Patent Application
20130260376
This invention concerns gene sets relevant to the treatment of epithelial cancers, and methods for assigning treatment options to epithelial cancer patients based upon knowledge derived from gene expression studies of cancer tissue.
Previous work has shown that epithelial-to-mesenchymal transition (“EMT”) is associated with metastasis and cancer stem cells (Creighton et al., 2009; Mani et al., 2008; Morel et al., 2008; Yang et al., 2006; Yang et al., 2004; Yauch et al., 2005) Importantly, induction of EMT across epithelial cancer types (e.g., lung, breast) also results in resistance to cancer therapies, including chemotherapies and kinase-targeted anti-cancer agents (e.g., erlotinib). Those skilled in the art will recognize that the EMT produces cancer cells that are invasive, migratory, and have stem-cell characteristics, which are all hallmarks of cells that have the potential to generate metastases.
EMT is a process in which adherent epithelial cells shed their epithelial characteristics and acquire, in their stead, mesenchymal properties, including fibroblastoid morphology, characteristic gene expression changes, increased potential for motility, and in the case of cancer cells, increased invasion, metastasis and resistance to chemotherapy. (See Kalluri et al., J Clin Invest 119(6):1420-28 (2009); Gupta et al., Cell 138(4):645-59 (2009)). Recent studies have linked EMTs with both metastatic progression of cancer (see Yang et al., Cell 117(7):927-39 (2004); Frixen et al., J Cell Biol 113(1):173-85 (1991); Sabbah et al., Drug Resist Updat 11(4-5):123-51 (2008)) and acquisition of stem-cell characteristics (see Mani et al., Cell 133(4):704-15 (2008); Morel et al., PLoS One 3(8):e288 (2008)), leading to the hypothesis that cancer cells that undergo an EMT are capable of metastasizing through their acquired invasiveness and, following dissemination, through their acquired self-renewal potential; the latter trait enables them to spawn the large cell populations that constitute macroscopic metastases.
Given these observations, one might predict that cancers harboring significant populations (or subpopulations) of cells having undergone EMT would be likely to exhibit reduced responsiveness to chemotherapies and anti-kinase targeted therapies.
The present invention is a method for deriving a molecular signature of epithelial cancers that would not be responsive to chemotherapies and anti-kinase targeted therapies. The present invention also covers any patient stratification scheme that takes advantage of the biomarkers described herein, whether for the purpose of treatment selection and/or prognosis determination. Treatment selection could be either positive or negative and with respect to any class of anti-cancer agents. The method utilizes assays for the expression of biomarker genes that are upregulated in cancer cells post-EMT (Table 1) and assays for other biomarker genes upregulated in cells that have not undergone EMT (Table 2). Using these biomarker assays, it is possible to identify cancers that would not be responsive to conventional cancer therapies.
The invention provides methods of predicting the likelihood that a patient's epithelial cancer will respond to a standard-of-care therapy, following surgical removal of the primary tumor, by determining the expression level in cancer (i.e., in an epithelial cancer cell from the removed primary tumor) of genes in Tables 1 and/or 2, wherein the overexpression of genes in Table 1 indicates an increased likelihood that the tumor will be resistant to the standard-of-care therapy and overexpression of genes in Table 2 indicates an increased likelihood that the tumor will be sensitive to the standard-of-care therapy.
Overexpression of genes in Table 1 (or any suitable subset thereof) indicates an increased likelihood that the epithelial cancer will be resistant to standard-of-care therapies such as paclitaxel but sensitive to a cancer stem-cell selective agent (“CSS agent”) such as, for example, but not limited to, salinomycin. Moreover, underexpression of genes in Table 2 (or any suitable subset thereof) indicates an increased likelihood that the epithelial cancer will be resistant to standard-of-care therapy such as paclitaxel but sensitive to a CSS agent such as salinomycin.
Additionally, those skilled in the art will recognize that the underexpression of genes in Table 1 indicates an increased likelihood that the tumor will be sensitive to standard-of-care. Similarly, the overexpression of genes in Table 2 indicates an increased likelihood that the tumor will be resistant to standard-of-care therapy.
Those skilled in the art will recognize that determining the expression level of genes in Tables 1 and/or 2 occurs in vitro in the removed primary tumor.
Specifically, those skilled in the art will recognize that the overexpression of genes in Table 1 indicates an increased likelihood that the tumor will be resistant to standard-of-care therapy. For example, the overexpression of genes in Table 1 indicates an increased likelihood that the tumor will be resistant to paclitaxel.
Examples of standard-of-care therapy can include, but are not limited to, kinase-targeted therapy, such as EGFR-inhibition, radiation, a hormonal therapy, paclitaxel and/or any combination(s) thereof.
In various embodiments, those skilled in the art will recognize that the expression level of the genes assayed may constitute any subset of the genes in Table 1 and/or Table 2. Specifically, the gene subset is any subset of genes is one for which an appropriate statistical test (i.e., Gene Set Enrichment Analysis (“GSEA”)) demonstrates that the genes in the subset are differentially expressed in populations treated with a cancer therapy at a level of significance (e.g. p-value) less than 0.1, relative to an appropriate control population (e.g., DMSO treatment). Any appropriate statistical test(s) known to those skilled in the art and/or any appropriate control population(s) known to those skilled in the art can be used in identifying the gene subsets. For example, the appropriate control population(s) can be any population of cells (i.e., cancer cells) that have not been treated with a given cancer therapy.
Examples of cancer therapy may include, but are not limited to, salinomycin treatment and paclitaxel treatment. Moreover, in various embodiments, the subset of genes may include 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, or 30 of the genes in Table 1 and/or Table 2.
The overexpression of genes in Table 1 may also indicate an increased likelihood that the tumor will be sensitive to therapeutic agents that are toxic to cancer cells resistant to standard-of-care therapies. Moreover, the overexpression of genes in Table 1 may also indicate an increased likelihood that the tumor will be sensitive to therapeutic agents that are toxic to cancer stem cells or to therapeutic agents that target invasive and/or metastatic cancer cells. In still other embodiments, the overexpression of genes in Table 1 may indicate an increased likelihood that the tumor will be sensitive to therapeutic agents that are toxic to cancer cells that have undergone an epithelial-to-mesenchymal transition. Moreover, the overexpression of genes in Table 1 also indicates an increased likelihood that the tumor will be sensitive to a CSS agent (e.g., salinomycin).
Also provided are methods of predicting the likelihood that a patient's epithelial cancer will respond to standard-of-care therapy, following surgical removal of the primary tumor, comprising determining the expression level in cancer (i.e., in an epithelial cancer cell from the removed tumor) of genes in Table 2. Those skilled in the art will recognize that the reduced expression of genes in Table 2 indicates an increased likelihood that the tumor will be resistant to standard-of-care therapy. Standard-of-care therapy can include, but is not limited to, a kinase-targeted therapy, such as EGFR-inhibition; a radiation therapy; a hormonal therapy; paclitaxel; and/or any combination(s) thereof.
Those skilled in the art will recognize that determining the expression level of genes in Table 2 occurs in vitro in the removed primary tumor. Again, those skilled in the art will recognize that the expression level of the genes assayed may constitute any subset of the genes in Table 2. Specifically, the gene subset is any subset of genes is one for which an appropriate statistical test (i.e., Gene Set Enrichment Analysis (“GSEA”)) demonstrates that the genes in the subset are differentially expressed in populations treated with a cancer therapy at a level of significance (e.g. p-value) less than 0.1, relative to an appropriate control population (e.g., DMSO treatment). Any appropriate statistical test(s) known to those skilled in the art and/or any appropriate control population(s) known to those skilled in the art can be used in identifying the gene subsets. For example, the appropriate control population(s) can be any population of cells (i.e., cancer cells) that have not been treated with a given cancer therapy.
Examples of cancer therapy may include, but are not limited to, salinomycin treatment and paclitaxel treatment. Moreover, in various embodiments, the subset of genes may include 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, or 30 of the genes in Table 2.
In these methods, the reduced expression of genes in Table 2 may indicate an increased likelihood that the tumor will be sensitive to therapeutic agents that are toxic to cancer cells resistant to standard-of-care therapies. Similarly, the reduced expression of genes in Table 2 may indicate an increased likelihood that the tumor will be sensitive to therapeutic agents that are toxic to cancer stem cells. Likewise, the reduced expression of genes in Table 2 may indicate an increased likelihood that the tumor will be sensitive to therapeutic agents that are toxic to cancer cells that have undergone an epithelial-to-mesenchymal transition.
The invention further provides methods of identifying therapeutic agents that target cancer stem cells or epithelial cancers that have undergone an epithelial to mesenchymal transition by screening candidate agents to identify those that increase the levels of expression of the genes in Table 2, wherein an increase in the expression of genes in Table 2 indicates that the candidate agent targets cancer stem cells or epithelial cancers that have undergone an epithelial to mesenchymal transition. Moreover, the reduced expression of genes in Table 2 also indicates an increased likelihood that the tumor will be sensitive to a CSS agent (e.g., salinomycin).
Such methods are preferably performed in vitro on cancer (i.e., on epithelial cancer cells obtained following surgical removal of a primary tumor).
The methods of identifying therapeutic agents that target cancer stem cells or epithelial cancers that have undergone an EMT according to the invention can be performed independently, simultaneously, or sequentially.
Those skilled in the art will recognize that in these screening methods, any subset of genes in Table 2 is evaluated for its expression levels. Preferably, the subset of genes is one for which a statistical test demonstrates that the genes in the subset are differentially expressed in populations treated with a cancer therapy (e.g., salinomycin treatment or paclitaxel treatment) at a level of significance (e.g., p-value) less than 0.1, relative to an appropriate control population (e.g., DMSO treatment). For example, the subset of genes may include 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, or 30 of the genes in Table 2.
Any appropriate statistical test(s) known to those skilled in the art and/or any appropriate control population(s) known to those skilled in the art can be used in identifying the gene subsets. For example, the appropriate control population(s) can be any population of cells (i.e., cancer cells) that have not been treated with a given cancer therapy.
In still further embodiments, the invention provides methods of identifying therapeutic agents that target cancer stem cells or epithelial cancers that have undergone an epithelial to mesenchymal transition comprising screening candidate agents to identify those that decrease the levels of expression of the genes in Table 1, wherein a decrease in the expression of genes in Table 1 indicates that the candidate agent targets cancer stem cells or epithelial cancers that have undergone an epithelial to mesenchymal transition. Such methods are preferably performed in vitro on cancer (i.e., epithelial cancer cells obtained following surgical removal of a primary tumor).
In these methods, any subset of genes in Table 1 is evaluated for its expression levels. Preferably, the subset of genes is one for which a statistical test demonstrates that the genes in the subset are differentially expressed in populations treated with a cancer therapy (e.g., salinomycin treatment or paclitaxel treatment) at a level of significance (e.g., p-value) less than 0.1, relative to an appropriate control population (e.g., DMSO treatment). For example, the subset of genes may include 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, or 30 of the genes in Table 1.
Any appropriate statistical test(s) known to those skilled in the art and/or any appropriate control population(s) known to those skilled in the art can be used in identifying the gene subsets. For example, the appropriate control population(s) can be any population of cells (i.e., cancer cells) that have not been treated with a given cancer therapy.
In other embodiments, the invention provides methods of predicting the likelihood that a patient's epithelial cancer will respond to therapy, following surgical removal of the primary tumor, comprising determining the expression level in cancer of genes in Table 1. Those skilled in the art will recognize that the overexpression of genes in Table 1 indicates an increased likelihood that the tumor will be sensitive to therapy with salinomycin or other CSS agents. Moreover, the overexpression of genes in Table 1 indicates an increased likelihood that the tumor will be resistant to standard-of-care therapy such as, for example, paclitaxel.
Those skilled in the art will recognize that in such methods, determining the expression level of genes in Table 1 occurs in vitro in the removed primary tumor. In any of these methods of predicting the likelihood that a patient's epithelial cancer will respond to therapy, any subset of genes in Table 1 is evaluated for its expression levels. Preferably, the subset of the genes whose expression is evaluated is one for which a statistical test demonstrates that the genes in the subset are differentially expressed in populations treated with a cancer therapy (e.g., salinomycin treatment or paclitaxel treatment) at a level of significance (e.g., p-value) less than 0.1, relative to an appropriate control population (e.g., DMSO treatment). Those skilled in the art will recognize that the subset of genes can include 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, or 30 of the genes in Table 1.
Those skilled in the art will readily recognize that any appropriate statistical test(s) known to those skilled in the art and/or any appropriate control population(s) known to those skilled in the art can be used in identifying the gene subsets. For example, the appropriate control population(s) can be any population of cells (i.e., cancer cells) that have not been treated with a given cancer therapy.
In some embodiments, the methods of the invention provide intermediate information that may be useful to a skilled practitioner in selecting a future course of action, therapy, and/or treatment in a patient. For example, any of the methods described herein can further involve the step(s) of summarizing the data obtained by the determination of the gene expression levels. By way of non-limiting example, the summarizing may include prediction of the likelihood of long term survival of said patient without recurrence of the cancer following surgical removal of the primary tumor. Additionally (or alternatively), the summarizing may include recommendation for a treatment modality of said patient.
Also provided by the instant invention are kits containing, in one or more containers, at least one detectably labeled reagent that specifically recognizes one or more of the genes in Table 1 and/or Table 2. For example, the kits can be used to determine the level of expression of the one or more genes in Table 1 and/or Table 2 in cancer (i.e., in an epithelial cancer cell). In some embodiments, the kit is used to generate a biomarker profile of an epithelial cancer. Kits according to the invention can also contain at least one pharmaceutical excipient, diluent, adjuvant, or any combination(s) thereof.
Moreover, in any of the methods of the invention, the RNA expression levels are indirectly evaluated by determining protein expression levels of the corresponding gene products. For example, in one embodiment, the RNA expression levels are indirectly evaluated by determining chromatin states of the corresponding genes.
Those skilled in the art will readily recognize that the RNA is isolated from a fixed, wax-embedded breast cancer tissue specimen of said patient; the RNA is fragmented RNA; and/or the RNA is isolated from a fine needle biopsy sample.
In any of the methods described herein, the cancer may be an epithelial cancer, a lung cancer, breast cancer, prostate cancer, gastric cancer, colon cancer, pancreatic cancer, brain cancer, and/or melanoma cancer.
The invention additionally provides in vitro for determining whether or predicting the likelihood that a patient's epithelial cancer will respond to a standard-of-care therapy. Such methods involve the steps of determining the expression level in cancer (i.e., in an epithelial cancer cell obtained following surgical removal of a primary tumor from a patient having epithelial cancer) of genes in Tables 1 and/or 2, wherein the overexpression of genes in Table 1 indicates an increased likelihood that the patient's epithelial cancer will be resistant to the standard-of-care therapy and overexpression of genes in Table 2 indicates an increased likelihood that the patient's epithelial cancer will be sensitive to the standard-of-care therapy. More specifically, the overexpression of genes in Table 1 indicates an increased likelihood that the tumor will be resistant to standard-of-care therapy and/or an increased likelihood that the tumor will be resistant to paclitaxel. Moreover, the overexpression of genes in Table 1 indicates an increased likelihood that the tumor will be sensitive to therapeutic agents that are toxic to cancer cells resistant to standard-of-care therapies; an increased likelihood that the tumor will be sensitive to therapeutic agents that are toxic to cancer stem cells or to therapeutic agents that target invasive, metastatic, or invasive and metastatic cancer cells; and/or an increased likelihood that the tumor will be sensitive to therapeutic agents that are toxic to cancer cells that have undergone an epithelial-to-mesenchymal transition.
Similarly, the reduced expression of genes in Table 2 indicates an increased likelihood that the tumor will be resistant to standard-of-care therapy; an increased likelihood that the tumor will be sensitive to therapeutic agents that are toxic to cancer cells resistant to standard-of-care therapies; an increased likelihood that the tumor will be sensitive to therapeutic agents that are toxic to cancer stem cells; and/or an increased likelihood that the tumor will be sensitive to therapeutic agents that are toxic to cancer cells that have undergone an epithelial-to-mesenchymal transition.
Those skilled in the art will readily recognize that the standard-of-care therapy can be a kinase-targeted therapy, such as EGFR-inhibition; a radiation; a hormonal therapy; paclitaxel; and/or any combination thereof.
In any of these in vitro methods, the expression level of the genes assayed constitutes any subset of the genes in Table 1 and/or Table 2. Specifically, the subset of genes is one for which a statistical test (e.g., Gene Set Enrichment Analysis) demonstrates that the genes in the subset are differentially expressed in populations treated with a cancer therapy at a level of significance (e.g., p-value) less than 0.1, relative to an appropriate control population (e.g., DMSO treatment). Examples of cancer therapy include, but are not limited to salinomycin treatment and paclitaxel treatment. Those skilled in the art will recognize that the subset of genes assayed can include 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, or 30 of the genes in Table 1 and/or Table 2.
The details of one or more embodiments of the invention have been set forth in the accompanying description below. Although any methods and materials similar or equivalent to those described herein can be used in the practice or testing of the present invention, the preferred methods and materials are now described. Other features, objects, and advantages of the invention will be apparent from the description and from the claims. In the specification and the appended claims, the singular forms include plural references unless the context clearly dictates otherwise. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. All patents and publications cited in this specification are incorporated by reference in their entirety.
Prior to setting forth the invention, it may be helpful to an understanding thereof to set forth definitions of certain terms that will be used hereinafter.
A “biomarker” in the context of the present invention is a molecular indicator of a specific biological property; a biochemical feature or facet that can be used to detect and/or categorize an epithelial cancer. “Biomarker” encompasses, without limitation, proteins, nucleic acids, and metabolites, together with their polymorphisms, mutations, variants, modifications, subunits, fragments, protein-ligand complexes, and degradation products, protein-ligand complexes, elements, related metabolites, and other analytes or sample-derived measures. Biomarkers can also include mutated proteins or mutated nucleic acids. In the instant invention, measurement of mRNA is preferred.
A “biological sample” or “sample” in the context of the present invention is a biological sample isolated from a subject and can include, by way of example and not limitation, whole blood, blood fraction, serum, plasma, blood cells, tissue biopsies, a cellular extract, a muscle or tissue sample, a muscle or tissue biopsy, or any other secretion, excretion, or other bodily fluids.
The phrase “differentially expressed” refers to differences in the quantity and/or the frequency of a biomarker present in a sample taken from patients having for example, epithelial cancer as compared to a control subject. For example without limitation, a biomarker can be an mRNA or a polypeptide which is present at an elevated level (i.e., overexpressed) or at a decreased level (i.e., underexpressed) in samples of patients with cancer as compared to samples of control subjects. Alternatively, a biomarker can be a polypeptide which is detected at a higher frequency (i.e., overexpressed) or at a lower frequency (i.e., underexpressed) in samples of patients compared to samples of control subjects. A biomarker can be differentially present in terms of quantity, frequency or both.
Previous work has shown that agents that selectively target cells induced into EMT also selectively kill cancer stem cells. Since cancer cells induced into EMT are also highly invasive, the hypothesis is that anti-cancer therapies that target invasive and/or metastatic cancer cells are likely to also target cancer cells induced into EMT.
According to one embodiment, this invention provides a method for determining which patient subpopulations harbor tumors responsive to three classes of essentially overlapping anti-cancer therapies or treatments—i.e., (a) therapies that target invasive/metastatic cells, (b) therapies that target cancer stem cells and (c) therapies that target cells post-EMT. Specifically, the invention provides methods for determining which therapies or treatments would be effective in cancers that express genetic biomarkers that are upregulated in cancer cells post-EMT (Table 1) and would not be effective in cancers that express genetic markers upregulated in cancer cells that have not undergone an EMT (Table 2).
The cancers that the methods of this invention are contemplated to be useful for include any epithelial cancers, and specifically include breast cancer, melanoma, brain, gastric, pancreatic cancer and carcinomas of the lung, prostate, and colon.
The anti-cancer therapies and treatments in which the methods of this invention are contemplated to be useful for include standard-of-care therapies such as paclitaxel, DNA damaging agents, kinase inhibitors (e.g., erlotinib), and radiation therapies, as well as therapies that target cancer stem cells and/or therapies that target cells post-EMT, including, for example, CSS agents such as salinomycin.
A set of genes differentially expressed in cancer cells that have undergone an EMT (Table 1) and genes expressed in cancer cells that have not undergone an EMT (Table 2) was determined. These genes were obtained by collecting RNA and performing microarray gene-expression analyses on breast cancer cells that were cultured either expressing one of 5 EMT-inducing genetic factors or 2 control genetic factors that did not induce EMT (control vectors). Cells were cultured in triplicate for each treatment condition. A global analysis of the gene expression data is shown as a heatmap in
To demonstrate that the responsiveness of cancer cell populations to therapy can be both measured by and predicted by the various subsets of the genes identified in Tables 1 and 2, HMLER breast cancer populations were treated with a commonly used anti-cancer chemotherapy paclitaxel (Taxol) or with control DMSO treatment. mRNA was then isolated, and global gene expression data was collected. The collective expression levels of the genes in Tables 1 and 2 after paclitaxel treatment were then determined. For these analyses, which are shown in
Those skilled in the art will recognize that determining the expression level of genes in Tables 1 and/or 2 occurs in vitro in the removed primary tumor.
The analyses show that the genes expressed in Table 1 and/or many subsets thereof are over-expressed upon treatment with paclitaxel, indicating that these genes identify cancer cellular subpopulations that are resistant to treatment with paclitaxel. As a consequence, measurement of the expression of the genes in Table 1 would serve to identify tumors that would fail to be responsive to paclitaxel treatment when applied as a single agent.
Also covered in this invention is any subset of the genes in Table 1 for which a statistical test (such as, for example, Gene Set Enrichment Analysis (see Subramanian, Tamayo, et al., PNAS 102:15545-50 (2005) and Mootha, Lindgren et al., Nat. Genet 34:267-73 (2003), each of which is herein incorporated by reference in its entirety) demonstrates that the genes in the subset are over-expressed in paclitaxel-treated populations at a level of significance (e.g. p-value) less than 0.1, more preferably less than 0.05, relative to an appropriate control population (e.g., DMSO treatment). In one embodiment it was contemplated that the subset of genes from Table 1 comprises at least 2 genes, 10 genes, 15 genes, 20 genes or 30 genes (or any range intervening therebetween). For example, the subset might include 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, or 30 genes.
Those skilled in the art will recognize that any other appropriate statistical test(s) for gene enrichment or differential expression can also be used to identify the desired subset of genes from Table 1. For example, the summation of the log-transformed gene expression scores for the genes in a set could identify a metric that could be used to compare differential gene expression between two profiles using a t-test, modified t-test, or non-parametric test such as Mann-Whitney.
Moreover, those skilled in the art will also recognize that any appropriate control population(s) can also be used to identify the desired subset of genes from Table 1. For example, the appropriate control population(s) can be any population of cells (i.e., cancer cells) that have not been treated with a given cancer therapy.
Alternatively, the subsets of the genes in Table 1 may be identified as any subset for which a statistical test (such as, for example, Gene Set Enrichment Analysis) demonstrates that the genes in the subset are under-expressed in salinomycin-treated populations at a level of significance (e.g. p-value) less than 0.1, more preferably less that 0.05, relative to an appropriate control population (e.g., DMSO treatment). In one embodiment it was contemplated that the subset of genes from Table 1 comprises at least 2 genes, 10 genes, 15 genes, 20 genes or 30 genes (or any range intervening therebetween). For example, the subset might include 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, or 30 genes. For those skilled in the art, any other appropriate statistical test(s) for gene expression or differential expression can also be used to identify the desired subset of genes from Table 1. For example, the summation of the log-transformed gene expression scores for the genes in a set could identify a metric that could be used to compare differential gene expression between two profiles using a t-test, modified t-test, or non-parametric test such as Mann-Whitney.
Likewise, any appropriate control population(s) can also be used to identify the desired subset of genes from Table 1. For example, the appropriate control population(s) can be any population of cells (i.e., cancer cells) that have not been treated with a given cancer therapy.
Those skilled in the art will recognize that the statistical test used to determine suitable subsets of the genes in Table 1 could be Gene Set Enrichment Analysis (GSEA) (see Subramanian, Tamayo, et al., PNAS 102:15545-50 (2005) and Mootha, Lindgren et al., Nat. Genet 34:267-73 (2003), each of which is herein incorporated by reference in its entirety) as used for the purposes of elucidation in this application, or it could be any other statistical test of enrichment or expression known in the art. For example, the summation of the log-transformed gene expression scores for the genes in a set could identify a metric that could be used to compare differential gene expression between two profiles using a t-test, modified t-test, or non-parametric test such as Mann-Whitney.
The populations of cells being treated for the purposes of this evaluation could be cancer cells of any type or normal cellular populations.
The analyses also show that the genes in Table 2 and many subsets thereof are under-expressed upon treatment with paclitaxel, indicating that these genes identify cellular subpopulations that are sensitive to treatment with paclitaxel. As a consequence, measurement of the expression of the genes in Table 2 would serve to identify tumors that would be responsive to paclitaxel treatment when applied as a single agent.
Those skilled in the art will recognize that determining the expression level of genes in Table 2 occurs in vitro in the removed primary tumor.
Also covered in this invention is any subset of the genes in Table 2 for which a statistical test (such as, for example, Gene Set Enrichment Analysis) demonstrates that the genes in the subset are under-expressed in paclitaxel-treated populations at a level of significance (e.g. p-value) less than 0.1, more preferably less than 0.05, relative to an appropriate control population (e.g., DMSO treatment). In one embodiment it was contemplated that the subset of the genes from Table 2 comprises at least 2 genes, 6 genes, 10 genes, 15 genes, 20 genes or 30 genes (or any range intervening therebetween). For example, the subset might include 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, or 30 genes. Those skilled in the art will recognize that any other appropriate statistical test(s) for gene enrichment or differential expression can also be used to identify the desired subset of genes from Table 2. For example, the summation of the log-transformed gene expression scores for the genes in a set could identify a metric that could be used to compare differential gene expression between two profiles using a t-test, modified t-test, or non-parametric test such as Mann-Whitney.
Moreover, those skilled in the art will also recognize that any appropriate control population(s) can also be used to identify the desired subset of genes from Table 2. For example, the appropriate control population(s) can be any population of cells (i.e., cancer cells) that have not been treated with a given cancer therapy.
Alternatively, the subsets of the genes in Table 2 may be identified as any subset for which a statistical test (such as Gene Set Enrichment Analysis) demonstrates that the genes in the subset are over-expressed in salinomycin-treated populations at a level of significance (e.g. p-value) less than 0.1, more preferably less than 0.05, relative to an appropriate control population (e.g., DMSO treatment). In one embodiment it was contemplated that the subset of the genes from Table 2 comprises at least 2 genes, 6 genes, 10 genes, 15 genes, 20 genes or 30 genes (or any range intervening therebetween). For example, the subset might include 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, or 30 genes. Those skilled in the art will recognize that any other appropriate statistical test(s) for gene enrichment or differential expression can also be used to identify can also be used to identify the desired subset of genes from Table 2. For example, the summation of the log-transformed gene expression scores for the genes in a set could identify a metric that could be used to compare differential gene expression between two profiles using a t-test, modified t-test, or non-parametric test such as Mann-Whitney.
Likewise, those skilled in the art will also recognize that any appropriate control population(s) can also be used to identify the desired subset of genes from Table 2. For example, the appropriate control population(s) can be any population of cells (i.e., cancer cells) that have not been treated with a given cancer therapy.
The statistical test used could be Gene Set Enrichment Analysis (GSEA) (see Subramanian, Tamayo, et al., PNAS 102:15545-50 (2005) and Mootha, Lindgren et al., Nat. Genet 34:267-73 (2003), each of which is herein incorporated by reference in its entirety) as used for the purposes of elucidation in this application, or it could be any other statistical test of enrichment or expression known in the art. By way of non-limiting example, the summation of the log-transformed gene expression scores for the genes in a set could identify a metric that could be used to compare differential gene expression between two profiles using a t-test, modified t-test, or non-parametric test such as Mann-Whitney.
The populations of cells being treated for the purposes of this evaluation could be cancer cells of any type or normal cellular populations.
Next, identical analyses as those described above were performed in the context of treatment with a different anti-cancer agent—salinomycin—that was previously identified as specifically killing invasive cancer stem cells. The opposite expression change (relative to paclitaxel) was observed upon treatment with salinomycin. The analyses, shown in
The analyses also show that the genes expressed in Table 2 and any subset thereof are over-expressed upon treatment with salinomycin (relative to control), indicating that these genes identify cellular subpopulations that are resistant to treatment with a CSS agent such as salinomycin. As a consequence, measurement of the expression of the genes in Table 2 (or any appropriate subsets thereof identified according to the methods disclosed herein) would serve to identify tumors that would fail to be responsive to a CSS agent (e.g., salinomycin treatment) when applied as a single agent.
It follows that measurement of the expression of the genes in Tables 1 and/or 2 as well as various subsets thereof for which a statistical test demonstrates that the genes in the subset are differentially expressed in response to treatment with a cancer treatment (e.g., salinomycin treatment or paclitaxel treatment) at a level of significance (e.g., p value) less than 0.1, relative to an appropriate control population (e.g., DMSO treatment) can be used to identify cancer cell populations that are or are not responsive to any given therapy or treatment. Distinct subpopulations of cells are identified using the expression levels of the genes in Tables 1 and/or 2 (or any appropriate subsets thereof) and these distinct subpopulations could respond distinctively to any particular therapeutic or treatment regimen, thereby allowing these genes to serve as biomarkers dictating therapy choice following primary tumor removal.
All documents and patents or patent applications referred to herein are fully incorporated by reference.
While the invention has been described in conjunction with the detailed description thereof, the foregoing description is intended to illustrate and not limit the scope of the invention, which is defined by the scope of the appended claims. Other aspects, advantages, and modifications are within the scope of the following claims.
This application claims priority to U.S. Ser. No. 61/369,928, filed on Aug. 2, 2010, which is herein incorporated by reference in its entirety.
| Filing Document | Filing Date | Country | Kind | 371c Date |
|---|---|---|---|---|
| PCT/US11/46325 | 8/2/2011 | WO | 00 | 6/10/2013 |
| Number | Date | Country | |
|---|---|---|---|
| 61369928 | Aug 2010 | US |
