US20140011748A1

US20140011748A1 - Methods Of Predicting Response To JNK Inhibitor Therapy

Info

Publication number: US20140011748A1
Application number: US13/980,522
Authority: US
Inventors: Lynda Chin; Ronald A. DePinho; Giulio Draetta; Timothy P. Heffernan
Original assignee: Dana Farber Cancer Institute Inc
Current assignee: Dana Farber Cancer Institute Inc
Priority date: 2011-01-21
Filing date: 2012-01-19
Publication date: 2014-01-09
Also published as: US20120187486A1; US8487378B2; TW201232780A; CN102610642A; US20140024182A1; US8916444B2; CN102610642B; TWI481033B

Abstract

This present invention provides methods of treating of accessing/monitoring the responsiveness of a cancer cell to JNK inhibitor therapy.

Description

RELATED APPLICATIONS

This patent application claims priority to U.S. Provisional Application No. 61/434,983, filed Jan. 21, 2011, which is incorporated herein by reference in its entirety.

STATEMENT AS TO FEDERALLY SPONSORED RESEARCH

This invention was made with U.S. government support under the National Institutes of Health grant RO1 CA099041. The government has certain rights in the invention.

FIELD OF THE INVENTION

The invention relates to generally to the predicting a subjects response to JNK inhibitor therapy.

BACKGROUND OF THE INVENTION

Mammalian cells respond to extracellular stimuli by activating signaling cascades that are mediated by members of the mitogen-activated protein (MAP) kinase family, which include the extracellular signal regulated kinases (ERKs), the p38 MAP kinases and the c-Jun N-terminal kinases (JNKs). MAP kinases (MAPKs) are activated by a variety of signals including growth factors, cytokines, UV radiation, and stress-inducing agents. MAPKs are serine/threonine kinases and their activation occurs by dual phosphorylation of threonine and tyrosine at the Thr-X-Tyr segment in the activation loop. MAPKs phosphorylate various substrates including transcription factors, which in turn regulate the expression of specific sets of genes and thus mediate a specific response to the stimulus.
One particularly interesting kinase family is the c-Jun NH₂-terminal protein kinases, also known as JNKs. Three distinct genes, JNK1, JNK2, and JNK3 have been identified and at least ten different splicing isoforms of JNKs exist in mammalian cells.
JNKs, along with other MAPKs, have been implicated in having a role in mediating cellular response to cancer, thrombin-induced platelet aggregation, immunodeficiency disorders, autoimmune diseases, cell death, allergies, osteoporosis and heart disease.
Thus, a need exists for the identification of biomarkers that are capable of predicting a therapeutic effect to JNK inhibitor treatment.

SUMMARY OF THE INVENTION

The invention features method of accessing the effectiveness of a JNK inhibitor treatment regimen of a subject having cancer by obtaining a sample from the subject and detecting c-JUN expression or the presence or absence of a mutation in the EGFR or KRAS polypeptide. The presence of c-JUN expression or a KRAS mutation indicates that the subject is responsive to JNK inhibitor treatment. The presence of an EGFR mutation indicates the subject is non-responsive to JNK inhibitor treatment. JNK inhibitors suitable for therapy include for example JIP-1, CC-410, 1,9-pyrazoloanthrone, 2-benzothiazoleacetonitrile, XG-102, BI78D3 and BI87G9. The cancer is any cancer capable of being treated with a JNK inhibitor. For example, the cancer is melanoma, non-small cell lung cancer or colorectal cancer. In some aspects the subject has not received treatment for the cancer. Alternatively, the subject has received treatment for the cancer.
The method is implemented in a flow-cytometry (FC), immuno-histochemistry (IHC), immuno-fluorescence (IF) assay format or a polymerase chain reaction (PCR) sequencing assay format.
Also provided by methods of treating a subject having melanoma by identifying a subject having a melanoma expressing c-JUN and administering to the subject a JNK inhibitor. Also included by the invention is a method treating a subject having non-small cell lung cancer or colon cancer by identifying a subject having a KRAS mutation and administering to the subject a JNK inhibitor. In a further aspect the invention provides a method of treating a subject having non-small cell lung cancer by identifying a subject not having an EGFR mutation and administering to the subject a JNK inhibitor.
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 the case of conflict, the present specification, including definitions, will control. In addition, the materials, methods, and examples are illustrative only and are 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

FIG. 1. JNK2 is required to maintain the growth of established melanoma xenografts. A.) M619 melanoma cells, expressing DOX-inducible JNK2 shRNAs, were injected subcutaneously into NUDE mice. When xenografts reached 150 mm³in size the animals were fed +/−DOX to induce JNK2 shRNA expression. Tumor growth was then measured until control tumors reached 2 cm³. B.) Weight of tumors at study endpoint. C.) Western immunoblot analysis of JNK2 expression in tumor lysates +/−DOX. D.) QPCR of the same tumor lysates measuring RNA expression of JNK2.

FIG. 2. cJUN expression predicts sensitivity of melanoma cell lines to JNK inhibition. A.) M619 melanoma cells were transduced with lentivirus expressing shRNAs against JNK2 and anchorage independent growth measured in soft agar. B.) Western immunoblot p-cJUN expression in melanoma cell lines. C.) Compilation data showing response of 10 melanoma cell lines to JNK inhibition. Red: Responder (sensitive to JNK inhibition); Blue: Non-responder.

FIG. 3. KRAS mutation predicts sensitivity to JNK inhibition. A.) A549 cells were transduced with lentivirus expressing shRNAs against KRAS or JNK1/JNK2 and cell growth measured in 2D (A.) and 3D (B.). NTC refers to the scrambled non-targeting control shRNA. C.) Compilation data showing the response of 7 NSCLC cell lines to JNK inhibition. Red: Responder (sensitive to JNK inhibition); Blue: Non-responder.

FIG. 4. JNK pathway activation correlates with NRAS expression. A.) Schematic of DOX-inducible NRAS expression and melanoma formation in FVB mice. Note the regression of tumors upon removal of DOX. B.) RPPA analysis of p-cJUN expression in melanomas with high and low NRAS expression. C.) Average log 2 expression of p-cJUN in each cohort.

FIG. 5. JNK pathway is activated in KRAS mutant NSCLC. Lung adenocarcinoma specimens from genetically engineered mouse models were processed and stained for p-cJUN expression by IHC.

DETAILED DESCRIPTION OF THE INVENTION

The present invention relates to the identification of biomarkers associated with the responsiveness to c-Jun N-terminal kinase (JNK) inhibitor treatment. Specifically, a genetic mechanism has been identified that stratifies melanoma, non-small cell lung cancer (NSCLC) and colorectal cancer (CRC) patients for treatment with a JNK inhibitor. In particular using these genetic strategies it has been shown that cJUN expression in melanoma, and a KRAS mutations in NSCLC and CRC patient predict sensitivity to JNK inhibition, while an EGFR mutation in NSCLC patients serves as a negative predictor of response.
JNK inhibitors suitable for human administration are know in the art and include for example, JIP-1, CC-410, 1,9-pyrazoloanthrone, 2-benzothiazoleacetonitrile, XG-102, BI78D3 and BI87G9.
While, JNK inhibitor therapy alone or in combination has shown a clinical benefit in patients having cancer, currently there are no known biomarkers capable of stratifying responders and non-responders. Accordingly a need exist is identify biomarkers that are predictive of responsiveness JNK inhibitor therapy.
Our previous work demonstrated that the JNK pathway functions in a context specific manner to promote either cell growth or cell death. Using a context-specific in vivo genetic screen we identified JNK pathway components as key tumor maintenance targets in BRAF^V600Emelanomas. (See, U.S. Ser. No. 61/297,143 filed Jan. 21, 2010 and PCT/US2011/022085 filed Jan. 21, 2011, the contents of each are incorporated by reference in their entireties.) These data provide a clinical path hypothesis guiding development of agents targeting the JNK pathway in specific melanoma patients. Here we have shown that a subset of melanoma patients stratified by levels of cJUN expression, are sensitive to JNK inhibitors. In contrast to BRAF, EGFR, and HER2, efforts to develop clinically effective KRAS inhibitors have been unsuccessful. Thus, KRAS mutant cancers constitute a major unmet clinical need. Here we have shown that KRAS mutation predicts response to JNK inhibition, and thus JNK inhibitors may serve as an effective treatment option for NSCLC and CRC patients.
Our data further suggest EGFR mutant NSCLC cell lines are resistant to JNK inhibition. Thus EGFR mutation status may serve as a negative predictor of response to JNK inhibitors.
Consistent with the requirement of JNK for the proliferation of RAS mutant cells, we and others have shown increased JNK pathway activation in RAS-driven cancers. Here we utilized a well characterized genetically engineered mouse model of melanoma driven by dox-inducible NRAS^Q61Kallele (FIG. 4). Addition of dox to the mouse chow induced expression of NRAS and the formation of melanoma with an average latency of 20 weeks. Upon removal of dox, NRAS expression is blunted and tumors regress. To gauge the activation status of the JNK pathway in RAS mutant cancers we measured p-cJUN expression in tumors with high and low RAS. A positive correlation was observed between NRAS expression and JNK pathway activation. A correlation between KRAS expression and JNK pathway activation was also observed in lung adenocarcinomas from genetically engineered mouse models. Here, we stained tumor specimens from KRAS, EGFR, or PIK3CA mutant tumors for p-cJUN by IHC. Consistent with our data in melanoma, we observed high JNK pathway activity in KRAS, but not EGFR or PIK3CA mutant lung adenocarcinomas (FIG. 5). The observed activity correlated with the response of KRAS, but not EGFR mutant NSCLC cell lines to JNK inhibition. In aggregate, RAS mutant cancers may express, and rely upon the JNK pathway for transformation, and thus JNK inhibitors are effective in treating RAS mutant NSCLC and CRC.
Accordingly, the invention provides methods of determining the responsiveness, e.g., sensitivity or resistance, of a cancer cell to treatment with JNK inhibitors. These methods are also useful for monitoring subjects undergoing treatments and therapies for cancer such as melanoma, NSCLC and CRC, and for selecting therapies and treatments that would be efficacious in subjects having cancer, wherein selection and use of such treatments and therapies slow the progression of cancer. More specifically, the invention provides methods of determining the whether a patient with melanoma, NSCLC or CRC will be responsive to JNK inhibitor therapy.
Because of the remarkable progress in genomic technology now allows us much greater power for deep discovery of genomic alterations in cancer that we could use these technologies to identify positive or negative predictive markers JNK inhibitor therapy response that had been previously missed.

DEFINITIONS

“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.
“Biomarker” in the context of the present invention encompasses, without limitation, proteins, nucleic acids, and metabolites, together with their polymorphisms, mutations, variants, modifications, subunits, fragments, protein-ligand complexes, and degradation products, protein-ligand complexes, elements, related metabolites, and other analytes or sample-derived measures. Biomarkers can also include mutated proteins or mutated nucleic acids. Biomarkers also encompass non-blood borne factors or non-analyte physiological markers of health status, such as “clinical parameters” defined herein, as well as “traditional laboratory risk factors”, also defined herein. Biomarkers also include any calculated indices created mathematically or combinations of any one or more of the foregoing measurements, including temporal trends and differences. Where available, and unless otherwise described herein, biomarkers which are gene products are identified based on the official letter abbreviation or gene symbol assigned by the international Human Genome Organization Naming Committee (HGNC) and listed at the date of this filing at the US National Center for Biotechnology Information (NCBI) web site.
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-analyte biomarkers of subject health status or other characteristics, such as, without limitation, age (Age), ethnicity (RACE), gender (Sex), or family history (FamHX).
“FN” is false negative, which for a disease state test means classifying a disease subject incorrectly as non-disease or normal.
“FP” 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, or statistical technique 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 sums, ratios, and regression operators, such as coefficients or exponents, biomarker 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 biomarkers are linear and non-linear equations and statistical classification analyses to determine the relationship between biomarkers detected in a subject sample and the subject's responsiveness to chemotherapy. 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 established techniques such as cross-correlation, Principal Components Analysis (PCA), factor rotation, Logistic Regression (LogReg), Linear Discriminant Analysis (LDA), Eigengene Linear Discriminant Analysis (ELDA), Support Vector Machines (SVM), Random Forest (RF), Recursive Partitioning Tree (RPART), as well as other related decision tree classification techniques, Shrunken Centroids (SC), StepAIC, 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 as forward selection, backwards selection, or stepwise selection, complete enumeration of all potential panels of a given size, genetic algorithms, 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 studies, or cross-validated in 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 may be estimated by value permutation according to techniques known in the art. A “health economic utility function” is a formula that is derived from a combination of the expected probability of a range of clinical outcomes in an idealized applicable patient population, both before and after the introduction of a diagnostic or therapeutic intervention into the standard of care. It encompasses estimates of the accuracy, effectiveness and performance characteristics of such intervention, and a cost and/or value measurement (a utility) associated with each outcome, which may be derived from actual health system costs of care (services, supplies, devices and drugs, etc.) and/or as an estimated acceptable value per quality adjusted life year (QALY) resulting in each outcome. The sum, across all predicted outcomes, of the product of the predicted population size for an outcome multiplied by the respective outcomes expected utility is the total health economic utility of a given standard of care. The difference between (i) the total health economic utility calculated for the standard of care with the intervention versus (ii) the total health economic utility for the standard of care without the intervention results in an overall measure of the health economic cost or value of the intervention. This may itself be divided amongst the entire patient group being analyzed (or solely amongst the intervention group) to arrive at a cost per unit intervention, and to guide such decisions as market positioning, pricing, and assumptions of health system acceptance. Such health economic utility functions are commonly used to compare the cost-effectiveness of the intervention, but may also be transformed to estimate the acceptable value per QALY the health care system is willing to pay, or the acceptable cost-effective clinical performance characteristics required of a new intervention.
For diagnostic (or prognostic) interventions of the invention, as each outcome (which in a disease classifying diagnostic test may be a TP, FP, TN, or FN) bears a different cost, a health economic utility function may preferentially favor sensitivity over specificity, or PPV over NPV based on the clinical situation and individual outcome costs and value, and thus provides another measure of health economic performance and value which may be different from more direct clinical or analytical performance measures. These different measurements and relative trade-offs generally will converge only in the case of a perfect test, with zero error rate (a.k.a., zero predicted subject outcome misclassifications or FP and FN), which all performance measures will favor over imperfection, but to differing degrees.
“Measuring” or “measurement,” or alternatively “detecting” or “detection,” means assessing the presence, absence, quantity or amount (which can be an effective amount) of either a given substance within a clinical or subject-derived sample, including the derivation of qualitative or quantitative concentration levels of such substances, or otherwise evaluating the values or categorization of a subject's non-analyte clinical parameters.
“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 A S, Jacobson R M, “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^thedition 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, 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. Finally, hazard ratios and absolute and relative risk ratios within subject cohorts defined by a test are a further measurement of clinical accuracy and utility. Multiple methods are frequently used to defining abnormal or disease values, including reference limits, discrimination limits, and risk thresholds.
“Analytical accuracy” refers to the reproducibility and predictability of the measurement process itself, and may be summarized in such measurements as coefficients of variation, and tests of concordance and calibration of the same samples or controls with different times, users, equipment and/or reagents. These and other considerations in evaluating new biomarkers are also summarized in Vasan, 2006.
“Performance” is a term that relates to the overall usefulness and quality of a diagnostic or prognostic test, including, among others, clinical and analytical accuracy, other analytical and process characteristics, such as use characteristics (e.g., stability, ease of use), health economic value, and relative costs of components of the test. Any of these factors may be the source of superior performance and thus usefulness of the test, and may be measured by appropriate “performance metrics,” such as AUC, time to result, shelf life, etc. as relevant.
“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, as in the responsiveness to treatment, cancer recurrence or survival 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 low risk cohorts 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/(1−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, the rate of occurrence of the event or conversion from one disease state. Risk evaluation can also comprise prediction of future clinical parameters, traditional laboratory risk factor values, or other indices of cancer, either in absolute or relative terms in reference to a previously measured population. The methods of the present invention may be used to make continuous or categorical measurements of the responsiveness to treatment thus diagnosing and defining the risk spectrum of a category of subjects defined as being responders or non-responders. In the categorical scenario, the invention can be used to discriminate between normal and other subject cohorts at higher risk for responding.
A “sample” in the context of the present invention is a biological sample isolated from a subject and can include, by way of example and not limitation, tissue biopsies, whole blood, serum, plasma, blood cells, endothelial cells, lymphatic fluid, ascites fluid, interstitital fluid (also known as “extracellular fluid” and encompasses the fluid found in spaces between cells, including, inter alia, gingival crevicular fluid), bone marrow, cerebrospinal fluid (CSF), saliva, mucous, sputum, sweat, urine, or any other secretion, excretion, or other bodily fluids. A “sample” may include a single cell or multiple cells or fragments of cells. The sample is also a tissue sample. The sample is or contains a circulating endothelial cell or a circulating tumor cell. The sample includes a primary tumor cell, primary tumor, a recurrent tumor cell, or a metastatic tumor cell.
“Sensitivity” of an assay is calculated by TP/(TP+FN) or the true positive fraction of disease subjects.
“Specificity” of an assay 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 p-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 considered highly significant at a p-value of 0.05 or less. Preferably, the p-value is 0.04, 0.03, 0.02, 0.01, 0.005, 0.001 or less.
A “subject” in the context of the present invention is preferably a mammal. The mammal can be a human, non-human primate, mouse, rat, dog, cat, horse, or cow, but are not limited to these examples. Mammals other than humans can be advantageously used as subjects that represent animal models of cancer. A subject can be male or female.
“TN” 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.
“Traditional laboratory risk factors” correspond to biomarkers isolated or derived from subject samples and which are currently evaluated in the clinical laboratory and used in traditional global risk assessment algorithms. Traditional laboratory risk factors for tumor recurrence include for example Proliferative index, tumor infiltrating lymphocytes. Other traditional laboratory risk factors for tumor recurrence known to those skilled in the art.
“Effectiveness” is the capability of producing a desired result in a subject when administered a JNK inhibitor, e.g., the tumor cells or tumor tissues of the subject undergo apoptosis and/or necrosis, or any other desired results for cancer treatment known in the art.

Methods and Uses of the Invention

The methods disclosed herein are used with subjects undergoing treatment and/or therapies for cancer, subjects who are at risk for developing a reoccurrence of cancer (e.g. melanoma, non-small cell lung cancer (NSCLC) or colorectal cancer (CRC), and subjects who have been diagnosed with cancer. The methods of the present invention are to be used to monitor or select a treatment regimen for a subject who has cancer, and to evaluate the predicted survivability and/or survival time of a cancer-diagnosed subject. Treatment regimens include JNK inhibitor therapies such as JIP-1, CC-410, 1,9-pyrazoloanthrone, 2-benzothiazoleacetonitrile, XG-102, BI78D3 and BI87G9.
Responsiveness (e.g., resistance or sensitivity) of a melanoma patient to JNK inhibitor therapy is determined by detecting cJUN expression in a test sample (e.g., a subject derived sample such as for example a cancer cell). The expression of cJUN indicates the patient will be responsive (i.e., sensitive) to JNK inhibitor therapy. In contrast, the absence of cJUN expression indicates the patient will be non-responsive (i.e., resistant) to JNK inhibitor therapy.
Responsiveness (e.g., resistance or sensitivity) of a NSCLC or CRC patient to JNK inhibitor therapy is determined by detecting a KRAS mutation in a test sample (e.g., a subject derived sample such as for example a cancer cell). The presence of a KRAS mutation indicates the patient will be responsive (i.e., sensitive) to JNK inhibitor therapy. In contrast, the absence of a KRAS mutation indicates the patient will be non-responsive (i.e., resistant) to JNK inhibitor therapy.
Responsiveness (e.g., resistance or sensitivity) of a NSCLC to JNK inhibitor therapy is determined by detecting an EGFR mutation in a test sample (e.g., a subject derived sample such as for example a cancer cell). The presence of an EGFR mutation indicates the patient will be non-responsive (i.e., resistant) to JNK inhibitor therapy. In contrast, the absence of an EGFR mutation indicates the patient will be responsive (i.e., sensitive) to JNK inhibitor therapy.
By resistance to a therapy it is meant that a patient fails to respond to an agent. For example, resistance to JNK inhibitor therapy means the cancer cell is not damaged or killed by the drug. By sensitivity to a therapy it is meant that that the cell responds to an agent. For example, sensitivity to JNK inhibitor therapy means the cell is damaged or killed by the drug.
The methods of the present invention are useful to treat, alleviate the symptoms of, monitor the progression of or delay the onset of cancer.
Preferably, the methods of the present invention are used to identify and/or diagnose subjects who are asymptomatic for a cancer recurrence. “Asymptomatic” means not exhibiting the traditional symptoms.
Identification of cJUN expression, KRAS mutation or EGFR mutation allows for the determination of whether a subject will derive a benefit from a particular course of treatment. In this method, a biological sample is provided from a subject before undergoing treatment, e.g., JNK inhibitor therapy or combinations thereof. By “derive a benefit” it is meant that the subject will respond to the course of treatment. By responding it is meant that the treatment decreases in size, prevalence, or metastatic potential of a cancer (e.g., melanoma, NSCLC or CRC) in a subject. When treatment is applied prophylactically, “responding” means that the treatment retards or prevents a cancer (e.g., melanoma, NSCLC or CR) recurrence from forming or retards, prevents, or alleviates a symptom. Assessments of cancers are made using standard clinical protocols.
The present invention can also be used to screen patient or subject populations in any number of settings. For example, a health maintenance organization, public health entity or school health program can screen a group of subjects to identify those requiring interventions, as described above, or for the collection of epidemiological data. Insurance companies (e.g., health, life or disability) may screen applicants in the process of determining coverage or pricing, or existing clients for possible intervention. Data collected in such population screens, particularly when tied to any clinical progression to conditions like cancer, will be of value in the operations of, for example, health maintenance organizations, public health programs and insurance companies. Such data arrays or collections can be stored in machine-readable media and used in any number of health-related data management systems to provide improved healthcare services, cost effective healthcare, improved insurance operation, etc. See, for example, U.S. Patent Application No. 2002/0038227; U.S. Patent Application No. US 2004/0122296; U.S. Patent Application No. US 2004/0122297; and U.S. Pat. No. 5,018,067. Such systems can access the data directly from internal data storage or remotely from one or more data storage sites as further detailed herein.
Each program can be implemented in a high level procedural or object oriented programming language to communicate with a computer system. However, the programs can be implemented in assembly or machine language, if desired. The language can be a compiled or interpreted language. Each such computer program can be stored on a storage media or device (e.g., ROM or magnetic diskette or others as defined elsewhere in this disclosure) readable by a general or special purpose programmable computer, for configuring and operating the computer when the storage media or device is read by the computer to perform the procedures described herein. The health-related data management system of the invention may also be considered to be implemented as a computer-readable storage medium, configured with a computer program, where the storage medium so configured causes a computer to operate in a specific and predefined manner to perform various functions described herein.
Differences in the genetic makeup of subjects can result in differences in their relative abilities to metabolize various drugs, which may modulate the symptoms or risk factors of cancer or metastatic events. Subjects that have cancer, or at risk for developing cancer or a metastatic event can vary in age, ethnicity, and other parameters. Accordingly, detection of the biomarkers disclosed herein, both alone and together in combination with known genetic factors for drug metabolism, allow for a pre-determined level of predictability that a putative therapeutic or prophylactic to be tested in a selected subject will be suitable for treating cancer in the subject.

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, predictive, or prognostic test, assay, or method concerns the ability of the test, assay, or method to distinguish between subjects responsive to therapeutic treatment and those that are not, is based on whether the subjects express c-JUN or has a KRAS or EGFR mutation.
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 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 0.80, desirably at least 0.85, more desirably at least 0.875, preferably at least 0.90, more preferably at least 0.925, and most preferably at least 0.95.
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 therapeutic unresponsiveness, and the bottom quartile comprising the group of subjects having the lowest relative risk for therapeutic unresponsiveness. 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; such is the case with total cholesterol and for many inflammatory biomarkers with respect to their prediction of future events. 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 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, Calif.).

Detection of Cjun Expression, KRAS or EGFR Mutation

The actual detection of c-JUN expression or the KRAS or EGFR mutation can be determined at the protein or nucleic acid level using any method known in the art.
c-Jun or mutation-specific reagents useful in the practice of the disclosed methods include, among others, mutant polypeptide specific antibodies and AQUA peptides (heavy-isotope labeled peptides) corresponding to, and suitable for detection and quantification of, polypeptide expression in a biological sample. A polypeptide-specific reagent is any reagent, biological or chemical, capable of specifically binding to, detecting and/or quantifying the presence/level of expressed polypeptide in a biological sample. Whereas, a mutant polypeptide-specific reagent is any reagent, biological or chemical, capable of specifically binding to, detecting and/or quantifying the presence/level of expressed mutant polypeptide (i.e., KRAS or EGFR mutation) in a biological sample, while not binding to or detecting wild type. The term includes, but is not limited to, the preferred antibody and AQUA peptide reagents discussed below, and equivalent reagents are within the scope of the present invention.
Reagents suitable for use in practice of the methods of the invention include a polypeptide-specific antibody.
Polypeptide-specific antibodies generated against human mutant may also bind to highly homologous and equivalent epitopic peptide sequences in other mammalian species, for example murine, rat, feline, pig, or rabbit, and vice versa. Antibodies useful in practicing the methods of the invention include (a) monoclonal antibodies, (b) purified polyclonal antibodies that specifically bind to the target polypeptide (e.g. an epitope comprising the KRAS or EGFR mutation point, (c) antibodies as described in (a)-(b) above that bind equivalent and highly homologous epitopes or phosphorylation sites in other non-human species (e.g. mouse, rat), and (d) fragments of (a)-(c) above that bind to the antigen (or more preferably the epitope) bound by the exemplary antibodies disclosed herein.
The term “antibody” or “antibodies” as used herein refers to all types of immunoglobulins, including IgG, IgM, IgA, IgD, and IgE. The antibodies may be monoclonal or polyclonal and may be of any species of origin, including (for example) mouse, rat, rabbit, horse, or human, or may be chimeric antibodies. See, e.g., M. Walker et al., Molec. Immunol. 26: 403-11 (1989); Morrision et al., Proc. Nat'l. Acad. Sci. 81:6851 (1984); Neuberger et al., Nature 312:604 (1984)). The antibodies may be recombinant monoclonal antibodies produced according to the methods disclosed in U.S. Pat. No. 4,474,893 (Reading) or U.S. Pat. No. 4,816,567 (Cabilly et al.) The antibodies may also be chemically constructed specific antibodies made according to the method disclosed in U.S. Pat. No. 4,676,980 (Segel et al.)
The invention is not limited to use of antibodies, but includes equivalent molecules, such as protein binding domains or nucleic acid aptamers, which bind, in a mutant-protein or truncated-protein specific manner, to essentially the same epitope to which a mutant polypeptide-specific antibody useful in the methods of the invention binds. See, e.g., Neuberger et al., Nature 312: 604 (1984). Such equivalent non-antibody reagents may be suitably employed in the methods of the invention further described below.
Polyclonal antibodies useful in practicing the methods of the invention may be produced according to standard techniques by immunizing a suitable animal (e.g., rabbit, goat, etc.) with an antigen encompassing a desired mutant-protein specific epitope (e.g. the sequence comprising the G724S mutation site) collecting immune serum from the animal, and separating the polyclonal antibodies from the immune serum, and purifying polyclonal antibodies having the desired specificity, in accordance with known procedures. The antigen may be a synthetic peptide antigen comprising the desired epitopic sequence, selected and constructed in accordance with well-known techniques. See, e.g., ANTIBODIES: A LABORATORY MANUAL, Chapter 5, p. 75-76, Harlow & Lane Eds., Cold Spring Harbor Laboratory (1988); Czernik, Methods In Enzymology, 201:264-283 (1991); Merrifield, J. Am. Chem. Soc. 85: 21-49 (1962)). Polyclonal antibodies produced as described herein may be screened and isolated as further described below.
Monoclonal antibodies may also be beneficially employed in the methods of the invention, and may be produced in hybridoma cell lines according to the well-known technique of Kohler and Milstein. Nature 265:495-97 (1975); Kohler and Milstein, Eur. J. Immunol. 6: 511 (1976); see also, CURRENT PROTOCOLS IN MOLECULAR BIOLOGY, Ausubel et al., Eds. (1989). Monoclonal antibodies so produced are highly specific, and improve the selectivity and specificity of assay methods provided by the invention. For example, a solution containing the appropriate antigen (e.g. a synthetic peptide comprising the mutant junction of polypeptide) may be injected into a mouse and, after a sufficient time (in keeping with conventional techniques), the mouse sacrificed and spleen cells obtained. The spleen cells are then immortalized by fusing them with myeloma cells, typically in the presence of polyethylene glycol, to produce hybridoma cells. Rabbit mutant hybridomas, for example, may be produced as described in U.S. Pat. No. 5,675,063, K. Knight, Issued Oct. 7, 1997. The hybridoma cells are then grown in a suitable selection media, such as hypoxanthine-aminopterin-thymidine (HAT), and the supernatant screened for monoclonal antibodies having the desired specificity, as described below. The secreted antibody may be recovered from tissue culture supernatant by conventional methods such as precipitation, ion exchange or affinity chromatography, or the like.
Monoclonal Fab fragments may also be produced in Escherichia coli by recombinant techniques known to those skilled in the art. See, e.g., W. Huse, Science 246:1275-81 (1989); Mullinax et al., Proc. Nat'l Acad. Sci. 87: 8095 (1990). If monoclonal antibodies of one isotype are preferred for a particular application, particular isotypes can be prepared directly, by selecting from the initial mutant, or prepared secondarily, from a parental hybridoma secreting a monoclonal antibody of different isotype by using the sib selection technique to isolate class-switch variants (Steplewski, et al., Proc. Nat'l. Acad. Sci., 82:8653 (1985); Spira et al., J. Immunol. Methods, 74.307 (1984)). The antigen combining site of the monoclonal antibody can be cloned by PCR and single-chain antibodies produced as phage-displayed recombinant antibodies or soluble antibodies in E. coli (see, e.g., ANTIBODY ENGINEERING PROTOCOLS, 1995, Humana Press, Sudhir Paul editor.)
Further still, U.S. Pat. No. 5,194,392, Geysen (1990) describes a general method of detecting or determining the sequence of monomers (amino acids or other compounds) which is a topological equivalent of the epitope (i.e., a “mimotope”) which is complementary to a particular paratope (antigen binding site) of an antibody of interest. More generally, this method involves detecting or determining a sequence of monomers which is a topographical equivalent of a ligand which is complementary to the ligand binding site of a particular receptor of interest. Similarly, U.S. Pat. No. 5,480,971, Houghten et al., (1996) discloses linear C.sub.1-C-alkyl peralkylated oligopeptides and sets and libraries of such peptides, as well as methods for using such oligopeptide sets and libraries for determining the sequence of a peralkylated oligopeptide that preferentially binds to an acceptor molecule of interest. Thus, non-peptide analogs of the epitope-bearing peptides of the invention also can be made routinely by these methods.
Antibodies useful in the methods of the invention, whether polyclonal or monoclonal, may be screened for epitope and mutant protein specificity according to standard techniques. See, e.g. Czernik et al., Methods in Enzymology, 201: 264-283 (1991). For example, the antibodies may be screened against a peptide library by ELISA to ensure specificity for both the desired antigen and, if desired, for reactivity only with a mutant polypeptide of the invention and not with wild type. The antibodies may also be tested by Western Notting against cell preparations containing target protein to confirm reactivity with the only the desired target and to ensure no appreciable binding to other mutants not containing the G724S point mutation. The production, screening, and use of mutant protein-specific antibodies are known to those of skill in the art, and have been described.
Antibodies employed in the methods of the invention may be further characterized by, and validated for, use in a particular assay format, for example FC, IHC, and/or ICC. The use of polypeptide-specific antibodies in such methods is further described below. Antibodies may also be advantageously conjugated to fluorescent dyes (e.g. Alexa488, PE), or labels such as quantum dots, for use in multi-parametric analyses along with other signal transduction (phospho-AKT, phospho-Erk 1/2) and/or cell marker (cytokeratin) antibodies, as further described below.
The AQUA methodology employs the introduction of a known quantity of at least one heavy-isotope labeled peptide standard (which has a unique signature detectable by LC-SRM chromatography) into a digested biological sample in order to determine, by comparison to the peptide standard, the absolute quantity of a peptide with the same sequence and protein modification in the biological sample. Briefly, the AQUA methodology has two stages: peptide internal standard selection and validation and method development; and implementation using validated peptide internal standards to detect and quantify a target protein in sample. The method is a powerful technique for detecting and quantifying a given peptide/protein within a complex biological mixture, such as a cell lysate, and may be employed, e.g., to quantify change in protein phosphorylation as a result of drug treatment, or to quantify differences in the level of a protein in different biological states.
Generally, to develop a suitable internal standard, a particular peptide (or modified peptide) within a target protein sequence is chosen based on its amino acid sequence and the particular protease to be used to digest. The peptide is then generated by solid-phase peptide synthesis such that one residue is replaced with that same residue containing stable isotopes The result is a peptide that is chemically identical to its native counterpart formed by proteolysis, but is easily distinguishable by MS via a 7-Da mass shift. The newly synthesized AQUA internal standard peptide is then evaluated by LC-MS/MS. This process provides qualitative information about peptide retention by reverse-phase chromatography, ionization efficiency, and fragmentation via collision-induced dissociation. Informative and abundant fragment ions for sets of native and internal standard peptides are chosen and then specifically monitored in rapid succession as a function of chromatographic retention to form a selected reaction monitoring (LC-SRM) method based on the unique profile of the peptide standard.
The second stage of the AQUA strategy is its implementation to measure the amount of a protein or modified protein from complex mixtures. Whole cell lysates are typically fractionated by SDS-PAGE gel electrophoresis, and regions of the gel consistent with protein migration are excised. This process is followed by in-gel proteolysis in the presence of the AQUA peptides and LC-SRM analysis. (See Gerber et al. supra.) AQUA peptides are spiked in to the complex peptide mixture obtained by digestion of the whole cell lysate with a proteolytic enzyme and subjected to immunoaffinity purification as described above. The retention time and fragmentation pattern of the native peptide formed by digestion (e.g. trypsinization) is identical to that of the AQUA internal standard peptide determined previously; thus, LC-MS/MS analysis using an SRM experiment results in the highly specific and sensitive measurement of both internal standard and analyte directly from extremely complex peptide mixtures.
Since an absolute amount of the AQUA peptide is added (e.g. 250 fmol), the ratio of the areas under the curve can be used to determine the precise expression levels of a protein or phosphorylated form of a protein in the original cell lysate. In addition, the internal standard is present during in-gel digestion as native peptides are formed, such that peptide extraction efficiency from gel pieces, absolute losses during sample handling (including vacuum centrifugation), and variability during introduction into the LC-MS system do not affect the determined ratio of native and AQUA peptide abundances.
An AQUA peptide standard is developed for a known sequence previously identified by the IAP-LC-MS/MS method within in a target protein. If the site is modified, one AQUA peptide incorporating the modified form of the particular residue within the site may be developed, and a second AQUA peptide incorporating the unmodified form of the residue developed. In this way, the two standards may be used to detect and quantify both the modified an unmodified forms of the site in a biological sample.
Peptide internal standards may also be generated by examining the primary amino acid sequence of a protein and determining the boundaries of peptides produced by protease cleavage. Alternatively, a protein may actually be digested with a protease and a particular peptide fragment produced can then be sequenced. Suitable proteases include, but are not limited to, serine proteases (e.g. trypsin, hepsin), metallo proteases (e.g. PUMP1), chymotrypsin, cathepsin, pepsin, thermolysin, carboxypeptidases, etc.
A peptide sequence within a target protein is selected according to one or more criteria to optimize the use of the peptide as an internal standard. Preferably, the size of the peptide is selected to minimize the chances that the peptide sequence will be repeated elsewhere in other non-target proteins. Thus, a peptide is preferably at least about 6 amino acids. The size of the peptide is also optimized to maximize ionization frequency. Thus, peptides longer than about 20 amino acids are not preferred. The preferred ranged is about 7 to 15 amino acids. A peptide sequence is also selected that is not likely to be chemically reactive during mass spectrometry, thus sequences comprising cysteine, tryptophan, or methionine are avoided.
A peptide sequence that does not include a modified region of the target region may be selected so that the peptide internal standard can be used to determine the quantity of all forms of the protein. Alternatively, a peptide internal standard encompassing a modified amino acid may be desirable to detect and quantify only the modified form of the target protein. Peptide standards for both modified and unmodified regions can be used together, to determine the extent of a modification in a particular sample (i.e. to determine what fraction of the total amount of protein is represented by the modified form). For example, peptide standards for both the phosphorylated and unphosphorylated form of a protein known to be phosphorylated at a particular site can be used to quantify the amount of phosphorylated form in a sample.
The peptide is labeled using one or more labeled amino acids (i.e. the label is an actual part of the peptide) or less preferably, labels may be attached after synthesis according to standard methods. Preferably, the label is a mass-altering label selected based on the following considerations: The mass should be unique to shift fragments masses produced by MS analysis to regions of the spectrum with low background; the ion mass signature component is the portion of the labeling moiety that preferably exhibits a unique ion mass signature in MS analysis; the sum of the masses of the constituent atoms of the label is preferably uniquely different than the fragments of all the possible amino acids. As a result, the labeled amino acids and peptides are readily distinguished from unlabeled ones by the ion/mass pattern in the resulting mass spectrum. Preferably, the ion mass signature component imparts a mass to a protein fragment that does not match the residue mass for any of the 20 natural amino acids.
The label should be robust under the fragmentation conditions of MS and not undergo unfavorable fragmentation. Labeling chemistry should be efficient under a range of conditions, particularly denaturing conditions, and the labeled tag preferably remains soluble in the MS buffer system of choice. The label preferably does not suppress the ionization efficiency of the protein and is not chemically reactive. The label may contain a mixture of two or more isotopically distinct species to generate a unique mass spectrometric pattern at each labeled fragment position. Stable isotopes, such as ²H, ¹³C, ¹⁵N, ¹⁷O, ¹⁸O, or ³⁴S, are among preferred labels. Pairs of peptide internal standards that incorporate a different isotope label may also be prepared. Preferred amino acid residues into which a heavy isotope label may be incorporated include leucine, proline, valine, and phenylalanine.
Peptide internal standards are characterized according to their mass-to-charge (m/z) ratio, and preferably, also according to their retention time on a chromatographic column (e.g. an HPLC column). Internal standards that co-elute with unlabeled peptides of identical sequence are selected as optimal internal standards. The internal standard is then analyzed by fragmenting the peptide by any suitable means, for example by collision-induced dissociation (CID) using, e.g., argon or helium as a collision gas. The fragments are then analyzed, for example by multi-stage mass spectrometry (MS) to obtain a fragment ion spectrum, to obtain a peptide fragmentation signature. Preferably, peptide fragments have significant differences in m/z ratios to enable peaks corresponding to each fragment to be well separated, and a signature is that is unique for the target peptide is obtained. If a suitable fragment signature is not obtained at the first stage, additional stages of MS are performed until a unique signature is obtained.
Fragment ions in the MS/MS and MS³spectra are typically highly specific for the peptide of interest, and, in conjunction with LC methods, allow a highly selective means of detecting and quantifying a target peptide/protein in a complex protein mixture, such as a cell lysate, containing many thousands or tens of thousands of proteins. Any biological sample potentially containing a target protein/peptide of interest may be assayed. Crude or partially purified cell extracts are preferably employed. Generally, the sample has at least 0.01 mg of protein, typically a concentration of 0.1-10 mg/mL, and may be adjusted to a desired buffer concentration and pH.
A known amount of a labeled peptide internal standard, preferably about 10 femtomoles, corresponding to a target protein to be detected/quantified is then added to a biological sample, such as a cell lysate. The spiked sample is then digested with one or more protease(s) for a suitable time period to allow digestion. A separation is then performed (e.g. by HPLC, reverse-phase HPLC, capillary electrophoresis, ion exchange chromatography, etc.) to isolate the labeled internal standard and its corresponding target peptide from other peptides in the sample. Microcapillary LC is a preferred method.
Each isolated peptide is then examined by monitoring of a selected reaction in the MS. This involves using the prior knowledge gained by the characterization of the peptide internal standard and then requiring the MS to continuously monitor a specific ion in the MS/MS or MSⁿspectrum for both the peptide of interest and the internal standard. After elution, the area under the curve (AUC) for both peptide standard and target peptide peaks is calculated. The ratio of the two areas provides the absolute quantification that can be normalized for the number of cells used in the analysis and the protein's molecular weight, to provide the precise number of copies of the protein per cell. Further details of the AQUA methodology are described in Gygi et al., and Gerber et al. supra. AQUA internal peptide standards (heavy-isotope labeled peptides) may desirably be produced, as described above, to detect and quantify any unique site (e.g. the KRAS or EGFR mutation point) within a polypeptide of the invention. For example, an AQUA phosphopeptide may be prepared that corresponds to the KRAS or EGFR peptide sequence immediately encompassing the mutation point. Peptide standards may be produced and such standards employed in the AQUA methodology to detect and quantify the presence of mutant KRAS or EGFR (i.e. the presence of the peptide sequence encompassing the point mutation) in a biological sample.
For example, an exemplary AQUA peptide of the invention comprises the amino acid sequence EVA, which corresponds to the three amino acids immediately flanking each side of the mutation point in the polypeptide. It will be appreciated that larger AQUA peptides comprising the mutant junction sequence (and additional residues downstream or upstream of it) may also be constructed. Similarly, a smaller AQUA peptide comprising less than all of the residues of such sequence (but still comprising the point of mutant junction itself) may alternatively be constructed. Such larger or shorter AQUA peptides are within the scope of the present invention, and the selection and production of preferred AQUA peptides may be carried out as described above (see Gygi et al., Gerber et al., supra.).
Mutant-specific reagents provided by the invention also include nucleic acid probes and primers suitable for detection of a mutant EGFR and KRAS polynucleotide. The specific use of such probes in assays includes such as fluorescence in-situ hybridization (FISH) or polymerase chain reaction (PCR) amplification.
Immunoassays useful in the practice of the methods of the invention may be homogenous immunoassays or heterogeneous immunoassays. In a homogeneous assay the immunological reaction usually involves a polypeptide-specific reagent, a labeled analyte, and the biological sample of interest. The signal arising from the label is modified, directly or indirectly, upon the binding of the antibody to the labeled analyte. Both the immunological reaction and detection of the extent thereof are carried out in a homogeneous solution. Immunochemical labels that may be employed include free radicals, radio-isotopes, fluorescent dyes, enzymes, bacteriophages, coenzymes, and so forth. Semi-conductor nanocrystal labels, or “quantum dots”, may also be advantageously employed, and their preparation and use has been well described. See generally, K. Barovsky, Nanotech. Law & Bus. 1(2): Article 14 (2004) and patents cited therein.
In a heterogeneous assay approach, the reagents are usually the biological sample, a polypeptide-specific reagent, and suitable means for producing a detectable signal. Biological samples as further described below may be used. The antibody is generally immobilized on a support, such as a bead, plate or slide, and contacted with the sample suspected of containing the antigen in a liquid phase. The support is then separated from the liquid phase and either the support phase or the liquid phase is examined for a detectable signal employing means for producing such signal. The signal is related to the presence of the analyte in the biological sample. Means for producing a detectable signal include the use of radioactive labels, fluorescent labels, enzyme labels, quantum dots, and so forth. For example, if the antigen to be detected contains a second binding site, an antibody which binds to that site can be conjugated to a detectable group and added to the liquid phase reaction solution before the separation step. The presence of the detectable group on the solid support indicates the presence of the antigen in the test sample. Examples of suitable immunoassays are the radioimmunoassay, immunofluorescence methods, enzyme-linked immunoassays, and the like.
Immunoassay formats and variations thereof, which may be useful for carrying out the methods disclosed herein, are well known in the art. See generally E. Maggio, Enzyme-Immunoassay, (1980) (CRC Press, Inc., Boca Raton, Fla.); see also, e.g., U.S. Pat. No. 4,727,022 (Skold et al., “Methods for Modulating Ligand-Receptor Interactions and their Application”); U.S. Pat. No. 4,659,678 (Forrest et al., “Immunoassay of Antigens”); U.S. Pat. No. 4,376,110 (David et al., “Immunometric Assays Using Monoclonal Antibodies”). Conditions suitable for the formation of reagent-antibody complexes are well known to those of skill in the art. See id. Polypeptide-specific monoclonal antibodies may be used in a “two-site” or “sandwich” assay, with a single hybridoma cell line serving as a source for both the labeled monoclonal antibody and the bound monoclonal antibody. Such assays are described in U.S. Pat. No. 4,376,110. The concentration of detectable reagent should be sufficient such that the binding of polypeptide is detectable compared to background.
Antibodies useful in the practice of the methods disclosed herein may be conjugated to a solid support suitable for a diagnostic assay (e.g., beads, plates, slides or wells formed from materials such as latex or polystyrene) in accordance with known techniques, such as precipitation. Antibodies or other polypeptide-binding reagents may likewise be conjugated to detectable groups such as radiolabels (e.g., ³⁵S, ¹²⁵I, ¹³¹I), enzyme labels (e.g., horseradish peroxidase, alkaline phosphatase), and fluorescent labels (e.g., fluorescein) in accordance with known techniques.
Cell-based assays, such flow cytometry (FC), immuno-histochemistry (IHC), or immunofluorescence (IF) are particularly desirable in practicing the methods of the invention, since such assay formats are clinically-suitable, allow the detection of polypeptide expression in vivo, and avoid the risk of artifact changes in activity resulting from manipulating cells obtained from, e.g. a tumor sample in order to obtain extracts. Accordingly, in some preferred embodiment, the methods of the invention are implemented in a flow-cytometry (FC), immuno-histochemistry (IHC), or immunofluorescence (IF) assay format.
Flow cytometry (FC) may be employed to determine the expression of polypeptide in a mammalian sample before, during, and after treatment with a drug targeted at inhibiting JNK activity. For example, tumor cells from a bone marrow sample may be analyzed by flow cytometry for polypeptide expression and/or activation, as well as for markers identifying cancer cell types, etc., if so desired. Flow cytometry may be carried out according to standard methods. See, e.g. Chow et al., Cytometry (Communications in Clinical Cytometry) 46: 72-78 (2001). Briefly and by way of example, the following protocol for cytometric analysis may be employed: fixation of the cells with 2% paraformaldehyde for 10 minutes at 37.degree. C. followed by permeabilization in 90% methanol for 30 minutes on ice. Cells may then be stained with the primary polypeptide-specific antibody, washed and labeled with a fluorescent-labeled secondary antibody. The cells would then be analyzed on a flow cytometer (e.g. a Beckman Coulter FC500) according to the specific protocols of the instrument used. Such an analysis would identify the level of expressed polypeptide in the tumor.
Immunohistochemical (IHC) staining may be also employed to determine the expression and/or activation status of polypeptide in a mammalian cancer (e.g. AML) before, during, and after treatment with a drug targeted at inhibiting JNK activity. IHC may be carried out according to well-known techniques. See, e.g., ANTIBODIES; A LABORATORY MANUAL, Chapter 10, Harlow & Lane Eds., Cold Spring Harbor Laboratory (1988). Briefly, and by way of example, paraffin-embedded tissue (e.g. tumor tissue from a biopsy) is prepared for immunohistochemical staining by deparaffinizing tissue sections with xylene followed by ethanol; hydrating in water then PBS; unmasking antigen by heating slide in sodium citrate buffer; incubating sections in hydrogen peroxide; blocking in blocking solution; incubating slide in primary anti-polypeptide antibody and secondary antibody; and finally detecting using ABC avidin/biotin method according to manufacturer's instructions.
Immunofluorescence (IF) assays may be also employed to determine the expression and/or activation status of polypeptide in a mammalian cancer before, during, and after treatment with a drug targeted at inhibiting JNK activity. IF may be carried out according to well-known techniques. See, e.g., J. M. Polak and S. Van Noorden (1997) INTRODUCTION TO IMMUNOCYTOCHEMISTRY, 2nd Ed.; ROYAL MICROSCOPY SOClETY MICROSCOPY HANDBOOK 37, BioScientific/Springer-Verlag. Briefly, and by way of example, patient samples may be fixed in paraformaldehyde followed by methanol, blocked with a blocking solution such as horse serum, incubated with the primary antibody against polypeptide followed by a secondary antibody labeled with a fluorescent dye such as Alexa 488 and analyzed with an epifluorescent microscope.
Antibodies employed in the above-described assays may be advantageously conjugated to fluorescent dyes (e.g. Alexa488, PE), or other labels, such as quantum dots, for use in multi-parametric analyses along with other signal transduction (, phospho-AKT, phospho-Erk 1/2) and/or cell marker (cytokeratin) antibodies.
A variety of other protocols, including enzyme-linked immunosorbent assay (ELISA), radio-Immunoassay (RIA), and fluorescent-activated cell sorting (FACS), for measuring polypeptide are known in the art and provide a basis for diagnosing altered or abnormal levels of polypeptide expression. Normal or standard values for polypeptide expression are established by combining body fluids or cell extracts taken from normal mammalian subjects, preferably human, with antibody to polypeptide under conditions suitable for complex formation. The amount of standard complex formation may be quantified by various methods, but preferably by photometric means. Quantities of polypeptide expressed in subject, control, and disease samples from biopsied tissues are compared with the standard values. Deviation between standard and subject values establishes the parameters for diagnosing disease.
Similarly, AQUA peptides for the detection/quantification of expressed polypeptide in a biological sample comprising cells from a tumor may be prepared and used in standard AQUA assays, as described above. Accordingly, in some preferred embodiments of the methods of the invention, the polypeptide-specific reagent comprises a heavy isotope labeled phosphopeptide (AQUA peptide) corresponding to a peptide sequence comprising the mutant junction of polypeptide, as described above.
Polypeptide-specific reagents useful in practicing the methods of the invention may also be mRNA, oligonucleotide or DNA probes that can directly hybridize to, and detect, mutant or truncated polypeptide expression transcripts in a biological sample. Briefly, and by way of example, formalin-fixed, paraffin-embedded patient samples may be probed with a fluorescein-labeled RNA probe followed by washes with formamide, SSC and PBS and analysis with a fluorescent microscope.
Polynucleotides encoding polypeptide may also be used for diagnostic purposes. The polynucleotides that may be used include oligonucleotide sequences, antisense RNA and DNA molecules. The polynucleotides may be used to detect and quantitate gene expression in biopsied tissues in which expression of polypeptide or mutated KRAS or EGFR polypeptide may be correlated with disease. For example, the diagnostic assay may be used to distinguish between absence, presence, and excess expression of polypeptide, and to monitor regulation of polypeptide levels during therapeutic intervention.
In one preferred embodiment, hybridization with PCR probes which are capable of detecting polynucleotide sequences, including genomic sequences, encoding polypeptide or mutated KRAS or EGFR polypeptide, or closely related molecules, may be used to identify nucleic acid sequences which encode polypeptide. The construction and use of such probes is described above. The specificity of the probe, whether it is made from a highly specific region, e.g., 10 unique nucleotides in the mutant junction, or a less specific region, e.g., the 3′ coding region, and the stringency of the hybridization or amplification (maximal, high, intermediate, or low) will determine whether the probe identifies only naturally occurring sequences encoding polypeptide, alleles, or related sequences.
Probes may also be used for the detection of related sequences, and should preferably contain at least 50% of the nucleotides from any of the polypeptide encoding sequences. The hybridization probes of the subject invention may be DNA or RNA and derived from the nucleotide sequence and encompassing the mutation point, or from genomic sequence including promoter, enhancer elements, and introns of the naturally occurring polypeptides but comprising the mutation point sequence.
A mutant EGFR of KRAS polynucleotide may be used in Southern or northern analysis, dot blot, or other membrane-based technologies; in PCR technologies; or in dip stick, pin, ELISA or chip assays utilizing fluids or tissues from patient biopsies to detect altered EGFR or KRAS polypeptide expression. Such qualitative or quantitative methods are well known in the art. Mutant EGFR or KRAS polynucleotides may be labeled by standard methods, and added to a fluid or tissue sample from a patient under conditions suitable for the formation of hybridization complexes. After a suitable incubation period, the sample is washed and the signal is quantitated and compared with a standard value. If the amount of signal in the biopsied or extracted sample is significantly altered from that of a comparable control sample, the nucleotide sequences have hybridized with nucleotide sequences in the sample, and the presence of altered levels of nucleotide sequences encoding polypeptide in the sample indicates the presence of the associated disease. Such assays may also be used to evaluate the efficacy of a particular therapeutic treatment regimen in animal studies, in clinical trials, or in monitoring the treatment of an individual patient.
In order to provide a basis for the diagnosis of disease characterized by expression of polypeptide, a normal or standard profile for expression is established. This may be accomplished by combining body fluids or cell extracts taken from normal subjects, either animal or human, with a sequence, or a fragment thereof, which encodes polypeptide, under conditions suitable for hybridization or amplification. Standard hybridization may be quantified by comparing the values obtained from normal subjects with those from an experiment where a known amount of a substantially purified polynucleotide is used. Standard values obtained from normal samples may be compared with values obtained from samples from patients who are symptomatic for disease. Deviation between standard and subject values is used to establish the presence of disease.
Once disease is established and a treatment protocol is initiated, hybridization assays may be repeated on a regular basis to evaluate whether the level of expression in the patient begins to approximate that which is observed in the normal patient. The results obtained from successive assays may be used to show the efficacy of treatment over a period ranging from several days to months.
Additional diagnostic uses for mutant EGFR or KRAS polynucleotides of the invention may involve the use of polymerase chain reaction (PCR), a preferred assay format that is standard to those of skill in the art. See, e.g., MOLECULAR CLONING, A LABORATORY MANUAL, 2nd. edition, Sambrook, J., Fritsch, E. F. and Maniatis, T., eds., Cold Spring Harbor Laboratory Press, Cold Spring Harbor, N.Y. (1989). PCR oligomers may be chemically synthesized, generated enzymatically, or produced from a recombinant source. Oligomers will preferably consist of two nucleotide sequences, one with sense orientation (5′ to 3′) and another with antisense (3′ to 5′), employed under optimized conditions for identification of a specific gene or condition. The same two oligomers, nested sets of oligomers, or even a degenerate pool of oligomers may be employed under less stringent conditions for detection and/or quantitation of closely related DNA or RNA sequences.
Methods which may also be used to quantitate the expression of polypeptide include radiolabeling or biotinylating nucleotides, coamplification of a control nucleic acid, and standard curves onto which the experimental results are interpolated (Melby et al., J. Immunol. Methods, 159:235-244 (1993); Duplaa et al. Anal. Biochem. 229-236 (1993)). The speed of quantitation of multiple samples may be accelerated by running the assay in an ELISA format where the oligomer of interest is presented in various dilutions and a spectrophotometric or calorimetric response gives rapid quantitation.
Other suitable methods for nucleic acid detection, such as minor groove-binding conjugated oligonucleotide probes (see, e.g. U.S. Pat. No. 6,951,930, “Hybridization-Triggered Fluorescent Detection of Nucleic Acids”) are known to those of skill in the art.

Example 1

General Methods

Cell Lines and Plasmids

All human cell lines were propagated at 37° C. and 5% CO₂in humidified atmosphere in RPMI 1640 medium (Invitrogen) supplemented with 10% heat-inactivated FB S. hTERT/CDK4(R24C)/p53DD/BRAF(V600E) melanocytes (HMEL) have been described previously. Mouse melanocytes were isolated from Ink/Arf −/− mice according to standard protocols and grown in RPMI 1640 medium supplemented with 10% heat-inactivated FBS, 2 nM TPA (Sigma), and 2 nM cholera toxin (Sigma). Mouse melanocytes were propagated at 37° C. and 10% CO₂in humidified atmosphere. Inducible JNK2 shRNAs were purchased from Open Biosystems. Lentivius was prepared and cells transduced according to established protocols.

2D Cell Proliferation

Cells were plated into 96 well plates (10,000 cells per well) in quadruplicate and analyzed for confluence using the IncuCyte imager. The plates were scanned in the IncuCyte at 8-hour intervals for 72 hours. The data were analyzed by the IncuCyte software.

Anchorage-Independent Growth

Soft-agar assays were performed on 6-well plates in triplicate. For each well, 1×10⁴cells were mixed thoroughly in cell growth medium containing 0.4% SeaKem LE agarose (Fisher) in RPMI plus 10% FBS, followed by plating onto bottom agarose prepared with 0.65% agarose in RPMI and 10% FBS. Each well was allowed to solidify and subsequently covered in 1 ml RPMI and 10% FBS, which was refreshed every 4 days. When appropriate, doxycycline was added to agarose and growth medium at a final concentration of 2 ug/ml. Colonies were stained with 0.05% (w/v) iodonitrotetrazolium chloride (Sigma) and scanned at 1,200 dots per inch (d.p.i.) using a flatbed scanner, and counted.

Immunohistochemistry

Melanoma tissue microarrays (Biomax) were stained with p-cJUN (Cell Signaling) using established protocols. Briefly, antigen retrieval was performed on 5 micron FFPE sections in decloaking chambers with citrate buffer. Following retrieval, sections were incubated with primary antibody Phospho-cJUN (ser73) (cell signaling 9164) overnight at 4° C. in a hybridization chamber. Dako EnVision+HRP and DAB chromogen were used for antibody detection.

Xenograft Studies

For in vivo studies, melanoma xenogaft cells stably expressing inducible JNK2 shRNA were subcutaneously implanted into female nude animals (Taconic) at 1×106 cells per site on both flanks. For analysis of tumor growth mice were fed normal H2O or H2O containing 2 mg/ml doxycycline and 2% sucrose. To determine is JNK expression was required for tumor maintenance, cells were implanted and tumors allowed to reach approximately 200 mm³, after which time animals were randomized into separate cohorts for treatment with H2O or H2O containing 2 mg/ml doxycycline and 2% sucrose. Tumor volumes were measured after dox administration. Tumor volume was determined by measuring in two directions with vernier calipers and formulated as tumor volume=(length×width²)/2. Growth curves and end-point scatter plots were plotted as tumor volume for each group. Percentage tumor growth inhibition was determined as (1-(TIN))×100, in which T is the mean change in tumor volume of the treated group and N is the mean change in tumor volume of the control group at the assay end-point. Two-tailed t-test calculations were performed using Prism 5 (Graphpad).

Western Immunoblot Analyses

Cells were harvested by trypsinization, washed once in PBS, and resuspended in RIPA (10 mM Tris-HCl (pH 7.4), 150 mM NaCl, 1 mM EDTA, 1% Nonidet P-40, 0.25% Na-deoxycholate) supplemented with Complete Protease Inhibitor Cocktail (Roche) and 1× phosphates inhibitor (Calbiochem). After clarifying the extract by centrifugation, protein concentration was determined using the Bradford Assay Reagent (Bio-Rad, Hercules, Calif.). Samples containing equal amounts of protein were mixed with 4×NuPAGE LDS Sample Buffer (Invitrogen) containing 5% β-mercaptoethanol, boiled, and separated by SDS-PAGE. Proteins were transferred to PVDF membrane and probed with antibodies against cJUN, p-cJUN, JNK, p-JNK, HSP90 (Cell Signaling Technology); Actin (Santa Cruz Biotechnology).

Example 2

cJUN Expression Predicts Sensitivity to JNK Inhibition

To elucidate the role of JNK in melanoma genesis we engineered a panel of melanoma cell lines with two independent shRNA targeting JNK2 in a doxycycline-inducible vector system. RNAi-mediated knockdown of JNK2 impaired tumorigenicity as we observed a dramatic inhibition of anchorage independent growth in 6 of 10 human melanoma cell lines tested (FIG. 1). Interestingly, a correlation was observed between cJUN expression and the response of melanoma cell lines to JNK knockdown. When responder cell lines were transplanted into immunodeficient hosts, expression of the JNK2 shRNA via doxycycline administration resulted in 50% inhibition of established xenograft growth (FIG. 2). Taken together, these data suggest the effective concentration of cJUN may predict response of melanoma patients to JNK inhibition.

Example 3

RAS Mutation Predicts Sensitivity to JNK Inhibition

JNK pathway deregulation was found to cooperate with BRAF^V600Eto transform human melanocytes in vivo. However, 3 NRAS mutant melanoma cell lines were found to be sensitive to JNK knockdown, suggesting that RAS mutation may predict response to JNK inhibition. These data motivated efforts to expand our analysis beyond melanoma to test the response of KRAS mutant NSCLC cell lines to JNK knockdown. Consistent with our observations in melanoma, combined knockdown of JNK1 and JNK2 impaired 2D and 3D growth of NSCLC cell lines (FIG. 3). We observed impaired tumorigenicity in 3 of 4 KRAS mutant NSCLC cell lines. Additionally, a cell line that was WT for KRAS but harbors a LOF mutation in the RAS GTPase activating protein NF1 (resulting in enhanced RAS activation) also responded to JNK inhibition. Together, these data reinforce our conclusion that RAS mutation (melanoma, NSCLC, and CRC) predicts response to JNK inhibition.

Example 4

EGFR Mutation Predicts Resistance to JNK Inhibition

In contrast to the response of KRAS mutant NSCLC cell lines to JNK knockdown, EGFR mutant NSCLC lines failed to respond to JNK inhibition (FIG. 3). The lack of response suggests EGFR mutation may serve as a mechanism to stratify NSCLC for treatment with JNK inhibitors.

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.

Claims

What is claimed is:

1. A method of accessing the effectiveness of a JNK inhibitor treatment regimen of a subject having cancer comprising, obtaining a sample from the subject and detecting:

a) c-JUN expression, wherein the expression of C-JUN indicates the subject is responsive to a JNK inhibitor treatment;

b) the presence or absence of a mutation in the KRAS polypeptide, wherein the presence of the mutation indicates the subject is responsive to a JNK inhibitor treatment; or

c) the presence or absence of a mutation in the EGFR polypeptide, wherein the presence of the mutation indicates the subject is non-responsive to a JNK inhibitor treatment.

2. The method of claim 1, wherein the JNK inhibitor is JIP-1, CC-410, 1,9-pyrazoloanthrone, 2-benzothiazoleacetonitrile, XG-102, BI78D3 or BI87G9.

3. The method of claim 1, wherein the cancer is melanoma, non-small cell lung cancer or colorectal cancer.

4. The method of claim 1, wherein said subject has not received treatment for the cancer.

5. The method of claim 1, wherein said subject has received treatment for the cancer.

6. The method of claim 1, wherein the method is implemented in a flow-cytometry (FC), immuno-histochemistry (IHC), or immuno-fluorescence (IF) assay format.

7. The method of claim 1, wherein the method is implemented in a polymerase chain reaction (PCR) sequencing assay format.

8. A method of treating a subject having melanoma comprising identifying a subject having a melanoma expressing c-JUN and administering to the subject a JNK inhibitor.

9. A method treating a subject having non-small cell lung cancer or colon cancer comprising identifying a subject having a KRAS mutation and administering to the subject a JNK inhibitor.

10. A method of treating a subject having non-small cell lung cancer comprising identifying a subject not having an EGFR mutation and administering to the subject a JNK inhibitor.