Patent Grant
7932031
A compact disc labeled “Copy 1” contains the Sequence Listing as 10338 NP.ST25.txt. The Sequence Listing is 1686 KB in size and was recorded Nov. 29, 2005. 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.
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.
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, mdr1 and mrp1 (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. Pat. 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 (L. J. 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(1):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.
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.
In one aspect, the invention provides 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) measuring in the mammal the level of at least one biomarker selected from the biomarkers of Table 1 and Table 2; (b) exposing a biological sample from the mammal to the agent; (c) following the exposing in step (b), measuring in said biological sample the level of the at least one biomarker, wherein an increase 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), predicts that the mammal will respond therapeutically to said method of treating cancer when said biomarker is from Table 1, and predicts that the mammal will not respond therapeutically to said method of treating cancer when said biomarker is from Table 2.
In another aspect, the invention provides a method for determining whether a mammal is responding therapeutically to a microtubule-stabilizing agent, 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 1 and Table 2, wherein an increase 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 is responding to said agent when said biomarker is from Table 1, and indicates that the mammal is not responding to said agent when said biomarker is from Table 2.
In another aspect, the invention provides 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) exposing a biological sample from the mammal to the microtubule-stabilizing agent; (b) following the exposing of step (a), measuring in said biological sample the level of at least one biomarker selected from the biomarkers of Table 1 or Table 2, wherein an increase 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, predicts that the mammal will respond therapeutically to said method of treating cancer when said biomarker is from Table 1, and predicts that the mammal will not respond therapeutically to said method of treating cancer when said biomarker is from Table 2.
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 1 and Table 2, wherein an increase in the level of said at least one biomarker measured in step (b), compared to the level of the biomarker in a mammal that has not been exposed to said agent, indicates that the agent stabilizes microtubules and has cytotoxic activity against rapidly proliferating cells when said biomarker is from Table 1, and indicates that the agent does not stabilize microtubules and does not have cytotoxic activity against rapidly proliferating cells when said biomarker is from 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 1 and Table 2. The biomarkers of the invention comprise sequences selected from the nucleotide and amino acid sequences provided in Table 1 and 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 1 and Table 2.
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 1 and 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 1 and 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. In another aspect, the instructions comprise the steps of (a) measuring in the mammal the level of at least one biomarker selected from the biomarkers of Table 1 and Table 2, (b) exposing the mammal to the microtubule-stabilizing agent, (c) following the exposing of step (b), measuring in the mammal 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) predicts that the mammal will respond therapeutically to said method of treating cancer when said biomarker is from Table 1, and predicts that the mammal will not respond therapeutically to said method of treating cancer when said biomarker is from Table 2.
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 said 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.
The patent or application file contains at least one drawing executed in color. Copies of this patent or patent application publication with color drawing(s) will be provided by the Office upon request and payment of the necessary fee.
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 Table 1, Table 2, and the Sequence Listing, including both polynucleotide and polypeptide sequences.
The biomarkers provided in Table 1 include the nucleotide sequences of SEQ ID NOS:1-100 and the amino acid sequences of SEQ ID NOS:201-299.
sapiens cDNA, 3 end /clone = IMAGE-
Caenorhabditis elegans C. elegans”
The biomarkers provided in Table 2 include the nucleotide sequences of SEQ ID NOS:101-200 and the amino acid sequences of SEQ ID NOS:300-395.
Homo sapiens mRNA; cDNA
Drosophila)
laevis)
Certain biomarkers were of particular interest. Microtubule-associated protein tau was identified as one of the resistance markers, and has been shown to bind at the close site of microtubule where Taxol® binds to. It is believed that Taxol® interferes microtubule and Tau interaction, but Tau's interaction seems more resistant than Taxol® (R. Dye et al., J. Biological Chem., 268, 6847-6850 (1993)). Therefore, this further validates the observation that Tau expressing cells are more resistant to ixabepilone treatment as ixabepilone binds at the same site of Taxol® in tubulin. Another interesting resistance biomarker is estrogen receptor. In general, estrogen-receptor status is predictive of response to hormonal treatments. (J. C. Chang et al., Lancet, 362, 362-369 (2003)). However, it was interesting to observe estrogen receptor as a strong marker for the resistance to ixabepilone. ER has not been previously suggested as a predictive marker of a patient's response to chemotherapy. More interestingly, microtubule associated protein tau is estrogen induced (M. West et al., P. N. A. S. USA, 98, 11462-11467 (2001)). ER and Tau were also found as resistance markers in an analysis of Taxol® (data not provided), and this suggests that Tau and ER both are likely to be the resistance markers for microtubule-stabilizing agents such as ixabepilone and Taxol®.
Several other genes appear promising as potential markers including transporter genes (ATP-binding cassette, sub-family G (WHITE), member 1 and ATP-binding cassette, sub-family A (ABC1), member 3), Midline 1 (C. Berti et al., BMC Cell Biol., February 29; 5(1):9 (2004)), LMP7 and etc. The differential expression patterns of these biomarkers were distinct between the two phenotypes of the cell lines (sensitive and resistant). In addition, their biological functions are involved in drug resistance mechanism or related with microtubule functions. Furthermore, their differential expression patterns observed within tumors support their potential as response markers.
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.
In 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; WO01/81342; WO00/66589; WO00/58254; 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 1 and 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 1 and 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. In 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 1 and 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.
Methods
Cell Lines and Cytotoxicity Assay
23 breast cancer cell lines were assayed for their sensitivity to ixabepilone. Each cell line was exposed to ixabepilone for 72 hours, and growth inhibition was assessed by the CellTiter 96® Aqueous Non-Radioactive Cell proliferation Assay (Promega) for IC50 measurements. Then, the concentration of the ixabepilone required for 50% growth inhibition was calculated as the IC50. For each experimental condition, at least triplicate measurements were carried out for each cell line. The 23 cell lines were assayed for their IC50 measurements twice, and these two separate IC50 data sets were used for the following analysis.
Training Set Selection
For analysis, training cell lines were chosen in the following manner. The 23 cell lines were assigned into the classes “sensitive” or “resistant” using IC50 values; log(IC50) values were normalized based on the mean and the standard deviation (SD) across the 23 cell lines for each IC50 data set. (J. E. Staunton et al., P. N. A. S. USA. 98, 10787-10792 (2001)) The cell lines with the normalized log(IC50) below the mean of log(IC50)s were classified as sensitive and above as resistant. Subsequently, classification of the cell lines were compared in two separate experiments and 18 cell lines that exhibited consistent IC50 and classification were chosen as a training set for subsequent marker analysis. Five cell lines with inconsistent IC50 and classification were considered to be intermediate and were eliminated from the analysis.
RNA Extraction and Gene Expression Data
The 23 breast cancer cell lines were grown to 50-70% confluent in RPMI media with FBS 10% at 37° C. and 5% CO2. RNA was isolated using the RNeasy Mini kit (Qiagen) according to the manufacturer's instructions. 10 ug of total RNA was used to prepare biotinylated cRNA targets as described in Affymetrix protocol. Targets were hybridized to Affymetrix high-density HU133 A and B set that consist of 44,000 probe sets containing ˜32,000 genes. The chips were washed and stained using recommended procedures for GeneChip®. Expression values were calculated and scaled to 1500 by using Affymetrix GeneChip® software.
k-Nearest Neighbors (KNN) Analysis
GeneCluster software was used to find a set of marker genes. First, genes with greater than 100 average difference were filtered. Then, genes were excluded if they varied by less than 2-fold and 1000 average difference change across 18 training cell lines. Subsequently, intensity units across the cell lines for each gene were normalized to the mean and variance. The genes were ranked according to the correlation between their expression level, and the sensitivity and resistance profile of the training cell lines. A marker gene selection process was carried out by KNN algorithm which fed only the genes with higher correlation with the target class. The KNN algorithm sets the class of the data point to the majority class appearing in the k closest training set samples. This marker selection is done by sorting the genes according to the signal-to-noise statistics, [μ1(g)−μ2(g)]/[σ1(g)+σ2(g)], described as the correlation function where [μ1(g), μ2(g)] and [σ1(g), σ2(g)] denote the means and SDs of the expression levels of gene g for the samples in class 1 and class 2, respectively. The magnitude of correlation values indicates the strength of the correlation between gene expression and class distinction.
Leave-One-Out Cross-Validation Analysis and Random Classification
Predictors with 1-250 genes were used for cross-validation of the training set. For each predictor, cross-validation was performed with the entire training set; one cell line was removed, the classifier was trained on the remaining cell lines and then tested for its ability to classify the withheld cell line. This procedure was repeated for each cell line in the training set. For random classification analysis, GeneCluster was used to generate random class vectors and calculate error rates.
Clustering and Tree View
Gene expression data were analyzed by the software Cluster and TreeView.
Breast Tumors and Gene Expression Data
RNAs extracted from 175 breast tumors resected at the surgery were obtained from the Karolinska Institute (Stockholm, Sweden). These RNA samples were profiled using Affymetrix Human U133 sets and their gene expression data were used for the analysis.
Results
Drug sensitivity data (IC50) was used as a template for determining the phenotype of the cell lines as resistant or sensitive. Initially, two separate IC50 data sets were generated for 23 breast cancer cell lines. As a first step for the analysis, the log(IC50) value for each cell line was calculated and normalized using the mean of log(IC50)s and SD across the cell lines (J. E. Staunton et al., P. N. A. S. USA. 98, 10787-10792 (2001)) in each IC50 data panel. Then the cell lines were divided into two classes using the following method; the normalized log(IC50)s above the mean are defined as resistant and below as sensitive (
Subsequently, the gene expression data of the 18 cell lines were analyzed to identify genes that were highly correlated with observed phenotype defined as sensitive or resistant. From the GeneCluster analysis, classifiers that consisted of up to 250 correlated genes were selected and tested through leave-one out cross validation; by holding back one cell line, training on the remaining cell lines, predicting the class of the withheld cell line, and repeating this cycle for each cell line in the training set. Each gene was ranked according to the correlation in the training set between its expression level and the sensitivity-resistance class distinction. Each classifier identified from the analysis was evaluated with the error rate as shown in the
From the GeneCluster analysis, 200 genes (Tables 1 and 2) were identified whose expression levels were highly correlated with the sensitivity-resistance class distinction based on the KNN analysis and the T-test. Among these genes, the 50 marker candidates most closely correlated with sensitivity-resistance class distinction (first 25 sensitive markers of Table 1 and first 25 resistant markers of Table 2) were selected for further analysis.
As shown in
Among 200 genes identified, it is interesting to find estrogen receptor (ER) as one of the resistance markers. As shown in
Among the sensitivity markers identified, LMP7 is particularly interesting because it appears to be connected to Tau's function. In general, the proteasome is a multicatalytic proteinase complex responsible for the degradation of most intracellular proteins, including proteins crucial to cell cycle regulation and programmed cell death, or apoptosis (P. Voorhees et al., Clin. Cancer Res., 9, 6316-6325 (2003)). Among many proteins processed by the proteasome, Tau is degraded by the 20S proteasome in vitro in an ubiquitin-independent manner (D. David et al., J. of Neurochemistry, 83, 176-185 (2002)). This supports LMP7 as one of the sensitivity markers because LMP7 presumably facilitates interaction between ixabepilone and microtubules by degrading Tau and making ixabepilone more accessible to the microtubules.
One type of drug resistance mechanism is based on the function of a group of transporter proteins, able to prevent the intracellular accumulation of anticancer drugs by an efflux mechanisms (F. Leonessa et al., Endocr. Relat. Cancer., 10, 43-73 (2003)). Several transporter genes were identified as potential resistance markers as they were highly expressed in the resistant cell lines. These genes include ATP-binding cassette, sub-family G (WHITE), member 1 and ATP-binding cassette, sub-family A (ABC1), member 3. They are ATP dependent transporters which may be involved in lipid transport, and act as an efflux pump for chemotherapeutics drugs respectively (M. Gottesman et al., Nat. Rev. Cancer., January; 2(1):48-58 (2002)).
In addition, genes implied in microtubule functions are particularly interesting since ixabepilone is a microtubule-stabilizing agents. Microtubules are essential components of the cytoskeleton and involved in cell motility and transport, and maintenance of cell shape. The dynamic nature of a microtubule whose ability to polymerize and depolymerize, is essential for the segregation of chromosomes during mitosis (C. Bode et al., Biochemistry, 41, 3870-3874 (2002)). Therefore, marker genes such as midline 1 (C. Berti et al., BMC Cell Biol., February 29; 5(1):9 (2004)) and annexin A1 (L. C. Alldridge et al., Exp. Cell Res., October 15; 290(1):93-107 (2003)) that are implied in those functions can be involved in the mechanism of drug resistance. The biomarkers are categorized by their biological functions in Tables 1 and 2.
To study the use of these genes as response prediction markers in vivo, the expression pattern of these 50 genes in 175 breast cancer biopsies obtained from the Karolinska Institute was examined. The 50 genes were used to cluster the expression patterns of tumors. As shown in
Estrogen receptor (ER) and tau (Tau) were identified as biomarkers since their expression patterns were highly correlated with resistance to ixabepilone. In addition, it was found that the ER pathway was the most implicated biological network for resistance to ixabepilone based on the pathway analysis using preclinical candidate markers (
Methods
CA163-080 Study
CA163-080 is 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≧3 cm 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 −70° C. until use.
Evaluation of Pathological Response
Pathological response was assessed using the Sataloff classification system (D. Sataloff et al., J. Am. Coll. Surg., 180(3):297-306 (1995)) and used as an end point for the pharmacogenomic analysis. The pathologic response was evaluated in the primary tumor site at the end of treatment and prior to surgery by assessing histologic changes compared with baseline as following: At the primary tumor site, cellular modifications were evaluated in both the infiltrating tumoral component and in the possible ductal component, to determine viable residual infiltrating component (% of total tumoral mass); residual ductal component (% of total tumoral mass); the mitotic index. Pathologic Complete Response (pCR) in the breast only was defined as T-A, Total or near total therapeutic effect in primary site. Based on this criteria, responders included patients with pCR while non-responders included patients who failed to demonstrate pCR. The response rate was defined as the number of responders divided by the number of treated patients.
Gene Expression Profiling
Total RNA was isolated using the RNeasy Mini kit (Qiagen) according to the manufacturer's instructions by Karolinska Institute (Stockholm, Sweden). A total of 134 patients with more than 1 μg of total RNA with good quality were included in the data set for the final genomic analysis. Samples were profiled in a randomized order by batches to minimize the experimental bias. Each batch consisted of about 15 subject samples and 2 experimental controls using RNA extracted from HeLa cells. The expression profiling was done following a complete randomization with an effort to balance the number of samples from two tissue collection procedures (RNAlater and liquid nitrogen), two mRNA preparation methods (standard and DNA supernatants), tissue collection sites, and time of RNA sample preparation within in each batch. The mRNA samples from each subject was processed with HG-U133A 2.0 GeneChip® arrays on the Affymetrix platform and quantitated with GeneChip® Operating Software (GCOS) V1.0 (Affymetrix). The HG-U133A 2.0 GeneChip® array consists of about 22,276 probe sets, each containing about 15 perfect match and corresponding mismatch 25 mer oligonucleotide probes from specific gene sequences.
Gene Expression Data Processing
The gene expression data were transformed using base two logarithm. The Robust Multichip Average (RMA) method (C. Clopper et al., Biometrika, 26:404-13 (1934)) was used to normalize the raw expression data. The gene expression measures of each gene were centered at zero and rescaled to have a 1-unit standard deviation.
Stromal Effects in Tumor Biopsies
A hierarchical clustering analysis was performed in order to examine molecular profiles of tumor biopsies (
Statistical Analysis
Logistic regression (F. Hsieh et al., Stat. Med., 17(14):1623-34 (1998)) was used to explore the relationships between the expression of genes and response to ixabepilone. The following model was fitted for each gene separately:
where Y=1 represents a responder and X is the gene expression measure. For each gene, the probability from a two-tailed Score test of whether the estimate of b1=0 was used to rank the most interesting genes for further investigation. eb1 is the odds ratio of being a non-responder for a one unit increase in gene expression relative to the average expression for the sample of subjects. Odds ratios and 95% confidence limits were reported.
Subjects were randomly assigned to the equal sized training set (n=67) or the test set (n=67), for responders and non-responders groups separately. The gene expression of ER and Tau were considered as potential predictors for response. Single logistic regression (SLR) was used to build the predictive model based on the training set, and the model performance was assessed on the test set. The prediction error, sensitivity, specificity, PPV (positive predictive value), and NPV (negative predictive value) as well as their 95% confidence intervals of the SLR model were estimated.
Results
Estrogen Receptor (ER) and Tau
For ER, patients whose predicted probability of being responders was greater than 0.3 were classified as responders. The ER prognostic sensitivity, specificity, PPV, NPV, and their 95% confidence intervals of the SLR model are 0.64 (0.35, 0.85), 0.79 (0.66, 0.87), 0.37 (0.19, 0.59), and 0.92 (0.80, 0.97), respectively. For Tau, patients whose predicted probability of being responders was greater than 0.25 were classified as responders. The Tau prognostic sensitivity, specificity, PPV, NPV, and their 95% confidence intervals of the SLR model are 0.55 (0.28, 0.79), 0.73 (0.60, 0.83), 0.29 (0.14, 0.50), and 0.89 (0.77, 0.95), respectively.
Estrogen Receptor (ER) and Tau without Russian Sites 16, 24 and 25
For ER and Tau separately as SLR predictors, patients whose predicted probability of being responders was greater than 0.5 were classified as responders. The estrogen receptor 1 prognostic sensitivity, specificity, PPV, NPV and their 95% confidence intervals of the SLR model are 0.67 (0.35, 0.88), 0.83 (0.69, 0.92), 0.46 (0.23, 0.71), and 0.92 (0.79, 0.97), respectively. The Tau prognostic sensitivity, specificity, PPV, NPV, and their 95% confidence intervals of the SLR model are 0.44 (0.19, 0.73), 0.88 (0.75, 0.95), 0.44 (0.19, 0.73), and 0.88 (0.75, 0.95), respectively.
Conclusion/Discussion
A total of 164 patients were enrolled in CA163-080 study. The quality and quantity of RNA samples obtained from pre-treatment biopsies was fairly good as 134 patients (85%) had RNA samples with >1 μg good quality which did not require additional amplification for gene expression profiling. Stromal contamination in the tumor biopsies was raised as a potential problem for the analysis. It appeared that the three Russian sites 16, 24 and 25 might have more stromal tissues in the samples compared to others based on the hierarchical clustering analysis. In addition, the tumors from these three Russian sites were larger than others at baseline. Although further analysis is needed to confirm this hypothesis, it raised an important issue for analyzing clinical samples that are inherently heterogeneous.
Among preclinical candidate markers, ER and Tau were examined in CA163-080 for their predictability. In our preclinical work, ER and Tau had been identified as biomarkers since their expression patterns were highly correlated with resistance to ixabepilone. In CA163-080, ER predicted well for pCR whether or not the three Russian sites were included in the analysis. However, the highest PPV was obtained when these sites were excluded. In another study finding predictive markers of response to combination chemotherapy with paclitaxel, 5-Fluorouracil, adriamycin and cyclophosphamide, the ER regulated gene Tau was identified as the best predictor of response (M. Ayers et al., J. Clin. Oncol., 22(12):2284-93 (2004)). Work done by this group has also demonstrated in vitro that knocking down Tau levels using small interfering RNA (siRNA) increases the sensitivity of breast cancer cell lines to paclitaxel treatment (R. Rouzier et al., P.N.A.S., June 7; 102(23):8315-20 (2005)). The proposed mechanism is that high levels of Tau inhibit binding of paclitaxel to the taxane binding site on β-tubulin. Tau gene expression was therefore also examined for ability to predict response to ixabepilone. The PPV (0.44) with this gene was similar to that for ER (0.46) in the subset excluding the 3 Russian sites.
Thus, ER and Tau demonstrated their utility as a predictors for response to ixabepilone and can be used as biomarkers for identifying the pCR responders to ixabepilone.
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° C.), and supplemented to contain about 10 g/l 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. In 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.
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° C. 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 g 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++ containing 0.2% BSA (blocker) at 0-4° C. 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° C. 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.
This application claims the benefit of U.S. Provisional Application No. 60/631,993 filed Nov. 30, 2004, and is a continuation of U.S. Non-Provisional application Ser. No. 11/289,102 filed Nov. 29, 2005 now abandoned, whose contents are hereby incorporated by reference in there entirety.
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| Number | Date | Country | |
|---|---|---|---|
| Parent | 11289102 | Nov 2005 | US |
| Child | 11906248 | US |
