EP2596131A2 - Profilage d'expression génique pour l'identification du cancer du poumon - Google Patents

Profilage d'expression génique pour l'identification du cancer du poumon

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Publication number
EP2596131A2
EP2596131A2 EP11810327.4A EP11810327A EP2596131A2 EP 2596131 A2 EP2596131 A2 EP 2596131A2 EP 11810327 A EP11810327 A EP 11810327A EP 2596131 A2 EP2596131 A2 EP 2596131A2
Authority
EP
European Patent Office
Prior art keywords
lung cancer
subject
sample
gene
constituent
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Withdrawn
Application number
EP11810327.4A
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German (de)
English (en)
Other versions
EP2596131A4 (fr
Inventor
Karl Wassmann
Danute M. Bankaitis-Davis
Kathleen Storm
Lisa Siconolfi
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
DxTerity Diagnostics
Original Assignee
DxTerity Diagnostics
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Publication date
Application filed by DxTerity Diagnostics filed Critical DxTerity Diagnostics
Publication of EP2596131A2 publication Critical patent/EP2596131A2/fr
Publication of EP2596131A4 publication Critical patent/EP2596131A4/fr
Withdrawn legal-status Critical Current

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    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
    • C12Q1/6883—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material
    • C12Q1/6886—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B25/00—ICT specially adapted for hybridisation; ICT specially adapted for gene or protein expression
    • G16B25/10—Gene or protein expression profiling; Expression-ratio estimation or normalisation
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/158—Expression markers
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B25/00—ICT specially adapted for hybridisation; ICT specially adapted for gene or protein expression

Definitions

  • 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).
  • COPD chronic obstructive pulmonary disease
  • 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.
  • SCLC small cell lung cancer
  • NSCLC non-small cell lung cancer
  • NSCLC 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.
  • Adenocarcinomas e.g., bronchioloalveolar carcinoma
  • 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.
  • 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.
  • 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.
  • smoking low tar or "light” cigarettes reduces the risk of lung cancer.
  • Mentholated cigarettes may increase the risk of developing lung cancer.
  • non-smokers are at risk for lung cancer due to second hand smoke.
  • 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.
  • age increased risk in the elderly population, nearly 70% of people diagnosed are over age 65
  • genetic predisposition e.g., genetic predisposition
  • exposure to high levels of arsenic in drinking water, asbestos fibers, and/or long term radon contamination each more pronounced in smokers
  • lung cancer remains asymptomatic until it reaches an advanced stage and spreads beyond the lungs.
  • symptoms 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).
  • COPD chronic obstructive pulmonary disease
  • 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;
  • 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,
  • 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.
  • the invention is in based in part upon the identification of gene expression profiles (Precision ProfilesTM) 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.
  • Precision ProfilesTM gene expression profiles associated with lung cancer.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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 TM 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.
  • 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.
  • 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.
  • 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 genes 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.
  • the female only 19-gene model was more satble (smaller fall off in the validation data) than the model developed on males only.
  • 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.
  • “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.
  • 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 TM ) resulting from evaluation of a biological sample (or population or set of samples) under a desired biological condition that is used for
  • 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.
  • the desired biological condition may be health of a subject or a population or set of subjects.
  • 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.
  • a condition in this context may be chronic or acute or simply transient.
  • 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.
  • CEC circulating endothelial cell
  • CTC circulating tumor cell
  • 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 TM of a subject's health status or other characteristics, such as, without limitation, age (AGE), ethnicity (RACE), gender (SEX), and family history of cancer.
  • COPD Chironic obstructive pulmonary disease
  • 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
  • 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.
  • a profile data set from a sample includes determining a set of values associated with constituents of a Gene Expression Panel (Precision Profile TM ) either (i) by direct measurement of such constituents in a biological sample.
  • Precision Profile TM Gene Expression Panel
  • 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.
  • 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.”
  • “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.
  • Precision Profile TM Of particular use in combining constituents of a Gene Expression Panel (Precision Profile TM ) 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 TM ) detected in a subject sample and the subject's risk of lung cancer.
  • 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.
  • PCA Principal Components Analysis
  • KS Logistic Regression Analysis
  • KS Linear Discriminant Analysis
  • ELDA Eigengene Linear Discriminant Analysis
  • SVM Support Vector Machines
  • RF Random Forest
  • RPART Recurs
  • 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).
  • FDR false discovery rates
  • a "Gene Expression Panel” (Precision Profile TM ) 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
  • Precision Profile TM 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.
  • Stage 1 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
  • NDV Neuronal predictive value
  • ROC Receiver Operating Characteristics
  • 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.
  • PSV Positive predictive value
  • “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 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 TM ) combinations and
  • 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.
  • Specificity is calculated by TN/(TN+FP) or the true negative fraction of non-disease or normal subjects.
  • 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 TM ), 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.
  • 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.
  • TP TP is true positive, which for a disease state test means correctly classifying a disease subject.
  • a degree of repeatability of measurement of better than twenty percent may be used as providing measurement conditions that are "substantially repeatable".
  • expression levels for a constituent in a Gene Expression Panel 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.
  • 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.
  • 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.
  • 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.
  • 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 ProfileTM) disclosed herein (i.e.
  • 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.
  • 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 .
  • CDK2, CXCR3, CD22, CDK6, ERCC2, ABCC5, CDH1 , CDK 1B, CDKN2A, CREB3, ICAM1 , IGF2BP2, MMP8, NCAM1 , SOCS 1 , ST14, STK4, TNFRSF10B, and TNFRSF1B are measured.
  • 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.
  • the qualitative measure of the constituent is compared to a reference or baseline level or value (e.g. a baseline profile set).
  • 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)).
  • 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.
  • 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.
  • 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.
  • 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.
  • 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).
  • a diagnostically relevant 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.
  • 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.
  • 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.
  • 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
  • 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.
  • 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.
  • 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.
  • 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.
  • the baseline level may be taken or derived from one or more individuals who have not been exposed to the treatment.
  • 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 TM ) 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.
  • 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 TM ) 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-clin
  • RNA 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,
  • 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);
  • inflammation promotes malignancy via proinflammatory cytokines, including but not limited to IL- ⁇ , which enhance immune suppression through the induction of myeloid suppressor cells, and that these cells down regulate immune
  • 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.
  • 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.
  • a sample is run through a panel in replicates of three for each target gene
  • test that is, a sample is divided into aliquots and for each aliquot the concentrations of each constituent in a Gene Expression Panel (Precision Profile TM ) 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.
  • internal control e.g., an endogenous marker such as 18S rRNA, or an exogenous marker
  • 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%.
  • 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.
  • 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.
  • cells may be lysed and RNA eluted in a suitable solution in which to conduct a DNAse reaction.
  • 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.
  • quantitative PCR is performed using amplification, reporting agents and instruments such as those supplied commercially by Applied Biosystems (Foster City, CA).
  • 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.
  • 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.
  • quantitative gene expression techniques may utilize 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.
  • 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.
  • the selected primer-probe combination is associated with a set of features:
  • the reverse primer should be complementary to the coding DNA strand.
  • 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.)
  • 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:
  • 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% C0 2 for 30 minutes. Cells are lysed and nucleic acids, e.g., RNA, are extracted by various standard means.
  • nucleic acids e.g., RNA
  • 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 (RNAqueousTM, Phenol-free Total RNA Isolation Kit, Catalog #1912, version 9908; Austin, Texas).
  • 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.
  • 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).
  • 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.
  • fluorescent-tagged detection oligonucleotide probes see, for example, TaqmanTM PCR Reagent Kit, Protocol, part number 402823, Revision A, 1996, Applied Biosystems, Foster City CA
  • 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 TM 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.
  • any tissue, body fluid, or cell(s) 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).
  • ELISA Enzyme Linked Immunosorbent Assay
  • mass spectroscopy mass spectroscopy
  • 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).
  • microcentrifuge tube for example, remove 10 ⁇ ⁇ RNA and dilute to 20 ⁇ ⁇ with RNase / DNase free water, for whole blood RNA use 20 ⁇ ⁇ total RNA
  • PCR QC should be run on all RT samples using 18S and ⁇ -actin.
  • first strand cDNA 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 ProfileTM) is performed using the ABI Prism® 7900 Sequence Detection System as follows:
  • 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.
  • Cepheid GeneXpert® self contained cartridge preloaded with a lyophilized
  • Clinical sample (whole blood, RNA, etc.)
  • the use of the primer probe with the first strand cDNA as described above to permit measurement of constituents of a Gene Expression Panel is performed using a QPCR assay on the Roche LightCycler ® 480 Real-Time PCR System as follows:
  • the endogenous control gene may be dual labeled with either VIC-MGB or VIC-TAMRA.
  • LightCycler ® 480 Real-Time PCR System
  • 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 TM ).
  • the detection limit may be reset and the "undetermined" constituents may be "flagged".
  • 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 ( ⁇ C T ) and relative expression calculations that have used re-set FAM C T values are also flagged.
  • 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.
  • 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.
  • a sample may be taken before stimulation or after stimulation with an exogenous compound or substance, such as before or after therapeutic treatment.
  • 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
  • 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.
  • the calibrated profile data sets may be reproducible within 20%, and typically within 10%.
  • 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 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.
  • 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.
  • 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.
  • using a network may include accessing a global computer network.
  • 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.
  • 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.
  • 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).
  • a computer system is further provided including derivative modules for deriving a first data set and a calibration profile data set.
  • 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.
  • 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 TM ). 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.
  • 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
  • I is the index
  • Mi is the value of the member i of the profile data set
  • Ci is a constant
  • 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.
  • Ci is a constant
  • 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.
  • 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
  • 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 TM (listed in Tables 1-5) described herein) correlates with a large value of the index function.
  • 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.
  • the index can be constructed, in relation to a normative Gene
  • 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 TM (listed in Tables 1-5).
  • 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.
  • Mi and M 2 are values of the member i of the profile data set
  • Ci is a constant determined without reference to the profile data set
  • PI and P2 are powers to which Mi and M 2 are raised.
  • the constant Co serves to calibrate this expression to the biological population of interest that is characterized by having lung cancer.
  • 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)].
  • 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.
  • 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.
  • the performance and thus absolute and relative clinical usefulness of the invention may be assessed in multiple ways as noted above.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • pre-test probability the greater the likelihood that the condition being screened for is present in an individual or in the population
  • a positive result has limited value (i.e., more likely to be a false positive).
  • a negative test result is more likely to be a false negative.
  • 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).
  • 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.
  • 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.
  • diagnostic accuracy is 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.
  • 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.
  • the degree of diagnostic accuracy i.e., cut points on a ROC curve
  • defining an acceptable AUC value 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • the number of sites displaying a detectable signal provides a quantitative indication of the amount of lung cancer genes present in the sample.
  • 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).
  • 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.
  • the substrate array can be a solution array, i.e., Luminex, Cyvera, Vitra and Quantum Dots' Mosaic.
  • nucleic acid probes i.e., oligonucleotides, aptamers, siRNAs, antisense oligonucleotides, against any of the lung cancer genes listed in Tables 1-5.
  • 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).
  • MNDO medically defined non-smoking normals
  • 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.
  • 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.
  • 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.
  • 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.

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Abstract

L'invention concerne des modèles génétiques utiles dans le criblage de patients à risque pour des cancers du poumon, comprenant des fumeurs présentant ou non une maladie pulmonaire obstructive chronique (MPOC).
EP11810327.4A 2010-07-21 2011-07-20 Profilage d'expression génique pour l'identification du cancer du poumon Withdrawn EP2596131A4 (fr)

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WO2006080597A1 (fr) * 2005-01-31 2006-08-03 Digital Genomics Inc. Marqueurs destines au diagnostic de cancer du poumon
WO2008063413A2 (fr) * 2006-11-13 2008-05-29 Source Precision Medicine, Inc. Détermination du profil de l'expression génique dans l'identification, la surveillance et le traitement du cancer du poumon

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WO2006080597A1 (fr) * 2005-01-31 2006-08-03 Digital Genomics Inc. Marqueurs destines au diagnostic de cancer du poumon
WO2008063413A2 (fr) * 2006-11-13 2008-05-29 Source Precision Medicine, Inc. Détermination du profil de l'expression génique dans l'identification, la surveillance et le traitement du cancer du poumon

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