WO2008115561A2 - Biomarkers and methods for determining sensitivity to microtubule-stabilizing agents - Google Patents

Abstract

Biomarkers that are useful for identifying a mammal that will respond therapeutically or is responding therapeutically to a method of treating cancer that comprises administering a microtubule-stabilizing agent. In one aspect, the cancer is breast cancer, and the microtubule-stabilizing agent is an epothilone or analog or derivative thereof, or ixabepilone.

Description

BIOMARKERS AND METHODS FOR DETERMINING SENSITIVITY TO MICROTUBULE-STABILIZING AGENTS

SEQUENCE LISTING: A compact disc labeled "Copy 1" contains the Sequence Listing as 10979

PCT.ST25.txt. The Sequence Listing is 2357 KB in size and was recorded March 21, 2008. The compact disk is 1 of 2 compact disks. A duplicate copy of the compact disc is labeled "Copy 2" and is 2 of 2 compact discs.

The compact disc and duplicate copy are identical and are hereby incorporated by reference into the present application.

FIELD OF THE INVENTION

The present invention relates generally to the field of pharmacogenomics, and more specifically to methods and procedures to determine drug sensitivity in patients to allow the identification of individualized genetic profiles which will aid in treating diseases and disorders.

' BACKGROUND OF THE INVENTION:

Cancer is a disease with extensive histoclinical heterogeneity. Although conventional histological and clinical features have been correlated to prognosis, the same apparent prognostic type of tumors varies widely in its responsiveness to therapy and consequent survival of the patient.

New prognostic and predictive markers, which would facilitate an individualization of therapy for each patient, are needed to accurately predict patient response to treatments, such as small molecule or biological molecule drugs, in the clinic. The problem may be solved by the identification of new parameters that could better predict the patient's sensitivity to treatment. The classification of patient samples is a crucial aspect of cancer diagnosis and treatment. The association of a patient's response to a treatment with molecular and genetic markers can open up new opportunities for treatment development in non-responding patients, or distinguish a treatment's indication among other treatment choices because of higher confidence in the efficacy. Further, the pre-selection of patients who are likely to respond well to a medicine, drug, or combination therapy may reduce the number of patients needed in a clinical study or accelerate the time needed to complete a clinical development program (M. Cockett et al., Current Opinion in Biotechnology, 11 :602-609 (2000)).

The ability to predict drug sensitivity in patients is particularly challenging because drug responses reflect not only properties intrinsic to the target cells, but also a host's metabolic properties. Efforts to use genetic information to predict drug sensitivity have primarily focused on individual genes that have broad effects, such as the multidrug resistance genes, mdrl and mrpl (P. Sonneveld, J. Intern. Med., 247:521-534 (2000)).

The development of microarray technologies for large scale characterization of gene mRNA expression pattern has made it possible to systematically search for molecular markers and to categorize cancers into distinct subgroups not evident by traditional histopathological methods (J. Khan et al., Cancer Res., 58:5009-5013 (1998); A.A. Alizadeh et al., Nature, 403:503-511 (2000); M. Bittner et al., Nature, 406:536-540 (2000); J. Khan et al., Nature Medicine, 7(6):673-679 (2001); T.R. Golub et al., Science, 286:531-537 (1999); U. Alon et al., P. N. A. S. USA, 96:6745- 6750 (1999)). Such technologies and molecular tools have made it possible to monitor the expression level of a large number of transcripts within a cell population at any given time (see, e.g., Schena et al., Science, 270:467-470 (1995); Lockhart et al., Nature Biotechnology, 14:1675-1680 (1996); Blanchard et al., Nature Biotechnology, 14: 1649 (1996); U.S. Patent No. 5,569,588 to Ashby et al.).

Recent studies demonstrate that gene expression information generated by microarray analysis of human tumors can predict clinical outcome (LJ. van't Veer et al., Nature, 415:530-536 (2002); T. Sorlie et al., P. N. A. S. USA, 98:10869-10874 (2001); M. Shipp et al., Nature Medicine, 8(l):68-74 (2002); G. Glinsky et al., The Journal of Clin. Invest., 113(6):913-923 (2004)). These findings bring hope that cancer treatment will be vastly improved by better predicting the response of individual tumors to therapy.

Needed are new and alternative methods and procedures to determine drug sensitivity in patients to allow the development of individualized genetic profiles which are necessary to treat diseases and disorders based on patient response at a molecular level. SUMMARY OF THE INVENTION:

The invention provides methods and procedures for determining patient sensitivity to one or more microtubule-stabilizing agents. The invention also provides methods of determining or predicting whether an individual requiring therapy for a disease state such as cancer will or will not respond to treatment, prior to administration of the treatment, wherein the treatment comprises administration of one or more microtubule-stabilizing agents.

A method for identifying a mammal that will respond therapeutically to a method of treating cancer comprising administering a microtubule-stabilizing agent, wherein the method comprises: (a) exposing a biological sample from the mammal to said agent; (b) following the exposing of step (a), measuring in said biological sample the level of the at least one biomarker selected from the biomarkers of Table 2, wherein a difference in the level of the at least one biomarker measured in step (b), compared to the level of the at least one biomarker in a mammal that has not been exposed to said agent, indicates that the mammal will respond therapeutically to said method of treating cancer. In one aspect, the at least one biomarker comprises CAPG and/or TACC3. hi another aspect, the invention provides a method for determining whether a mammal is responding therapeutically to a microtubule-stabilizing agent, comprising: (a) exposing a biological sample from the mammal to said agent; (b) following the exposing of step (a), measuring in said biological sample the level of the at least one biomarker selected from the biomarkers of Table 2, wherein a difference in the level of the at least one biomarker measured in step (b), compared to the level of the at least one biomarker in a mammal that has not been exposed to said agent, indicates that the mammal will respond therapeutically to said method of treating cancer.

A method for predicting whether a mammal will respond therapeutically to a method of treating cancer comprising administering a microtubule-stabilizing agent, wherein the method comprises: (a) measuring in the mammal the level of at least one biomarker selected from the biomarkers of Table 2; (b) exposing a biological sample from said mammal to said agent; (c) following the exposing of step (b), measuring in said biological sample the level of the at least one biomarker, wherein a difference in the level of the at least one biomarker measured in step (c) compared to the level of the at least one biomarker measured in step (a) indicates that the mammal will respond therapeutically to said method of treating cancer

In another aspect, the invention provides a method for determining whether an agent stabilizes microtubules and has cytotoxic activity against rapidly proliferating cells, such as, tumor cells or other hyperproliferative cellular disease in a mammal, comprising: (a) exposing the mammal to the agent; and (b) following the exposing of step (a), measuring in the mammal the level of at least one biomarker selected from the biomarkers of Table 2.

As used herein, respond therapeutically refers to the alleviation or abrogation of the cancer. This means that the life expectancy of an individual affected with the cancer will be increased or that one or more of the symptoms of the cancer will be reduced or ameliorated. The term encompasses a reduction in cancerous cell growth or tumor volume. Whether a mammal responds therapeutically can be measured by many methods well known in the art, such as PET imaging. The amount of increase in the level of the at least one biomarker measured in the practice of the invention can be readily determined by one skilled in the art. In one aspect, the increase in the level of a biomarker is at least a two-fold difference, at least a three-fold difference, or at least a four-fold difference in the level of the biomarker. The mammal can be, for example, a human, rat, mouse, dog, rabbit, pig sheep, cow, horse, cat, primate, or monkey.

The method of the invention can be, for example, an in vitro method wherein the step of measuring in the mammal the level of at least one biomarker comprises taking a biological sample from the mammal and then measuring the level of the biomarker(s) in the biological sample. The biological sample can comprise, for example, at least one of whole fresh blood, peripheral blood mononuclear cells, frozen whole blood, fresh plasma, frozen plasma, urine, saliva, skin, hair follicle, bone marrow, or tumor tissue.

The level of the at least one biomarker can be, for example, the level of protein and/or mRNA transcript of the biomarker(s).

The invention also provides an isolated biomarker selected from the biomarkers of Table 2. The biomarkers of the invention comprise sequences selected from the nucleotide and amino acid sequences provided in Table 2 and the Sequence Listing, as well as fragments and variants thereof.

The invention also provides a biomarker set comprising two or more biomarkers selected from the biomarkers of Table 2. The invention also provides a method for predicting whether a mammal will respond therapeutically to a method of treating cancer comprising administering ixabepilone, wherein the method comprises: (a) measuring in a biological sample from the mammal the expression levels of a set of biomarkers comprising the biomarkers of Table 4; (b) exposing the mammal to ixabepilone; (c) following the exposing of step (b), measuring in a second biological sample the expression levels of the set of biomarkers, wherein a large weighted combination of the expression levels indicates that the mammal will respond therapeutically to the method of treating cancer, and wherein a small weighted combination of the expression levels indicates that the mammal will not respond therapeutically to the method of treating cancer, hi one aspect, the expression levels of the set of biomarkers is the level of the DNA or RNA sequences of Table 4. In another aspect, the expression levels of the set of biomarkers is the level of the amino acid sequences of Table 4.

The invention also provides a method for predicting whether a mammal will respond therapeutically to a method of treating cancer comprising administering ixabepilone, wherein the method comprises: (a) exposing the mammal to ixabepilone; (b) following the exposing of step (a), measuring in a biological sample from the mammal the expression levels of a set of biomarkers comprising the biomarkers of Table 4, wherein a large weighted combination of the expression levels indicates that the mammal will respond therapeutically to the method of treating cancer, and wherein a small weighted combination of the expression levels indicates that the mammal will not respond therapeutically to the method of treating cancer. In one aspect, the expression levels of the set of biomarkers is the level of the DNA or RNA sequences of Table 4. In another aspect, the expression levels of the set of biomarkers is the level of the amino acid sequences of Table 4. The invention also provides a method for predicting whether a mammal will respond therapeutically to a method of treating cancer comprising administering ixabepilone, wherein the method comprises: (a) measuring in a biological sample from the mammal the expression levels of a set of biomarkers comprising the biomarkers of Table 5; (b) exposing the mammal to ixabepilone; (c) following the exposing of step (b), measuring in a second biological sample the expression levels of the set of biomarkers, wherein a large weighted combination of the expression levels indicates that the mammal will not respond therapeutically to the method of treating cancer, and wherein a small weighted combination of the expression levels indicates that the mammal will respond therapeutically to the method of treating cancer, hi one aspect, the expression levels of the set of biomarkers is the level of the DNA or RNA sequences of Table 5. In another aspect, the expression levels of the set of biomarkers is the level of the amino acid sequences of Table 5.

The invention also provides a method for predicting whether a mammal will respond therapeutically to a method of treating cancer comprising administering ixabepilone, wherein the method comprises: (a) exposing the mammal to ixabepilone; (b) following the exposing of step (a), measuring in a biological sample from the mammal the expression levels of a set of biomarkers comprising the biomarkers of Table 5, wherein a large weighted combination of the expression levels indicates that the mammal will not respond therapeutically to the method of treating cancer, and wherein a small weighted combination of the expression levels indicates that the mammal will respond therapeutically to the method of treating cancer. In one aspect, the expression levels of the set of biomarkers is the level of the DNA or RNA sequences of Table 5. hi another aspect, the expression levels of the set of biomarkers is the level of the amino acid sequences of Table 5.

The invention also provides kits for determining or predicting whether a patient would be susceptible or resistant to a treatment that comprises one or more microtubule-stabilizing agents. The patient may have a cancer or tumor such as, for example, a breast cancer or tumor.

In one aspect, the kit comprises a suitable container that comprises one or more specialized microarrays of the invention, one or more microtubule-stabilizing agents for use in testing cells from patient tissue specimens or patient samples, and instructions for use. The kit may further comprise reagents or materials for monitoring the expression of a biomarker set at the level of mRNA or protein. In another aspect, the invention provides a kit comprising two or more biomarkers selected from the biomarkers of Table 2.

In yet another aspect, the invention provides a kit comprising at least one of an antibody and a nucleic acid for detecting the presence of at least one of the biomarkers selected from the biomarkers of Table 2. In one aspect, the kit further comprises instructions for determining whether or not a mammal will respond therapeutically to a method of treating cancer comprising administering a microtubule-stabilizing agent.

The invention also provides screening assays for determining if a patient will be susceptible or resistant to treatment with one or more microtubule-stabilizing agents.

The invention also provides a method of monitoring the treatment of a patient having a disease, wherein the disease is treated by a method comprising administering one or more microtubule-stabilizing agents. The invention also provides individualized genetic profiles which are necessary to treat diseases and disorders based on patient response at a molecular level.

The invention also provides specialized microarrays, e.g., oligonucleotide microarrays or cDNA microarrays, comprising one or more biomarkers having expression profiles that correlate with either sensitivity or resistance to one or more microtubule-stabilizing agents.

The invention also provides antibodies, including polyclonal or monoclonal, directed against one or more biomarkers of the invention.

The invention will be better understood upon a reading of the detailed description of the invention when considered in connection with the accompanying figures.

BRIEF DESCRIPTION OF THE FIGURES: FIG. 1 illustrates the results obtained from GO analysis with GSEA enrichment scores. The ranking scores from GSEA were used to run the ErmineJ Gene Ontology program. FIG. 2 illustrates the microtubule network built by GeneGo.. The top 100 genes based upon ranking scores from GSEA were used for gene network analysis with the GeneGo program. 22 out of 51 genes within the microtubule network were found (highlighted with dots; p = 5.27e-45). FIG. 3 illustrates the distribution of normalized, median-centered expression levels for a probe set representing transforming, acidic coiled-coil containing protein 3 (TACC3, 218308_at), for pCR subjects in CAl 63080 (BMS) and MDAl 33 (MDA). P-value is from a t-test of the mean difference between the two studies.

FIG. 4 illustrates the distribution of normalized, median-centered expression levels for a probe set representing chromosome condensation protein G (CAPG,

218662_s_at), for pCR subjects in CAl 63080 (BMS) and MDAl 33 (MDA). P-value is from a t-test of the mean difference between the two studies.

FIG. 5 illustrates ROC curves for a predictive model based on TACC3 gene expression. This model showed predictive utility for CAl 63080 subjects but not for MDAl 33 subjects.

FIG. 6 illustrates ROC curves for a predictive model based on CAPG gene expression. This model showed predictive utility for CA163080 subjects but not for MDAl 33 subjects.

FIG. 7 illustrates ROC curves for the 26 biomarker model fit to the CA163080 subjects. Each ROC curve is the point- wise mean of 200 individual curves, from 50 replicates of 4-fold cross-validation of the entire model-building process.

FIG. 8 illustrates ROC curves for the 20 biomarker model fit to the MDAl 33 subjects. Each ROC curve is the point-wise mean of 200 individual curves, from 50 replicates of 4-fold cross-validation of the entire model-building process. FIG. 9 illustrates mRNA expression level of CAPG in breast cancer cell lines.

The cell lines are in order of increasing IC₅₀ values from left to right.

FIG. 10 illustrates CAPG down-regulation by siRNA in cells was confirmed by gene expression profiling and western blot. Fold change in gene expression is the ratio of CAPG gene expression level in siRNA-transfected MDA-MB-231 cells to that in negative control cells. FIG. 11 illustrates CAPG down regulation increased resistance to ixabepilone (P<0.001), but not to paclitaxel. Fold change is the ratio of IC₅₀ for siRNA- transfected cells to IC₅₀ for the negative control.

DETAILED DESCRIPTION OF THE INVENTION:

The invention provides biomarkers that correlate with microtubule- stabilization agent sensitivity or resistance. These biomarkers can be employed for predicting response to one or more microtubule-stabilization agents. In one aspect, the biomarkers of the invention are those provided in Tables 2, 4, and 5, and the Sequence Listing, including both polynucleotide and polypeptide sequences.

The biomarkers provided in Tables 2, 4, and 5 include the nucleotide and amino acid sequences provided in the sequence listing and, also, the nucleotide sequences that, due to the degeneracy of the genetic code, encode the amino acid sequences of the sequence listing.

MICROTUBULE-STABILIZING AGENTS

Agents that affect microtubule-stabilization are well known in the art. These agents have cytotoxic activity against rapidly proliferating cells, such as, tumor cells or other hyperproliferative cellular disease. hi one aspect, the microtubule-stabilizing agent is an epothilone, or analog or derivative thereof. The epothilones, including analogs and derivatives thereof, may be found to exert microtubule-stabilizing effects similar to paclitaxel (Taxol^®) and, hence, cytotoxic activity against rapidly proliferating cells, such as, tumor cells or other hyperproliferative cellular disease. Suitable microtubule-stabilizing agents are disclosed, for example, in the following PCT publications hereby incorporated by reference : WO93/ 10121;

WO98/22461; WO99/02514; WO99/58534; WO00/39276; WO02/14323;

WO02/72085; WO02/98868; WO03/070170; WO03/77903; WO03/78411;

WO04/80458; WO04/56832; WO04/14919; WO03/92683; WO03/74053; WO03/57217; WO03/22844; WO03/103712; WO03/07924; WO02/74042;

WO02/67941; WOO 1/81342; WO00/66589; WOOO/58254; WO99/43320; WO99/42602; WO99/39694; WO99/16416; WO 99/07692; WO99/03848; WO99/01124; and WO 98/25929.

In another aspect, the microtubule-stabilizing agent is ixabepilone. Ixabepilone is a semi-synthetic analog of the natural product epothilone B that binds to tubulin in the same binding site as paclitaxel, but interacts with tubulin differently. (P. Giannakakou et al., P. N. A. S. USA, 97, 2904-2909 (2000)).

In another aspect, the microtubule-stabilizing agent is a taxane. The taxanes are well known in the art and include, for example, paclitaxel (Taxol^®) and docetaxel (Taxotere^® ).

BIOMARKERS AND BIOMARKER SETS

The invention includes individual biomarkers and biomarker sets having both diagnostic and prognostic value in disease areas in which microtubule-stabilization and/or cytotoxic activity against rapidly proliferating cells, such as, tumor cells or other hyperproliferative cellular disease is of importance, e.g., in cancers or tumors. The biomarker sets comprise a plurality of biomarkers such as, for example, a plurality of the biomarkers provided in Table 2, that highly correlate with resistance or sensitivity to one or more microtubule-stabilizing agents.

The biomarker sets of the invention enable one to predict or reasonably foretell the likely effect of one or more microtubule-stabilizing agents in different biological systems or for cellular responses. The biomarker sets can be used in in vitro assays of microtubule-stabilizing agent response by test cells to predict in vivo outcome. In accordance with the invention, the various biomarker sets described herein, or the combination of these biomarker sets with other biomarkers or markers, can be used, for example, to predict how patients with cancer might respond to therapeutic intervention with one or more microtubule-stabilizing agents.

A biomarker set of cellular gene expression patterns correlating with sensitivity or resistance of cells following exposure of the cells to one or more microtubule-stabilizing agents provides a useful tool for screening one or more tumor samples before treatment with the microtubule-stabilizing agent. The screening allows a prediction of cells of a tumor sample exposed to one or more microtubule- stabilizing agents, based on the expression results of the biomarker set, as to whether or not the tumor, and hence a patient harboring the tumor, will or will not respond to treatment with the microtubule-stabilizing agent.

The biomarker or biomarker set can also be used as described herein for monitoring the progress of disease treatment or therapy in those patients undergoing treatment for a disease involving a microtubule-stabilizing agent.

The biomarkers also serve as targets for the development of therapies for disease treatment. Such targets may be particularly applicable to treatment of breast cancers or tumors. Indeed, because these biomarkers are differentially expressed in sensitive and resistant cells, their expression patterns are correlated with relative intrinsic sensitivity of cells to treatment with microtubule-stabilizing agents.

Accordingly, the biomarkers highly expressed in resistant cells may serve as targets for the development of new therapies for the tumors which are resistant to microtubule-stabilizing agents.

The level of biomarker protein and/or mRNA can be determined using methods well known to those skilled in the art. For example, quantification of protein can be carried out using methods such as ELISA, 2-dimensional SDS PAGE, Western blot, immunopreciptation, immunohistochemistry, fluorescence activated cell sorting (FACS), or flow cytometry. Quantification of mRNA can be carried out using methods such as PCR, array hybridization, Northern blot, in-situ hybridization, dot- blot, Taqman, or RNAse protection assay.

MICROARRAYS

The invention also includes specialized microarrays, e.g., oligonucleotide microarrays or cDNA microarrays, comprising one or more biomarkers, showing expression profiles that correlate with either sensitivity or resistance to one or more microtubule-stabilizing agents. Such microarrays can be employed in in vitro assays for assessing the expression level of the biomarkers in the test cells from tumor biopsies, and determining whether these test cells are likely to be resistant or sensitive to microtubule-stabilizing agents. For example, a specialized microarray can be prepared using all the biomarkers, or subsets thereof, as described herein and shown in Table 2. Cells from a tissue or organ biopsy can be isolated and exposed to one or more of the microtubule-stabilizing agents. Following application of nucleic acids isolated from both untreated and treated cells to one or more of the specialized microarrays, the pattern of gene expression of the tested cells can be determined and compared with that of the biomarker pattern from the control panel of cells used to create the biomarker set on the microarray. Based upon the gene expression pattern results from the cells that underwent testing, it can be determined if the cells show a resistant or a sensitive profile of gene expression. Whether or not the tested cells from a tissue or organ biopsy will respond to one or more of the microtubule- stabilizing agents and the course of treatment or therapy can then be determined or evaluated based on the information gleaned from the results of the specialized microarray analysis.

ANTIBODIES

The invention also includes antibodies, including polyclonal or monoclonal, directed against one or more of the polypeptide biomarkers. Such antibodies can be used in a variety of ways, for example, to purify, detect, and target the biomarkers of the invention, including both in vitro and in vivo diagnostic, detection, screening, and/or therapeutic methods.

KITS The invention also includes kits for determining or predicting whether a patient would be susceptible or resistant to a treatment that comprises one or more microtubule-stabilizing agents. The patient may have a cancer or tumor such as, for example, a breast cancer or tumor. Such kits would be useful in a clinical setting for use in testing a patient's biopsied tumor or other cancer samples, for example, to determine or predict if the patient's tumor or cancer will be resistant or sensitive to a given treatment or therapy with a microtubule-stabilizing agent. The kit comprises a suitable container that comprises: one or more microarrays, e.g., oligonucleotide microarrays or cDNA microarrays, that comprise those biomarkers that correlate with resistance and sensitivity to microtubule-stabilizing agents; one or more microtubule- stabilizing agents for use in testing cells from patient tissue specimens or patient samples; and instructions for use. hi addition, kits contemplated by the invention can further include, for example, reagents or materials for monitoring the expression of biomarkers of the invention at the level of mRNA or protein, using other techniques and systems practiced in the art such as, for example, RT-PCR assays, which employ primers designed on the basis of one or more of the biomarkers described herein, immunoassays, such as enzyme linked immunosorbent assays (ELISAs), immunoblotting, e.g., Western blots, or in situ hybridization, and the like, as further described herein.

APPLICATION OF BIOMARKERS AND BIOMARKER SETS

The biomarkers and biomarker sets may be used in different applications. Biomarker sets can be built from any combination of biomarkers listed in Table 2 to make predictions about the likely effect of any microtubule-stabilizing agent in different biological systems. The various biomarkers and biomarkers sets described herein can be used, for example, as diagnostic or prognostic indicators in disease management, to predict how patients with cancer might respond to therapeutic intervention with a microtubule-stabilizing agent, and to predict how patients might respond to therapeutic intervention that affects microtubule-stabilization and/or cytotoxic activity against rapidly proliferating cells, such as, tumor cells or other hyperproliferative cellular disease.

The biomarkers have both diagnostic and prognostic value in diseases areas in which microtubule-stabilization and/or cytotoxic activity against rapidly proliferating cells, such as, tumor cells or other hyperproliferative cellular disease is of importance.

In accordance with the invention, cells from a patient tissue sample, e.g., a tumor or cancer biopsy, can be assayed to determine the expression pattern of one or more biomarkers prior to treatment with one or more microtubule-stabilizing agents. In one aspect, the tumor or cancer is breast cancer. Success or failure of a treatment can be determined based on the biomarker expression pattern of the cells from the test tissue (test cells), e.g., tumor or cancer biopsy, as being relatively similar or different from the expression pattern of a control set of the one or more biomarkers. Thus, if the test cells show a biomarker expression profile which corresponds to that of the biomarkers in the control panel of cells which are sensitive to the microtubule- stabilizing agent, it is highly likely or predicted that the individual's cancer or tumor will respond favorably to treatment with the microtubule-stabilizing agent. By contrast, if the test cells show a biomarker expression pattern corresponding to that of the biomarkers of the control panel of cells which are resistant to the microtubule- stabilizing agent, it is highly likely or predicted that the individual's cancer or tumor will not respond to treatment with the microtubule-stabilizing agent. The invention also provides a method of monitoring the treatment of a patient having a disease treatable by one or more microtubule-stabilizing agents. The isolated test cells from the patient's tissue sample, e.g., a tumor biopsy or tumor sample, can be assayed to determine the expression pattern of one or more biomarkers before and after exposure to a microtubule-stabilizing agent. The resulting biomarker expression profile of the test cells before and after treatment is compared with that of one or more biomarkers as described and shown herein to be highly expressed in the control panel of cells that are either resistant or sensitive to a microtubule-stabilizing agent. Thus, if a patient's response is sensitive to treatment by a microtubule- stabilizing agent, based on correlation of the expression profile of the one or biomarkers, the patient's treatment prognosis can be qualified as favorable and treatment can continue. Also, if, after treatment with a microtubule-stabilizing agent, the test cells don't show a change in the biomarker expression profile corresponding to the control panel of cells that are sensitive to the microtubule-stabilizing agent, it can serve as an indicator that the current treatment should be modified, changed, or even discontinued. This monitoring process can indicate success or failure of a patient's treatment with a microtubule-stabilizing agent and such monitoring processes can be repeated as necessary or desired.

The biomarkers of the invention can be used to predict an outcome prior to having any knowledge about a biological system. Essentially, a biomarker can be considered to be a statistical tool. Biomarkers are useful in predicting the phenotype that is used to classify the biological system.

Although the complete function of all of the biomarkers are not currently known, some of the biomarkers are likely to be directly or indirectly involved in microtubule-stabilization and/or cytotoxic activity against rapidly proliferating cells. In addition, some of the biomarkers may function in metabolic or other resistance pathways specific to the microtubule-stabilizing agents tested. Notwithstanding, knowledge about the function of the biomarkers is not a requisite for determining the accuracy of a biomarker according to the practice of the invention.

EXAMPLES: EXAMPLE 1 - IDENTIFICATION OF BIOMARKERS

The CA 163-080 study was an exploratory genomic phase II study that was conducted in breast cancer patients who received ixabepilone as a neoadjuvant treatment. The primary objective of this study was to identify predictive markers of response to ixabepilone through gene expression profiling of pre-treatment breast cancer biopsies. Patients with invasive stage IIA-IIIB breast adenocarcinoma (tumor size >3cm diameter) received 40 mg/m² ixabepilone as a 3-hour infusion on Day 1 for up to four 21 -day cycles, followed by surgery within 3-4 weeks of completion of chemotherapy. A total of 164 patients were enrolled in this study. Biopsies for gene expression analysis were obtained both pre- and post-treatment. Upon isolation of biopsies from the patients, samples were either snap frozen in liquid nitrogen or placed into RNAlater solution overnight, followed by removal from the RNAlater solution. All samples were kept at -70⁰C until use.

The MDAl 33 study was a biomarker discovery trial. All patients received 24 weeks of sequential paclitaxel and fluorouracil-doxorubicin-cyclophosphamide (T/FAC) preoperative chemotherapy. (K. Hess et al., Journal of Clinical Oncology 24:4236-4244 (2006))

Gene expression profiles were generated for 134 patients in study CAl 63080 from RNA isolated from core needle biopsies obtained before treatment with ixabepilone. Gene expression profiles also were generated for 133 patients in clinical study MDAl 33 from RNA isolated from fine needle biopsies obtained before treatment with T/FAC.

In study CA 163080, estrogen-receptor-negative (ER-negative) subjects, as determined by IHC, were previously found to have a higher pCR rate than ER- positive subjects. Therefore, only ER-negative subjects were included in analyses to build multi-predictor models and when evaluating the predictive performance of all models. All subjects were included in gene set enrichment and network analyses. A binary response measure was used for all analyses, defined as pathological complete response (pCR) in breast tissue, based on central review of biopsies.

Gene expression was assayed using Affymetrix microarrays: array HU133A2 for CAl 63080 and HUl 33A for MDAl 33. Data from the two studies were normalized together by Robust Microarray Analysis (R. Irizarry et al., Biostatistics, 4: 249-264 (2003)), as implemented in Bioconductor (R. Gentleman et al., Genome Biology, 5(10):R80 (2004)). The summary Iog₂(intensity) measurements produced by RMA for each probe set were used for subsequent analyses. The 22,215 probe sets per microarray were filtered by removing (a) exogenous control probe sets, (b) probe sets with low expression over all samples, defined as a maximum Iog₂(intensity) < 5, and (c) probe sets with low variance over all samples, defined as a coefficient of variation < 5%. This yielded 14,839 probe sets for subsequent analyses.

The two clinical trials from which the data were combined are assumed to have sampled the same patient population. They were, however, conducted independently of each other, at different times and locations. Different types of biopsies were used, and the mRNA preparation and microarray assays were performed in different laboratories. Such differences, known and unknown, between the two studies led to clear differences in the within-study distributions of pre- treatment expression levels. To reduce such differences, the normalized Iog₂(intensity) values were centered by subtracting the study-specific median for each probe set. Study-specific centering ensured that the median expression level was the same in the two studies, and the same for all probe sets. The median centered data set was used to perform gene set enrichment and network analyses. For subsequent multi-probe set model building, the normalized Iog2(intensity) values were centered by subtracting the study-specific mean for each probe set, and rescaled by dividing by the pooled within-study standard deviation for each probe set. Study-specific centering ensured that the mean expression level was the same in the two studies, and the same for all probe sets. Rescaling ensured that the spread of expression levels was the same for all probe sets. Gene set enrichment and network analyses: A gene set enrichment program developed by the Broad Institute at MIT (A. Subramanian et al., P.N.A.S. USA., 102(43): 15545-50 (2005)) was used to generate ranking scores based upon a signal-to-noise ratio. The top 100 genes were then further analyzed by two different gene network analysis programs: GeneGo and ErmineJ (H. Lee et al., BMC Bioinformatics, 6:269 (2005)). Statistical model building:

Because so many (14,839) potential predictors remained after filtering of probe sets, a two-stage approach was adopted to building multi-probe set models for predicting response to treatment: (1) single probe set analyses for dimension reduction; and (2) fitting multi -probe set predictive models by threshold gradient descent (TGD) combined with recursive feature elimination (RFE). Single Probe Set Analyses:

Single probe set analyses for selecting predictors to include in the multi- predictor model-building process were designed to search for probe sets that showed a difference between the two studies in the relationship between expression level and response status. Two different types of models were used, logistic regression and linear regression. All models were fit using R (R. Ihaka et al., J. Comput. Graph. Stat., 5, 299-314 (1996)). For logistic regression, the binary response variable was pCR status. The explanatory variables were expression level, as represented by the normalized, standardized Iog₂(intensity), a study indicator variable, and the additive interaction between study and expression level. Probe sets were selected based on significance of a likelihood ratio test of the interaction term.

For the linear regression models, the response variable was expression level. The explanatory variables were a study indicator variable, the pCR status indicator variable, and the additive interaction between the two. The models included study- specific residual variances, and thus were fit by generalized least squares. Probe sets were selected based on significance of an F test of the interaction term.

All probe sets with a p-value < 0.01 for at least one of the two tests were selected. If necessary in order to yield 100 probe sets, additional probe sets with smallest minimum p-values were selected. This second selection step turned out to be unnecessary for the full data set, but was important for cross-validation during model performance assessment. Multi-Probe set Model Building:

Starting with pre-selected probe sets, for which the relationship between expression level and response status had opposite directions in the two studies, multi- probe set predictive models were fit to each study separately using a TGD method for regularized classification (J. Friedman et al., Gradient directed regularization for linear regression and classification. Stanford University, Department of Statistics. Technical Report (March 29, 2004)). This approach works well when the number of predictors in a model is much greater than the number of subjects, and allowed use of a loss function robust to misclassification of response status. After obtaining an initial fitted model for a given study, RPE was applied (I. Guyon et al., Machine Learning, Vol. 46, pp. 389-422 (2002)) to attempt to simplify the model without appreciable loss of predictive accuracy.

In the RPE procedure, the ten probe sets with smallest scaled absolute coefficients were dropped from the model, and the model containing the remaining probe sets was refit. This procedure was repeated until a model containing 100 probe sets was obtained. Then, the procedure was continued, except that one probe set at a time was dropped, instead often, until a model containing only a single probe set was obtained. This produced two distinct series of nested models, one for each study.

The performance characteristics of each model series, applied to both studies separately, were estimated by four-fold cross-validation. The cross-validation partitions were balanced by study and response status. For each learning set, the entire model-building procedure - single probe set analyses for probe set selection, followed by multi-predictor model fitting via TGD and RPE for each study separately - was repeated. From each validation set, performance of every model in each of the two series was assessed for each study separately. For example, a model fit to learning-set subjects from CAl 63080 was applied to validation-set subjects from CAl 63080 to assess performance, and then separately applied to validation-set subjects from MDAl 33.

For every model in each series, the cross-validated area under the response operating characteristic curve (AUC) was calculated for each study separately (F.

Harrell, Logistic Regression, and Survival Analysis. New York: Springer- Verlag, 568 pp. (2001)). The entire cross-validation procedure was repeated 50 times, using 50 different random partitions of subjects, and the mean of the AUC estimates over the replicates obtained. These mean cross-validation estimates of AUC were used to compare the predictive accuracy of different models. From the model series fit to all subjects from the CA163080 study, the model with maximum difference between the mean cross-validation AUC estimate for study CAl 63080 and that for study MDAl 33 was selected. A single model also was selected from the model series fit to all subjects from the MDAl 33 study by the same procedure.

A second layer of cross validation was not added to account for the process of picking the best-performing model from a series. This process is equivalent to estimating one parameter, the model size. Excluding this from the cross-validation could slightly increase over-optimism of the final performance estimates. However, with only eight responders in one study, it is believed that a second layer of cross validation would have increased the variance of the cross-validation estimators too much to be compensated by any potential decrease in bias achieved. Cross validation, RFE, and performance measure estimation were implemented in S-Plus 7.0 (S-PLUS 7.0 for UNIX User's Guide, Insightful Corporation, Seattle, Washington (2005)). TGD was implemented as an S-Plus wrapper to a FORTRAN executable provided by Jerome Friedman. (J. Friedman et al., Gradient directed regularization for linear regression and classification. Stanford University, Department of Statistics, Technical Report (March 29, 2004)) siRNA study:

Small interfering RNA experiments were performed to examine whether down-regulation of candidates of differential biomarkers identified altered the sensitivity of breast cancer cell line to ixabepilone and/or paclitaxel. hi detail, MDA- MB-231 breast cancer cells were plated and cultured in 6-well and 96- well dishes in media without antibiotics. Twenty- four hours after plating, cells were transfected with a pool of four separate siRNAs specific to human CAPG RNA (Dharmacon Lafayette, Colorado) using Lipofectamine 2000 transfection reagent (Sigma St. Louis, Missouri). Six hours after transfection, media was removed and cells were re-plated in normal antibiotic-free media. Subsequently, cells were treated with various concentrations of ixabepilone or paclitaxel (25x, 5x, Ix, 0.2x, 0.04 and 0.008x of IC₅₀ for each respective drug, and an additional dose of 35x for ixabepilone and 29x for paclitaxel). The concentration of DMSO was kept below 0.1% in media. Seventy- two hours after drug treatment, cells in 96-well plates were tested for proliferation by MTT assay (Cat# 30-101 OK ATCC, Manassas, Virginia). Cells in 6-well dishes were harvested for the assessment of protein and mRNA level. Down-regulation of mRNA was confirmed by transcriptional profiling. Down-regulation of protein was confirmed by western blot using a rabbit polyclonal antibody to human CAPG (Bethyl Laboratories, Montgomery, Texas). Chemosensitivity was determined from four separate experiments, each performed in triplicate. Results:

The numbers of ER-negative responders and non-responders in each study are provided in Table 1.

Table 1 - Observed distribution of ER-negative responders and non-responders in the two studies

Gene set enrichment analysis:

For GSEA, expression levels for the subjects with pathological complete response (pCR) from study CAl 63080 were compared to those from study MDAl 33. Ranking scores were calculated by signal-to-noise ratio with 100 permutations. Table 2 A lists the top 100 probe set IDs that have highest ranking scores in this comparison. There were many microtubule-associated genes with very high scores. It should be noted that GSEA databases lack gene sets curated from the microtubule functional network. To further understand if statistically significant scores were enriched for genes associated with microtubules, a gene ontology (GO) search program named ErmineJ (H. Lee et al., BMC Bioinformatics, 6:269 (2005)) was applied to the ranking scores of the probe sets identified by GSEA. As expected, two interesting GO functional processes, cytoplasmic microtubule and microtubule organizing center, were identified (FIG. 1). To further support this result, an independent gene network program named GeneGo was used to examine the same top 100 probe sets from the GSEA. Within a curated microtubule network that contains 51 genes, 22 of them were from the top 100 probe set list and had very small p-values (FIG. 2).

This novel finding is important since it is believed that both ixabepilone and paclitaxel are microtubule stabilizing agents. There have been several studies showing that the two compounds target different sites, but both within the microtubule microenvironment. The result also suggests that additional markers to differentiate between ixabepilone and paclitaxel could be identified via these significant microtubule networks.

Table 2A - Top 100 genes with highest GSEA ranking scores from the comparison of

CAl 63080 and MDAl 33 pCR subjects

Table 2 - Biomarkers of Table 2A

-33-

To prioritize these potential differential markers, the following criteria were defined: (1) relatively high expression range between the two studies; (2) a high GSEA ranking score; and (3) relatively high area under the Receiver Operating Characteristic curve (AUC). Table 3 provides the list of genes that met these criteria.

Table 3 - 10 probe sets with largest mean difference between CAl 63080 and MDAl 33 pCR subjects, sorted by GSEA ranking score in descending order

FIGS. 3 and 4 summarize the distributions of normalized, median-centered expression levels for TACC3 and CAPG probe sets, for pCR subjects in CAl 63080 and MDA 133 separately. Differences between the distributions for the two studies are evident. TACC3 proteins play important roles in interactions with both microtubules and tubulin, and in regulation of the cell cycle. (F. Gergley, P.N.A.S. U S A, 97(26): 14352-7 (2000)) CAPG is involved in mitosis, may be a proliferation marker, and is a potential prognostic indicator in cancer (D. Jager et al., Cancer Research 60, 3584-3591 (2000)).

Single-probe set models to predict pCR, based on TACC3 and CAPG gene expression separately, were fit by TGD to data for the CAl 63080 subjects. Response operating characteristic (ROC) curves for the TACC3 model (FIG. 5), applied to each study separately, indicated predictive utility of this marker in CAl 63080 (AUC = 0.84) but lack of utility in MDA133 (AUC = 0.46). The pCR rate for CA163080 subjects with predicted probabilities of response > 0.5 from the TACC3 model was 33%, compared to 13% for all ER-negative CAl 63080 subjects.

Response operating characteristic (ROC) curves for the CAPG model (FIG. 6), applied to each study separately, indicated predictive utility of this marker in CA163080 (AUC = 0.76) but lack of utility in MDA133 (AUC = 0.48). The pCR rate for CA163080 subjects with predicted probabilities of response > 0.5 from the CAPG model was 27%. For both TACC3 and CAPG, predictive models fit to data from the MDAl 33 subjects instead of the CAl 63080 subjects yielded AUC estimates identical to those described here (results not shown). This is as expected from statistical theory.

Building and assessing performance of multi-predictor models: Starting with expression levels for 14,839 probe sets, 707 were selected by the single probe set analyses applied to all ER-negative subjects in the two studies. The interaction analyses for probe set selection compared differences between responders and non-responders in one study to such differences in the other study. That is, the interaction terms were built from within-study contrasts of mean expression levels. Differences in mean expression levels between the two studies, such as those observed before standardization, would not affect these interaction estimates. Differences in scale between the two studies, however, can affect the interaction estimates and inferences about them. A difference between the within-study standard deviations, for example, can lead to a difference in magnitude of the slope relating log-odds of response to expression level in the logistic model. It seems less likely, however, that a difference in scale between the two studies can lead to a difference in sign of the slope. This difference in sign was termed a "crossover interaction". Fortunately, all but 47 of the 707 selected probe sets exhibited such a crossover interaction.

Two multi-predictor models that appear to differentiate response in the two studies were identified and are provided in Tables 4 and 5.

Table 4 — 26 biomarker model

Table 5 — 20 biomarker model

For the 26 and 20 biomarker models, the prediction of response is obtained from a weighted combination of the expression levels of all the biomarkers in the model.

For the 20 biomarker model, a large weighted combination yields an increased estimated probability of response in the MDA 133 study, and a decreased probability of response in CAl 63080. A small weighted combination yields a decreased probability of response in MDAl 33 and increased probability of response in CA163080.

For the 26 biomarker model, a large weighted combination yields an increased probability of response in CA 163080. A small weighted combination yields a decreased probability of response in CAl 63080. The model is not predictive for study MDAl 33. For both these models, one cannot make a general statement about how up- regulation or down-regulation of individual biomarkers contributes to the probability of response. The direction of effect for a biomarker depends on the sign of its weight in the model: some are positive and some are negative.

Table 6 — Coefficients of 20 biomarker model

Probe Set Coefficient

204922_ at -0.2485995

214820_ at 0.3889511

220451_ _s_at -0.2685128

215125_ _s_at -0.2256988

204736_ s_at -0.4088082

222369_ _at -0.2788129

204262_ _s_at -0.1691269

204640_. _s_at 0.2406481 202804_ at -0.3203045

210141_ sat 0.2254955

207388_ _s_at -0.2582779

206999 _at 0.3378347

213256_ at 0.2914438

210662_ at -0.3975818

220298_ s_at 0.3380620

215930_ _s_at -0.2296385

204773_ at 0.2042484

218337_ .at -0.2584266

202920_ .at -0.3818751

221622_ _s_at -0.1827982

Intercept 0.1853529

As shown, up-regulation of 7 probe sets and down-regulation of 13 probe sets yields a larger weighted combination. The "Intercept" is a constant included in the model so that a patient with the average expression level for all 20 probe sets - if such a patient were to exist - would have a 50% probability of response in the MDAl 33 study.

Table 7 - Coefficients of 26 probe set model

Probe Set Coefficient

219214 _s_at 0.11780890

204922 .at 0.09652516

208433_ s_at 0.08864199

203965_ .at 0.11500080

220057_. at 0.07201534

213633_ at 0.11636990

218609_ s_at -0.13527770

211376_ s_at -0.09828885

219600_ _s_at -0.12866110

209994_ _s_at -0.10539430 218815_._s_at 0.10332810

219491_ _at 0.11332000

212738_ at -0.10155180

203178_ at -0.08674573

202938_ x_ at 0.10687110

206789_. _s_at 0.11870280

202315_ _s_at 0.09606586

202103_ _at 0.11152710

211914_. X at 0.09237500

210486_ at 0.08023373

212752_ at 0.09569042

212145_. at -0.10161070

46665_at 0.11269490

213324_. at 0.09002145

218457_. _s_at 0.09728418

212564 _at 0.11498180

Intercept -1.07642700

As shown, up-regulation of 19 probe sets and down-regulation of 7 probe sets yields a larger weighted combination. The "Intercept" is a constant included in the model so that a patient with the average expression level for all 26 probe sets - if such a patient were to exist - would have a 50% probability of response in the CAl 63080 study.

With regard to the coefficients provided in Tables 6 and 7, it should be recognized that one can obtain a predicted value for each subject by the formula: Intercept + al * Il + a2 * 12 + ... + ap * Ip, where Ii, i = l,...,p, are normalized, standardized log-intensity values, and the ai are the coefficients for the model. A simple transformation of the predicted value yields a probability of response. Weighted combinations of expression levels other than those provided in Tables 6 and 7 for the specified probe sets can also be used.

The 26 biomarker model (Table 4) fit to the CAl 63080 subjects appears to have predictive utility in CAl 63080 but not in MDAl 33 (Table 4, FIG. 7). Nineteen of these 26 probe sets exhibited crossover interactions. The 20 biomarker model (Table 5) fit to the MDAl 33 subjects appears to have predictive utility in both studies, but in opposite directions (Table 5, FIG. 8). All but one of these 20 probe sets exhibited crossover interactions. A large predicted probability from this model indicates that a subject is likely to respond in the MDAl 33 study but unlikely to respond in CAl 63080. A small predicted probability indicates that a subject is unlikely to respond in the MDAl 33 study but likely to respond in CAl 63080.

Estimates of several performance measures for the four predictive models described here are summarized in Table 8. Baseline response rates were 28/51 = 0.55 for MDA133 and 8/62 = 0.13 for CA163080. Proportion Above Threshold (PAT), Negative Predictive Value (NPV) , Positive Predictive Value (PPV), Sensitivity (Sens), and Specificity (Spec) were calculated using an arbitrary classification threshold of 0.5 on a probability scale. To calculate actual NPV and PPV for model 4 as it would be applied to study MDA 133, use 1 - NPV for PPV and 1 - PPV for NPV. To calculate actual Sens, Spec, and AUC for model 4 as it would be applied to study MDAl 33, use 1 - the value listed in table.

Table I 5 - Model performance estimates

Down-regulation of CAPG expression in breast cancer cells increases resistance to ixabepilone, but not to paclitaxel: It has been discovered that higher expression of CAPG was predictive of response in CA163080 but not in MDA133. Therefore, it was investigated whether this difference may have been due to a difference between ixabepilone and paclitaxel. CAPG6 gene expression was assessed in 26 breast cancer cell lines and selected MDA-MB-231 for a siRNA study, as this cell line is ER-negative and expresses CAPG at a relatively high level (FIG. 9). In addition, its sensitivity to ixabepilone and paclitaxel is at an intermediate level, so it was hypothesized that changes in sensitivity could be observed in either direction upon down-regulation of CAPG. siRNA transfection efficiency was estimated to be about 60% (FIG. 10), determined by both gene expression profiling and western blot. CAPG down- regulation lasted for at least 96 hours after transfection (data not shown). Decreased CAPG expression significantly increased the resistance of MDA-MB-231 cells to ixabepilone compared with negative control siRNA cells (FIG. 11). No significant change was observed in sensitivity of CAPG siRNA cells to paclitaxel treatment (FIG. 11).

This observation is consistent with the finding that high CAPG expression is predictive of response in the ixabepilone study, CAl 63080, but not in the T/FAC study, MDAl 33. It suggests that the difference between the two studies in predictive utility of CAPG may have been due to a difference between ixabepilone and paclitaxel treatment per se. Furthermore, it suggests that CAPG down-regulation confers resistance of cells to ixabepilone but not to paclitaxel. This may indicate a potential difference in underlying mechanism of action between ixabepilone and paclitaxel.

Thus, four candidate predictive models have been identified that differentiate response in a clinical trial of ixabepilone from that in a trial of paclitaxel. Two of the models are based on single genes, TACC3 and CAPG, identified through gene set enrichment and network analyses. The involvement of CAPG in differentiating response to ixabepilone and paclitaxel was supported by siRNA studies. Two of the models to differentially predict response to the two compounds are based on expression levels for 26 and 20 genes, with one gene in common. EXAMPLE 2 - PRODUCTION OF ANTIBODIES AGAINST THE BIOMARKERS

Antibodies against the biomarkers can be prepared by a variety of methods. For example, cells expressing a biomarker polypeptide can be administered to an animal to induce the production of sera containing polyclonal antibodies directed to the expressed polypeptides. In one aspect, the biomarker protein is prepared and isolated or otherwise purified to render it substantially free of natural contaminants, using techniques commonly practiced in the art. Such a preparation is then introduced into an animal in order to produce polyclonal antisera of greater specific activity for the expressed and isolated polypeptide. In one aspect, the antibodies of the invention are monoclonal antibodies (or protein binding fragments thereof). Cells expressing the biomarker polypeptide can be cultured in any suitable tissue culture medium, however, it is preferable to culture cells in Earle's modified Eagle's medium supplemented to contain 10% fetal bovine serum (inactivated at about 56 ⁰C), and supplemented to contain about 10 g/1 nonessential amino acids, about 1 ,00 U/ml penicillin, and about 100 μg/ml streptomycin.

The splenocytes of immunized (and boosted) mice can be extracted and fused with a suitable myeloma cell line. Any suitable myeloma cell line can be employed in accordance with the invention, however, it is preferable to employ the parent myeloma cell line (SP2/0), available from the ATCC (Manassas, VA). After fusion, the resulting hybridoma cells are selectively maintained in HAT medium, and then cloned by limiting dilution as described by Wands et al. (1981, Gastroenterology, 80:225-232). The hybridoma cells obtained through such a selection are then assayed to identify those cell clones that secrete antibodies capable of binding to the polypeptide immunogen, or a portion thereof.

Alternatively, additional antibodies capable of binding to the biomarker polypeptide can be produced in a two-step procedure using anti-idiotypic antibodies. Such a method makes use of the fact that antibodies are themselves antigens and, therefore, it is possible to obtain an antibody that binds to a second antibody, hi accordance with this method, protein specific antibodies can be used to immunize an animal, preferably a mouse. The splenocytes of such an immunized animal are then used to produce hybridoma cells, and the hybridoma cells are screened to identify clones that produce an antibody whose ability to bind to the protein-specific antibody can be blocked by the polypeptide. Such antibodies comprise anti-idiotypic antibodies to the protein-specific antibody and can be used to immunize an animal to induce the formation of further protein-specific antibodies.

EXAMPLE 3 - IMMUNOFLUORESCENCE ASSAYS

The following immunofluorescence protocol may be used, for example, to verify biomarker protein expression on cells or, for example, to check for the presence of one or more antibodies that bind biomarkers expressed on the surface of cells. Briefly, Lab-Tek II chamber slides are coated overnight at 4 ⁰C with 10 micrograms/milliliter (μg/ml) of bovine collagen Type II in DPBS containing calcium and magnesium (DPBS++). The slides are then washed twice with cold DPBS++ and seeded with 8000 CHO-CCR5 or CHO pC4 transfected cells in a total volume of 125 μl and incubated at 37 °C in the presence of 95% oxygen / 5% carbon dioxide. The culture medium is gently removed by aspiration and the adherent cells are washed twice with DPBS++ at ambient temperature. The slides are blocked with DPBS-H- containing 0.2% BSA (blocker) at 0-4 ⁰C for one hour. The blocking solution is gently removed by aspiration, and 125 μl of antibody containing solution (an antibody containing solution may be, for example, a hybridoma culture supernatant which is usually used undiluted, or serum/plasma which is usually diluted, e.g., a dilution of about 1/100 dilution). The slides are incubated for 1 hour at 0-4 ⁰C. Antibody solutions are then gently removed by aspiration and the cells are washed five times with 400 μl of ice cold blocking solution. Next, 125 μl of 1 μg/ml rhodamine labeled secondary antibody (e.g., anti-human IgG) in blocker solution is added to the cells. Again, cells are incubated for 1 hour at 0-4 °C.

The secondary antibody solution is then gently removed by aspiration and the cells are washed three times with 400 μl of ice cold blocking solution, and five times with cold DPBS++. The cells are then fixed with 125 μl of 3.7% formaldehyde in DPBS++ for 15 minutes at ambient temperature. Thereafter, the cells are washed five times with 400 μl of DPBS++ at ambient temperature. Finally, the cells are mounted in 50% aqueous glycerol and viewed in a fluorescence microscope using rhodamine filters. Although the invention has been described in some detail by way of illustration and example for purposes of clarity and understanding, it will be apparent that certain changes and modifications may be practiced within the scope of the appended claims.

Claims

CLAIMS:What is claimed is:

1. A method for predicting whether a mammal will respond therapeutically to a method of treating cancer comprising administering a microtubule-stabilizing agent, wherein the method comprises:

(a) measuring in the mammal the level of at least one biomarker selected from the biomarkers of Table 2;

(b) exposing a biological sample from said mammal to said agent;

(c) following the exposing of step (b), measuring in said biological sample the level of the at least one biomarker, wherein a difference in the level of the at least one biomarker measured in step (c) compared to the level of the at least one biomarker measured in step (a) indicates that the mammal will respond therapeutically to said method of treating cancer.

2. The method of claim 1 wherein said agent is an epothilone or analog or derivative thereof.

3. The method of claim 1 wherein said agent is ixabepilone.

4. The method of claim 1 wherein said agent is a taxane.

5. The method of claim 1 wherein said at least one biomarker comprises CAPG.

6. The method of claim 1 wherein said at least one biomarker comprises

TACC3.

7. A method for identifying a mammal that will respond therapeutically to a method of treating cancer comprising administering a microtubule-stabilizing agent, wherein the method comprises: (a) exposing a biological sample from the mammal to said agent;

(b) following the exposing of step (a), measuring in said biological sample the level of the at least one biomarker selected from the biomarkers of Table 2, wherein a difference in the level of the at least one biomarker measured in step (b), compared to the level of the at least one biomarker in a mammal that has not been exposed to said agent, indicates that the mammal will respond therapeutically to said method of treating cancer.

8. The method of claim 7 wherein said agent is an epothilone or analog or derivative thereof.

9. The method of claim 7 wherein said agent is ixabepilone.

10. The method of claim 7 wherein said agent is a taxane.

11. The method of claim 7 wherein said at least one biomarker comprises

CAPG.

12. The method of claim 7 wherein said at least one biomarker comprises TACC3.

13. A method for predicting whether a mammal will respond therapeutically to a method of treating cancer comprising administering ixabepilone, wherein the method comprises:

(a) measuring in a biological sample from the mammal the expression levels of a set of biomarkers comprising the biomarkers of Table 4;

(b) exposing said mammal to ixabepilone; (c) following the exposing of step (b), measuring in a second biological sample the expression levels of said set of biomarkers, wherein a large weighted combination of said expression levels indicates that the mammal will respond therapeutically to said method of treating cancer, and wherein a small weighted combination of said expression levels indicates that the mammal will not respond therapeutically to said method of treating cancer.

14. A method for predicting whether a mammal will respond therapeutically to a method of treating cancer comprising administering ixabepilone, wherein the method comprises:

(a) exposing said mammal to ixabepilone; (b) following the exposing of step (a), measuring in a biological sample from said mammal the expression levels of a set of biomarkers comprising the biomarkers of Table 4, wherein a large weighted combination of said expression levels indicates that the mammal will respond therapeutically to said method of treating cancer, and wherein a small weighted combination of said expression levels indicates that the mammal will not respond therapeutically to said method of treating cancer.

15. A method for predicting whether a mammal will respond therapeutically to a method of treating cancer comprising administering ixabepilone, wherein the method comprises:

(a) measuring in a biological sample from the mammal the expression levels of a set of biomarkers comprising the biomarkers of Table 5;

(b) exposing said mammal to ixabepilone;

(c) following the exposing of step (b), measuring in a second biological sample the expression levels of said set of biomarkers, wherein a large weighted combination of said expression levels indicates that the mammal will not respond therapeutically to said method of treating cancer, and wherein a small weighted combination of said expression levels indicates that the mammal will respond therapeutically to said method of treating cancer.

16. A method for predicting whether a mammal will respond therapeutically to a method of treating cancer comprising administering ixabepilone, wherein the method comprises :

(a) exposing said mammal to ixabepilone;

(b) following the exposing of step (a), measuring in a biological sample from said mammal the expression levels of a set of biomarkers comprising the biomarkers of Table 5, wherein a large weighted combination of said expression levels indicates that the mammal will not respond therapeutically to said method of treating cancer, and wherein a small weighted combination of said expression levels indicates that the mammal will respond therapeutically to said method of treating cancer.