WO2013071066A1 - Signatures associated with the response to cancer therapy - Google Patents

Abstract

The present invention provides methods of treating cancer and methods of assessing/monitoring the responsiveness of a cancer cell to a therapeutic compound. The present invention further provides methods of screening for an identifying therapeutic compounds that induce cancer cell death or inhibit cancer cell proliferation. Markers differentially expressed in response to compounds that are selectively toxic to cancer stem cell -like cells were identified for use in any of the methods of the present invention. In particular, cancer stem cell -like breast cancer cell lines generated by knowcking down E-cadherin and Twist in human breast epithelial cells (HMLE_sh_Ecad, HMLE_sh_Twist). Markers differentially expressed in response to compounds ML239, ML245, CID50904149 (analog of ML 245), ML243 and Salinomycin, which were selectively toxic to the CSC- like cells, were identified. The NF-kappaB pathway was the most significantly associated with the genes regulated in response to ML239.

Description

SIGNATURES ASSOCIATED WITH THE RESPONSE TO CANCER THERAPY

RELATED APPLICATIONS

[0001] This application claims the benefit of U.S. Provisional Application Nos. 61/558,782, filed November 11, 2011 and 61/692,466, filed August 23, 2012 the contents of each are incorporated herein by reference in their entireties.

FIELD OF THE INVENTION

[0002] The present invention relates generally to the identification of biomarkers and methods of using such biomarkers to identify responsiveness to therapy of a subject having cancer.

BACKGROUND OF THE INVENTION

[0003] Cancer stem cells (CSCs), which drive tumor growth, are known to be resistant to standard chemotherapy and radiation treatment. This raises a significant unmet need to find therapies that can target CSCs within tumors because these cells are proposed to be responsible for recurrence, the primary cause of patient mortality.

Furthermore, there is a need for assays to determine the responsiveness of CSCs to therapeutic compound. There is also a need for identification of biomarkers associated with the response to therapy.

SUMMARY OF THE INVENTION

[0004] The invention is based upon the discovery that certain signature genes referred to herein as RESPONSEMARKERS are associated with responsiveness to cancer therapy.

[0005] The invention features methods of assessing the effectiveness of a treatment regimen of a subject having a cancer by detecting the level of an effective amount of one or more RESPONSEMARKERS in a sample from the subject and comparing the level of the effective amount of the one or more RESPONSEMARKERS to a reference value. For example, the cancer is breast cancer.

[0006] The invention also features methods of monitoring a treatment regimen of a subject with cancer by detecting the level of an effective amount of one or more

RESPONSEMARKERS in a first sample from the subject at a first period of time;

detecting the level of an effective amount of one or more RESPONSEMARKERS in a second sample from the subject at a second period of time; and comparing the level of the effective amount of one or more RESPONSEMARKERS detected in the first step to the amount detected in the second step, or to a reference value. For example, the cancer is breast cancer.

[0007] The invention further features methods of determining whether a subject with cancer would derive a benefit from a treatment regimen by detecting the level of an effective amount of one or more RESPONSEMARKERS and comparing the level of the effective amount of one or more RESPONSEMARKERS detected in the first step to a reference value.

[0008] The invention further features methods of identifying a biological target by identifying one or more RESPONSEMARKERS that are differentially expressed in cancer cell or cancer stem cell compared to a non-cancer cell to produce a gene target listand identifying one or more genes on said target list that is associated with toxicity against the cancer cell or cancer stem cell. Also included are methods of identifying a compound that modulates the expression or activity of the biological target identified by contacting a cell with a test compound and detecting modulation of the expression or activity of the biological target.

[0009] The invention further features methods of screening for a compound that induces cancer cell death and/or inhibits cancer cell proliferation by detecting the level of an effective amount of one or more RESPONSEMARKERS in a cell that has been contacted with the compound, and comparing the level of the effective amount of one or more RESPONSEMARKERS listed in any one of Tables 1-10, where a similarity of the level of the RESPONSEMARKERS detected in the first step with the level of

RESPONSEMARKERS listed in Tables 1-10 indicates that the compound induces cancer cell death and/or inhibits cancer cell proliferation.

[00010] The invention further provides methods of screening for a test compound that induces cancer cell death and/or inhibits cancer cell proliferation by detecting the level of an effective amount of one or more RESPONSEMARKERS in a cell that has been contacted with the test compound, and comparing the level of the effective amount of one or more RESPONSEMARKERS in a cell that has been treated with a reference compound, where the similarity of the level of the RESPONSEMARKERS detected in step (a) with the level of RESPONSEMARKERS in the cell treated with the reference compound indicates that the test compound induces cancer cell death and/or inhibits cancer cell proliferation.

[00011] In any of the foregoing methods, the reference compound is ML239, ML245, ML243 or CID50904149 (an analogue of ML 245).

[00012] In any of the foregoing methods, the cancer cell is a cancer stem cell.

[00013] In some aspects, the treatment regimen is ML239 and the one or more RESPONSEMARKERS are selected from Tables 1 or 2. In some aspects, the treatment regimen is ML245 and the one or more RESPONSEMARKERS are selected from Tables 3 and 4. In other aspects, the treatment regimen is ML243 and the one or more

RESPONSEMARKERS are selected from Tables 5 and 6. Alternatively, the treatment regimen is CID50904149 (an analogue of ML 245) and the one or more

RESPONSEMARKERS are selected from Tables 7 and 8. In some aspects, the treatment regimen is salinomycin and the one or more RESPONSEMARKERS are selected from Tables 9 and 10.

[00014] The invention disclosed herein also provides methods of treating or alleviating a symptom of cancer comprising administering a compound that modulates the expression of one or more RESPONSEMARKERS.

[00015] The invention further provides methods of treating or alleviating a symptom of cancer comprising administering a compound that modulates the expression of one or more genes in the NF-κΒ pathway.

[00016] In any of the foregoing methods, the RESPONSEMARKERS include, but are not limited to, at least one selected from the group comprising: ALDH1L2, ASNS, C10RF24, CBS, CDKN1A, EIF1, FBX032, GDF15, HERPUD1, HSPA5, LOC729779, MTHFD2, NCRNA00219, P8, PCK2, PSATl, RND3, SLC7A1, SNHG8, SQSTMl, and TRIB3.

[00017] In any of the foregoing methods, the cancer is a breast cancer.

[00018] 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 pertains. Although methods and materials similar or equivalent to those described herein can be used in the practice of the present invention, suitable methods and materials are described below. All publications, patent applications, patents, and other references mentioned herein are expressly incorporated by reference in their entirety. In cases of conflict, the present specification, including definitions, will control. In addition, the materials, methods, and examples described herein are illustrative only and are not intended to be limiting.

[00019] Other features and advantages of the invention will be apparent from and encompassed by the following detailed description and claims.

BRIEF DESCRIPTION OF THE FIGURES

[00020] Figure 1 is a plot depicting the cancer stem cell- selective toxicity of compounds to HMLE_sh_ECad cells. Each compound was tested in duplicate and were highly correlated. The signal was normalized to neutral (DMSO) and positive

(Puromycin) controls, and a mean of 75% inhibition was used as a cutoff to define a hit. Compounds that were determined to be "hits" are represented by the medium grey squares in the lower left of the graph (3,190 compounds); inactive compounds are represented by black squares in the upper right of the graph; and compounds that yielded inconclusive results are represented by light grey squares in the middle of the graph.

[00021] Figure 2 is two graphs showing the representative dose curve data for ML239. (A) HMLE_sh_ECad (IC₅₀ = 1.16 μΜ; n=4) (black circles) compared to control cell line HMLE_sh_GFP (26.7 μΜ, n=5) (grey squares) toxicity. (B) Selective toxicity towards HMLE_Twist (0.1 μΜ; n=4) (black diamonds) as compared to control cell line HMLE_sh_GFP (grey squares).

[00022] Figure 3 is a table and images summarizing CID5417654, CID24816775, and ML239 toxicity results towards HMLE_sh_ECad (sh_ECad), HMLE_sh_GFP (sh_GFP), and HMLE_sh_Twist (sh_Twist), MDA-MB-231 cells lines and SUM159 tumorspheres (n= 3-5). (A) A table with the results from the toxicity assay. (B) Representative tumorsphere images after treating with DMSO or puromycin. Scale bar represents 50 micrometers.

[00023] Figure 4 shows the gene expression profiling and characterizaing of identified RESPONSEMARKERS in HMLE_sh_ECad cells. (A) Measurement of gene expression in response to treatment with ML239. Upregulated genes (black, right side of graph); downregulated genes (grey, left side of graph). (B) 24 genes were identified with highest significant changes (p>0.005) with more than 2.5-fold change in expression. (C)

Ingenuity Network analysis was performed on the 24 genes from (B) to identify the pathways associated with the affected genes. Genes identified in the screen (solid lines); other known members of the pathway not identified in the screen (dotted lines).

DETAILED DESCRIPTION OF THE INVENTION

[00024] The present invention relates to the identification of signatures associated with responsiveness to cancer therapy.

[00025] Cancer stem cells (CSCs), which drive tumor growth, are known to be resistant to standard chemotherapy and radiation treatment. Six previously identified compounds have been shown to selectively kill breast cancer stem cells. These compounds include salinomycin, CID49843203 (also referred to herein as ML239), CID50904134 (also referred to herein as ML245, CID50910523 (also referred to herein as ML243), CID50904149 (also referred to herein as Analog of ML245); CID5417654 (also referred to herein as Analog of ML239) and CID24816775 (also referred to herein as Analog of ML239).

[00026] Salinomycin is represented by Formula I below:

[00027] ML239 is represented by Formula II below:

[00028] ML245 is represented by Formula III below:

[00029] ML243 is represented by Formula IV below:

[00030] CID50904149 represented by Formula V below:

[00031] CID5417654 is represented by Formula VI below:

[00032] CID24816775 is represented by Formula VII below:

[00033] In order to gain insight into the possible mechanism or signaling pathways that these compounds could be targeting, gene expression studies were conducted on breast cancer stem cells and control breast cancer cells. Genes that were differentially expressed in treated cells compared to non-treated cells were identified. These differentially expressed genes are listed on Tables 1-10 and are collectively referred to herein as RESPONSEMARKERS. Examples of RESPONSEMARKERS can include, but are not limited to, the nucleic acids or polypeptide sequences of: ALDH1L2, ASNS, C10RF24, CBS, CDKN1A, EIF1, FBX032, GDF15, HERPUD1, HSPA5, LOC729779, MTHFD2, NCRNA00219, P8, PCK2, PSAT1, RND3, SLC7A1, SNHG8, SQSTM1, and TRIB3.

[00034] These RESPONSEMARKERS are useful for monitoring subjects undergoing treatments and therapies for cancer, and for assessing therapies and treatments that would be efficacious in subjects having cancer, wherein selection and use of such treatments and therapies slow the progression of the tumor, or substantially delay or prevent its onset, or reduce or prevent the incidence of tumor metastasis.

[00035] These RESPONSEMARKERS are also useful for screening and identifying compounds that inhibit cancer cell proliferation or induce cancer cell death.

[00036] Definitions

[00037] "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.

[00038] "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.

[00039] "RESPONSEMARKER" OR "RESPONSEMARKERS" encompass one or more of all nucleic acids or polypeptides whose levels are changed in a subject in response to a therapy. Individual RESPONSEMARKERS are collectively referred to herein as, inter alia, "response-associated proteins" "response-associated polypeptides", "RESPONSEMARKER polypeptides", or "RESPONSEMARKER proteins". The corresponding nucleic acids encoding the polypeptides are referred to as "response- associated nucleic acids", "response-associated genes", "RESPONSEMARKER nucleic acids", or "RESPONSEMARKER genes". Unless indicated otherwise,

"RESPONSEMARKER", "response-associated proteins", "response -associated nucleic acids" are meant to refer to any of the biomarkers disclosed herein, e.g, Tables 1-10. Examples of RESPONSEMARKERS include, but are not limited to, the nucleic acids or polypeptide sequences of: ALDH1L2, ASNS, C10RF24, CBS, CDKN1A, EIF1,

FBX032, GDF15, HERPUD1, HSPA5, LOC729779, MTHFD2, NCRNA00219, P8, PCK2, PSAT1, RND3, SLC7A1, SNHG8, SQSTM1, and TRIB3.

[00040] The corresponding metabolites of the RESPONSEMARKER proteins or nucleic acids can also be measured, as well as any of the aforementioned traditional risk marker metabolites.

[00041] An "effective amount of RESPONSEMARKER" encompasses the

measurement of an appropriate number of RESPONSEMARKER (which may be one or more) is different than the predetermined cut-off point (or threshold value) for that RESPONSEMARKER(S) and therefore indicates a particular phenotype. As used herein, the measurement of an appropriate number of RESPONSEMARKER encompasses an increase or decrease in expression level of at least one nucleic acid or polypeptide RESPONSEMARKER sequence compared to a reference value. In some instances, an effective amount of RESPONSEMARKER encompasses the gene signature that is indicative of a particular phenotype. A phenotype may be, for example, a particular stage of cancer, or a particular response to a cancer treatment regimen or administration of a compound. For example, the gene signature may be a gene or set of genes that indicates teatment/administration of the compound. In other aspects, the gene signature is gene or set of genes that indicates a particular stage of a cancer, such as breast cancer or metastasis. In other aspects, the gene signature is a gene or set of genes that indicate a particular response to a cancer therapeutic regimen.

[00042] 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.

[00043] "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).

[00044] "FN" is false negative, which for a disease state test means classifying a disease subject incorrectly as non-disease or normal.

[00045] "FP" is false positive, which for a disease state test means classifying a normal subject incorrectly as having disease.

[00046] 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 (ELD A), 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, Hidden Markov Models, and Ingenuity Network 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 outcome's 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.

[00047] 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.

[00048] "Inhibit" as used herein, in reference to cancer stem cells, encompasses inhibiting, decreasing, preventing, or reducing the growth, proliferation, size, activity, or metastatic potential. "Inhibiting cancer stem cells" can also mean killing, either directly or indirectly, cancer stem cells, i.e. by apoptosis or immunologic response.

[00049] "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.

[00050] "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.

[00051] See, e.g., O'Marcaigh AS, Jacobson RM, "Estimating The Predictive Value Of A Diagnostic Test, How To Prevent Misleading Or Confusing Results," Clin. Ped. 1993, 32(8): 485-491, which discusses specificity, sensitivity, and positive and negative predictive values of a test, e.g., a clinical diagnostic test. Often, for binary disease state classification approaches using a continuous diagnostic test measurement, the sensitivity and specificity is summarized by Receiver Operating Characteristics (ROC) curves according to Pepe et al, "Limitations of the Odds Ratio in Gauging the Performance of a Diagnostic, Prognostic, or Screening Marker," Am. J. Epidemiol 2004, 159 (9): 882-890, and summarized by the Area Under the Curve (AUC) or c-statistic, an indicator that allows representation of the sensitivity and specificity of a test, assay, or method over the entire range of test (or assay) cut points with just a single value. See also, e.g., Shultz, "Clinical Interpretation Of Laboratory Procedures," chapter 14 in Teitz, Fundamentals of Clinical Chemistry, Burtis and Ashwood (eds.), 4th edition 1996, W.B. Saunders Company, pages 192-199; and Zweig et al., "ROC Curve Analysis: An Example

Showing The Relationships Among Serum Lipid And Apolipoprotein Concentrations In Identifying Subjects With Coronory Artery Disease," Clin. Chem., 1992, 38(8): 1425- 1428. An alternative approach using likelihood functions, 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.

[00052] 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.

[00053] "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.

[00054] "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.

[00055] "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.

[00056] "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/(l-p) where p is the probability of event and (1- p) is the probability of no event) to no-conversion.

[00057] "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. Such differing use may require different

RESPONSEMARKER combinations and individualized panels, mathematical algorithms, and/or cut-off points, but be subject to the same aforementioned

measurements of accuracy and performance for the respective intended use.

[00058] 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 biopies, 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. Samples may also include cells in culture, such as those utilized for in vitro assays.

Samples can include various animal models for use in in vivo assays. For example, the sample can be a mouse tumor model or a mouse cancer model.

[00059] "Sensitivity" is calculated by TP/(TP+FN) or the true positive fraction of disease subjects.

[00060] "Specificity" is calculated by TN/(TN+FP) or the true negative fraction of non-disease or normal subjects.

[00061] 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.

[00062] 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.

[00063] "TN" is true negative, which for a disease state test means classifying a non- disease or normal subject correctly.

[00064] "TP" i_s rue positive, which for a disease state test means correctly classifying a disease subject.

[00065] "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.

[00066] Methods And Uses Of The Invention

[00067] 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 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 efficacy or benefit of a treatment regimen for a subject who has cancer. Treatment regimens include salinomycin, ML239, ML245, ML 243 aor CID50904149 (an analogue of ML 245).

[00068] Cancer includes solid tumors such as breast, ovarian, prostate, lung, kidney, gastric, colon, testicular, head and neck, pancreas, brain, melanoma, and other tumors of tissue organs and cancers of the blood cells, such as lymphomas and leukemias, including acute myelogenous leukemia, chronic lymphocytic leukemia, T cell

lymphocytic leukemia, and B cell lymphomas. Preferably, the cancer is breast cancer. [00069] A "cellular proliferative disorder" includes those disorders that affect cell proliferation, activation, adhesion, growth, differentiation, or migration processes. As used herein, a "cellular proliferation, activation, adhesion, growth, differentiation, or migration process" is a process by which a cell increases in number, size, activation state, malignancy, or content, by which a cell develops a specialized set of characteristics which differ from that of other cells, or by which a cell moves closer to or further from a particular location or stimulus. Disorders are characterized by aberrantly regulated growth, activation, adhesion, differentiation, or migration. "Cell proliferative disorders" include autoimmune diseases and inflammation. For example, an inflammatory or immune system disorder, and/or a cellular proliferative disorder.

[00070] Subjects have varying degrees of responsiveness to therapy and methods are needed to distinguish the capability of the treatment in a dynamic manner.

Responsiveness (e.g., resistance or sensitivity) of a cell to therapy is determined by measuring an effective amount of RESPONSEMARKER proteins, nucleic acids, polymorphisms, metabolites, and other analytes (which may be two or more) in a test sample (e.g., a subject derived sample), and comparing the effective amounts to reference or index values, often utilizing mathematical algorithms or formula in order to combine information from results of multiple individual RESPONSEMARKER and from non- analyte clinical parameters into a single measurement or index.

[00071] By resistance it is meant that a cell fails to respond to an agent. For example, resistance to therapy means the cell is not damaged or killed by the drug. By sensitivity it is meant that that the cell responds to an agent. For example, sensitivity to therapy means the cell is damaged or killed by the drug.

[00072] The methods of the present invention are useful to treat, alleviate the symptoms of, monitor the progression of or delay the onset of cancer.

[00073] Expression of an effective amount of RESPONSEMARKER proteins, nucleic acids or metabolites allows for 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. 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 in a subject. When treatment is applied prophylactically, "responding" means that the treatment retards or prevents a cancer recurrence from forming or retards, prevents, or alleviates a symptom. Assessment of cancers are made using standard clinical protocols.

[00074] Expression of an effective amount of RESPONSEMARKER proteins, nucleic acids or metabolites also allows for the course of treatment of cancer to be monitored. In this method, a biological sample is provided from a subject undergoing treatment.

[00075] If desired, biological samples are obtained from the subject at various time points before, during, or after treatment. Expression of an effective amount of

RESPONSEMARKER proteins, nucleic acids or metabolites is then determined and compared to a reference value are then identified, e.g. a control individual or population whose cancer state is known or an index value. The reference sample or index value may be taken or derived from one or more individuals who have been exposed to the treatment. Alternatively, the reference sample or index value may be taken or derived from one or more individuals who have not been exposed to the treatment. For example, samples may be collected from subjects who have received initial treatment for cancer and subsequent treatment for cancer to monitor the progress of the treatment.

[00076] A reference value can be relative to a number or value derived from

population studies, including without limitation, such subjects having the same cancer, subject having the same or similar age range, subjects in the same or similar ethnic group, subjects having family histories of cancer, or relative to the starting sample of a subject undergoing treatment for a cancer. Such reference values can be derived from statistical analyses and/or risk prediction data of populations obtained from mathematical algorithms and computed indices of cancer recurrence. Reference RESPONSEMARKER indices can also be constructed and used using algorithms and other methods of statistical and structural classification.

[00077] In one embodiment of the present invention, the reference value is the amount of RESPONSEMARKERS in a control sample derived from one or more subjects who are responsive to therapy. In another embodiment of the present invention, the reference value is the amount of RESPONSEMARKERS in a control sample derived from one or more subjects who have higher disease free or overall survival rate from cancer. In the other embodiment of the present invention, the reference value is the amount of RESPONSEMARKERS in a control sample derived from one or more subjects who are not at risk or at low risk for developing a recurrence of cancer. In a further embodiment, such subjects are monitored and/or periodically retested for a diagnostically relevant period of time ("longitudinal studies") following such test to verify continued absence of cancer (disease free or overall survival). Such period of time may be one year, two years, two to five years, five years, five to ten years, ten years, or ten or more years from the initial testing date for determination of the reference value. Furthermore, retrospective measurement of RESPONSEMARKERS in properly banked historical subject samples may be used in establishing these reference values, thus shortening the study time required.

[00078] A reference value can also comprise the amounts of

RESPONSEMARKERS derived from subjects who show an improvement in risk factors as a result of treatments and/or therapies for the cancer. A reference value can also comprise the amounts of RESPONSEMARKERS derived from subjects who show an improvement in responsiveness to therapy as a result of treatments and/or therapies for the cancer. A reference value can also comprise the amounts of RESPONSEMARKERS derived from subjects who have higher disease free /overall rate, or are at high risk for developing cancer, or who have suffered from cancer.

[00079] In another embodiment, the reference value is an index value or a baseline value. An index value or baseline value is a composite sample of an effective amount of RESPONSEMARKERS from one or more subjects who do not have a cancer or subjects who are asymptomatic for a cancer. A baseline value can also comprise the amounts of RESPONSEMARKERS in a sample derived from a subject who has shown an improvement in cancer responsiveness to therapy or disease free /overall survival rate as a result of cancer treatments or therapies. In this embodiment, to make comparisons to the subject-derived sample, the amounts of RESPONSEMARKERS are similarly calculated and compared to the index value. Optionally, subjects identified as having cancer, or being at increased risk of developing a cancer are chosen to receive a therapeutic regimen to slow the progression the cancer, or decrease or prevent the risk of developing cancer.

[00080] The progression of a cancer, or effectiveness of a cancer treatment regimen can be monitored by detecting a RESPONSEMARKER in an effective amount (which may be two or more) of samples obtained from a subject over time and comparing the amount of RESPONSEMARKERS detected. For example, a first sample can be obtained prior to the subject receiving treatment and one or more subsequent samples are taken after or during treatment of the subject. The cancer is considered to be progressive (or, alternatively, the treatment does not prevent progression) if the amount of

RESPONSEMARKER changes over time relative to the reference value, whereas the cancer is not progressive if the amount of RESPONSEMARKERS remains constant over time (relative to the reference population, or "constant" as used herein). The term "constant" as used in the context of the present invention is construed to include changes over time with respect to the reference value.

[00081] Additionally, therapeutic or prophylactic agents suitable for administration to a particular subject can be identified by detecting one or more of the

RESPONSEMARKERS in an effective amount (which may be two or more) in a sample obtained from a subject, exposing the subject-derived sample to a test compound that determines the amount (which may be two or more) of RESPONSEMARKERS in the subject-derived sample. Accordingly, treatments or therapeutic regimens for use in subjects having a cancer, or subjects with non-responsiveness to therapy or lower disease free /overall survival rate can be selected based on the amounts of

RESPONSEMARKERS in samples obtained from the subjects and compared to a reference value. Two or more treatments or therapeutic regimens can be evaluated in parallel to determine which treatment or therapeutic regimen would be the most efficacious for use in a subject to delay onset, or slow progression of the cancer.

[00082] The present invention further provides a method for screening for changes in marker expression associated with a cancer, by determining one or more of the

RESPONSEMARKERS in a subject-derived sample, comparing the amounts of the RESPONSEMARKERS in a reference sample, and identifying alterations in amounts in the subject sample compared to the reference sample.

[00083] If the reference sample, e.g., a control sample, is from a subject that does not have a cancer, from cells that are sensitive to a therapeutic compound or radiation, or if the reference sample reflects a value that is relative to a person that has a high likelihood of responsiveness to the therapy, low risk of developing recurrence or higher rate of disease free /overall survival, a similarity in the amount of the RESPONSEMARKER in the test sample and the reference sample indicates that the treatment is efficacious.

However, a difference in the amount of the RESPONSEMARKER in the test sample and the reference sample indicates a less favorable clinical outcome or prognosis. In contrast, if the reference sample, e.g., a control sample is from cells that are resistant to a therapeutic compound or if the reference sample reflects a value that is relative to a person that has a high likelihood of non-responsiveness to the therapy, high risk of developing a recurrence or lower rate of disease free /overall survival, then a similarity in the amount of the RESPONSEMARKER proteins in the test sample and the reference sample indicates that the treatment with that compound will result in a less favorable clinical outcome or prognosis. However, a change in the amount of the

RESPONSEMARKER in the test sample and the reference sample indicates that treatment with that therapeutic compound will be efficacious.

[00084] In another embodiment, the reference value is the level of an effective amount of RESPONSEMARKERS detected after treatment with any one of the probes disclosed herein. In some embodiments, the reference value is the level of an effective amount of RESPONSEMARKERS detected after treatment with ML239.

[00085] By "efficacious", it is meant that the treatment leads to a decrease in the amount or activity of a RESPONSEMARKER protein, nucleic acid, polymorphism, metabolite, or other analyte. Assessment of the risk factors disclosed herein can be achieved using standard clinical protocols. Efficacy can be determined in association with any known method for diagnosing, identifying, or treating a cancer.

[00086] The present invention also comprises a kit with a detection reagent that binds to two or more of the RESPONSEMARKERS proteins, nucleic acids, polymorphisms, metabolites, or other analytes. Also provided by the invention is an array of detection reagents, e.g., antibodies and/or oligonucleotides that can bind to two or more

RESPONSEMARKER proteins or nucleic acids, respectively.

[00087] 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. Patent 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.

[00088] 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.

[00089] 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 are at risk for developing cancer or a metastatic event can vary in age, ethnicity, and other parameters. Accordingly, detection of the RESPONSEMARKER 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.

[00090] The present invention also provides methods for screening for a test compound that induces cancer cell death and/or inhibits cancer cell proliferation. To identify such compounds, a cell (i.e., a cell isolated from a subject) can be incubated in the presence of a candidate compound and the level of an effective amount of

RESPONSEMARKER in the test sample can be measured by the various assays described herein. The pattern or level of an effective amount of RESPONSEMARKERS in a cell that has been contacted with the test compound can be compared to a reference profile. The level of an effective amount of RESPONSEMARKERS in a cell that has been contacted with the test compound can also be compared to the level of the effective amount of one or more RESPONSEMARKERS listed in any one of Tables 1-10, wherein a similarity indicates the compound' s ability to induce cancer cell death and/or inhibit cancer cell proliferation. Alternatively, the level of an effective amount of

RESPONSEMARKER in a cell that has been contacted with the test compound can also be compared to the level of an effective amount of one or more RESPONSEMARKERS in a cell treated with a reference compound. Examples of RESPONSEMARKERS include, but are not limited to, the nucleic acids or polypeptide sequences of: ALDH1L2, ASNS, C10RF24, CBS, CDKN1A, EIF1, FBX032, GDF15, HERPUD1, HSPA5, LOC729779, MTHFD2, NCRNA00219, P8, PCK2, PSAT1, RND3, SLC7A1, SNHG8, SQSTM1, and TRIB3.

[00091] The compound, or test compound, can be any agent, drug, compound, or composition or combination thereof, including, dietary supplements. For example, the test agents are agents frequently used in cancer treatment regimens.

[00092] The reference compound of any of the methods disclosed herein can be

ML239, ML245, ML 243 or CID50904149 (an analogue of ML 245).

[00093] Therapeutic Methods

[00094] The methods disclosed herein are useful to treat, alleviate the symptoms of, diagnose, prognose, monitor the progression, predict the progression of, or delay the onset of cancer in a subject. [00095] In one aspect, the method of treating or alleviating a symptom of cancer comprises administering a compound that modulates the expression of one or more RESPONSEMARKERS. In other aspects, the method of treating or alleviating a symptom of cancer comprises administering a compound that modulates the expression of one or more genes in the NF-κΒ pathway. The RESPONSEMARKERS include, but are not limited to, ALDH1L2, ASNS, C10RF24, CBS, CDKN1A, EIF1, FBX032, GDF15, HERPUD1, HSPA5, LOC729779, MTHFD2, NCRNA00219, P8, PCK2, PSAT1, RND3, SLC7A1, SNHG8, SQSTM1, and TRIB3.

[00096] The term "treating" in its various grammatical forms in relation to the present invention refers to preventing, curing, reversing, attenuating, alleviating, ameliorating, minimizing, suppressing, or halting the deleterious effects of a cancer or a cell proliferative disorder, cancer or disorder progression.

[00097] The term "alleviate" or "ameliorate" in its various grammatical forms in relation to the present invention is meant to describe a process by which the severity of a sign or symptom of a cancer or cell proliferative disorder is decreased. Importantly, a sign or symptom can be alleviated without being eliminated. In a preferred embodiment, the administration of a compound as disclosed herein leads to the elimination of a sign or symptom, however, elimination is not required. Therapeutically effective dosages are expected to decrease the severity of a sign or symptom. As used herein, the term

"symptom" is defined as an indication of disease, illness, injury, or something that is not right in the body.

[00098] The proliferation or growth of cells is inhibited, e.g., reduced by contacting a cell with a compound that modulates expression of one or more RESPONSEMARKERS. By inhibition of cell proliferation or growth is meant the cell divides at a lower rate or has decreased viability compared to a cell not exposed to the composition. Cell growth is measured by methods know in the art such as, the MTT cell proliferation assay, BrDU incorporation, immunohistochemical staining for proliferation markers or measurement of total GFP from GFP expressing cell lines.

[00099] By inducing cell death is meant inducing apoptosis. The process of apoptosis is characterized by, but not limited to, several events. Cells lose their cell junctions and microvilli, the cytoplasm condenses and nuclear chromatin marginates into a number of discrete masses. As the nucleus fragments, the cytoplasm contracts and mitochondria and ribosomes become densely compacted. After dilation of the endoplasmic reticulum and its fusion with the plasma membrane, the cell breaks up into several membrane-bound vesicles, apoptotic bodies, which are usually phagocytosed by adjacent bodies. As fragmentation of chromatin into oligonucleotides fragments is characteristic of the final stages of apoptosis, DNA cleavage patterns can be used as and in vitro assay for its occurrence (Cory, Nature 367: 317-18, 1994). Many methods for measuring apoptosis, including those described herein, are known to the skilled artisan including, but not limited to, the classic methods of DNA ladder formation by gel electrophoresis, immunohistochemical staining for apoptotic markers, measurement of apoptotic gene expression and of morphologic examination by electron microscopy. The more recent and readily used method for measuring apoptosis is flow cytometry.

[000100] Cells are directly contacted with a compound of the present invention.

Alternatively, the compound is administered systemically. Compounds are administered in an amount sufficient to decrease (e.g., inhibit) cell proliferation or induce apoptosis.

[000101] In preferred embodiments, the cells are cancer cells, i.e., cancer stem cells. In some embodiments, the cancer cells are breast cancer cells. As used herein, "cancer stem cells" collectively refer to cells found within tumors or hematological cancers that possess stem cell properties, e.g., the ability to give rise to all cell types found in a particular cancer sample. Cancer stem cells (CSCs) may generate tumors through the processes of self-renewal and differentiation into multiple cell types. Such cells may persist in tumors as a distinct population (e.g., after surgical or radiation treatment, or therapeutic regimens) and cause relapse and metastasis by giving rise to new tumors. CSCs may also possess the ability to undergo epithelial-mesenchymal transition (EMT).

[000102] Treatment is efficacious if the treatment leads to clinical benefit such as, a decrease in size, prevalence, or metastatic potential of the tumor in the subject. When treatment is applied prophylactically, "efficacious" means that the treatment retards or prevents tumors from forming or prevents or alleviates a clinical symptom of the tumor. Efficaciousness is determined in association with any known method for diagnosing or treating the particular tumor type. [000103] The compounds described herein can be formulated into pharmaceutical compositions for treating or alleviating a symptom of cancer in a subject. The

compounds can be administered to a subject using methods known in the art. Preferably, the compound is administered orally, rectally, nasally, topically or parenterally, e.g., subcutaneously, intraperitoneally, intramuscularly, and intravenously. The inhibitors are optionally formulated as a component of a cocktail of therapeutic drugs to treat cancers.

[000104] Performance And Accuracy Measures Of The Invention

[000105] 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 treatment and those that are not, is based on whether the subjects have an "effective amount" or a "significant alteration" in the levels of a

RESPONSEMARKER. By "effective amount" or "significant alteration," it is meant that the measurement of an appropriate number of RESPONSEMARKER (which may be one or more) is different than the predetermined cut-off point (or threshold value) for that RESPONSEMARKER(S) and therefore indicates that the subject responsiveness to therapy or disease free/overall survival for which the RESPONSEMARKER(S) is a determinant. The difference in the level of RESPONSEMARKER between normal and abnormal is preferably statistically significant. As noted below, and without any limitation of the invention, achieving statistical significance, and thus the preferred analytical and clinical accuracy, generally but not always requires that combinations of several RESPONSEMARKER be used together in panels and combined with

mathematical algorithms in order to achieve a statistically significant

RESPONSEMARKER index.

[000106] 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.

[000107] 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.

[000108] 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.

[000109] 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.

[000110] 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.

[000111] 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.

[000112] 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, California).

[000113] Construction Of Clinical Algorithms

[000114] Any formula may be used to combine RESPONSEMARKER results into indices useful in the practice of the invention. As indicated above, and without limitation, such indices may indicate, among the various other indications, the probability, likelihood, absolute or relative chance of responding to chemotherapy or chemoradiotherapy. This may be for a specific time period or horizon, or for remaining lifetime risk, or simply be provided as an index relative to another reference subject population.

[000115] Although various preferred formula are described here, several other model and formula types beyond those mentioned herein and in the definitions above are well known to one skilled in the art. The actual model type or formula used may itself be selected from the field of potential models based on the performance and diagnostic accuracy characteristics of its results in a training population. The specifics of the formula itself may commonly be derived from RESPONSEMARKER results in the relevant training population. Amongst other uses, such formula may be intended to map the feature space derived from one or more RESPONSEMARKER inputs to a set of subject classes (e.g. useful in predicting class membership of subjects as normal, responders and non-responders), to derive an estimation of a probability function of risk using a Bayesian approach (e.g. the risk of cancer or a metastatic event), or to estimate the class-conditional probabilities, then use Bayes' rule to produce the class probability function as in the previous case.

[000116] Preferred formulas include the broad class of statistical classification algorithms, and in particular the use of discriminant analysis. The goal of discriminant analysis is to predict class membership from a previously identified set of features. In the case of linear discriminant analysis (LDA), the linear combination of features is identified that maximizes the separation among groups by some criteria. Features can be identified for LDA using an eigengene based approach with different thresholds (ELD A) or a stepping algorithm based on a multivariate analysis of variance (MAN OVA). Forward, backward, and stepwise algorithms can be performed that minimize the probability of no separation based on the Hotelling-Lawley statistic.

[000117] Eigengene-based Linear Discriminant Analysis (ELD A) is a feature selection technique developed by Shen et al. (2006). The formula selects features (e.g.

biomarkers) in a multivariate framework using a modified eigen analysis to identify features associated with the most important eigenvectors. "Important" is defined as those eigenvectors that explain the most variance in the differences among samples that are trying to be classified relative to some threshold.

[000118] A support vector machine (SVM) is a classification formula that attempts to find a hyperplane that separates two classes. This hyperplane contains support vectors, data points that are exactly the margin distance away from the hyperplane. In the likely event that no separating hyperplane exists in the current dimensions of the data, the dimensionality is expanded greatly by projecting the data into larger dimensions by taking non-linear functions of the original variables (Venables and Ripley, 2002).

Although not required, filtering of features for SVM often improves prediction. Features (e.g., biomarkers) can be identified for a support vector machine using a non-parametric Kruskal-Wallis (KW) test to select the best univariate features. A random forest (RF, Breiman, 2001) or recursive partitioning (RPART, Breiman et al., 1984) can also be used separately or in combination to identify biomarker combinations that are most important. Both KW and RF require that a number of features be selected from the total. RPART creates a single classification tree using a subset of available biomarkers.

[000119] Other formula may be used in order to pre-process the results of individual RESPONSEMARKER measurement into more valuable forms of information, prior to their presentation to the predictive formula. Most notably, normalization of biomarker results, using either common mathematical transformations such as logarithmic or logistic functions, as normal or other distribution positions, in reference to a population's mean values, etc. are all well known to those skilled in the art. Of particular interest are a set of normalizations based on Clinical Parameters such as age, gender, race, or sex, where specific formula are used solely on subjects within a class or continuously combining a Clinical Parameter as an input. In other cases, analyte-based biomarkers can be combined into calculated variables which are subsequently presented to a formula. [000120] In addition to the individual parameter values of one subject potentially being normalized, an overall predictive formula for all subjects, or any known class of subjects, may itself be recalibrated or otherwise adjusted based on adjustment for a population's expected prevalence and mean biomarker parameter values, according to the technique outlined in D'Agostino et al, (2001) JAMA 286: 180-187, or other similar normalization and recalibration techniques. Such epidemiological adjustment statistics may be captured, confirmed, improved and updated continuously through a registry of past data presented to the model, which may be machine readable or otherwise, or occasionally through the retrospective query of stored samples or reference to historical studies of such parameters and statistics. Additional examples that may be the subject of formula recalibration or other adjustments include statistics used in studies by Pepe, M.S. et al, 2004 on the limitations of odds ratios; Cook, N.R., 2007 relating to ROC curves. Finally, the numeric result of a classifier formula itself may be transformed post-processing by its reference to an actual clinical population and study results and observed endpoints, in order to calibrate to absolute risk and provide confidence intervals for varying numeric results of the classifier or risk formula. An example of this is the presentation of absolute risk, and confidence intervals for that risk, derived using an actual clinical study, chosen with reference to the output of the recurrence score formula in the Oncotype Dx product of Genomic Health, Inc. (Redwood City, CA). A further modification is to adjust for smaller sub-populations of the study based on the output of the classifier or risk formula and defined and selected by their Clinical Parameters, such as age or sex.

[000121] MEASUREMENT OF RESPONSEMARKERS

[000122] The actual measurement of levels or amounts of the RESPONSEMARKERS can be determined at the protein or nucleic acid level using any method known in the art. For example, at the nucleic acid level, Northern and Southern hybridization analysis, as well as ribonuclease protection assays using compounds which specifically recognize one or more of these sequences can be used to determine gene expression. Alternatively, amounts of RESPONSEMARKERS can be measured using reverse-transcription-based PCR assays (RT-PCR), e.g., using primers specific for the differentially expressed sequence of genes or by branch-chain RNA amplification and detection methods by Panomics, Inc. Other methods for measuring amounts of RESPONSEMARKERS include, but are not limited to DNA microarrays, such as Bioanalyzer Chips (Agilent) or Human HT-12 Expression BeadChip (Illumina); SAGE (Serial analysis of gene expression); tiling arrays; and RNA-Seq.

[000123] Amounts of RESPONSEMARKERS can also be determined at the protein level, e.g., by measuring the levels of peptides encoded by the gene products described herein, or subcellular localization or activities thereof using technological platform such as for example AQUA. Such methods are well known in the art and include, e.g., immunoassays based on antibodies to proteins encoded by the genes, aptamers or molecular imprints. Any biological material can be used for the detection/quantification of the protein or its activity. Alternatively, a suitable method can be selected to determine the activity of proteins encoded by the marker genes according to the activity of each protein analyzed.

[000124] The RESPONSEMARKER proteins, polypeptides, mutations, and

polymorphisms thereof can be detected in any suitable manner, but is typically detected by contacting a sample from the subject with an antibody which binds the

RESPONSEMARKER protein, polypeptide, mutation, or polymorphism and then detecting the presence or absence of a reaction product. The antibody may be

monoclonal, polyclonal, chimeric, or a fragment of the foregoing, as discussed in detail above, and the step of detecting the reaction product may be carried out with any suitable immunoassay. The sample from the subject is typically a biological fluid as described above, and may be the same sample of biological fluid used to conduct the method described above.

[000125] Immunoassays carried out in accordance with the present invention may be homogeneous assays or heterogeneous assays. In a homogeneous assay the

immunological reaction usually involves the specific antibody (e.g., anti- RESPONSEMARKER protein antibody), a labeled analyte, and the 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 can be carried out in a homogeneous solution. Immunochemical labels which may be employed include free radicals, radioisotopes, fluorescent dyes, enzymes, bacteriophages, or coenzymes. [000126] In a heterogeneous assay approach, the reagents are usually the sample, the antibody, and means for producing a detectable signal. Samples as described above may be used. The antibody can be immobilized on a support, such as a bead (such as protein A and protein G agarose beads), plate or slide, and contacted with the specimen 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 sample. Means for producing a detectable signal include the use of radioactive labels, fluorescent labels, or enzyme labels. 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 oligonucleotides, immunoblotting, immunofluorescence methods, immunoprecipitation, quantum dots, multiplex fluorochromes, chemiluminescence methods,

electrochemiluminescence (ECL) or enzyme-linked immunoassays.

[000127] Those skilled in the art will be familiar with numerous specific immunoassay formats and variations thereof which may be useful for carrying out the method disclosed herein. See generally E. Maggio, Enzyme-Immunoassay, (1980) (CRC Press, Inc., Boca Raton, Fla.); see also U.S. Pat. No. 4,727,022 to Skold et al. titled "Methods for

Modulating Ligand-Receptor Interactions and their Application," U.S. Pat. No. 4,659,678 to Forrest et al. titled "Immunoassay of Antigens," U.S. Pat. No. 4,376,110 to David et al., titled "Immunometric Assays Using Monoclonal Antibodies," U.S. Pat. No.

4,275,149 to Litman et al., titled "Macromolecular Environment Control in Specific Receptor Assays," U.S. Pat. No. 4,233,402 to Maggio et al., titled "Reagents and Method Employing Channeling," and U.S. Pat. No. 4,230,767 to Boguslaski et al., titled

"Heterogenous Specific Binding Assay Employing a Coenzyme as Label."

[000128] Antibodies can be conjugated to a solid support suitable for a diagnostic assay (e.g., beads such as protein A or protein G agarose, microspheres, plates, slides or wells formed from materials such as latex or polystyrene) in accordance with known techniques, such as passive binding. Antibodies as described herein may likewise be conjugated to detectable labels or groups such as radiolabels (e.g., 35S, 1251, 1311), enzyme labels (e.g., horseradish peroxidase, alkaline phosphatase), and fluorescent labels (e.g., fluorescein, Alexa, green fluorescent protein, rhodamine) in accordance with known techniques. Highly sensitivity antibody detection strategies may be used that allow for evaluation of the antigen-antibody binding in a non-amplified configuration. In addition, antibodies may be conjugated to oligonucleotides, and followed by Polymerase Chain Reaction (PCR) and a variety of oligonucleotide detection methods.

[000129] Antibodies can also be useful for detecting post-translational modifications of RESPONSEMARKER proteins, polypeptides, mutations, and polymorphisms, such as tyrosine phosphorylation, threonine phosphorylation, serine phosphorylation,

glycosylation (e.g., O-GlcNAc). Such antibodies specifically detect the phosphorylated amino acids in a protein or proteins of interest, and can be used in immunoblotting, immunofluorescence, and ELISA assays described herein. These antibodies are well- known to those skilled in the art, and commercially available. Post-translational modifications can also be determined using metastable ions in reflector matrix-assisted laser desorption ionizationtime of flight mass spectrometry (MALDI-TOF) (Wirth, U. et al. (2002) Proteomics 2(10): 1445-51). In addition to post-translation modifications, these processes may be coupled to localization of the protein, such that a re-localization process is monitored, and the RESPONSEMARKER is evaluated in a relative fashion exhibited by the constancy or change to the ratio of the RESPONSEMARKER in different compartments. Important to several of the proteins in RESPONSEMARKERS, nuclear, nuclear foci, and cytoplasmic sites in tumor cells are evident.

[000130] For RESPONSEMARKER proteins, polypeptides, mutations, and

polymorphisms known to have enzymatic activity, the activities can be determined in vitro using enzyme assays known in the art. Such assays include, without limitation, kinase assays, phosphatase assays, reductase assays, among many others. Modulation of the kinetics of enzyme activities can be determined by measuring the rate constant KM using known algorithms, such as the Hill plot, Michaelis-Menten equation, linear regression plots such as Lineweaver-Burk analysis, and Scatchard plot.

[000131] Using sequence information provided by the database entries for the

RESPONSEMARKER sequences, expression of the RESPONSEMARKER sequences can be detected (if present) and measured using techniques well known to one of ordinary skill in the art. For example, sequences within the sequence database entries

corresponding to RESPONSEMARKER sequences, or within the sequences disclosed herein, can be used to construct probes for detecting RESPONSEMARKER RNA sequences in, e.g., Northern blot hybridization analyses or methods which specifically, and, preferably, quantitatively amplify specific nucleic acid sequences. As another example, the sequences can be used to construct primers for specifically amplifying the RESPONSEMARKER sequences in, e.g., amplification-based detection methods such as reverse-transcription based polymerase chain reaction (RT-PCR). When alterations in gene expression are associated with gene amplification, deletion, polymorphisms, and mutations, sequence comparisons in test and reference populations can be made by comparing relative amounts of the examined DNA sequences in the test and reference cell populations.

[000132] Expression of the genes disclosed herein can be measured at the RNA level using any method known in the art. For example, Northern hybridization analysis using probes which specifically recognize one or more of these sequences can be used to determine gene expression. Alternatively, expression can be measured using reverse transcription- based PCR assays (RT-PCR), e.g., using primers specific for the differentially expressed sequences. RNA can also be quantified using, for example, other target amplification methods (e.g., TMA, SDA, NASBA), or signal amplification methods (e.g., bDNA), and the like.

[000133] Alternatively, RESPONSEMARKER protein and nucleic acid metabolites can be measured. The term "metabolite" includes any chemical or biochemical product of a metabolic process, such as any compound produced by the processing, cleavage or consumption of a biological molecule (e.g., a protein, nucleic acid, carbohydrate, or lipid). Metabolites can be detected in a variety of ways known to one of skill in the art, including the refractive index spectroscopy (RI), ultra-violet spectroscopy (UV), fluorescence analysis, radiochemical analysis, near-infrared spectroscopy (near-IR), nuclear magnetic resonance spectroscopy (NMR), light scattering nalysis (LS), mass spectrometry, pyrolysis mass spectrometry, nephelometry, dispersive Raman

spectroscopy, gas chromatography combined with mass spectrometry, liquid chromatography combined with mass spectrometry, matrix-assisted laser desorption ionization-time of flight (MALDI-TOF) combined with mass spectrometry, ion spray spectroscopy combined with mass spectrometry, capillary electrophoresis, NMR and IR detection. (See, WO 04/056456 and WO 04/088309, each of which are hereby

incorporated by reference in their entireties) In this regard, other RESPONSEMARKER analytes can be measured using the above-mentioned detection methods, or other methods known to the skilled artisan. For example, circulating calcium ions (Ca2+) can be detected in a sample using fluorescent dyes such as the Fluo series, Fura-2A, Rhod-2, among others. Other RESPONSEMARKER metabolites can be similarly detected using reagents that are specifically designed or tailored to detect such metabolites.

[000134] KITS

[000135] The invention also includes a RESPONSEMARKER-detection reagent, e.g., nucleic acids that specifically identify one or more RESPONSEMARKER nucleic acids by having homologous nucleic acid sequences, such as oligonucleotide sequences, complementary to a portion of the RESPONSEMARKER nucleic acids or antibodies to proteins encoded by the RESPONSEMARKER nucleic acids packaged together in the form of a kit. The oligonucleotides can be fragments of the RESPONSEMARKER genes. For example the oligonucleotides can be 200, 150, 100, 50, 25, 10 or less nucleotides in length. The kit may contain in separate containers a nucleic acid or antibody (either already bound to a solid matrix or packaged separately with reagents for binding them to the matrix), control formulations (positive and/or negative), and/or a detectable label such as fluorescein, green fluorescent protein, rhodamine, cyanine dyes, Alexa dyes, luciferase, radiolabels, among others. Instructions (e.g., 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 a Northern hybridization or a sandwich ELISA as known in the art.

[000136] For example, RESPONSEMARKER detection reagents can be immobilized on a solid matrix such as a porous strip to form at least one RESPONSEMARKER detection site. The measurement or detection region of the porous strip may include a plurality of sites containing a nucleic acid. A test strip may also contain sites for negative and/or positive controls. Alternatively, control sites can be located on a separate strip from the test strip. Optionally, the different detection sites may contain different amounts of immobilized nucleic acids, e.g., a higher amount in the first detection site and lesser amounts in subsequent sites. Upon the addition of test sample, the number of sites displaying a detectable signal provides a quantitative indication of the amount of RESPONSEMARKERS 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.

[000137] Alternatively, the kit contains a nucleic acid substrate array comprising one or more nucleic acid sequences. The nucleic acids on the array specifically identify one or more nucleic acid sequences represented by RESPONSEMARKERS. The substrate array can be on, e.g., a solid substrate, e.g., a "chip" as described in U.S. Patent No.5,744,305. Alternatively, the substrate array can be a solution array, e.g., xMAP (Luminex, Austin, TX), Cyvera (Illumina, San Diego, CA), CellCard (Vitra Bioscience, Mountain View, CA) and Quantum Dots' Mosaic (Invitrogen, Carlsbad, CA).

EXAMPLES

[000138] EXAMPLE 1: IDENTIFICATION OF RESPONSEMARKERS

[000139] Generation and culture of CSC-like breast cancer cell lines

[000140] Human breast epithelial cells (HMLE) were induced into an epithelial-to- mesenchymal transdifferentiation to generate enriched populations of cancer stem cells (CSCs). HMLE cell lines can be driven to a CSC-like state by knocking down genes that regulate the epithelial-mesenchymal transition, such as E-Cadherin and Twist. Two such cell lines, HMLE_sh_ECad (HMLE expressing a short hairpin targeting E-Cadherin) and HMLE_sh_Twist (HMLE expressing a short hairpin targeting Twist), were used in the following experiments. An isogenic control, which is absent in most other screens, was created by introduction of a short hairpin targeting the GFP (green fluorescent protein) gene (HMLE_sh_GFP). This control minimized the probability of finding spurious compound hits that are not selective for CSCs and target unknown or uncharacterized genetic differences between the screened cell line and other commonly used cellular models. HMLE_sh_ECad, HMLE_sh_Twist, and HMLE_sh_GFP were propagated in 1: 1 mixture of 10% fetal bovine serum (FBS; HyClone), 1% Penicillin/Streptomycin (Pen/Strep; Cellgro), 1% Glutamax-1 (Invitrogen), 70 nM Hydrocortisone (Sigma), 12 μg/ml Insulin (Sigma), 50 μg/ml Gentamicin (Sigma), 12.5 μg/ml Plasmocin

(InVivogen), 10 ng/ml EGF in DMEM (Cellgro) with Mammary Epithelial Cell Growth Medium (MEGM complete medium; Lonza, Basel, Switzerland) at 37 °C, 5% C0₂.

[000141] Initial screen for RESPONSEMARKERS

[000142] CID49843203 (ML239), CID50904134 (ML245), CID50904149 (Analog 2 of ML 245), CID50910523 (ML243) and Salinomycin (Sal) were selectively toxic to HMLE_sh_ECad cell lines (over HMLE_sh_GFP cell lines) in 3-day toxicity assays. The IC50 concenttion of toxicity towards HMLE_sh_ECad cell line in these 3-day toxicity assay was the same concentration used for experiments below (1.2 μΜ, 0.54 μΜ, 6.7 μΜ, 2 μΜ, and 1 μΜ). Replicate samples (3-4) of HMLE_sh_ECad and

HMLE_sh_GFP cells were treated with vehicle, ML239, ML245, CID50904149 (an analogue of ML245) ML243, or Salinomycin (Sal)) at the above concentrations for 24 hours prior to isolation of RNA. Total RNA was isolated using the RNeasy Protect Mini Kit (Qiagen). Quality control (QC) processing of the RNA samples and the gene expression analysis were performed by the Genome Analysis Platform (GAP) at the Broad Institute.

[000143] Briefly, RNA samples were analyzed for quality using Aglient Bioanalyzer Chips. cDNA synthesis from the total RNA samples passing QC was prepared for analysis on a HumanHT-12 Expression BeadChip (Illumina, San Diego, CA) according to the manufacturer's instructions. QC checks, and analyses were done with

GenomeStudio (version 2010.3; Illumina, San Diego, CA). All of the samples were run in replicates (3-4) and the entire data set was normalized using the quantile module available in open source program GenePattern (http://genepattern.broadinstitute.org/). After the samples were normalized, gene expression was compared between the DMSO- treated and compound-treated using the ComparitiveSelection Module in GenePattern. Those genes with a significant difference (p>0.005) and had 1.50-fold change in expression were selected.

[000144] EXAMPLE 2: A SCREEN FOR ADDITIONAL PROBES IDENTIFIED ML239.

[000145] The Molecular Libraries Small Molecule Repository (MLSMR) was screened in a high-throughput assay to identify additional selective and tractable compounds for use as probes. These probes, or chemical tools, were then used to gain insight into the biology of cancer stem cells and identify additional RESPONSEMARKERS associated with responsiveness to cancer treatment. A total of 300,718 compounds from the

MLSMR were assessed for selective toxicity towards the breast CSC-like population HMLE_sh_Ecad

(http://pubchem.ncbi.nlm.nih.gov/assay/assay.cgi?aid=2717&loc=ea_ras).

[000146] For screening, the cells were counted and resuspended in complete media without serum. Next, 2,000 cells were plated per well in white, tissue culture-treated, 384-well plates (Corning). The cells were incubated at 37 °C, 5% C0₂ for at least 4 hours and pinned with compounds from the MLSMR library of compounds

(http://mli.nih.gov/mli/). After 72 hours, toxicity was assessed after treating the 2,000 HMLE_sh_ECad cells/well with compound for 72 hours using CellTiter-Glo (Promega). CellTiter-Glo (Promega) was diluted 1:3 with PBS and added to each well. After 12 minutes of incubation, each plate was read using the En Vision (PerkinElmer;

Luminescence 0.1 sec/well). Each assay plate is normalized to the 32 neutral control (DMSO-treated) wells and the 32 positive control (Puromycin-treated) wells on each plate. The signal was normalized to the neutral (DMSO) and positive (Puromycin) controls. Hits were defined as showing a greater than 75% reduction in viability (i.e ATP level) cutoff at an average screening concentration of 7.5 μΜ was used to define a hit. Using these criteria, 3,190 hits were identified as inhibitors of HMLE_sh_Ecad (Figure 1). Duplicate samples correlated highly with one another showing that the data were reproducible.

[000147] To remove promiscuously active compounds, the list of hits was narrowed by discarding compounds that were active in >10% of assays conducted within the

Molecular Libraries Probe Production Center Network (MLPCN). Additionally, compounds that scored positively in mammalian cell toxicity screens listed in PubChem as of April 2010 were also removed from the list of hits as non- selective. The top 2,500 compounds were requested and, of these, 2,244 compounds in DMSO stock were available and sourced for retest. In order to determine selectivity, these compounds were retested in dose concentrations against the primary cell line, HMLE_sh_ECad and the counterscreen control HMLE_sh_GFP cell line, respectively. From the 2,244 compounds retested in the primary assay against HMLE_sh_ECad at dose concentrations, an unprecedented 97% (2181 compounds) inhibited at least 50% at the highest concentration tested (20 μιη).

[000148] In parallel, these 2,244 compounds were included in HMLE_sh_GFP viability assays to determine if these compounds affected the viability of the control cell line. Taken together, only 26 compounds were identified as having an IC50 of < 5 μΜ and were 25-fold more toxic towards HMLE_sh_ECad versus HMLE_sh_GFP cells (Figures 2A and B).

[000149] After retesting and determining the IC₅₀ of the compounds from DMSO stocks in the primary and counterscreen, 19 exact or nearly identical compounds were ordered from commercial vendors and purified. Dose responses in the primary assay and counterscreens were obtained for these 19 dry powder compounds after quality controlled to determine if these compounds retained potency and selectivity. From this set of dry powder compounds, two acyl hydrazone compounds (CID5417654 and CID24816775, Figure 3) retained selectivity, and this scaffold was prioritized for medicinal chemistry efforts. Based on the potency and selectivity of this scaffold, 53 structurally modified compounds were tested against HMLE_sh_ECad and HMLE_sh_GFP cell lines

(Carmody, LC et al., Molecular Library Probe Center Network Probe Report 2011;

Germain, AR et al., Bioorganic & Medicinal Chemistry Letters 2012, In press).

[000150] A single compound, ML239, was identified as the best in the series with an IC₅₀=1.16 μΜ and 23-fold selectivity for HMLE_sh_ECad vs. HMLE_sh_GFP cells (Figures 2A, 2B, and 3A). To confirm that ML239 was not specifically toxic to the HMLE_sh_ECad cell line, another CSC-like cell line, HMLE_Twist was treated with ML239. Twist is a transcription factor that down regulates the expression of E-Cadherin, Therefore, overexpression of Twist promotes an EMT-induced model of breast CSC-like cells (Gupta, PB et al., Cell 2009, 138:645-659; Mani, SA et al., Cell 2008, 133:704- 715). Similar to the results observed in the HMLE_sh_ECad cell line, ML239 was potently toxic, inhibiting HMLE_Twist with an IC₅₀ - 0.1 μΜ (Figure 3A). Furthermore, ML239 displayed potent toxicity (ICso=2.81 μΜ) to the breast carcinoma cell line, MDA- MB-231 (Figure 3A). Taken together, this suggests that ML239 is potently toxic to CSCs and at least one other cancer cell line, but does not display toxicity to control mammary epithelial cells (HMLE_sh_GFP) . [000151] The compounds CID24816775 and CID5417654 were toxic towards the HMLE_sh_ECad cells with IC₅₀ values of 2.51 μΜ and 3.3μΜ, respectively. To confirm that they retained this selectivity in other CSC-like cell lines, they were also tested for toxicity the against HMLE_Twist line. Although CID5417654 and CID24816775 were not as potent against HMLE_Twist, they were toxic in the low μΜ range (ICso=2.89 μΜ and 2.21 μΜ, respectively). Interestingly, CID5417654 did not significantly inhibit the control line HMLE_sh_GFP at the highest concentration tested (20 μΜ), whereas CID24816775 was significantly toxic to HMLE_sh_GFP (IC₅₀=14.95 μΜ). These two compounds were significantly less toxic to the MDA-MB-231 cells. CID24816775 inhibited viability with an IC₅₀ of 14.7μΜ, while CID5417654 was only toxic at very high concentrations (IC₅₀ =47.6 μΜ) (Figure 3).

[000152] EXAMPLE 3: IDENTIFICATION OF CSC PATHWAYS USING ML239

[000153] Gene expression profiling was performed using ML239 to evaluate breast cancer stem cell pathways that may be used for future target development. This assay identified early gene expression alterations, additional RESPONSEMARKERS associated with responsiveness to cancer treatment, and a potential pathway for targeting by future therapeutics.

[000154] Triplicate samples of HMLE_sh_ECad were treated with vehicle (DMSO) or compound ML239

(http://pubchem.ncbi.nlm.nih.gov/summary/summary.cgi?cid=49843203&loc= ec_rcs) at an IC_5o dose determined in the 3-day toxicity assay for 24 hours prior to isolation of

RNA. Total RNA was isolated using the RNeasy Protect Mini Kit (Qiagen). Quality control (QC) processing of the RNA samples and the gene expression analysis were performed by the Genome Analysis Platform (GAP) at the Broad Institute. Briefly, RNA samples were analyzed for quality using Aglient Bioanalyzer Chips. RNA synthesis from the total RNA samples passing QC was prepared for analysis on a HumanHT-12

Expression BeadChip (Illumina, San Diego, CA) according to the manufacturer's instructions. Quantile normalization of the raw gene expression data, QC checks, and analyses were performed with GenePattern (genepattern.broadinstitute.org, Broad Institute). After the samples were normalized, gene expression was compared and those with a statistically significant difference (p>0.005) were identified. The raw data is available the scientific community on the web at:

http://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-884. Genes identified to have approximately 2.5-fold change in expression were analyzed for gene set enrichment using Ingenuity (http://www.ingenuity.com/, Ingenuity Systems).

[000155] There were significant changes in gene expression in the HMLE_sh_Ecad cells, as shown in Figure 4A. Numerous genes are either up regulated (grey, left) or down regulated (black, right) after treatment with ML239 as compared to DMSO treated cells. The 21 genes with the most significant changes (p>0.005) and greater than 2.5-fold change in expression were: ALDH1L2, ASNS, C10RF24, CBS, CDKN1A, EIF1, FBX032, GDF15, HERPUDl, HSPA5, LOC729779, MTHFD2, NCRNA00219, P8, PCK2, PSATl, RND3, SLC7A1, SNHG8, SQSTMl, and TRIB3 (Figure 3B). Genes identified play a role in signaling pathways involved in free radical scavenging, cell death, and protein expression regulation, including ASNS, CBS, CDKN1A, FBX032, GDF15, HERPUDl, HSPA5, MTHFD2, PCK2, PSATl, RND3, SLC7A1, SQSTMl, and TRIB3.

[000156] A similar study was conducted with the control line, HMLE_sh_GFP. Only 5 genes were differentially regulated in HMLE_sh_GFP after treatment with ML239 (ATP6V0C, PKM2, PPDPF, RPL23, and SERINC2), and none overlapped with genes regulated in HMLE_sh_ECad cells, indicating that there were different responses to the compound in the two cell types.

[000157] For additional insight into the interaction of the gene set identified, an Ingenuity Network analysis was performed on the 24 genes. The most significant pathway associated with the affected genes was the NF-κ-Β pathway with several genes involved in free radical scavenging, cell death, and protein synthesis. Fourteen of the 24 genes have been linked to this pathway (ASNS, CBS, CDKN1A, FBX032, GDF15, HERPUDl, HSPA5, MTHFD2, PCK2, PSATl, RND3, SLC7A1, SQSTMl, and TRIB3) (Figure 4C), primarily centering on TRIB3 (Tribbles 3 homolog). TRIB3 is a putative protein kinase that is induced by NF-κ B and that is correlated with both pro-apoptotic and anti-apoptotic features. High levels of TRIB3 have been correlated with poor prognosis in breast cancer and TRIB3 expression is increased in hypoxic conditions. ML239 increases TRIB3 expression in HMLE_sh_ECad (Figure 4) and this increase may induce TRIB 3 -regulated apoptosis.

[000158] Taken together, these data verify the utility of ML239 as a probe for identifying gene signatures and RESPONSEMARKERS associated with responsiveness to cancer treatment. The gene-profiling experiments suggest that the NF-kB pathway is critical for the probe's selective toxicity towards breast cancer stem cells. Although the direct target of ML239 has not ben identified, these collective results suggest that developing future therapeutics to target the NF-kB pathway would produce more efficient breast cancer therapies. Moreover, identification of the direct targets or pathways of the probes, or reference compounds, disclosed herein will aid in the development and study of the selective toxicity of breast cancer stem cells, and may identify novel proteins or pathways to target with future therapeutics.

Table 1: Comparison between DMSO-treated andML239 Treated HMLE_shECad Cells

Table 2: Comparison between DMSO-treated and ML239 Treated HMLE_shGFPCells

Table 3: Comparison between DMSO-treated and ML245 Treated HMLE_shECad Cells

Table 4: Comparison between DMSO treated and ML245 treated HMLE_shGFP cells

Table 5: Com arison between DMSO treated and ML243 treated HMLE_shECad cells

Table 6: Com arison between DMSO treated andML243 treated HMLE_shGFP cells

Table 7: Com arison between DMSO treated and CID50904149 (an analogue of ML245) treated HMLE_shECad cells

Table 8: Comparison between DMSO treated and CID50904149 (an analogue of ML245) treated HMLE_shGFP Cells

Table 9: Com arison between DMSO treated and Salinom cin Treated HMLE_shECad

Table 10: Comparison between DMSO treated and Salinomycin treated HMLE_shGFP Cells

Claims

We claim:

1. A method of assessing the effectiveness of a treatment regimen of a subject having a cancer comprising

a) detecting the level of an effective amount of one or more

RESPONSEMARKERS in a sample from the subject; and

b) comparing the level of the effective amount of the one or more

RESPONSEMARKERS detected in step (a) to a reference value.

2. A method of monitoring a treatment regimen of a subject with a cancer comprising

a) detecting the level of an effective amount of one or more

RESPONSEMARKERS in a first sample from the subject at a first period of time;

b) detecting the level of an effective amount of one or more

RESPONSEMARKERS in a second sample from the subject at a second period of time; c) comparing the level of the effective amount of one or more

RESPONSEMARKERS detected in step (a) to the amount detected in step (b), or to a reference value.

3. A method of determining whether a subject with a cancer would derive a benefit from a treatment regimen comprising

a) detecting the level of an effective amount of one or more

RESPONSEMARKERS; and

b) comparing the level of the effective amount of one or more

RESPONSEMARKERS detected in step (a) to a reference value.

4. A method of screening for a test compound that induces cancer cell death and/or inhibits cancer cell proliferation comprising:

a) detecting the level of an effective amount of one or more

RESPONSEMARKERS in a cancer cell that has been contacted with the test compound; and b) comparing the level of the effective amount of one or more

RESPONSEMARKERS listed in any one of Tables 1-10,

wherein in a similarity of the level of the RESPONSEMARKERS detected in step (a) with the level of RESPONSEMARKERS listed in Tables 1-10 indicates that the test compound induces cancer cell death and/or inhibits cancer cell proliferation.

5. A method of screening for a test compound that induces cancer cell death and/or inhibits cancer cell proliferation comprising:

a) detecting the level of an effective amount of one or more

RESPONSEMARKERS in a cancer cell that has been contacted with the test compound; and

b) comparing the level of the effective amount of one or more

RESPONSEMARKERS in a cancer cell that has been treated with a reference compound, wherein in a similarity of the level of the RESPONSEMARKERS detected in step (a) with the level of RESPONSEMARKERS in the cancer cell treated with the reference compound indicates that the test compound induces cancer cell death and/or inhibits cancer cell proliferation.

6. A method of identifying a biological target comprising:

a) identifying one or more RESPONSEMARKERS that are differentially expressed in a cancer cell or a cancer stem cell compared to a non-cancer cell to produce a gene target list; and

b) identifying one or more genes on said target list that is associated with toxicity against the cancer cell or the cancer stem cell,

thereby identifying a biological target.

7. A method of identifying a compound that modulates the expression or activity of the biological target identified in claim 6, comprising contacting a cancer cell with a test compound and detecting modulation of the expression or activity of the biological target.

8. The method of claim 5 wherein the reference compound is ML 239, ML245, ML 243 or CID50904149 (an analogue of ML245).

9. The method of any one of claims 4, 5 or 7, wherein the cancer cell is a cancer stem cell.

10. The method according to any of claims 1-3, wherein the treatment regimen is ML239 and the one or more RESPONSEMARKERS are selected from Tables 1 or 2.

11. The method according to of claims 1-3, wherein the treatment regimen is ML245 and the one or more RESPONSEMARKERS are selected from Tables 3 and 4.

12. The method according to of claims 1-3, wherein the treatment regimen is ML243 and the one or more RESPONSEMARKERS are selected from Tables 5 and 6.

13. The method according to any of claims 1-3, wherein the treatment regimen is CID50904149 (an analogue of ML245) and the one or more RESPONSEMARKERS are selected from Tables 7 and 8.

14. The method according to any of claims 1-3, wherein the treatment regimen is salinomycin and the one or more RESPONSEMARKERS are selected from Tables 9 and 10.

15. The method according to any one of claims 1-5, wherein the

RESPONSEMARKERS are at least one selected from the group comprising: ALDH1L2, ASNS, C10RF24, CBS, CDKN1A, EIF1, FBX032, GDF15, HERPUD1, HSPA5, LOC729779, MTHFD2, NCRNA00219, P8, PCK2, PSAT1, RND3, SLC7A1, SNHG8, SQSTM1, and TRIB3.

16. A method of treating or alleviating a symptom of cancer comprising

administering a compound that modulates the expression of one or more

RESPONSEM ARKERS .

17. The method of claim 16, wherein the RESPONSEMARKERS are at least one selected from the group comprising: ALDH1L2, ASNS, C10RF24, CBS, CDKNIA, EIFl, FBX032, GDF15, HERPUDl, HSPA5, LOC729779, MTHFD2, NCRNA00219, P8, PCK2, PSAT1, RND3, SLC7A1, SNHG8, SQSTM1, and TRIB3.

18. A method of treating or alleviating a symptom of cancer comprising

administering a compound that modulates the expression of one or more genes in the NF- k-B pathway.

19. The method according to any of the preceeding claims, wherein the cancer is breast cancer.