WO2009089521A2 - Prédicteurs pour évaluer une réponse à une thérapie du cancer - Google Patents

Prédicteurs pour évaluer une réponse à une thérapie du cancer Download PDF

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WO2009089521A2
WO2009089521A2 PCT/US2009/030719 US2009030719W WO2009089521A2 WO 2009089521 A2 WO2009089521 A2 WO 2009089521A2 US 2009030719 W US2009030719 W US 2009030719W WO 2009089521 A2 WO2009089521 A2 WO 2009089521A2
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genes
patient
cancer
predictor
patients
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WO2009089521A3 (fr
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W. Fraser Symmans
Christos Hatzis
Lajos Pusztai
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Nuvera Biosciences, Inc.
The Board Of Regents Of The University Of Texas System
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/53—Immunoassay; Biospecific binding assay; Materials therefor
    • G01N33/574—Immunoassay; Biospecific binding assay; Materials therefor for cancer
    • G01N33/57484—Immunoassay; Biospecific binding assay; Materials therefor for cancer involving compounds serving as markers for tumor, cancer, neoplasia, e.g. cellular determinants, receptors, heat shock/stress proteins, A-protein, oligosaccharides, metabolites
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B25/00—ICT specially adapted for hybridisation; ICT specially adapted for gene or protein expression
    • G16B25/10—Gene or protein expression profiling; Expression-ratio estimation or normalisation
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B50/00—ICT programming tools or database systems specially adapted for bioinformatics
    • G16B50/30—Data warehousing; Computing architectures
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2800/00—Detection or diagnosis of diseases
    • G01N2800/52—Predicting or monitoring the response to treatment, e.g. for selection of therapy based on assay results in personalised medicine; Prognosis
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2800/00—Detection or diagnosis of diseases
    • G01N2800/60—Complex ways of combining multiple protein biomarkers for diagnosis
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B25/00—ICT specially adapted for hybridisation; ICT specially adapted for gene or protein expression
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B25/00—ICT specially adapted for hybridisation; ICT specially adapted for gene or protein expression
    • G16B25/20—Polymerase chain reaction [PCR]; Primer or probe design; Probe optimisation
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B50/00—ICT programming tools or database systems specially adapted for bioinformatics

Definitions

  • Embodiments of this invention are directed generally to biology and medicine.
  • the invention relates to a gene set whose levels of expression are evaluated and used to prognose and/or derive a survival indicator for a patient who has undergone therapy, who is undergoing therapy, or who is a candidate for therapy.
  • a "predictor score”, “predictor index”, or “index” or any variation of these terms when used herein includes the computation of a numeric score obtained from the expression levels of the genes identified in Table 2, 4, or 6 multiplied by a prespecified positive or negative constant for each gene and added up to produce a single value. The value of the predictor is comparable to a reference score or scale form which assessment of the patient sample is made.
  • pCR pathologic complete response
  • dichotomization of response as pCR or residual disease (RD) may be simplistic for the objective of assay discovery and validation, particularly because residual disease (RD) after neoadjuvant treatment includes a broad range of actual tumor shrinkage.
  • RD residual disease
  • the response outcome blurs the prognostic distinction between pCR and RD.
  • Expression markers are chosen for the ability to classify and/or identify patients as to probability for response (or non response) to therapy.
  • Response to therapy is commonly classified by the RECIST criteria established by the World Health Organization, the National Cancer Institute and the European Organization for Research and Treatment of Cancer.
  • the RECIST criteria classify response as progressive disease (PD), stable disease (SD), partial response (PR), and complete response (CR).
  • PD progressive disease
  • SD stable disease
  • PR partial response
  • CR complete response
  • a good response is typically considered to include PR+CR (collectively referred to herein as Objective Response).
  • Certain aspects of the invention include methods of evaluating a cancer patient comprising one or more of the steps of (a) evaluating gene expression levels in a patient sample comprising cancer cells or an RNA sample isolated from one or more a patient samples, wherein a plurality of genes to be evaluated are selected from 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 20, 25, 30, 40, 50, 60, 70, 80, 90, 100, 125, 130, or all of the genes identified in Tables 2, 4, or 6 including all ranges and values there between and all subsets and combinations thereof (5, 10, 15, 20, 25, 100, 125, 130 or more such genes can be specifically excluded, including all values and ranges there between)(in certain aspects, the gene subset is selected based on the rank, e.g.
  • the method may further comprise classifying a patient prior to evaluation.
  • classification can include identifying a cancer patient with a disease state classified as a residual disease state or other clinically defined state prior to evaluation.
  • a predictor includes but is not limited to a measure for distant relapse-free survival (DRFS).
  • a gene expression profile or transcriptional profile comprises 5, 10, 20, 30, 60, 120, 125, 130, 135, 240, or all the genes of Table 2, 4 or 6 including all values and ranges there between as well as a number of subsets of these genes which may include some genes from one or more tables and exclude others from the same table or other tables.
  • a patient may be stratified or analyzed by using other factors such as protein expression, demographic information, family history, and other biological or medical states.
  • the method may include determining Her2-neu and/or estrogen receptor status of the patient sample and/or evaluation of tumor size, cellularity of tumor bed, and/or nodal burden to name a few.
  • the methods may also provide a treatment recommendation depending on the assessment derived from analysis of the gene expression profile as well as other factors.
  • the recommendation may be based on residual cancer burden (RCB) classification or the like.
  • a treatment is typically a standard treatment or a more aggressive non-standard treatment depending on the analysis.
  • a treatment may be a combination of one or more cancer therapies, such as hormonal therapy and/or chemotherapy.
  • Hormonal therapy includes, but is not limited to tamoxifen therapy, aromatase inhibitor therapy, or SERM therapy.
  • preparing a gene expression index or transcriptional profile can include one or more of the following steps: (a) obtaining data associated with a plurality of cancer patients, such as breast cancer, melanoma, ovarian cancer, testicular cancer or the like comprising measuring expression levels of a plurality of genes in samples from a plurality of patients; (b) partitioning the data into a first and second dataset; (c) evaluating the data and identifying data associated with a particular treatment outcome; (d) selecting a set of genes whose expression levels are indicative of therapeutic outcome.
  • selecting a transcriptional profile includes evaluation of survival of the patient population sampled for all or part of the reference population of tumor samples, such as the distant relapse-free survival (DRFS) of the patient population.
  • DRFS distant relapse-free survival
  • kits to determine responsiveness of a cancer or cancer patient to a treatment or therapy comprising one or more of (a) reagents for determining expression levels of a plurality of genes selected from Table 2 or ER positive genes of Table 4 or ER negative genes of Table 4 or ER positive genes of Table 6 or ER negative genes of Table 6 or combinations thereof, such as probe sets that identify and measure the levels of gene transcripts, transcription, or protein levels; and (b) software encoding methods for designing, gathering, inputting, analyzing and/or assessing various data, which includes an algorithm for calculating a predictor score based on the analysis of the gene expression levels.
  • the invention includes an apparatus, or system for providing assessment of a sample relative to a gene expression profile, the system comprising (a) an application server comprising an input manager to receive expression data from a user for a plurality of genes selected from Table 2 or ER positive genes of Table 4 or ER negative genes of Table 4 or ER positive genes of Table 6 or ER negative genes of Table 6 or combinations thereof or subsets thereof obtained from a patient sample or an RNA sample from such patient sample; and (b) a network server comprising an output manager constructed and arranged to provide an assessment to the user.
  • the invention includes a computer readable medium having software modules for performing the one or more of the methods described herein comprising the acts of: (a) comparing gene expression data obtained from a patient sample for a plurality of genes selected from Table 2 or ER positive genes of Table 4 or ER negative genes of Table 4 or ER positive genes of Table 6 or ER negative genes of Table 6 or combinations thereof with a reference; and (b) providing a predictor score to a physician for use in determining an appropriate therapeutic regimen for a patient.
  • the invention includes a computer system, having a processor, memory, external data storage, input/output mechanisms, a display, for performing the method of the invention, comprising (a) a database; (b) logic mechanisms in the computer for generating the transcriptional profile index; and (c) a comparing mechanism in the computer for comparing the gene expression reference to expression data from a patient sample or an RNA sample from such a patient sample to calculate a predictor score.
  • An internet accessible portal may be use to provide biological information constructed and arranged to execute a computer-implemented methods for providing: (a) a comparison of gene expression data of a plurality of genes of claim 1 in a patient sample with a transcriptional profile index; and (b) providing a predictor score to a physician for use in determining an appropriate therapeutic regime for a patient.
  • the words “comprising” (and any form of comprising, such as “comprise” and “comprises”), “having” (and any form of having, such as “have” and “has”), "including” (and any form of including, such as “includes” and “include”) or “containing” (and any form of containing, such as “contains” and “contain”) are inclusive or open-ended and do not exclude additional, unrecited elements or method steps.
  • FIG. 1 Characteristics of the patient cohort used in developing the predictors shown with stratification by ER status and Her-2 status.
  • FIG. 2 Schematic of predictor development and evaluation protocol employing an external cross-validation loop to validate the predictor performance while accounting for selection bias and an internal cross-validation loop to optimize gene selection and parameter tuning.
  • a Monte-Carlo step repeats the external cross- validation process several times to obtain average performance.
  • FIG. 3 Predictive performance of RCB-O predictor on Her2 -normal and Her2-amplified patients.
  • FIG. 4 Predictive performance of RCB-0/I predictor on Her2-normal and Her2-amplified patients.
  • FIG. 5 Plot of survival outcomes in predicted responders by the RCB-0/I predictor
  • FIG. 6 Plot of actual patient outcomes measured as distant relapse-free survival in responders predicted by RCB-0/I predictor stratified by ER status.
  • FIG. 7 Predictive performance of RCB-0/I predictor developed for sample robustness in Her2 -normal patients.
  • FIG. 8 Plot of actual patient outcomes measured as distant relapse-free survival in responders predicted by RCB-0/I gene predictor.
  • FIG. 9 Plot of actual patient outcomes measured as distant relapse-free survival in responders predicted by RCB-0/I predictor stratified by ER status. DETAILED DESCRIPTION OF THE INVENTION
  • pathologic complete response As an in vivo model for marker development and validation, pre-operative (neoadjuvant) chemotherapy provides an opportunity to gain access to samples that directly describe tumor response to therapy. Furthermore, complete eradication of all invasive cancer from the breast and regional lymph nodes, called pathologic complete response (pCR), is associated with excellent long-term cancer-free survival (Fisher, 1998; Kuerer, 1999). Therefore, the goal in developing treatment-directed response markers is to evaluate gene expression profiles in order to predict who may achieve pCR versus residual disease (RD). Pathologic CR is a meaningful clinical end-point to predict because these patients experience prolonged disease-free and overall survival compared to patients with lesser response (Cleator, 2005; Fisher, 1998; Kaufmann, 2006; Wolmark, 2001).
  • a cohort of 82 patients was used for predictor discovery of pCR to preoperative T/FAC chemotherapy using fine needle biopsies taken before treatment and by analyzing gene profiles generated from a commercially available standard gene expression profiling technology (Affymetrix, Santa Clara, CA). Although several analytic techniques and resulting gene sets for response prediction were studied, the nominally best predictor for pCR with the least number of genes, called DLDA-30, was selected for independent validation in 51 additional patients. The predictor showed substantially higher sensitivity (a measure of how well a predictor identifies responsiveness or non-responsiveness to a therapy, e.g.
  • a sensitivity of 100% means that the test recognizes all patient as either responsive to therapy or non-responsive to therapy. Typically, sensitivity alone does not tell us how well the test predicts other classes (that is, about the negative cases).
  • Sensitivity is not the same as the positive predictive value (ratio of true positives to combined true and false positives), which is as much a statement about the proportion of actual positives in the population being tested as it is about the test.
  • the calculation of sensitivity typically does not take into account indeterminate test results. If a test cannot be repeated, the options are to exclude indeterminate samples from analyses (but the number of exclusions should be stated when quoting sensitivity), or, alternatively, indeterminate samples can be treated as false negatives (which gives the worst-case value for sensitivity and may therefore underestimate it).
  • pCR pathologic complete response
  • dichotomization of response as pCR or residual disease (RD) may be simplistic for the objective of assay discovery and validation, particularly because residual disease (RD) after neoadjuvant treatment includes a broad range of actual tumor shrinkage.
  • RD residual disease
  • the response outcome blurs the prognostic distinction between pCR and RD.
  • a measure of residual disease or residual cancer burden may be useful as a variable to characterize response to treatment (Symmans, 2007). This measure is derived from the primary tumor dimensions, cellularity of the tumor bed, and axillary nodal burden. Each component contributes meaningful pathologic information and can be obtained using routine pathologic materials and methods of interpretation that could easily be implemented in routine diagnostic practice. RCB measurements can provide a continuous parameter of residual disease and thus of response, so that all subject responses contribute to the analysis.
  • RCB is divided into four survival-related classes (RCB-O to RCB-III) where patients with minimal residual disease (RCB-I) have the same 5 -year relapse-free survival as those with pCR (RCB-O), irrespective of the type of neoadjuvant chemotherapy administered, adjuvant hormonal therapy or the pathologic stage of RD. Therefore, the combination of RCB-O (pCR) and RCB-I expands the subset of patients who can be identified as having "good response" and to have benefited from the chemotherapy.
  • pCR RCB-O
  • RCB-I expands the subset of patients who can be identified as having "good response" and to have benefited from the chemotherapy.
  • Extensive residual disease (RCB-III), on the other hand, is associated with poor prognosis, irrespective of the type of neoadjuvant chemotherapy administered, adjuvant hormonal therapy, or the pathologic stage of RD.
  • all patients with RCB-III after T/FAC chemotherapy who did not receive adjuvant hormonal therapy, suffered distant relapse within 3 years (Symmans, 2007). This identifies an important subset of patients who are not responsive to chemotherapy, or with residual disease (after surgery) that is too extensive to be controlled by hormonal therapy alone.
  • RCB residual cancer burden
  • RCB categories can be employed with existing methods to define surrogate endpoints from neoadjuvant trials.
  • RCB is strongly and independently prognostic and the classes of RCB capture distinct sets of survival -based outcomes.
  • predictors specific to RCB-O pCR or complete response
  • RCB-0/I pCR+near-pCR called good response
  • RCB-III resistance
  • the inventors have also accounted for tumor sub-types based on the status of two receptors, Her2-neu and ER, allowing for the predictors to capture heterogeneity within breast cancers and achieve acceptable diagnostic performance.
  • Outcome can be represented in various forms to indicate probability of survival or the likely survival outcome after surgery and therapy.
  • survival rate is a part of survival analysis, indicating the percentage of people in a study or treatment group who are alive for a given period of time after diagnosis. Survival rates are important for prognosis; for example, whether a type of cancer has a good or bad prognosis can be determined from its survival rate or survival outcome.
  • Relative survival is calculated by dividing the overall survival after diagnosis of a disease by the survival as observed in a similar population that was not diagnosed with that disease.
  • a similar population is composed of individuals with at least age and gender similar to those diagnosed with the disease.
  • Cause specific survival is calculated by treating deaths from other causes than the disease as withdrawals from the population that don't lower survival, comparable to patients who are not observed any longer, e.g. due to reaching the end of the study period.
  • Relative survival has the advantage that it does not depend on accuracy of the reported cause of death; cause-specific survival has the advantage that it does not depend on the ability to find a similar population of people without the disease.
  • Survival is not the only endpoint that can be used as a metric in developing predictors such as those described herein. Endpoints or therapeutic outcomes can include survival or distant relapse-free survival (DRFS). Other endpoints are discussed in Cooper and Kaanders, Biological surrogate end-points in cancer trials: Potential uses, benefits and pitfalls, European Journal of Cancer, Volume 41, Issue 9, Pages 1261-1266, which is incorporated herein by reference.
  • a "surrogate marker” or “surrogate endpoint” or “secondary endpoint” typically will refer to a biological or clinical parameter that is measured in place of the biologically definitive or clinically most meaningful parameter, i.e., survival.
  • Primary endpoints may also include limitation of pharmacologic therapies, reduction of time to death, or reduction in the progression of the disease, disorder, or condition.
  • Surrogate markers are pathophysiologic parameters determined by medical or clinical laboratory diagnosis that are associated and have been correlated with the prognosis, progression, predisposition, or risk analysis with a disease, disorder, or condition that are not directly related to the primary diagnosed pathophysiologic condition.
  • Secondary endpoints are those that supplement the primary endpoint.
  • secondary endpoints include reduction in pharmacologic therapy, reduction in requirement of a medical device, or alteration of the progression of the disease disorder, or condition.
  • a clinical endpoint may refer to a disease, symptom, or sign that constitutes one of the target outcomes of the therapy or clinical trial.
  • the results of a therapy or clinical trial generally indicate the number of people enrolled who reached the predetermined clinical endpoint during the study interval, compared with the overall number of people who were enrolled. Once a patient reaches the endpoint, he or she is generally excluded from further experimental intervention (the origin of the term endpoint). For example, a clinical trial investigating the ability of a medication to prevent heart attack might use chest pain as a clinical endpoint. Any patient enrolled in the trial who develops chest pain over the course of the trial, then, would be counted as having reached that clinical endpoint. The results would ultimately reflect the fraction of patients who reached the endpoint of having developed chest pain, compared with the overall number of people enrolled.
  • the method used to identify predictive genes involved first, applying one or more filters to the gene expression data of all probes on an array to select the top probe sets to be used in signature development using the above described algorithm.
  • Gene filtering can be based on the regularized t-test for the selected response endpoint such as RCB-O (same as pathologic complete response), or RCB-0/I (very good response).
  • Other methods for gene filtering include methods that utilize non-specific global filtering criteria. These include, but are not limited to intensity-based filtering, which aims to remove genes that are not expressed at all in the samples studied or variability-based filtering, which aims to remove genes with low variability across samples.
  • a multivariate method was used to simultaneously select the signature genes and to calculate the predictor score.
  • the final predictor is determined by level of penalization, which determines the number of genes included in the predictive signature, and the choice of a decision threshold to dichotomize the classification score into a good prognosis and a bad prognosis group.
  • level of penalization determines the number of genes included in the predictive signature
  • a decision threshold to dichotomize the classification score into a good prognosis and a bad prognosis group.
  • performance - this step determines the signature probe sets and their weights.
  • a decision threshold is selected in order to optimize the predictive values of the predictor.
  • Evaluation of the predictors was based on the joint confidence interval of the positive predictive value (PPV) and the negative predictive value (NPV) of the predictor at 5% significance level (low 95% confidence limit of PPV > baseline response rate and low 95% confidence limit of NPV > 1 - baseline response rate).
  • TPR true positive rate
  • FPR false positive rate
  • the best possible prediction method would yield a point in the upper left corner or coordinate (0,1) of the ROC space, representing 100% sensitivity (all true positives are found) and 100% specificity (no false positives are found).
  • the (0,1) point is also called a perfect classification.
  • a completely random guess would give a point along a diagonal line (the so-called line of no- discrimination) from the left bottom to the top right corners. The diagonal line divides the ROC space in areas of good or bad classification/diagnostic.
  • a 5 -fold internal cross-validation can be used to select the optimal set of genes for the predictor and to tune the parameters of the predictor, e.g. , the degree of penalization. Since different optimal reporter gene sets might result from the different internal cross-validation folds, the number of times each gene is selected is tracked to provide a measure of its importance or its reliability. The trained predictor is then tested on the 1/5 hold-out part of the training dataset and its performance is evaluated based on the AUC.
  • the trained predictor or outcome predictor can be evaluated on the test set (1/3 of the original data) that was not used in training the predictor.
  • the permutation predictive performance of the predictor was estimated by randomly scrambling the outcome labels in the test dataset. The entire process of randomly splitting the data to a training and a test set was repeated a number of times to obtain the distributions and summary statistics of the performance metrics.
  • the decision threshold is varied along all possible values and for each value predictor performance (accuracy, positive predictive value (PPV), negative predictive value (NPV)) is determined.
  • the threshold is selected that yields the best compromise between PPV and NPV, as typically increasing PPV results in decreasing NPV.
  • the objective is to maximize both.
  • Protein expression can be detected in tumor tissue, cell material obtained by biopsy and the like.
  • a biopsy sample can be immobilized and contacted with an antibody, an antibody fragment or an aptamer that binds selectively to the protein to be detected.
  • the sample can be assayed to determine whether the antibody, fragment or aptamer has bound to the protein by techniques well known in the art.
  • Protein expression can be measured by a variety of methods including but not limited to Western blot, immunoblot, enzyme- linked immunosorbant assay (ELISA), radioimmunoassay (RIA), immunoprecipitation, surface plasmon resonance, immunohistochemical (IHC) analysis, mass spectrometry, fluorescence activated cell sorting (FACS) and flow cytometry.
  • ELISA enzyme- linked immunosorbant assay
  • RIA radioimmunoassay
  • IHC immunohistochemical
  • FACS fluorescence activated cell sorting
  • IHC analysis is used to measure protein expression.
  • the level of expression for a sample is determined by IHC by staining the sample for a particular expression marker and developing a score for the staining.
  • monoclonal antibodies can be used to stain for the expression of a marker of interest.
  • Mouse antibodies are known for use in the staining of the marker PTEN.
  • Samples can be evaluated for the frequency of cells stained for each sample and the intensity of the stain. Typically, a score based on the frequency (rated from 0-4) and intensity (rated from 0-4) of the stained sample is developed as a measure of overall expression. Exemplary but non-limiting methods for IHC and criteria for scoring expression are described in detail in Handbook of Immunohistochemistry and In Situ Hybridization in Human Carcinomas, M. Hayat Ed., 2004, Academic Press.
  • a predictor or transcriptional profile index is used to measure the expression of many genes that provide predictive information about a likely outcome for a particular patient.
  • the invention includes the methods for standardizing the expression values of future samples to a normalization standard that will allow direct comparison of the results to past samples, such as from a clinical trial.
  • the invention also includes the biostatistical methods to calculate and report such results.
  • a sample as used herein can comprise any number of cells that is sufficient for a clinical diagnosis or prognosis, and typically contain at least, at most or about 100 target cells.
  • Microarrays provide a suitable method to measure gene expression from clinical samples. mRNA levels measured by microarrays, such as Affymetrix Ul 33 A gene chips, in fine needle aspirates (FNA), core needle biopsy, resected tissue samples from excised tumor and/or frozen tumor tissue samples of breast cancer correlated closely with protein expression by enzyme immunoassay and by routine immunohistochemistry.
  • FNA fine needle aspirates
  • Estrogen receptor and Her2-neu status ER-positive breast cancer includes a continuum of ER expression that might reflect a continuum of biologic behavior and endocrine sensitivity. Others have reported that some breast cancers are difficult to predict as ER-positive based on transcriptional profile and described non-estrogenic growth effects, such as HER-2, more frequently in this small subset of tumors with aggressive natural history (Kun et al., 2003). Indeed, ER mRNA levels are lower in breast cancers that are positive for both ER and HER2 (Konecny et al, 2003).
  • Diagnostic tools are needed not merely for prognosis, but, for providing a biological rationale and to demonstrate clinical benefit when they are used to guide the selection and duration of therapies, particularly in light of the cost, complexity, toxicity, benefits and other factors related to such therapies.
  • An index or predictor can be used to predict the likelihood of response rather than intrinsic prognosis.
  • hormone therapies may be employed in the treatment of patients identified as having hormone sensitive cancers.
  • Hormones, or other compounds that stimulate or inhibit these pathways can bind to hormone receptors, blocking a cancer's ability to get the hormones it needs for growth. By altering the hormone supply, hormone therapy can inhibit growth of a tumor or shrink the tumor.
  • these cancer treatments only work for hormone-sensitive cancers. If a cancer is hormone sensitive, a patient might benefit from hormone therapy as part of cancer treatment. Sensitive to hormones is usually determined by taking a sample of a tumor (biopsy) and conducting analysis in a laboratory.
  • Chemotherapy is the use of chemical substances to treat disease. In its modern-day use, it refers to cytotoxic drugs used to treat cancer or the combination of these drugs into a standardized treatment regimen. There are a number of strategies in the administration of chemotherapeutic drugs used today. Chemotherapy may be given with a curative intent or it may aim to prolong life or to palliate symptoms.
  • Combined modality chemotherapy is the use of drugs with other cancer treatments, such as radiation therapy or surgery.
  • Combination chemotherapy is a similar practice which involves treating a patient with a number of different drugs simultaneously, e.g., T/FAC therapy.
  • the drugs typically differ in their mechanism and side effects. The biggest advantage is minimizing the chances of resistance developing to any one agent.
  • initial chemotherapy is aimed for shrinking the primary tumor, thereby rendering local therapy (surgery or radiotherapy) less destructive or more effective.
  • Adjuvant chemotherapy postoperative treatment
  • the chemotherpay is a taxane chemotherapy.
  • the taxanes are diterpenes produced by the plants of the genus Taxus (yews). As their name suggests, they were first derived from natural sources, but some have been synthesized artificially. Taxanes include, but is not limited to Docetaxel, Larotaxel, Ortataxel, Paclitaxel, and Tesetaxel. Taxanes prevent growth of cancer cells by inhibiting the breakdown of microtubules, which normally occurs once a cell stops dividing. Thus, treated cells become so clogged with microtubules that they cannot grow and divide.
  • Paclitaxel is isolated from the bark of the ash tree, Taxus brevifolia. It binds to tubulin (at a site distinct from that used by the vinca alkaloids) and promotes the assembly of microtubules. It has activity against malignant melanoma and carcinoma of the ovary. Maximal doses are 30 mg/m per day for 5 days or 210 to 250 mg/m given once every 3 weeks. Of course, all of these dosages are exemplary, and any dosage in-between these points is also expected to be of use in the invention. Docetaxel, a compound that is similar to paclitaxel, and is also used to treat cancer. Docetaxel comes from the needles of the yew tree. The FDA has approved docetaxel to treat advanced breast, lung, and ovarian cancer.
  • Steroids can inhibit tumor growth or the associated edema (tissue swelling), and may cause regression of lymph node malignancies.
  • Prostate cancer is often sensitive to finasteride, an agent that blocks the peripheral conversion of testosterone to dihydrotestosterone.
  • Breast cancer cells often highly express the estrogen and/or progesterone receptor. Inhibiting the production (with aromatase inhibitors) or action (with tamoxifen) of these hormones can often be used as an adjunct to therapy.
  • Gonadotropin-releasing hormone agonists such as goserelin possess a paradoxic negative feedback effect followed by inhibition of the release of FSH (follicle-stimulating hormone) and LH (luteinizing hormone), when given continuously.
  • FSH follicle-stimulating hormone
  • LH luteinizing hormone
  • Hormone therapy may be used in combination with other types of cancer treatments, including surgery, radiation and chemotherapy.
  • a hormone therapy can be used before a primary cancer treatment, such as before surgery to remove a tumor. This is called neoadjuvant therapy.
  • Hormone therapy can sometimes shrink a tumor to a more manageable size so that it's easier to remove during surgery.
  • Hormone therapy is sometimes given in addition to the primary treatment — usually after — in an effort to prevent the cancer from recurring (adjuvant therapy).
  • hormone therapy is sometimes used as a primary treatment.
  • Hormone therapy can be given in several forms, including: (A) Surgery — Surgery can reduce the levels of hormones in your body by removing the parts of your body that produce the hormones, including: Testicles (orchiectomy or castration), Ovaries (oophorectomy) in premenopausal women, Adrenal gland (adrenalectomy) in postmenopausal women, Pituitary gland (hypophysectomy) in women. Because certain drugs can duplicate the hormone-suppressive effects of surgery in many situations, drugs are used more often than surgery for hormone therapy. And because removal of the testicles or ovaries will limit an individual's options when it comes to having children, younger people are more likely to choose drugs over surgery.
  • Radiation Radiation is used to suppress the production of hormones. Just as is true of surgery, it's used most commonly to stop hormone production in the testicles, ovaries, and adrenal and pituitary glands.
  • Pharmaceuticals Various drugs can alter the production of estrogen and testosterone. These can be taken in pill form or by means of injection.
  • Anti-hormones that block the cancer cell's ability to interact with the hormones that stimulate or support cancer growth. Though these drugs do not reduce the production of hormones, anti-hormones block the ability to use these hormones.
  • Anti-hormones include the anti-estrogens tamoxifen (Nolvadex) and toremifene (Fareston) for breast cancer, and the anti-androgens flutamide (Eulexin) and bicalutamide (Casodex) for prostate cancer.
  • Aromatase inhibitors target enzymes that produce estrogen in postmenopausal women, thus reducing the amount of estrogen available to fuel tumors. AIs are only used in postmenopausal women because the drugs can't prevent the production of estrogen in women who haven't yet been through menopause. Approved AIs include letrozole (Femara), anastrozole (Arimidex) and exemestane (Aromasin).
  • LH-RH agonists and antagonists LH-RH agonists and antagonists
  • LH-RH agonists sometimes called analogs
  • LH-RH antagonists reduce the level of hormones by altering the mechanisms in the brain that tell the body to produce hormones.
  • LH-RH agonists are essentially a chemical alternative to surgery for removal of the ovaries for women, or of the testicles for men. Depending on the cancer type, one might choose this route if they hope to have children in the future and want to avoid surgical castration. In most cases the effects of these drugs are reversible.
  • LH-RH agonists examples include: Leuprolide (Lupron, Viadur, Eligard) for prostate cancer, Goserelin (Zoladex) for breast and prostate cancers, Triptorelin (Trelstar) for ovarian and prostate cancers and abarelix (Plenaxis).
  • SERMs Selective Estrogen Receptor Modulators
  • SERMs block the action of estrogen in the breast and certain other tissues by occupying estrogen receptors inside cells.
  • SERMs include, but are not limited to tamoxifen (the brand name is Nolvadex, generic tamoxifen citrate); Raloxifene (brand name: Evista), and toremifene (brand name: Fareston).
  • kits for the measurement, analysis, and reporting of gene expression and transcriptional output include kits for the measurement, analysis, and reporting of gene expression and transcriptional output.
  • a kit may include, but is not limited to microarray, quantitative RT-PCR, antibodies, labeling or other reagents and materials, as well as hardware and/or software for performing at least a portion of the methods described.
  • custom microarrays or analysis methods for existing microarrays are contemplated.
  • methods of the invention include methods of accessing and using a reporting system that compares a single result to a scale of clinical trial results.
  • a digital standard for data normalization is contemplated so that the assay result values from future samples would be able to be directly compared with the assay value results from past samples, such as from specific clinical trials.
  • RNAlaterTM solution (Ambion, Austin TX) and stored at -80 0 C.
  • FNA samples on average contain 80% neoplastic cells and contain little or no stromal cells or normal breast epithelium.
  • Gene expression data generated from FNAs captures the molecular characteristics of the invasive cancer including the molecular class (Pusztai, 2003). At least 70% of all aspirations yielded at least 1 ⁇ g total RNA that is required for the gene expression profiling. The main reason for failure to obtain sufficient RNA was acellular aspirations. Two hundred and twenty seven (227) consecutively accrued patients with at least 1 ⁇ g RNA were included in this analysis.
  • pCR Pathologic complete response
  • FIG. 1 summarizes the stratification of the cases used in this example and the response rates within each subgroup.
  • RNA extraction and gene expression profiling - RNA was extracted from FNA samples using the RNAeasy KitTM (Qiagen, Valencia CA). The amount and quality of RNA was assessed with DU-640 U.V. Spectrophotometer (Beckman Coulter, Fullerton, CA) and was considered adequate for further analysis if the OD 260/280 ratio was >1.8 and the total RNA yield was >l ⁇ g.
  • cRNA generation and second-strand cDNA synthesis was performed as described previously (Rouzier, 2005; Stec, 2005; Symmans, 2003). No second round amplification was performed. Biotin-labeled cDNA was hybridized to Affymetrix Ul 33 A gene chips following the vendor's standard protocols.
  • Microarray data normalization and data analysis - Raw data were generated from Affymetrix chip reader were saved as CEL files. Bioconductor software, which can be found on the World Wide Web at bioconductor.org, was used to generate probe-level intensities and quality measures for each chip. Each chip was normalized using MAS5 (mean 600) using the Bioconductor/R software. Log2- transformed expression values for each probe set were used in subsequent analyses. A reference set of 1322 breast specific (invariant) genes ("housekeeping genes”) and their mean expression intensities were established from a reference breast cancer sample database obtained from MD Anderson Cancer Center.
  • a critical step when building prediction rules of treatment response or disease state in general from gene expression data is to select a small subset of informative genes that will be used as prognostic features in the predictor.
  • Most predictors employ univariate filtering to rank the candidate genes according to the p-value of a two- sample unequal variance t-test comparing the mean expression values of each gene in the two response classes (e.g., pCR and RD).
  • Univariate filtering methods have the disadvantage that they do not deal well with redundant features (genes that have similar expression profiles) and therefore the resulting predictors tend to be less robust (Lai, 2006).
  • the inventors used an approach that combines feature selection and model discovery using a multivariate penalized approach called Gradient Directed Regularization developed by Prof. J. Friedman at Stanford University, a description of which can be found on the World Wide Web at stat.stanford.edu/ ⁇ jhf/ftp/pathlite.pdf.
  • the informative genes are selected through penalization using the maximization of the area under the ROC curve (AUC) as the optimization criterion.
  • AUC area under the ROC curve
  • Ma and Huang have previously used a similar approach for disease classification (Ma, 2006).
  • the number of times each gene is selected is tracked to provide a measure of its importance or its reliability.
  • the trained predictor is then tested on the 1/5 hold-out part of the training dataset and its performance is evaluated based on the AUC.
  • the trained predictor is then evaluated on the test set (1/3 of the original data) that was not used in training the predictor.
  • the permutation predictive performance of the predictor was estimated by randomly scrambling the outcome labels in the test dataset. The entire process of randomly splitting the data to a training and a test set was repeated 100 times to obtain the distributions and summary statistics of the performance metrics.
  • the predictor is determined by level of penalization and the choice of a decision threshold.
  • the inventors selected the maximum level of penalization resulting in the smallest signatures that yield significant cross-validated predictor performance - this step determines the signature probe sets and their weights.
  • a decision threshold is selected in order to optimize the predictive values of the predictor. Evaluation of the predictors was based on the joint confidence interval of the positive predictive value (PPV) and the negative predictive value (NPV) of the predictor at 5% significance level (low 95% confidence limit of PPV > baseline response rate & low 95% confidence limit of NPV > 1 - baseline response rate).
  • RCB-O which constitutes complete pathologic response
  • Table 2 lists the 64 probes (probe set # on Affymetrix Ul 33 A gene chip) and corresponding genes that predict likelihood of achieving RCB-O. The genes are ranked, in decreasing order, according to their predictive power to predict RCB-O in a patient.
  • Table 2 Genes used for prediction of RCB-O in Her2 -normal patients after T/FAC chemotherapy
  • the predictor is not informative in the latter cohort indicating its specific value in patients whose Her2 receptor status is normal.
  • FIG. 5 shows the survival outcome of patients defined by distant relapse-free survival (DRFS) over a period of about 7 years. These patients have undergone surgery where it was considered appropriate and the ER-positive patients received hormonal therapy (tamoxifen) for 5 years after the surgery. ER-negative patients did not receive any treatment post-surgery.
  • the plot shows that predicted high responders to T/FAC chemotherapy (FIG. 5) show a high proportion of patients free of distant relapse (-90%) whereas the remainder show considerably lower relapse-free survival among the patients (-60% in about 7 years).
  • the two curves are statistically distinct (p ⁇ 0.001) indicating that the composite predictor of good response among patients can discriminate patients effectively for survival-related response with high statistical significance.
  • FIG. 6 shows the survival outcome as distant relapse-free survival (DRFS) in ER-stratif ⁇ ed patients over a period of about 7 years. These patients are the same as those shown in FIG. 5 but stratified for ER status. All patients have undergone surgery where it was considered appropriate and the ER-positive patients received hormonal therapy (tamoxifen) for 5 years after the surgery. ER-negative patients did not receive any treatment post-surgery. The analysis of performance separately in ER-stratified patients is important to examine and account for heterogeneity of tumors and the corresponding differences in response to therapy. The plots show that, in both groups, predicted high responders to T/FAC chemotherapy (FIG.
  • Example 3 Compared to Example 3, a difference in analysis methodology was employed in this example because different types of biological specimens might contain different numbers of tumor cells compared to stromal cells or cells from other contaminating organs (Symmans et al, 2003). Steps were taken, therefore, to improve the robustness of the predictors with respect to their applicability to different sample types.
  • a reference set of 1322 of breast cancer specific genes were identified from a set of core biopsies, fine needle aspirations and lymph node biopsies. A reference distribution for these breast cancer specific genes was established from a database of breast cancer fine needle aspiration samples. This reference distribution was then used to scale (normalize) microarray profiles generated from breast tumor biopsies.
  • probe sets were evaluated for concordance of expression in matched pairs of core biopsy and fine need aspiration breast cancer samples. Probe sets that exhibited concordance bias of more than 5% were removed from the candidate pool for predictor development. This resulted in a response predictor that yields considerably more concordant results when applied to core biopsies or fine need aspiration samples from the same tumor.
  • FIG. 7 shows the composite resubstitution performance of the predictors in predicting excellent response (RCB — 0/1) in the total patient dataset.
  • the different thresholds employed for ER-positive and ER-negative patients are also shown in the figure.
  • the response output by the predictor of all cases in this development cohort was reported correctly.
  • measurement of the resubstitution performance reflects an optimistic value of prediction efficiency whereas the predictor efficiency in an independent patient cohort may be somewhat lower.
  • FIG. 8 shows the survival outcome of patients defined by distant relapse-free survival (DRFS) over a period of about 7 years. These patients have undergone surgery where it was considered appropriate and the ER-positive patients received hormonal therapy (tamoxifen) for 5 years after the surgery. ER-negative patients did not receive any treatment post-surgery.
  • the plot shows that predicted high responders to T/FAC chemotherapy (FIG. 8) show a high proportion of patients free of distant relapse (-90% DRFS) whereas the remainder (FIG. 8) show considerably lower relapse-free survival among the patients (-60% in about 7 years).
  • the numbers shown represent the percentage of total patients who are in the respective category.
  • the two curves are statistically distinct (p ⁇ 0.001) indicating that the composite predictor of good response among patients can discriminate patients effectively for survival-related response with high statistical significance.
  • FIG. 9 shows the survival outcome as distant relapse-free survival (DRFS) in ER-stratified patients over a period of about 7 years. These patients are the same as those shown in FIG. 8 but stratified for ER status. All patients have undergone surgery where it was considered appropriate and the ER-positive patients received hormonal therapy (tamoxifen) for 5 years after the surgery. ER-negative patients did not receive any treatment post-surgery. The analysis of performance separately in ER-stratified patients is important to examine and account for heterogeneity of tumors and the corresponding differences in response to therapy. The plots show that, in both groups, predicted high responders to T/FAC chemotherapy (FIG.
  • FIG. 9 represents a significant advance over conventional techniques of therapy selection because of the predictor's ability to account for tumor heterogeneity and its inherent impact of response outcomes from therapy.
  • ER- positive tumors in general, have a better overall prognosis than ER-negative tumors, as evident from comparing the average baselines of DRFS between the left (ER- positive) and right (ER-negative) panels of FIG. 9.
  • the predictor exhibits strong predictive power in separating good responders (17.6% of patients) from remainder who are poor responders, where good response translates to excellent survival post-therapy. More significantly, in ER-negative and Her2- negative tumors (shown at right in FIG.
  • RNA yield and microarray gene expression profiles from fine -needle aspiration biopsy and core-needle biopsy samples of breast carcinoma Cancer 97(12): 2960-71.

Abstract

L'invention concerne des procédés d'évaluation d'un patient et de détermination de la probabilité d'un résultat de traitement par rapport à une ou quatre catégories de classification de charge de cancer résiduelle.
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