METHOD FOR PREDICTING RESPONSE TO BREAST CANCER THERAPEUTIC AGENTS AND METHOD OF TREATMENT OF BREAST CANCER
20220056541 · 2022-02-24
Assignee
Inventors
Cpc classification
A61P43/00
HUMAN NECESSITIES
C12Q2600/106
CHEMISTRY; METALLURGY
A61K45/06
HUMAN NECESSITIES
A61K31/4166
HUMAN NECESSITIES
C12Q2600/112
CHEMISTRY; METALLURGY
A61P35/00
HUMAN NECESSITIES
International classification
A61K31/4166
HUMAN NECESSITIES
Abstract
Methods for treating triple negative breast cancer with an androgen receptor inhibitor are provided, as well as methods for screening for the likelihood of the effectiveness of such treatment.
Claims
1. A method of treating triple negative breast cancer (TNBC) in a subject, said subject having a breast cancer comprising breast cancer cells that have been classified as other than basal-like subtype, said method comprising: testing the subject to determine a Weighted Basal and Luminal A classifier score of breast cancer cells of the subject; and administering a breast cancer treatment to the subject comprising an androgen receptor inhibitor, thereby treating the triple negative breast cancer in the subject; wherein the triple negative breast cancer cells of the subject are characterized by a Weighted Basal and Luminal A classifier score greater than −0.3 according to the formula:
Weighted Basal and Luminal A classifier score=−0.25(Basal Centroid classifier score)+0.27(Luminal A Centroid classifier score) wherein said Basal Centroid classifier score and said Luminal A Centroid classifier score are determined for the triple negative breast cancer cells of the subject from the expression by said cells of the set of intrinsic genes listed in Table 1 using a PAM50 classifier.
2. (canceled)
3. The method according to claim 1, wherein the breast cancer cells of the subject are characterized by a Weighted Basal and Luminal A classifier score greater than −0.2.
4. The method according to claim 3, wherein the breast cancer cells of the subject are characterized by a Weighted Basal and Luminal A classifier score greater than −0.25.
5-8. (canceled)
9. The method according to claim 1, wherein the breast cancer of the subject is characterized by the presence of androgen receptor-positive tumor cells.
10. The method according to claim 1, wherein the androgen receptor inhibitor is selected from the group consisting of enzalutamide, bicalutamide, flutamide, nilutamide, ARN509, ketoconazole, abiraterone acetate, VN/124-1 (TOK-001), orteronel (TAK-700), finasteride, galeterone, cyproterone acetate, andarine, and combinations thereof.
11. The method according to claim 10, wherein the androgen receptor inhibitor is enzalutamide.
12. The method according to claim 1, wherein the androgen receptor inhibitor is enzalutamide.
13. The method according to claim 3, wherein the androgen receptor inhibitor is enzalutamide.
14. The method according to claim 4, wherein the androgen receptor inhibitor is enzalutamide.
15. The method according to claim 11, wherein the enzalutamide is orally administered once daily at a dose of 160 mg.
16. The method according to claim 15, wherein the enzalutamide is administered as a single capsule comprising 160 mg enzalutamide.
17. The method according to claim 15, wherein the enzalutamide is administered as four capsules, each capsule comprising 40 mg enzalutamide.
18. The method according to claim 1, wherein the breast cancer treatment comprising an androgen receptor inhibitor further comprises one or more other anti-cancer agents that is not an androgen receptor inhibitor.
19. The method according to claim 18, wherein the other anti-cancer agent that is not an androgen receptor inhibitor is selected from the group consisting of cyclophosphamide, fluorouracil, 5-fluorouracil, methotrexate, thiotepa, carboplatin, cisplatin, taxanes, paclitaxel, protein-bound paclitaxel, docetaxel, vinorelbine, tamoxifen, raloxifene, toremifene, fulvestrant, gemcitabine, irinotecan, ixabepilone, temozolmide, topotecan, vincristine, vinblastine, eribulin, mutamycin, capecitabine, anastrozole, exemestane, letrozole, leuprolide, abarelix, buserelin, goserelin, megestrol acetate, risedronate, pamidronate, ibandronate, alendronate, denosumab, zoledronate, trastuzumab, tykerb, bevacizumab, and combinations thereof.
20. The method according to claim 19, wherein the other anti-cancer agent that is not an androgen receptor inhibitor is paclitaxel.
21. The method according to claim 1, further comprising a step of testing the subject to determine whether the subject has a breast cancer comprising breast cancer cells that are other than basal-like subtype.
22. (canceled)
23. (canceled)
24. The method according to claim 4, wherein the subject has received zero or one rounds of prior treatment with an anti-cancer agent, other than an androgen receptor inhibitor, for treatment of triple negative breast cancer.
25. A method of treating triple negative breast cancer in a subject in need of such treatment comprising: (a) providing a biological sample from the subject; (b) assaying the biological sample to determine whether the biological sample is classified as a basal-like subtype or another subtype using a PAM50 classifier; and (c) if the biological sample is classified as other than a basal-like subtype, administering a breast cancer treatment to the subject comprising an androgen receptor inhibitor, thereby treating the breast cancer in the subject.
26. The method of claim 25, wherein assaying the biological sample to determine whether the biological sample is classified as a basal-like subtype or another subtype is performed by detecting the expression of the intrinsic genes listed in Table 1 using the PAM50 classifier.
27. The method according to claim 26, comprising: (a) determining the Basal Centroid classifier score and the Luminal A Centroid classifier score of the sample from the expression of the set of intrinsic genes listed in Table 1 using the PAM50 classifier; and (b) calculating a Weighted Basal and Luminal A classifier score from the Basal Centroid classifier score and the Luminal A Centroid classifier score according to the following equation:
Weighted Basal and Luminal A classifier score=−0.25(Basal Centroid classifier score)+0.27(Luminal A Centroid classifier score); and wherein the breast cancer treatment is administered to the subject if the Weighted Basal and Luminal A classifier score is greater than −0.3.
28. The method according to claim 27, wherein the breast cancer treatment is administered if the Weighted Basal and Luminal A classifier score is greater than −0.2.
29. The method according to claim 28 wherein the breast cancer treatment is administered if the Weighted Basal and Luminal A classifier score is greater than −0.25.
30-33. (canceled)
34. The method according to claim 25, wherein the breast cancer of the subject is characterized by the presence of androgen receptor-positive tumor cells.
35. The method according to claim 25, wherein the androgen receptor inhibitor is selected from the group consisting of enzalutamide, bicalutamide, flutamide, nilutamide, ARN509, ketoconazole, abiraterone acetate, VN/124-1 (TOK-001), orteronel (TAK-700), finasteride, galeterone, cyproterone acetate, andarine, and combinations thereof.
36. The method according to claim 35, wherein the androgen receptor inhibitor is enzalutamide.
37. The method according to claim 27, wherein the androgen receptor inhibitor is enzalutamide.
38. The method according to claim 28, wherein the androgen receptor inhibitor is enzalutamide.
39. The method according to claim 29, wherein the androgen receptor inhibitor is enzalutamide.
40. The method according to claim 36, wherein the enzalutamide is orally administered once daily at a dose of 160 mg.
41. The method according to claim 40, wherein the enzalutamide is administered as a single capsule comprising 160 mg enzalutamide.
42. The method according to claim 40, wherein the enzalutamide is administered as four capsules, each capsule comprising 40 mg enzalutamide.
43. The method according to claim 25, wherein the breast cancer treatment comprising an androgen receptor inhibitor further comprises one or more other anti-cancer agents that is not an androgen receptor inhibitor.
44. The method according to claim 43, wherein the other anti-cancer agent that is not an androgen receptor inhibitor is selected from the group consisting of cyclophosphamide, fluorouracil, 5-fluorouracil, methotrexate, thiotepa, carboplatin, cisplatin, taxanes, paclitaxel, protein-bound paclitaxel, docetaxel, vinorelbine, tamoxifen, raloxifene, toremifene, fulvestrant, gemcitabine, irinotecan, ixabepilone, temozolmide, topotecan, vincristine, vinblastine, eribulin, mutamycin, capecitabine, anastrozole, exemestane, letrozole, leuprolide, abarelix, buserelin, goserelin, megestrol acetate, risedronate, pamidronate, ibandronate, alendronate, denosumab, zoledronate, trastuzumab, tykerb, bevacizumab, and combinations thereof.
45. The method according to claim 44, wherein the other anti-cancer agent that is not an androgen receptor inhibitor is paclitaxel.
46. The method according to claim 25, wherein the biological sample is selected from the group consisting of a cell, tissue, and bodily fluid.
47. The method according to claim 46, wherein the biological sample comprises breast tissue or cells.
48. The method of claim 47, wherein the tissue is obtained from a biopsy.
49. The method of claim 47, wherein the bodily fluid is selected from the group consisting of blood, lymph, urine, saliva, fluid from ductal lavage, and nipple aspirate.
50. The method according to claim 29, wherein the subject has received zero or one rounds of prior treatment with an anti-cancer agent, other than an androgen receptor inhibitor, for treatment of triple negative breast cancer.
51. The method according to claim 1, wherein detecting the expression of the set of intrinsic genes listed in Table 1 using the PAM50 classifier is conducted by RNA sequencing.
52. The method according to claim 25, wherein detecting the expression of the set of intrinsic genes listed in Table 1 using the PAM50 classifier is conducted by RNA sequencing.
53. The method according to claim 1, wherein prior to determining the Basal centroid classifier score, the sample expression data is normalized and adjusted such that the median expression value of each gene in Table 1 is equivalent to the median of a known subset from a subject with triple negative breast cancer.
54. The method according to claim 25, wherein prior to determining the Basal centroid classifier score, the sample expression data is normalized and adjusted such that the median expression value of each gene in Table 1 is equivalent to the median of a known subset from a subject with triple negative breast cancer.
55. The method according to claim 1, wherein (i) detecting the expression of the set of intrinsic genes listed in Table 1 is conducted by RNA sequencing; (ii) the sample expression data is aligned to human genome sequence hg19; (iii) gene and isoform level counts are estimated using RNA sequencing by expectations maximization; (iv) the gene level counts estimates are normalized to a fixed upper quartile; and (v) the resulting normalized gene expression estimates are then adjusted such that the median expression value of each gene is equivalent to the median of the triple negative subset of TCGA RNA sequence data.
56. The method according to claim 25, wherein (i) detecting the expression of the set of intrinsic genes listed in Table 1 is conducted by RNA sequencing; (ii) the sample expression data is aligned to human genome sequence hg19; (iii) gene and isoform level counts are estimated using RNA sequencing by expectations maximization; (iv) the gene level counts estimates are normalized to a fixed upper quartile; and (v) the resulting normalized gene expression estimates are then adjusted such that the median expression value of each gene is equivalent to the median of the triple negative subset of TCGA RNA sequence data.
Description
DESCRIPTION OF THE FIGURES
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Weighted Basal and Luminal A classifier score=−0.2468275(Basal Centroid classifier score)+0.2667110(Luminal A Centroid classifier score).
The enzalutamide response/non-response data was analyzed using Weighted Basal and Luminal A classifier score cut-offs of >−0.2 (
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DETAILED DESCRIPTION OF THE INVENTION
[0082] The present invention provides a method of treating TNBC in subjects afflicted with TNBC in which breast cancer cells of the TNBC-afflicted subject are characterized by a score derived from the expression by those cells of a certain set of intrinsic genes described more particularly below. The present invention also provides a method of assessing whether a TNBC treatment comprising an AR inhibitor is recommended (will likely be effective) for administration as a course of therapy for a patient afflicted with TNBC. Thus, the present invention provides in one embodiment a method of evaluating a treatment for triple negative breast cancer comprising the use of an androgen receptor inhibitor, the method comprising assaying a biological sample obtained from a subject to determine whether the biological sample obtained from the subject is classified as basal-like subtype or another subtype. If the biological sample is classified as other than a basal-like subtype, the breast cancer treatment comprising an androgen receptor inhibitor is more likely to be effective than if the sample were classified as basal-like subtype. Thus, the present invention provides in one embodiment a method of treating triple negative breast cancer in a subject having a cancer comprising breast cancer cells that have been previously classified as other than basal-like subtype. The method comprises administering a breast cancer treatment to the subject comprising an androgen receptor inhibitor, thereby treating the triple negative breast cancer in the subject.
[0083] The present invention further provides a method of treating TNBC by determining whether a TNBC patient should receive a treatment including AR inhibitor therapy, and then administering the optimal AR inhibitor treatment to the patient based on that determination. While the studies referenced herein were conducted on patient samples comprising tumor tissue staining positive by immunohistochemistry (IHC) for the AR receptor, the scope of the present invention is not so limited to the treatment and prognosis of AR(+) TNBC.
[0084] Studies of breast tumors based upon intrinsic gene analysis have identified five distinct subtypes of breast carcinomas: Luminal A (LumA), Luminal B (LumB), HER2-enriched (Her-2-E), Basal-like, and Normal-like (Perou et al. Nature, 406(6797):747-52 (2000); Sorlie et al. PNAS, 98(19):10869-74 (2001)). The HER2-enriched subtype may be referred to herein by “HER2”, it being understood that the latter also means the HER2-enriched subtype. The Basal-like subtype may be referred to herein as “Basal”, it being understood that the latter also means the Basal-like subtype. A breast cancer sample or cell is thus “classified” by assigning the cell or sample to an aforementioned subtype. A breast cancer sample or cell can also be considered “classified” in negative terms, i.e., a cell or sample may be classified as “non-Basal” or “other than Basal” upon determination that the cell or sample is of the LumA, LumB, HER2, or Normal-like sub-type.
[0085] We have unexpectedly found that the presence of the basal-like subtype is indicative of a likelihood of clinical non-response in TNBC to treatment with an AR inhibitor. We have found that a Basal Centroid classifier score of less than or equal to 0.9 is indicative of a likelihood of clinical response to an AR inhibitor. We have also unexpectedly found that an empirically determined weighted score based upon Basal-like and Luminal A subtype analysis conducted on biological samples from TNBC patients is indicative of a likelihood of clinical response to treatment with an AR inhibitor. Thus, in one embodiment, an assay is thus performed on a biological sample from a patient suffering from TNBC to determine the breast cancer subtype. In another embodiment, an assay is performed on a biological sample from a patient suffering from TNBC to determine the Basal Centroid classifier score, or both the Basal Centroid classifier score and the Luminal A classifier score.
[0086] The assay for determining whether the biological sample is classified as a subtype other than a basal-like subtype can comprise an assay for determining the presence of a basal-like subtype; a negative result indicates a non-basal subtype. Any assay capable of identifying the presence of a basal-like subtype may be utilized for this purpose. With approximately 70-90% of triple-negative carcinomas revealed to be basal-like breast carcinomas (Bertucci et al., Int. J. Cancer 2008, 123, 236-240; Wang et al, Eur. J. Clin. Invest. 2008, 38, 438-446), the tripe negative phenotype has been used as a surrogate for the basal-like subtype. However, studies have shown that triple-negative and basal-like breast tumors are not synonymous. See, e.g., Choo and Nielsen, Cancers 2010, 2, 1040-1065. Thus, care must be exercised in selecting an assay for identifying the basal-like subtype.
[0087] Recently, an assay for basal-like subtype has been announced that relies on the following profile which has been found to be characteristic of the basal-like subtype: ER negative, HER2 negative, and cytokeratin 5/6 and/or HER1 positive. A panel of four antibodies (ER, HER1, HER2, and cytokeratin 5/6) has thus been proposed as an immunohistochemical profile for identifying breast basal-like tumors (Nielsen et al., Clinical Cancer Research 2014; 10:5367-5374).
[0088] The Basal-like and Luminal A subtype analysis is performed by means of a gene expression assay which utilizes expression of intrinsic genes as classifier genes for breast cancer classification. Intrinsic genes, as described in Perou et al. (2000) Nature 406:747-752, are statistically selected to have low variation in expression between biological sample replicates from the same individual and high variation in expression across samples from different individuals. The present invention utilizes the PAM50 gene expression assay (Parker et al. J Clin Oncol., 27(8):1160-7 (2009) and U.S. Patent Application Publication No. 2011/0145176, both incorporated herein, by reference, in their entireties). The PAM50 gene expression assay can be used to identify intrinsic subtypes of breast cancer (Luminal A, Luminal B, HER2-enriched, Basal-like, and Normal-like) from standard biological samples, such as formalin fixed paraffin embedded tumor tissue. The PAM50 gene expression classifier is a supervised, centroid-based prediction method to classify breast cancers into one of the five aforesaid molecular subtypes using a 50-gene intrinsic gene signature.
[0089] As described in Parker et al. and in U.S. Patent Application Publication No. 2011/0145176, as well as in U.S. Patent application Publication No. 2013/0004482, the PAM50 gene expression assay method utilizes a supervised algorithm to classify subject samples according to breast cancer intrinsic subtype. This algorithm, referred to herein as the “PAM50 classification model” or “PAM50 classifier” is based on the gene expression profile of a defined subset of 50 intrinsic genes that has been identified for classifying breast cancer intrinsic subtypes. The subset of genes, along with primers specific for their detection, is provided in Table 1 of U.S. Patent Application Publication No. 2013/0004482 and reproduced below as Table 1 of this disclosure. Select sequences of the same 50 intrinsic genes are set forth in Table 2 below. The entire disclosure of Publication No. 2013/0004482, is incorporated herein by reference.
[0090] The detection and estimation of the expression of the set of 50 subtype predictor genes of Table 1 is performed by any suitable means.
[0091] The PAM50 gene expression classifier operates by using a supervised prediction algorithm developed based on the profiles of objectively-selected prototype samples for “training” the algorithm. The samples are selected and subtyped using an expanded intrinsic gene set according to the methods disclosed in U.S. Patent Publication No. 2009/0299640, the entire disclosure of which is incorporated herein by reference. After stratifying the training samples according to subtype, a centroid-based prediction algorithm is used to construct centroids for each molecular subtype based on the expression profile of the intrinsic gene set described in Table 1. The centroid is the average gene expression for each gene in each subtype (or “class”) divided by the within-class standard deviation for that gene. Nearest centroid classification takes the gene expression profile of a new sample, and compares it to each of these class centroids. Subtype prediction is done by calculating the Spearman's rank correlation of each test case to the five centroids of the PAM50 subtypes, and assigning a sample to a subtype based on the nearest centroid.
[0092] According to one embodiment, which does not necessarily involve assigning the patient sample to a PAM50 subtype, the Spearman rank correlation to the basal-like gene expression centroid is determined. The Spearman rank correlation between the sample and the basal-like centroid is assigned as the “Basal Centroid classifier score”. The Spearman rank correlation to the Luminal A gene expression centroid is determined. The Spearman rank correlation between the sample and the Luminal A centroid is assigned as the “Luminal A Centroid classifier score”. Methods for utilizing the PAM50-based signature to provide a Basal Centroid classifier score and a Luminal A Centroid classifier score are known to those skilled in the art. See, for example, U.S. Patent Application Publication No. 2009/0299640; Parker et al., J Clin. Oncol., 27(8):1160-7 (2009); U.S. Patent Application Publication No. 2011/0145176. Also see, for example, Prat et al., British Journal of Cancer, (2014) 111, 1532-1541, incorporated herein by reference.
[0093] We have found, as demonstrated by the clinical trial of TNBC patients treated with the AR inhibitor enzalutamide, that a Basal Centroid classifier score of less than or equal to 0.9 is indicative of a likelihood of clinical response to an AR inhibitor. In some embodiments, a Basal Centroid classifier scores of less than or equal to 0.9, from 0.2 to 0.8, from 0.4 to 0.7 are used to predict the likelihood of clinical response to an AR inhibitor. In one embodiment, a Basal Centroid classifier score of less than or equal to 0.6 is used to predict the likelihood of clinical response to an AR inhibitor.
[0094] We have further found that the Basal Centroid classifier score and Luminal A Centroid classifier score, when combined subject to certain empirically defined weighting factors, provides a score (“Weighted Basal and Luminal A classifier score”) that can be used to further predict responsiveness to androgen receptor inhibitor therapy in an individual TNBC patient. The Weighted Basal and Luminal A classifier score is determined from the following equation:
Weighted Basal and Luminal A classifier score=−0.25(Basal Centroid classifier score)+0.27(Luminal A Centroid classifier score).
[0095] In some embodiments, the equation for determining the Weighted Basal and Luminal A classifier score takes the form:
Weighted Basal and Luminal A classifier score=−0.2468275(Basal Centroid classifier score)+0.2667110(Luminal A Centroid classifier score).
[0096] As demonstrated by the clinical trial of TNBC patients treated with the AR inhibitor enzalutamide, if the Weighted Basal and Luminal A classifier score is greater than −0.3, the patient is identified as one likely responsive to AR inhibitor therapy. Alternatively, if the Weighted Basal and Luminal A classifier score is greater than −0.2, the patient may also be identified as one likely responsive to AR inhibitor therapy. Increased accuracy is obtained by selecting −0.25 as the cut-off for predicting responsiveness to AR inhibitor therapy. Thus, in a preferred embodiment, if the Weighted Basal and Luminal A classifier score is greater than −0.25, the patient is identified as one likely responsive to AR inhibitor therapy. If the TNBC patient is identified through determination of the Weighted Basal and Luminal A classifier score as one who is likely responsive to AR inhibitor therapy for TNBC, an appropriate AR inhibitor therapy may then be administered to treat the TNBC condition in the patient.
[0097] The utility of the Weighted Basal and Luminal A classifier score for predicting patient response to AR inhibitor therapy is illustrated in
[0098] The correlation between patient response and Weighted Basal and Luminal A classifier score is further illustrated in the Kaplan-Meier plot of
[0099] It was also found that the novel Weighted Basal and Luminal A classifier score as a predictor of responsiveness to AR inhibitor therapy for TNBC achieves even greater accuracy in patients who have either received no prior TNBC therapy, or have received no more than one round of prior TNBC therapy. As may be appreciated from a comparison of
[0100] This result is also illustrated in
[0101] The correlation between patient response and Weighted Basal and Luminal A classifier score is further illustrated in the Kaplan-Meier plots of
Gene Expression Detection
[0102] As the first step in determining the Basal Centroid Classifier Score or Weighted Basal and Luminal A classifier score of a TNBC patient, gene expression detection of the genes of the intrinsic gene set of Table 1 is carried out on patient samples by any method for determining the quantity or presence of an RNA transcript or its expression product of an intrinsic gene. Such methods are described in U.S. Patent Application Publication Nos. 2009/0299640 and 2013/0004482, incorporated herein by reference. They include, for example means, methods based on hybridization analysis of polynucleotides, methods based on sequencing of polynucleotides, immunohistochemistry methods, and proteomics-based methods. The methods generally detect expression products (e.g., mRNA) of the intrinsic genes listed in Table 1.
[0103] RNA sequencing as a method for assaying gene expression may be utilized in one embodiment. The assay for gene expression of the intrinsic gene set can also be performed by other technologies used to evaluate gene expression/quantification, including but not limited to real-time PCR, microarrays, microfluidic gene expression, and targeted gene sequencing. Such methods include, for example, hybridization analysis of polynucleotides, methods based on sequencing of polynucleotides, immunohistochemistry methods, and proteomics-based methods. PCR-based methods, such as reverse transcription PCR (RT-PCR) (Weis et al., TIG 8:263-64, 1992), and array-based methods such as microarray (Schema et al., Science 270:467-70, 1995) may be used.
[0104] General methods for RNA extraction are well known in the art and are disclosed in standard textbooks of molecular biology, including Ausubel et al, ed., Current Protocols in Molecular Biology, John Wiley & Sons, New York 1987-1999. Methods for RNA extraction from paraffin embedded tissues are disclosed, for example, in Rupp and Locker, Lab Invest. 56:A67, (1987); and De Andres et al., Biotechniques 18:42-44, (1995). Isolated RNA can be used in hybridization or amplification assays that include, but are not limited to, PCR analyses and probe arrays. Intrinsic gene expression product level determination in a sample may also involve nucleic acid amplification, for example, by RT-PCR (U.S. Pat. No. 4,683,202), ligase chain reaction, self-sustained sequence replication, transcriptional amplification, rolling circle replication, and other methods utilizing nucleic acid amplification method, followed by the detection of the amplified molecules using techniques well known to those of skill in the art.
[0105] Microarrays may be used for expression profiling. Each array includes a reproducible pattern of capture probes attached to a solid support. Labeled RNA or DNA is hybridized to complementary probes on the array and then detected by laser scanning Hybridization intensities for each probe on the array are determined and converted to a quantitative value representing relative gene expression levels. See, for example, U.S. Pat. Nos. 6,040,138, 5,800,992 and 6,020,135, 6,033,860, and 6,344,316. High-density oligonucleotide arrays are particularly useful for determining the gene expression profile for a large number of RNAs in a sample.
[0106] Total RNA for analysis of the intrinsic gene set may be isolated from a biological sample, such as a tumor. If the source of RNA is a primary tumor, RNA (e.g., mRNA) can be extracted, for example, from frozen or archived paraffin-embedded and fixed (e.g., formalin-fixed) tissue samples (e.g., pathologist-guided tissue core samples).
Gene Analysis and Data Processing
[0107] Patient sample gene expression data from the intrinsic gene set may be pre-processed by known techniques to achieve sequence data alignment, data normalization and mean centering of data, for example. Methods of normalization include, for example, (i) global normalization that uses all genes on the array; (ii) housekeeping genes normalization that uses constantly expressed housekeeping/invariant genes; and (iii) internal controls normalization that uses known amount of exogenous control genes added during hybridization (Quackenbush Nat. Genet. 32 (Suppl.), 496-501 (2002)). Gene count estimates can also be normalized to a fixed quartile, such as a fixed upper quartile. The resulting normalized gene expression estimates may then be adjusted such that the median expression value of each gene is equivalent to the median of a known subset, such as a gene subset from TNBC patients.
[0108] According to one embodiment, patient sample expression data for processing by the PAM50 classifier is first pre-processed by alignment and data centering techniques. RNA-sequence data is first aligned to Human (Homo sapiens) genome sequence hg19 (https://genome.ucsc.edu/cgi-bin/hgGateway?db=hg19) (http://www.ncbi.nlm.nih.gov/assembly/GCF_000001405.25/) using, for example, MapSplice (Nucleic Acids Res. 2010 October; 38(18):e178. doi: 10.1093/nar/gkq622). Gene and isoform level counts may be estimated, for example, using RNA-Seq by Expectation-Maximization (RSEM) (deweylab.biostat.wisc.edu/rsem/). Gene count estimates are normalized to a fixed upper quartile. The resulting normalized gene expression estimates may then be adjusted such that the median expression value of each gene is equivalent to the median of the triple negative subset of the TCGA RNA-seq data reported in “Comprehensive Molecular Portraits of Human Breast Tumors”, The Cancer Genome Atlas Network, Nature 490, 61-70 (Oct. 4, 2012) (www.nature.com/nature/journal/v490/n7418/full/nature11412.html.
[0109] Following pre-processing, the patient sample expression data from the PAM50 gene array is processed according to the known techniques for processing intrinsic gene set data. Complete instructions for processing of patient sample gene expression data from the PAM50 intrinsic gene set is described in detail in at least the following, and will not be detailed herein except by way of summary: Parker et al. J Clin Oncol., 27(8):1160-7 (2009); U.S. Patent Application Publication No. 2011/0145176; and U.S. Patent Application Publication No. 2013/0004482. (U.S. Patent Application Publication No. 2013/0004482 describes the application of the PAM50 classifier for screening breast cancer subjects' possible responsiveness to anthracycline therapy relying on, inter alia, classification of the patient tumor into the HER2 subtype by the PAM50 classifier.) The Spearman rank correlation to the basal-like gene expression centroid is determined. The Spearman rank correlation between the sample and the basal-like centroid is assigned as the Basal Centroid classifier score. The Spearman rank correlation to the Luminal A gene expression centroid is determined. The Spearman rank correlation between the sample and the Luminal A centroid is assigned as the Luminal A Centroid classifier score. The Basal Centroid classifier score and Luminal A Centroid classifier score so determined are then inserted into the equation,
Weighted Basal and Luminal A classifier score=−0.25(Basal Centroid classifier score)+0.27(Luminal A Centroid classifier score)
to provide the Weighted Basal and Luminal A classifier score for the patient sample.
SAMPLES
[0110] Samples for analysis of intrinsic subtype classification may comprise a biological sample comprising a cancer cell or tissue, such as a breast tissue sample or a primary breast tumor tissue sample. In some embodiments, the biological sample comprises breast tissue or cells. By “biological sample” is intended any sampling of cells, tissues, or bodily fluids in which expression of an intrinsic gene can be detected. Examples of such biological samples include, but are not limited to, biopsies and smears. Bodily fluids useful in the present disclosure include blood, lymph, urine, saliva, nipple aspirates, fluid from ductal lavage, gynecological fluids, or any other bodily secretion or derivative thereof. Blood can include whole blood, plasma, serum, or any derivative of blood. In some embodiments, the biological sample includes breast cells, and may particularly comprise breast tissue from a biopsy, such as a breast tumor tissue sample. Biological samples may be obtained from a subject by a variety of techniques including, for example, by scraping or swabbing an area, by using a needle to aspirate cells or bodily fluids, or by removing a tissue sample (i.e., biopsy). Methods for collecting various biological samples are well known in the art. In some embodiments, a breast tissue sample is obtained by, for example, fine needle aspiration biopsy, core needle biopsy, or excisional biopsy. In another embodiment, fluid is obtained by ductal lavage. A thin catheter is inserted into the natural opening of the milk duct. A saline solution is then infused through the catheter to rinse the duct, which loosens cells from the duct lining. The solution containing the loosened cells is withdrawn through the catheter and biopsied. Fixative and staining solutions may be applied to the cells or tissues for preserving the specimen and for facilitating examination. In one embodiment, the biological sample is a formalin-fixed, paraffin-embedded breast tissue sample, particularly a primary breast tumor sample. In various embodiments, the tissue sample is obtained from a pathologist-guided tissue core sample.
Therapeutic Agents
[0111] Androgen receptor inhibitors directly or indirectly inhibit the androgen receptor (AR) signaling pathway. In one embodiment, direct inhibitors of the AR receptor include enzalutamide, bicalutamide (Casodex), flutamide, nilutamide, ARN509, and the like. In another embodiment, indirect inhibitors of AR include Cyp 17 inhibitors such as ketoconazole, abiraterone acetate, VN/124-1 (TOK-001), orteronel (TAK-700) and the like. In another embodiment, AR inhibitors include finasteride, galeterone, cyproterone acetate, and andarine, and the like. The antigen receptor inhibitor may result in complete or partial inhibition of the biological activity of the androgen receptor.
[0112] In a preferred embodiment, the AR inhibitor is enzalutamide (Xtandi®), which has the systematic (IUPAC) name 4-(3-(4-cyano-3-(trifluoromethyl)phenyl)-5,5-dimethyl-4-oxo-2-thioxoimidazolidin-1-yl)-2-fluoro-N-methylbenzamide, directly binds the androgen receptor (AR) and has three sites of activity. It inhibits binding of androgens to AR, inhibits nuclear translocation of AR, and inhibits AR-mediated DNA binding.
[0113] In certain embodiments, the breast cancer treatment comprising an androgen receptor inhibitor further comprises one or more other anti-cancer agents that is not an androgen receptor inhibitor. Such non-AR inhibitor anticancer agents that may also be administered to patients in conjunction with AR inhibitor therapy include, for example, cyclophosphamide, fluorouracil (or 5-fluorouracil or 5-FU), methotrexate, thiotepa, carboplatin, cisplatin, taxanes, paclitaxel, protein-bound paclitaxel, docetaxel, vinorelbine, tamoxifen, raloxifene, toremifene, fulvestrant, gemcitabine, irinotecan, ixabepilone, temozolmide, topotecan, vincristine, vinblastine, eribulin, mutamycin, capecitabine, capecitabine, anastrozole, exemestane, letrozole, leuprolide, abarelix, buserlin, goserelin, megestrol acetate, risedronate, pamidronate, ibandronate, alendronate, denosumab, zoledronate, trastuzumab, tykerb or bevacizumab, or combinations thereof.
[0114] In one embodiment, the non-AR inhibitor anticancer agent is paclitaxel. In one embodiment, the AR inhibitor is enzalutamide and the non-AR inhibitor anticancer agent is paclitaxel. As described hereinafter, it has been found that the combination of enzalutamide and paclitaxel results in enhanced cytotoxicity in tumor cells that are positive for the prognostic marker consisting of a Weighted Basal and Luminal A classifier score of greater than −0.25.
[0115] A therapeutically effective amount of one or more AR inhibitors is administered to the subject according to the present invention, to treat TNBC utilizing dosing and treatment regimens that are typically employed when administering AR inhibitors in the treatment of cancer. The AR inhibitor can be administered in the breast cancer treatments described herein, by the routes by which such agents are typically administered. A representative regimen for one such AR inhibitor, enzalutamide, is 160 mg/day orally, once daily. The dosage form may comprise, for example, a capsule. The daily dose may be administered, for example, in the form of a capsule comprising 160 mg enzalutamide. In another embodiment, four capsules, each comprising 40 mg enzalutamide, are administered. Lower or higher doses may be utilized. The non-AR inhibitor agents are administered according to well-known dosages and treatment regimens for such agents as used in the treatment of breast cancer.
TABLE-US-00001 TABLE 1 PAM50 Intrinsic Gene List Genbank SEQ SEQ Gene Accession No. Forward Primer ID NO: Reverse Primer ID NO: ACTR3B NM_020445 AAAGATTCCTG 1 TGGGGCAGTTCT 51 NM_001040135 GGACCTGA GTATTACTTC ANLN NM_018685 ACAGCCACTTTC 2 CGATGGTTTTGT 52 AGAAGCAAG ACAAGATTTCTC BAG1 NM_004323 CTGGAAGAGTT 3 GCAAATCCTTGG 53 GAATAAAGAGC GCAGA BCL2 NM_000633 TACCTGAACCG 4 GCCGTACAGTTC 54 GCACCTG CACAAAGG BIRE5 NM_001012271 GCACAAAGCCA 5 GACGCTTCCTAT 55 TTCTAAGTC CACTCTATTC BKVRA BX647539 GCTGGCTGAGC 6 TTCCTCCATCAA 56 AGAAAG GAGTTCAACA CCNB1 NM_031966 CTTTCGCCTGAG 7 GGGCACATCCAG 57 CCTATTT ATGTTT CCNE1 BC035498 GGCCAAAATCG 8 GGGTCTGCACAG 58 ACAGGAC ACTGCAT CDC20 BG256659 CTGTCTGAGTGC 9 TCCTTGTAATGG 59 CGTGGAT GGAGACCA CDC6 NM_001254 GTAAATCACCTT 10 ACTTGGGATATG 60 CTGAGCCT TGAATAAGACC CDCA1 NM 031423 GGAGGCGGAAG 11 GGGGAAAGACA 61 AAACCAG AAGTTTCCA CDH3 BC041846 GACAAGGAGAA 12 ACTGTCTGGGTC 62 TCAAAAGATCA CATGGCTA GC CENPF NM_016343 GTGGCAGCAGA 13 GGATTTCGTGGT 63 TCACAA GGGTTC CEP55 AB091343 CCTCACGAATT 14 CCACAGTCTGTG 64 GCTGAACTT ATAAACGG CXXC5 BC006428 CATGAAATAGT 15 CCATCAACATTC 65 GCATAGTTTGCC TCTTTATGAACG EGFR NM_005228 ACACAGAATCT 16 ATCAACTCCCAA 66 ATACCCACCAG ACGGTCAC AGT ERBB2 NM_001005862 GCTGGCTCTCAC 17 GCCCTTACACAT 67 ACTGATAG CGGAGAAC ESR1 MM_001122742 GCAGGGAGAGG 18 GACTTCAGGGTG 68 AGTTTGT CTGGAC EXO1 NM_130398 CCCATCCATGTG 19 TGTGAAGCCAGC 69 AGGAAGTATAA AATATGTATC FGFR4 AB209631 CTTCTTGGACCT 20 TATTGGGAGGCA 70 TGGCG GGAGGTTTA FOXA1 NM_004496 GCTACTACGCA 21 CTGAGTTCATGT 71 GACACG TGCTGACC FOXC1 NM_001453 GATGTTCGAGT 22 GACAGCTACTAT 72 CACAGAGG TCCCGTT GPR160 AJ249248 TTCGGCTGGAA 23 TATGTGAGTAAG 73 GGAACC CTCGGAGAC GRB7 NM_005310 CGTGGCAGATG 24 AGTGGGCATCCC 74 TGAACGA GTAGA HSPC150 NM_014176 GGAGATCCGTC 25 AGTGGACATGCG 75 (UBE2T) AACTCCAAA AGTGGAG KIF2C NM_006845 TGGGTCGTGTC 26 CACCGCTGGAAA 76 AGGAAAC CTGAAC KNTC2 NM_006101 CGCAGTCATCC 27 CGTGCACATCCA 77 AGAGATGTG TGACCTT KRT14 BC042437 ACTCAGTACAA 28 GAGGAGATGACC 78 GAAAGAACCG TTGCC KRT17 AK095281 GTTGGACCAGT 29 GCCATAGCCACT 79 CAACATCTCTG GCCACT KRT5 M21389 TGTGGCTCATTA 30 CTTCGACTGGAC 80 GGCAAC TCTGT MAPT NM_001123066 GACTCCAAGCG 31 CAGACATGTTGG 81 CGAAAAC TATTGCACATT MDM2 M92424 CCAACAAAATA 32 AGGCGATCCTGG 82 TTCATGGTTCTT GAAATTAT G MELK NM_014791 CCAGTAGCATT 33 CCCATTTGTCTG 83 GTCCGAG TCTTCAC MIA BG765502 GTCTCTGGTAAT 34 CTGATGGTTGAG 84 GCACACT GCTGTT MK167 NM_002417 GTGGAATGCCT 35 CGCACTCCAGCA 85 GCTGACC CCTAGAC MLPH NM_024101 AGGGGTGCCCT 36 TCACAGGGTCAA 86 CTGAGAT ACTTCCAGT MMP11 NM_005940 CGAGATCGCCA 37 GATGGTAGAGTT 87 AGATGTT CCAGTGATT MYBL2 BX647151 AGGCGAACACA 38 TCTGGTCACGCA 88 CAACGTC GGGCAA MYC NM_002467 AGCCTCGAACA 39 ACACAGATGATG 89 ATTGAAGA GAGATGTC NAT1 BC013732 ATCGACTGTGT 40 AGTAGCTACATC 90 AAACAACTAGA TCCAGGTTCTCT GAAGA G ORC6L NM_014321 TTTAAGAGGGC 41 CGGATTTTATCA 91 AATGGAAGG ACGATGCAG PGR NM_000926 TGCCGCAGAAC 42 CATTTGCCGTCC 92 TCACTTG TTCATCG PHGDH AK093306 CCTCAGATGAT 43 GCAGGTCAAAAC 93 GCCTATCCA TCTCAAAG PTTG1 BE904476 CAGCAAGCGAT 44 AGCGGGCTTCTG 94 GGCATAGT TAATCTGA RRM2 AK123010 AATGCCACCGA 45 GCCTCAGATTTC 95 AGCCTC AACTCGT SERP1 BC036503 TCGAACTGAAG 46 CTGCTGAGAATC 96 GCTATTTACGA AAAGTGGGA G SLC39A6 NM_012319 GTCGAAGCCGC 47 GGAACAAACTGC 97 AATTAGG TCTGCCA TMEM45B AK098106 CAAACGTGTGT 48 ACAGCTCTTTAG 98 TCTGGAAGG CATTTGTGGA TYMS BQ056428 TGCCCTGTATGA 49 GGGACTATCAAT 99 TGTCAGGA GTTGGGTTCTC UBE2C BC032677 GTGAGGGGTGT 50 CACACAGTTCAC 100 CAGCTCAGT TGCTCCACA
TABLE-US-00002 TABLE 2 PAM50 Intrinsic Gene Sequences Genbank SEQ Genbank SEQ ID Gene Accession No. ID NO: Gene Accession No. NO: ACTR3B NM_020445 101 KIF2C NM_006845 127 NM_001040135 102 ANLN NM_018685 103 KNTC2 NM_006101 128 BAG1 NM_004323 104 KRT14 BC042437 129 BCL2 NM_000633 105 KRT17 AK095281 130 BIRC5 NM_001012271 106 KRT5 M21389 131 BKVRA BX647539 107 MAPT NM_001123066 132 CCNB1 NM_031966 108 MDM2 M92424 133 CCNE1 BC035498 109 MELK NM_014791 134 CDC20 BG256659 110 MIA BG765502 135 CDC6 NM_001254 111 MK167 NM_002417 136 CDCA1 NM_031423 112 MLPH NM_024101 137 CDH3 BC041846 113 MMP11 NM_005940 138 CENPF NM_016343 114 MYBL2 BX647151 139 CEP55 AB091343 115 MYC NM_002467 140 CXXC5 BC006428 116 NAT1 BC013732 141 EGFR NM_005228 117 ORC6L NM_014321 142 ERBB2 NM_001005862 118 PGR NM_000926 143 ESR1 NM_001122742 119 PHGDH AK093306 144 EXO1 NM_130398 120 PTTG1 BE904476 145 FGFR4 AB209631 121 RRM2 AK123010 146 FOXA1 NM_004496 122 SFRP1 BC036503 147 FOXC1 NM_001453 123 SLC39A6 NM_012319 148 GPR160 AJ249248 124 TMEM45B AK098106 149 GRB7 NM_005310 125 TYMS BQ056428 150 HSPC150 NM_014176 126 UBE2C BC032677 151 (UBE2T)
[0116] The practice of the invention is illustrated by the following non-limiting examples.
EXAMPLES
Example 1
Clinical Study Protocol
[0117] A clinical trial was conducted to determine clinical benefit of enzalutamide treatment in patients whose tumors are androgen receptor-positive (AR+) and triple-negative. In this study, AR+ is defined as any nuclear AR staining by immunohistochemistry (NC) and TNBC is defined as <1% staining by IHC for estrogen receptor (ER) and progesterone receptor (PgR), 0 or 1+ by IHC for human epidermal growth factor receptor 2 (HER2), or negative for HER2 amplification by in situ hybridization (ISH) for 2+ IHC disease. AR staining was carried out by IHC with two different antibodies each of which were individually optimized on breast cancer tissue. Enzalutamide (16 mg/day) was administered as four 40 mg soft gelatin capsules orally once daily with or without food. Patients received enzalutamide until disease progression per Response Evaluation Criteria in Solid Tumors version 1.1 (RECIST 1.1) was documented unless treatment was discontinued due to other reasons specified in the trial protocol. The study periods included prescreening (patients could sign consent to submit to tissue for testing for AR expression at any time in their disease course); screening (28 days before first dose of study drug); treatment (day 1 through discontinuation); safety follow-up (approximately 30 days after the last dose of study drug or before initiation of a new antitumor treatment, whichever occurs first); and long-term follow-up (assessment of subsequent breast cancer therapies and survival status every 3 to 6 months after treatment discontinuation). Objective response—complete response (CR) or partial response (PR)—was determined by investigators according to the RECIST 1.1.
[0118] The trial was a Simon 2-stage study where a minimum benefit was required in a pre-defined patient population prior to expanding the study to a larger size. In Stage 1, 42 patients enrolled into the study to obtain the pre-defined 26 Evaluable patients. The requisite clinical benefit to proceed to Stage 2 was observed in Stage 1 and an additional 76 patients were enrolled for a total of 118 patients overall. Patients who received prior treatment with an androgen receptor signaling inhibitor, who had central nervous system (CNS) metastases were excluded; there was no limit to number of prior therapies, and patients with patients measurable disease or bone-only nonmeasurable disease were eligible. Clinical Benefit Rate at 16 weeks (CBR16) was defined as the proportion of Evaluable Patients with a best response of complete remission (CR), partial response (PR) or stable disease (SD) ≥16 weeks (CBR16). The Clinical Benefit Rate at ≥24 weeks (CBR16) was also assessed.
[0119] In Stage 1, 42 patients were enrolled to get 26 Evaluable Patients (n=26). Evaluable patients were those who had both AR staining in ≥10% of tumor and at least 1 post-baseline tumor assessment. The Intent-To-Treat (ITT) population (n=42 in Stage 1) was defined as all enrolled patients who had centrally assessed AR+TNBC and received at least 1 dose of study drug. Twenty-six (62%) of 42 ITT patients were Evaluable, while 16 of 42 were not Evaluable. Of the 16 not meeting the criteria for Evaluable, 10 had AR expression below 10%; 6 had AR expression ≥10% but did not have a post-baseline assessment (2 were discovered to have CNS metastases shortly after study entry and were withdrawn from treatment prior to having a post-baseline tumor assessment). More than 50% of the patients received enzalutamide as their first or second line of therapy, while >30% had ≥3 prior regimens before receiving enzalutamide.
Intrinsic Gene Expression Analysis
[0120] Human breast tumors from TNBC patients were obtained from the aforementioned clinical study of enzalutamide, an AR antagonist. The patient breast cancer tissue was stained for AR expression. The patient staining was graded by a pathologist on both the staining intensity (3+, 2+ and 1+) as well as the percentage of tumor cells stained as given in the standard operating procedure. AR staining was evaluated both in the nucleus and cytoplasm.
[0121] RNA-seq data utilized in this study were pre-processed as follows. The RNA-seq data was aligned to Human (Homo sapiens) genome sequence hg19 from the Human Genome Browser—hg19 Assembly created by the Genome Bioinformatics Group of UC Santa Cruz (genome.ucsc.edu/cgi-bin/hgGateway?db=hg19) (www.ncbi.nlm.nih.gov/assembly/GCF_000001405.25/) using MapSplice (Nucleic Acids Res. 2010 October; 38(18):e178. doi: 10.1093/nar/gkq622). Gene and isoform level counts were estimated using RNA-Seq by Expectation-Maximization (RSEM) (deweylab.biostat.wisc.edu/rsem/). Gene count estimates were normalized to a fixed upper quartile. The resulting normalized gene expression estimates were adjusted such that the median expression value of each gene was equivalent to the median of the triple negative subset of the TCGA RNA-seq data reported in “Comprehensive Molecular Portraits of Human Breast Tumors”, The Cancer Genome Atlas Network, Nature 490, 61-70 (Oct. 4, 2012) (www.nature.com/nature/journal/v490/n7418/full/nature11412.html).
[0122] Intrinsic subtype classification was performed into the LumA, LumB, Basal, HER2 and Normal groups using the PAM50 classification model as described in Parker et al. J Clin Oncol., 27(8):1160-7 (2009). The intrinsic subtype classification was carried out on genomic data obtained from RNA sequencing of RNA obtained from formalin fixed, paraffin embedded tissue collected from subjects' breast tumors. The data was pre-processed as indicated above. Subtype classification was performed on a “Training and Test” set and a further “Validation” set. The Training and Test set consisted of 122 patient samples out of which 42 patients were from the pre-screened population but not enrolled in the study and 80 patients samples were from the enrolled population in the clinical study. The Validation set consisted of 55 patient samples which had 15 patients from the pre-screened population not enrolled on the study and 40 samples from the enrolled population.
[0123] The data was analyzed according to the known methods for analyzing PAM50 intrinsic gene set data, as described by Parker et al. et al., supra. Essentially, the detection and estimation of the expression of the set of 50 subtype predictor genes of Table 1 from patient tumor samples was carried out. The expression profile of the set of 50 subtype predictor genes by the described method that provides Basal-like, HER2, LumA, LumB and Normal subtype classifications was analyzed. The Spearman correlation was calculated for each sample and PAM50 centroid. These values were used as continuous estimates of distance or similarity of a sample to each centroid. The subtype of each sample was assigned as the closest (largest positive correlation) centroid. The underlying measures of correlation to each subtype were used to classify a sample as one of 4 tumor subtypes (Basal-like, HER2, LumA and LumB) or Normal-like.
[0124] Further, the Spearman rank correlation to the Basal-like gene expression centroid was evaluated. The Spearman rank correlation between the sample and the Basal-like centroid was assigned as the “Basal Centroid classifier score”. The Spearman rank correlation to the Luminal A gene expression centroid was evaluated. The Spearman rank correlation between the sample and the Luminal A centroid was assigned as the “Luminal A classifier score”.
[0125] In the enrolled patients (Intent-To-Treat (ITT) population, Basal-like subtype generally correlated with non-response to enzalutamide therapy, while existence of one of the other subtypes generally correlated with response to enzalutamide therapy. See
Example 2
[0126] The results of the clinical study of Example 1 were further analyzed utilizing the patient Basal Centroid classifier scores. The therapeutic response data was evaluated imposing a series of threshold cut-offs on the Basal Centroid classifier score. The enzalutamide response/non-response data was analyzed using Basal Centroid classifier score cut-offs of 0.2, 0.3, 0.4, 0.5, 0.6, 0.65, 0.7, 0.8 and 0.9. The data is set forth in
TABLE-US-00003 FIG. 2A/B 3A/B 4A/B 5A/B 6A/B 7A/B 8A/B 9A/B 10A/B Basal Centroid 0.2 0.3 0.4 0.5 0.6 0.65 0.7 0.8 0.9 classifier score
[0127] As shown in
Example 3
[0128] The results of the clinical study of Example 1 are further analyzed and summarized in
Example 4
[0129] The results of the clinical study of Example 1 are further analyzed and summarized in
Example 5
[0130] The effect of the novel prognostic signature utilizing a Basal Centroid classifier score of <0.6 as a predictor of response to AR inhibitor therapy is further illustrated in
Example 6
[0131] The results of the clinical study of Example 1 were further analyzed utilizing the patient Basal Centroid classifier and Luminal A classifier scores. The classifier scores and response data were analyzed. As a result of analysis, a Weighted Basal and Luminal A classifier score was empirically devised that predicted responsiveness to androgen receptor inhibitor therapy in the clinical trial. The Weighted Basal and Luminal A classifier score of patient samples was determined from the following formula:
Weighted Basal and Luminal A classifier score=−0.2468275(Basal Centroid classifier score)+0.2667110(Luminal A Centroid classifier score).
[0132] The therapeutic response data was then evaluated imposing a series of threshold cut-offs on the Weighted Basal and Luminal A classifier score. Specifically, the enzalutamide response/nota-response data was analyzed using Weighted Basal and Luminal A classifier score cut-offs of greater than −0.2, greater than −0.25, greater than −0.3 and greater than −0.35. The data is set forth in
[0133] As shown in
Example 7
[0134] The results of the clinical study of Example 1 are further analyzed and summarized in
[0135] Also shown in in
Example 8
[0136] The effect of the novel prognostic signature utilizing a Weighted Basal and Luminal A classifier score cut-off of >−0.2 as a predictor of response to AR inhibitor therapy is further illustrated in
Example 9
[0137] The effect of the novel prognostic signature utilizing a Weighted Basal and Luminal A classifier score cut-off of >−0.25 as a predictor of response to AR inhibitor therapy is further illustrated in 17 with respect to patient progression-free survival time to 56 weeks. The results demonstrate a prolonged progression-free survival in patients that were identified as meeting the prognostic signature condition of a Weighted Basal and Luminal A classifier score of greater than −0.25 (“PR-AR DX+: >−0.25”, top curve) versus less than or equal to −0.25 (“PR-AR DX−: <=−0.25”, bottom curve).
Example 10
[0138] The effect of the novel prognostic signature utilizing a Weighted Basal and Luminal A classifier score cut-off of >−0.3 as a predictor of response to AR inhibitor therapy is further illustrated in
Example 11
[0139] The effect of the novel prognostic signature utilizing a Weighted Basal and Luminal A classifier score cut-off of >−0.35 as a predictor of response to AR inhibitor therapy is further illustrated in
Example 12
[0140] The effect of the novel prognostic signature utilizing a Weighted Basal and Luminal A classifier score cut-off of >−0.25 as a predictor of response to AR inhibitor therapy is further illustrated in
[0141] The effect of the novel prognostic signature utilizing a Weighted Basal and Luminal A classifier score cut-off of >−0.25 as a predictor of response to AR inhibitor therapy in patients receiving from zero to one prior therapies for treatment of TNBC with a drug other than an androgen receptor inhibitor is further shown in
Example 13
[0142] The effect of the novel prognostic signature utilizing a Weighted Basal and Luminal A classifier score cut-off of >−0.25 as a predictor of response to AR inhibitor therapy is further illustrated in
Example 14
[0143] The effect of the novel prognostic signature utilizing a Weighted Basal and Luminal A classifier score cut-off of >−0.25 as a predictor of response to AR inhibitor therapy is further illustrated in
Example 15
[0144] A Phase II clinical trial of the androgen receptor antagonist bicalutamide has been reported. Ayca et al., “Phase II Trial of Bicalutamide in Patients with Androgen Receptor Positive, Hormone Receptor Negative Metastatic Breast Cancer”, Clin Cancer Res 19: 5505-5512 (Oct. 1, 2013). The trial was designed to study the effect of bicalutamide in treating metastatic breast cancer that is AR-positive, estrogen receptor (ER)-negative, and progesterone receptor (PgR)-negative.
[0145] Briefly, as described by Ayca et al., tumors from 452 patients with ER-negative/PgR-negative advanced breast cancer were tested centrally for AR by immunohistochemistry (IHC) (>10% nuclear staining considered positive). See Ayca et al., p. 5506 for additional eligibility criteria. If either the primary or a metastatic site was positive, patients were eligible to receive the AR antagonist bicalutamide at a dose of 150 mg daily. Twenty-eight patients were treated on study. Bicalutamide 150 mg was administered orally on a continuous daily schedule. Patients were treated until disease progression or unacceptable adverse events. A maximum of 2 dose reductions for grade ≥3 toxicity were allowed (100 and 50 mg). A maximum of 2 weeks was permitted for treatment delays due to toxicity. Two patients who initiated bicalutamide were removed from study, leaving 26 study participants with AR(+) ER/PgR(−) metastatic breast cancer. Five patients had stable disease >6 months (number of cycles completed: 6, 8, 10+, 13, 57+) as their best response on treatment. There were no confirmed complete or partial responses yielding a clinical benefit rate of 19% (95% CI, 7%-39%) in the target population (n=26). In an intention-to-treat analysis, a CBR of 18% (95% CI, 6%-37%) was observed. See Ayca et al., p. 5507.
[0146] Twenty-one of the 26 bicalutamide-treated study patients were determined to also be HER-2 negative, i.e., twenty-one patients had breast cancers that were triple negative (Her-2(−), ER(−) and PgR(−)). Following the study, patient tumor samples from the twenty-one TNBC patients that received bicalutamide therapy were subjected to intrinsic subtype classification into the Luminal A, Luminal B, Basal-like, HER2-enriched and Normal-like groups using the PAM50 classification model. Each subtype score for each sample is listed in Table 3. Also set forth in Table 3 is the Weighted Basal and Luminal A classifier score of each sample. Based on the results obtained in Example 6 from the clinical trial of the AR-receptor antagonist enzalutamide, a greater than −0.25 Weighted Basal and Luminal A classifier score (“PR-AR DX+>−0.25”) indicates that such patients are more likely to respond to the bicalutamide treatment than patients with a Weighted Basal and Luminal A classifier score of less than or equal to −0.25. Eight patients satisfied this criterion, and are designated in Table 3 as having a likely positive (“POS”) prognosis on bicalutamide treatment. Each of the 21 patient samples displayed a confidence level of 1, except for sample No. 16, which had a confidence level of 0.99.
TABLE-US-00004 TABLE 3 Weighted Basal Her2 LumA LumB Normal Basal/LumA No. Score Score Score Score Score Score Prognosis 1 0.542569 −0.02857 −0.59846 0.242161 −0.25186 −0.29354 NEG 2 0.405618 −0.17714 −0.11635 −0.30343 0.296423 −0.13115 POS 3 0.509628 0.038367 −0.3915 −0.20711 0.059208 −0.23021 POS 4 0.753469 0.003025 −0.59088 −0.28912 0.055078 −0.34357 NEG 5 0.766146 −0.00543 −0.69729 −0.08581 −0.07851 −0.37508 NEG 6 0.638896 −0.34665 −0.22439 −0.54103 0.447779 −0.21755 POS 7 0.75078 0.112509 −0.7188 −0.01945 −0.11001 −0.37702 NEG 8 0.795342 0.039808 −0.66511 −0.22968 0.052293 −0.37371 NEG 9 0.793421 −0.06708 −0.59818 −0.372 0.158127 −0.35538 NEG 10 0.699496 −0.23275 −0.43616 −0.26617 0.192221 −0.28898 NEG 11 0.634478 −0.15333 −0.33906 −0.49273 0.304298 −0.24704 POS 12 0.729556 −0.15188 −0.48984 −0.35529 0.206531 −0.31072 NEG 13 0.721104 0.015222 −0.66387 −0.074 −0.03558 −0.35505 NEG 14 0.747419 −0.26098 −0.42406 −0.40687 0.255414 −0.29758 NEG 15 0.702089 −0.04 −0.53719 −0.25522 0.095414 −0.31657 NEG 16 0.161104 −0.10146 −0.01647 −0.29834 0.383721 −0.04416 POS 17 0.571477 −0.12826 −0.27549 −0.34146 0.260024 −0.21453 POS 18 0.399184 −0.03741 −0.21268 −0.22113 0.090708 −0.15525 POS 19 0.622089 −0.18588 −0.31313 −0.58329 0.431741 −0.23706 POS 20 0.752797 −0.13546 −0.55064 −0.40072 0.161008 −0.33267 NEG 21 0.736567 −0.1346 −0.58339 −0.24216 0.082737 −0.3374 NEG
Example 16
[0147] The following study demonstrates the enhanced antitumor effect of the combination of enzalutamide plus paclitaxel in cells positive for the prognostic marker of a Weighted Basal and Luminal A classifier score greater than −0.25.
[0148] Triple negative breast cancer cell lines BT549, MDA-MB-436, MDA-MB-453 were selected for study. Messenger RNA datasets for the cell lines were down-loaded from the Cancer Cell Line Encyclopedia (CCLE) database. The Weighted Basal and Luminal A classifier score for each cell line was determined from the downloaded datasets. Applying a Weighted Basal and Luminal A classifier score of >−0.25 as a prognostic marker for responsiveness to AR inhibitor therapy, it was determined that MDA-MB-453, but not BT549 and MDA-MB-436, satisfied this criterion.
[0149] Cells were maintained in 10% FBS supplemented growth media. Viability assays were performed in 10% FBS, and measured by CellTiter-Glo reagent according to the manufacturer's protocol (Promega). To determine molecular effects of enzalutamide alone or in combination with paclitaxel on androgen receptor signaling, cells (BT549, MDA-MB-436 or MDA-MB-453) were seeded on day one in 10% FBS. The cells were treated with enzalutamide or paclitaxel or the combination in 2% charcoal-stripped serum and were stimulated with 10 nM DHT for 4 hours. Cell fractionation was isolated for cytosolic and nuclear fractions. Protein expression levels were determined using a Western blotting method. The IC.sub.50 for enzalutamide or paclitaxel for each cell line is shown in Table 4. Mean values are presented for each cell line (n=3). The prognostic marker-positive MDA-MB-453 cells exhibited greater sensitivity to enzalutamide compared to the prognostic marker-negative BT549 and MDA-MB-463 cells.
TABLE-US-00005 TABLE 4 Enzalutamide Paclitaxel Cell Line IC.sub.50 (μM) IC.sub.50 (nM) BT549 57.0 2.8 MDA-MB-436 73.0 6.7 MDA-MB-453 22.7 20.7
[0150] Viability of the cells was measured in the presence of the concentrations of enzalutamide (Enza) and paclitaxel (PTX) in
Example 17
[0151] To generate a mouse xenograft model, 5- to 6-week-old female NOD-SCID mice were injected orthotopically into the mammary gland with 6.0×10.sup.6 MDA-MB-453 cells. DHT (10.5 mg in a 60-day release pellet) or control pellets were implanted into animals. When tumor size reached ˜100 mm.sup.3, mice were treated by (i) oral gavage (PO) with enzalutamide (“Enza”) at 3 mg/kg/day (n=10), (ii) paclitaxel (“PTX”) at 6 mg/k QMWF (IP) (n=7), or (iii) the combination of (i) and (ii) (n=10). A control group of mice (n=8) was treated with vehicle (0.5% Methocel solution). Tumor size was measured by caliper. Tumor weights were determined at day 35. The results are shown in
[0152] Representative tumors from each treated group were selected to perform immunohistochemistry against AR, Ki67 or p-AKT. Immunohistochemistry staining for Ki67 or AKT phosphorylation was significantly reduced in the enzalutamide plus paclitaxel tumors compared to the enzalutamide or paclitaxel single treated group (data not shown).
[0153] The disclosures of each and every patent, patent application, and publication cited herein are hereby incorporated herein by reference in their entirety. One skilled in the art will readily appreciate that the present invention is well adapted to carry out the objects and obtain the ends and advantages mentioned, as well as those inherent therein. While the invention has been disclosed with reference to specific embodiments, it is apparent that other embodiments and variations of this invention may be devised by others skilled in the art without departing from the true spirit and scope used in the practice of the invention. The appended claims are intended to be construed to include all such embodiments and equivalent variations.