetts, called Latent Gold^®. Alternatively, other simpler modeling techniques may be employed in a manner known in the art. The index function for lung cancer may be constructed, for example, in a manner that a greater degree of lung cancer (as determined by the profile data set for the any of the Precision Profiles^™ (listed in Tables 1-5) described herein) correlates with a large value of the index function.

Just as a baseline profile data set, discussed above, can be used to provide an appropriate normative reference, and can even be used to create a Calibrated profile data set, as discussed above, based on the normative reference, an index that characterizes a Gene Expression Profile can also be provided with a normative value of the index function used to create the index. This normative value can be determined with respect to a relevant population or set of subjects or samples or to a relevant population of cells, so that the index may be interpreted in relation to the normative value. The relevant population or set of subjects or samples, or relevant population of cells may have in common a property that is at least one of age range, gender, ethnicity, geographic location, nutritional history, medical condition, clinical indicator, medication, physical activity, body mass, and environmental exposure.

As an example, the index can be constructed, in relation to a normative Gene

Expression Profile for a population or set of healthy subjects, in such a way that a reading of approximately 1 characterizes normative Gene Expression Profiles of healthy subjects. Let us further assume that the biological condition that is the subject of the index is lung cancer; a reading of 1 in this example thus corresponds to a Gene Expression Profile that matches the norm for healthy subjects. A substantially higher reading then may identify a subject experiencing lung cancer, or a condition related to lung cancer. The use of 1 as identifying a normative value, however, is only one possible choice; another logical choice is to use 0 as identifying the normative value. With this choice, deviations in the index from zero can be indicated in standard deviation units (so that values lying between -1 and +1 encompass 90% of a normally distributed reference population or set of subjects. Since it was determined that Gene Expression Profile values (and accordingly constructed indices based on them) tend to be normally distributed, the 0-centered index constructed in this manner is highly informative. It therefore facilitates use of the index in diagnosis of disease and setting objectives for treatment.

Still another embodiment is a method of providing an index pertinent to lung cancer or conditions related to lung cancer of a subject based on a first sample from the subject, the first sample providing a source of RNAs, the method comprising deriving from the first sample a profile data set, the profile data set including a plurality of members, each member being a quantitative measure of the amount of a distinct R A constituent in a panel of constituents selected so that measurement of the constituents is indicative of the presumptive signs of lung cancer, the panel including at least one constituent of any of the genes listed in the Precision Profiles^™ (listed in Tables 1-5). In deriving the profile data set, such measure for each constituent is achieved under measurement conditions that are substantially repeatable, at least one measure from the profile data set is applied to an index function that provides a mapping from at least one measure of the profile data set into one measure of the presumptive signs of lung cancer, so as to produce an index pertinent to the lung cancer or conditions related to lung cancer of the subject.

As another embodiment of the invention, an index function / of the form

I = Co +∑C_iMu ¹⁽ⁱ⁾ M_2l ^p2(i),

can be employed, where Mi and M₂ are values of the member i of the profile data set, Ci is a constant determined without reference to the profile data set, and PI and P2 are powers to which Mi and M₂ are raised. The role of Pl(i) and P2(i) is to specify the specific functional form of the quadratic expression, whether in fact the equation is linear, quadratic, contains cross-product terms, or is constant. For example, when PI = P2 = 0, the index function is simply the sum of constants; when PI = 1 and P2 = 0, the index function is a linear expression; when PI = P2 =1, the index function is a quadratic expression.

The constant Co serves to calibrate this expression to the biological population of interest that is characterized by having lung cancer. In this embodiment, when the index value equals 0, the odds are 50:50 of the subject having lung cancer vs a normal subject. More generally, the predicted odds of the subject having lung cancer is [exp(Ii)], and therefore the predicted probability of having lung cancer is [exp(Ii)]/[l+exp((Ii)]. Thus, when the index exceeds 0, the predicted probability that a subject has lung cancer is higher than 0.5, and when it falls below 0, the predicted probability is less than 0.5.

The value of Co may be adjusted to reflect the prior probability of being in this population based on known exogenous risk factors for the subject. In an embodiment where Co is adjusted as a function of the subject's risk factors, where the subject has prior probability pi of having lung cancer based on such risk factors, the adjustment is made by increasing (decreasing) the unadjusted Co value by adding to Co the natural logarithm of the following ratio: the prior odds of having lung cancer taking into account the risk factors/ the overall prior odds of having lung cancer without taking into account the risk factors.

Performance and Accuracy Measures of the Invention

The performance and thus absolute and relative clinical usefulness of the invention may be assessed in multiple ways as noted above. Amongst the various assessments of performance, the invention is intended to provide accuracy in clinical diagnosis and prognosis. The accuracy of a diagnostic or prognostic test, assay, or method concerns the ability of the test, assay, or method to distinguish between subjects having lung cancer is based on whether the subjects have an "effective amount" or a "significant alteration" in the levels of a cancer associated gene. By "effective amount" or "significant alteration", it is meant that the measurement of an appropriate number of cancer associated gene (which may be one or more) is different than the predetermined cut-off point (or threshold value) for that cancer associated gene and therefore indicates that the subject has lung cancer for which the cancer associated gene(s) is a determinant.

The difference in the level of cancer associated gene(s) between normal and abnormal is preferably statistically significant. As noted below, and without any limitation of the invention, achieving statistical significance, and thus the preferred analytical and clinical accuracy, generally but not always requires that combinations of several cancer associated gene(s) be used together in panels and combined with mathematical algorithms in order to achieve a statistically significant cancer associated gene index.

In the categorical diagnosis of a disease state, changing the cut point or threshold value of a test (or assay) usually changes the sensitivity and specificity, but in a qualitatively inverse relationship. Therefore, in assessing the accuracy and usefulness of a proposed medical test, assay, or method for assessing a subject's condition, one should always take both sensitivity and specificity into account and be mindful of what the cut point is at which the sensitivity and specificity are being reported because sensitivity and specificity may vary significantly over the range of cut points. Use of statistics such as AUC, encompassing all potential cut point values, is preferred for most categorical risk measures using the invention, while for continuous risk measures, statistics of goodness-of-fit and calibration to observed results or other gold standards, are preferred.

Using such statistics, an "acceptable degree of diagnostic accuracy", is herein defined as a test or assay (such as the test of the invention for determining an effective amount or a significant alteration of cancer associated gene(s), which thereby indicates the presence of a lung cancer in which the AUC (area under the ROC curve for the test or assay) is at least 0.60, desirably at least 0.65, more desirably at least 0.70, preferably at least 0.75, more preferably at least 0.80, and most preferably at least 0.85.

By a "very high degree of diagnostic accuracy", it is meant a test or assay in which the AUC (area under the ROC curve for the test or assay) is at least 0.75, desirably at least 0.775, more desirably at least 0.800, preferably at least 0.825, more preferably at least 0.850, and most preferably at least 0.875.

The predictive value of any test depends on the sensitivity and specificity of the test, and on the prevalence of the condition in the population being tested. This notion, based on Bayes' theorem, provides that the greater the likelihood that the condition being screened for is present in an individual or in the population (pre-test probability), the greater the validity of a positive test and the greater the likelihood that the result is a true positive. Thus, the problem with using a test in any population where there is a low likelihood of the condition being present is that a positive result has limited value (i.e., more likely to be a false positive). Similarly, in populations at very high risk, a negative test result is more likely to be a false negative.

As a result, ROC and AUC can be misleading as to the clinical utility of a test in low disease prevalence tested populations (defined as those with less than 1% rate of occurrences (incidence) per annum, or less than 10% cumulative prevalence over a specified time horizon). Alternatively, absolute risk and relative risk ratios as defined elsewhere in this disclosure can be employed to determine the degree of clinical utility. Populations of subjects to be tested can also be categorized into quartiles by the test's measurement values, where the top quartile (25% of the population) comprises the group of subjects with the highest relative risk for developing lung cancer, and the bottom quartile comprising the group of subjects having the lowest relative risk for developing lung cancer. Generally, values derived from tests or assays having over 2.5 times the relative risk from top to bottom quartile in a low prevalence population are considered to have a "high degree of diagnostic accuracy," and those with five to seven times the relative risk for each quartile are considered to have a "very high degree of diagnostic accuracy." Nonetheless, values derived from tests or assays having only 1.2 to 2.5 times the relative risk for each quartile remain clinically useful are widely used as risk factors for a disease. Often such lower diagnostic accuracy tests must be combined with additional parameters in order to derive meaningful clinical thresholds for therapeutic intervention, as is done with the aforementioned global risk assessment indices.

A health economic utility function is yet another means of measuring the performance and clinical value of a given test, consisting of weighting the potential categorical test outcomes based on actual measures of clinical and economic value for each. Health economic performance is closely related to accuracy, as a health economic utility function specifically assigns an economic value for the benefits of correct classification and the costs of misclassification of tested subjects. As a performance measure, it is not unusual to require a test to achieve a level of performance which results in an increase in health economic value per test (prior to testing costs) in excess of the target price of the test.

In general, alternative methods of determining diagnostic accuracy are commonly used for continuous measures, when a disease category or risk category (such as those at risk for having a bone fracture) has not yet been clearly defined by the relevant medical societies and practice of medicine, where thresholds for therapeutic use are not yet established, or where there is no existing gold standard for diagnosis of the pre-disease. For continuous measures of risk, measures of diagnostic accuracy for a calculated index are typically based on curve fit and calibration between the predicted continuous value and the actual observed values (or a historical index calculated value) and utilize measures such as R squared, Hosmer-Lemeshow P- value statistics and confidence intervals. It is not unusual for predicted values using such algorithms to be reported including a confidence interval (usually 90% or 95% CI) based on a historical observed cohort's predictions, as in the test for risk of future breast cancer recurrence commercialized by Genomic Health, Inc. (Redwood City,

California).

In general, by defining the degree of diagnostic accuracy, i.e., cut points on a ROC curve, defining an acceptable AUC value, and determining the acceptable ranges in relative concentration of what constitutes an effective amount of the cancer associated gene(s) of the invention allows for one of skill in the art to use the cancer associated gene(s) to identify, diagnose, or prognose subjects with a pre-determined level of predictability and performance.

Results from the cancer associated gene(s) indices thus derived can then be validated through their calibration with actual results, that is, by comparing the predicted versus observed rate of disease in a given population, and the best predictive cancer associated gene(s) selected for and optimized through mathematical models of increased complexity. Many such formula may be used; beyond the simple non-linear transformations, such as logistic regression, of particular interest in this use of the present invention are structural and synactic classification algorithms, and methods of risk index construction, utilizing pattern recognition features, including established techniques such as the Kth-Nearest Neighbor, Boosting, Decision Trees, Neural Networks, Bayesian Networks, Support Vector Machines, and Hidden Markov Models, as well as other formula described herein.

Furthermore, the application of such techniques to panels of multiple cancer associated gene(s) is provided, as is the use of such combination to create single numerical "risk indices" or "risk scores" encompassing information from multiple cancer associated gene(s) inputs. Individual B cancer associated gene(s) may also be included or excluded in the panel of cancer associated gene(s) used in the calculation of the cancer associated gene(s) indices so derived above, based on various measures of relative performance and calibration in validation, and employing through repetitive training methods such as forward, reverse, and stepwise selection, as well as with genetic algorithm approaches, with or without the use of constraints on the complexity of the resulting cancer associated gene(s) indices.

The above measurements of diagnostic accuracy for cancer associated gene(s) are only a few of the possible measurements of the clinical performance of the invention. It should be noted that the appropriateness of one measurement of clinical accuracy or another will vary based upon the clinical application, the population tested, and the clinical consequences of any potential misclassification of subjects. Other important aspects of the clinical and overall performance of the invention include the selection of cancer associated gene(s) so as to reduce overall cancer associated gene(s) variability (whether due to method (analytical) or biological (pre-analytical variability, for example, as in diurnal variation), or to the integration and analysis of results (post-analytical variability) into indices and cut-off ranges), to assess analyte stability or sample integrity, or to allow the use of differing sample matrices amongst blood, cells, serum, plasma, urine, etc.

Kits

The invention also includes a lung cancer detection reagent, i.e., nucleic acids that specifically identify one or more lung cancer or condition related to lung cancer nucleic acids {e.g., any gene listed in Tables 1-5, oncogenes, tumor suppression genes, tumor progression genes, angiogenesis genes and lymphogenesis genes; sometimes referred to herein as lung cancer associated genes or lung cancer associated constituents) by having homologous nucleic acid sequences, such as oligonucleotide sequences, complementary to a portion of the lung cancer genes nucleic acids or antibodies to proteins encoded by the lung cancer gene nucleic acids packaged together in the form of a kit. The oligonucleotides can be fragments of the lung cancer genes. For example the oligonucleotides can be 200, 150, 100, 50, 25, 10 or less nucleotides in length. The kit may contain in separate containers a nucleic acid or antibody (either already bound to a solid matrix or packaged separately with reagents for binding them to the matrix), control formulations (positive and/or negative), and/or a detectable label. Instructions (i.e., written, tape, VCR, CD-ROM, etc.) for carrying out the assay may be included in the kit. The assay may for example be in the form of PCR, a Northern hybridization or a sandwich ELISA, as known in the art.

For example, lung cancer gene detection reagents can be immobilized on a solid matrix such as a porous strip to form at least one lung cancer gene detection site. The measurement or detection region of the porous strip may include a plurality of sites containing a nucleic acid. A test strip may also contain sites for negative and/or positive controls. Alternatively, control sites can be located on a separate strip from the test strip. Optionally, the different detection sites may contain different amounts of immobilized nucleic acids, i.e., a higher amount in the first detection site and lesser amounts in subsequent sites. Upon the addition of test sample, the number of sites displaying a detectable signal provides a quantitative indication of the amount of lung cancer genes present in the sample. The detection sites may be configured in any suitably detectable shape and are typically in the shape of a bar or dot spanning the width of a test strip.

Alternatively, lung cancer detection genes can be labeled (e.g., with one or more fluorescent dyes) and immobilized on lyophilized beads to form at least one lung cancer gene detection site. The beads may also contain sites for negative and/or positive controls. Upon addition of the test sample, the number of sites displaying a detectable signal provides a quantitative indication of the amount of lung cancer genes present in the sample.

Alternatively, the kit contains a nucleic acid substrate array comprising one or more nucleic acid sequences. The nucleic acids on the array specifically identify one or more nucleic acid sequences represented by lung cancer genes (see Tables 1-5). In various embodiments, the expression of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 40 or 50 or more of the sequences represented by lung cancer genes (see Tables 1-5) can be identified by virtue of binding to the array. The substrate array can be on, i.e., a solid substrate, i.e., a "chip" as described in U.S. Patent No. 5,744,305. Alternatively, the substrate array can be a solution array, i.e., Luminex, Cyvera, Vitra and Quantum Dots' Mosaic.

The skilled artisan can routinely make antibodies, nucleic acid probes, i.e., oligonucleotides, aptamers, siRNAs, antisense oligonucleotides, against any of the lung cancer genes listed in Tables 1-5. OTHER EMBODIMENTS

While the invention has been described in conjunction with the detailed description thereof, the foregoing description is intended to illustrate and not limit the scope of the invention, which is defined by the scope of the appended claims. Other aspects, advantages, and modifications are within the scope of the following claims.

EXAMPLES

Example 1 : Patient Population

R A was isolated using the PAXgene System from blood samples obtained from a total of 293 subjects with suspicious imaged nodules undergoing resection surgery at NYU Medical Center and 298 control subjects without lung cancer included 97 COPD patients with 20+ pack year smoking history, 101 otherwise healthy subjects with 20+ pack year smoking history and 100 age and gender matched medically defined non-smoking normals (MNDO). Of the subjects with described above with suspicious imaged nodules; 192 cases were diagnosed with primary CaL, 38 secondary CaL and 63 non-malignant nodules after resection surgery. These 293 subjects were used as a training dataset.

An additional independent dataset consisting of 75 Primary CaL, 14 secondary CaL, 25 non-malignant, 38 COPD, 39 smokers, 40 MDNO were used to as a validation data set.

As shown in Figure 1 , the patient characteristic were well matched across both the training and validation sets. Primary Lung Cancer cases were assigned to training a validation groups by types as shown in Figure 2.

Example 2: Development of 19-gene models that is predictive of primary and secondary lung cancer vs. smokers with and without COPD

The data consists of AC_T values for each sample subject in each of G(k) genes obtained from a particular class k of genes.

An exploratory gene selection using a total of 346 genes were run in resection positive vs, resection negative traing set. From this study 24 candidate genes were identified. (Figure 3) This 24 gene candidate model discriminates resection positive from resection negative subjects. (Figure 4) This 24 candidate gene model was used to select a validated 19-gene model that is capable of discriminating smokers with lung cancer from smokers with and without COPD as well as an optimized 19-gene model that is capable of discriminating smokers with lung cancer from smokers with and without COPD that was developed on a female smoker training data set. Using a new step down algorithm (K-Component) a 19 gene model was developed on a training set (101 Primary CaL, 17 secondary CaL, 59 COPD, 62 smokers). These nineteen genes in the model are CDK2, CXCR3, CD22, CDK6, ERCC2, CCND2, ABCC5, CDH1, CDK 1B, CDK 2A, CREB3, ICAM1, MMP8, NCAM1, SOCS1, STK4, TNFRSF10B, TNFRSF1B, and TOPOR.

When using this 19-gene model, primary and secondary CaL vs. Smokers and COPD patients had sensitivity of 90.7%, specificity of 90.1% and AUC of 0.959. Using fixed parameters and cut-off, an independent data set (62 Primary CaL, 7 secondary CaL, 38 COPD, 39 smokers) validated 18-gene model with p-value of 2.1E-10. Primary and secondary CaL vs. smokers and COPD patients sensitivity of 62.3%, specificity of 84.4% AUC of 0.805. (Figure 9) A combined model using same 19 genes but re-estimated coefficients and cut-off had primary and secondary CaL vs. smokers and COPD patients with sensitivity of 87.2%, specificity of 85.4% and AUC of 0.923. (Figure 12)

When estimating seperate 19-gene models for males and females based upon the training data, it was ovserved that the 19-gene model developed for females excluding non- smokers was more stable (smallerfall off in the validation data) that the models developed for males. These nineteen genes in the model (female only) are CDK2, CXCR3, CD22, CDK6, ERCC2, ABCC5, CDH1, CDK 1B, CDK 2A, CREB3, ICAM1, IGF2BP2, MMP8, NCAM1, SOCS1, ST 14, STK4, TNFRSF10B, and TNFRSF1B

When using this 19 gene model, primary and secondary CaL vs. Smokers and COPD patients had sensitivity of 89.8%, specificity of 84.3 % and AUC of 0.942. Using fixed parameters and cut-off, an independent data set (62 Primary CaL, 7 secondary CaL, 38 COPD, 39 smokers) validated 18-gene model with p-value of 7.9E-132. Primary and secondary CaL vs. smokers and COPD patients sensitivity of 76.8%, specificity of 79.2% AUC of 0.844. (Figure 17) A combined model using same 19 genes but re-estimated coefficients and cut-off had primary and secondary CaL vs. smokers and COPD patients with sensitivity of 86.6%, specificity of 85.9% and AUC of 0.922. (Figure 21)

The step down algorithm is described in USSN 61/294,386 and PCT/US2011/020835, the contents of each are incorporated by reference their entireties. Briefly, this step down algorithm was based upon the observation that (i) one gene of the pair (referred to herein as a

"Prime" gene) is significant when used separately in a 1-gene model; (ii) the other gene of the pair (referred to herein as a "Proxy" gene) is NOT significant when used separately in a 1- gene model; (iii) however, when the Proxy gene is included in a 2-gene model with the Prime gene, the Proxy gene significantly improves the predictive area under the ROC curve of the Prime gene alone; (iv) in the 2-gene model, one gene has a significant positive coefficient, while the other gene has a significant negative coefficient; and (v) the two genes have moderate to high positive correlation (>0.6). The references listed below are hereby incorporated herein by reference.

References

Magidson, J. GOLDMineR User's Guide (1998). Belmont, MA: Statistical Innovations Inc. Vermunt and Magidson (2005). Latent GOLD 4.0 Technical Guide, Belmont MA: Statistical Innovations.

Vermunt and Magidson (2007). LG-Syntax™ User's Guide: Manual for Latent GOLD^® 4.5 Syntax Module, Belmont MA: Statistical Innovations.

Vermunt J.K. and J. Magidson. Latent Class Cluster Analysis in (2002) J. A. Hagenaars and A. L. McCutcheon (eds.). Applied Latent Class Analysis, 89-106. Cambridge:

Cambridge University Press.

Magidson, J. "Maximum Likelihood Assessment of Clinical Trials Based on an Ordered Categorical Response." (1996) Drug Information Journal, Maple Glen, PA: Drug Information Association, Vol. 30, No. l, pp 143-170.

Claims

What is claimed is:

1. A method for evaluating the presence of lung cancer in a subject based on a sample from the subject, the sample providing a source of RNAs, comprising:

a) determining a quantitative measure of the amount of CDK2, CXCR3, CD22,

CDK6, ERCC2, ABCC5, CDHl, CDKNIB, CDKN2A, CREB3, ICAMl, MMP8, NCAMl, SOCSl, STK4, TNFRSFIOB, and TNFRSFIB as a distinct RNA constituent in the subject sample subject sample, wherein such measure is obtained under measurement conditions that are substantially repeatable and the constituent is selected so that measurement of the constituent distinguishes between a normal subject and a lung cancer-diagnosed subject in a reference population with at least 75% accuracy; and

b) comparing the quantitative measure of the constituent in the subject sample to a reference value.

2. The method of claim 1, further comprising determining a quantitative measure of the amount of (a) CCND2 and TOPORS or (b) IGF2BP2 and ST14.

3. The method of claim 1, wherein said reference value is an index value.

4. The method of claim 1, wherein expression of said constituent in said subject is increased compared to expression of said constituent in a normal reference sample.

5. The method of claim 1, wherein expression of said constituent in said subject is decreased compared to expression of said constituent in a normal reference sample.

6. The method of claim 1, wherein the sample is selected from the group consisting of blood, a blood fraction, a body fluid, a cells and a tissue.

7. The method of claim 1, wherein the measurement conditions that are substantially repeatable are within a degree of repeatability of better than ten percent.

8. The method of claim 1, wherein the measurement conditions that are substantially repeatable are within a degree of repeatability of better than five percent.

9. The method of claim 1, wherein the measurement conditions that are substantially repeatable are within a degree of repeatability of better than three percent.

10. The method of claim 1, wherein efficiencies of amplification for all constituents are substantially similar.

11. The method of claim 1 , wherein the efficiency of amplification for all constituents is within ten percent.

12. The method of claim 1, wherein the efficiency of amplification for all constituents is within five percent.

13. The method of claim 1, wherein the efficiency of amplification for all constituents is within three percent.

14. The method of claim 1, wherein the subject is a smoker and/or has chronic obstructive pulmonary disease (COPD).

15 A kit for detecting lung cancer in a subject, comprising at least one reagent for the detection or quantification of any constituent measured according to any one of claims 1-14 and instructions for using the kit.