Patent Application
20140037620
The contents of the text file named “40448-514001US_ST25.txt”, which was created on Oct. 7, 2013 and is 257 KB in size, are hereby incorporated by reference in their entireties.
This disclosure relates generally to the field of cancer biology, and specifically, to the fields of detection and identification of specific cancer cell phenotypes and correlation with appropriate therapies.
Therapy including the nucleoside analog, gemcitabine, has proven to be effective against many types of tumors. However, the side effects associated with gemcitabine therapy, including neutropenia, anemia, liver and kidney changes, flu-like symptoms, loss of appetite, hair loss, shortness of breath, fatigue, loss of appetite, nausea and vomiting are severe. Alternative therapies with less severe side effects are known. Thus, there is a need in the art to determine types of cancer that respond best to gemcitabine based therapy and which types of cancer would be better to treat with non-gemcitabine based therapy. The present invention addresses these needs.
In one embodiment, this invention provides a method of predicting progression free survival in a subject having metastatic breast cancer comprising (a) providing a biological sample from the subject; and (b) assaying the biological sample to determine an intrinsic breast cancer subtype, the subtype selected from the group consisting of luminal A, luminal B, basal-like, and HER-2 enriched subtypes; wherein the intrinsic subtype is determined using a measurement of at least 40 of the genes listed in Table 1 and wherein the intrinsic subtype is used to predict progression free survival in said subject independent of the treatment that the subject has received or will receive. A determination of luminal A and B subtypes indicates a longer disease progression free survival time period and a determination of HER2-enriched or basal-like subtype indicates a shorter disease progression free survival time period. The assaying of the biological sample to determine whether intrinsic subtype is performed by detecting at least 10, at least 15, at least 20, at least 25, at least 40, 41, 42, 43, 44, 45, 46 47, 48, 49 or all 50 of the intrinsic genes listed in Table 1. In a preferred embodiment the intrinsic subtype is determined using at least 45 of the genes listed in Table 1.
The present invention also provides a method of predicting overall survival in a subject having breast cancer comprising, (a) providing a biological sample from the subject; and (b) assaying the biological sample to determine an intrinsic breast cancer subtype, the subtype selected from the group consisting of luminal A, luminal B, basal-like, and HER-2 enriched subtypes; wherein the intrinsic subtype is determined using a measurement of at least 40 of the genes listed in Table 1, wherein a determination of luminal A and luminal B subtypes indicates a longer overall survival and a determination of HER2-enriched or basal-like subtype indicates a shorter overall survival. The assaying of the biological sample to determine whether intrinsic subtype is performed by detecting at least 10, at least 15, at least 20, at least 25, at least 40, 41, 42, 43, 44, 45, 46 47, 48, 49 or all 50 of the intrinsic genes listed in Table 1. In a preferred embodiment the intrinsic subtype is determined using at least 45 of the genes listed in Table 1.
The present invention also provides a method of predicting overall survival in a subject having breast cancer. This method includes the steps of providing a biological sample from the subject; assaying the biological sample to determine whether the biological sample is classified as a basal-like subtype; wherein if the biological sample is classified as a basal-like subtype, a breast cancer treatment comprising gemcitabine is more likely to prolong overall survival of the subject. The breast cancer can be primary breast cancer, locally advanced breast cancer or metastatic breast cancer.
The assaying of the biological sample to determine whether the biological sample is classified as a basal-like subtype is performed using RNA expression profiling. The assaying the biological sample to determine whether the biological sample is classified as a basal-like subtype is performed by detecting at least 10, at least 15, at least 20, at least 25, at least 40, 41, 42, 43, 44, 45, 46 47, 48, 49 or all 50 of the intrinsic genes listed in Table 1. Preferably, detection is of all 50 of the intrinsic genes listed in Table 1. The expression of the members of the intrinsic gene list of Table 1 can be determined using a nanoreporter and the nanoreporter code system (nCounter® Analysis system).
The breast cancer treatment that includes gemcitabine can also include anthracycline, cyclophosphamide, fluorouracil (or 5-fluorouracil or 5-FU), methotrexate, thiotepa, carboplatin, cisplatin, taxanes, paclitaxel, protein-bound paclitaxel, docetaxel, vinorelbine, tamoxifen, raloxifene, toremifene, fulvestrant, 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. Preferably, the treatment that includes gemcitabine also includes one or more taxanes. Preferably, the taxanes are paclitaxel or docetaxel. The breast cancer treatment not comprising an gemcitabine includes anthracycline, cyclophosphamide, fluorouracil (or 5-fluorouracil or 5-FU), methotrexate, thiotepa, carboplatin, cisplatin, taxanes, paclitaxel, protein-bound paclitaxel, docetaxel, vinorelbine, tamoxifen, raloxifene, toremifene, fulvestrant, 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. Preferably, the treatment that does not include gemcitabine includes one or more taxanes. Preferably, the taxanes are paclitaxel or docetaxel.
The biological sample can be a cell, a tissue or a bodily fluid. The tissue can be sampled from a biopsy or smear. The sample can also be a sampling of bodily fluids. These bodily fluids can include blood, lymph, urine, saliva, nipple aspirates and gynecological fluids. The biological sample can be a formalin-fixed, paraffin-embedded sample.
The present invention provides a method of treating breast cancer in a subject in need thereof. This method includes the steps of providing a biological sample from the subject; assaying the biological sample to determine whether the biological sample is classified as a basal-like subtype; and administering a breast cancer treatment to the subject. If the biological sample is classified as a basal-like subtype, the subject is administered a breast cancer treatment including gemcitabine. If the biological sample is not a basal-like subtype, the subject is administered a breast cancer treatment without gemcitabine. The breast cancer can be primary breast cancer, locally advanced breast cancer or metastatic breast cancer.
The present invention also provides a method of treating breast cancer in a subject in need thereof comprising requesting a test providing the results of analysis determining whether a biological sample from the subject is classified as a basal-like subtype, and administering a breast cancer treatment including gemcitabine if the sample from the patient is classified as a basal-like subtype, or administering a breast cancer treatment without gemcitabine if the sample from the patient is classified as not a basal-like subtype. The breast cancer can be primary breast cancer, locally advanced breast cancer or metastatic breast cancer.
The assaying of the biological sample to determine whether the biological sample is classified as a basal-like subtype is performed using RNA expression profiling. The assaying the biological sample to determine whether the biological sample is classified as a basal-like subtype is performed by detecting at least 10, at least 15, at least 20, at least 25, at least 40, 41, 42, 43, 44, 45, 46 47, 48, 49 or all 50 of the intrinsic genes listed in Table 1. Preferably, detection is of all 50 of the intrinsic genes listed in Table 1. The expression of the members of the intrinsic gene list of Table 1 can be determined using a nanoreporter and the nanoreporter code system (nCounter® Analysis system).
The breast cancer treatment that includes gemcitabine can also include anthracycline, cyclophosphamide, fluorouracil (or 5-fluorouracil or 5-FU), methotrexate, thiotepa, carboplatin, cisplatin, taxanes, paclitaxel, protein-bound paclitaxel, docetaxel, vinorelbine, tamoxifen, raloxifene, toremifene, fulvestrant, 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. Preferably, the treatment that includes gemcitabine also includes one or more taxanes. Preferably, the taxanes are paclitaxel or docetaxel. The breast cancer treatment not comprising an gemcitabine includes anthracycline, cyclophosphamide, fluorouracil (or 5-fluorouracil or 5-FU), methotrexate, thiotepa, carboplatin, cisplatin, taxanes, paclitaxel, protein-bound paclitaxel, docetaxel, vinorelbine, tamoxifen, raloxifene, toremifene, fulvestrant, 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. Preferably, the treatment that does not include gemcitabine includes one or more taxanes. Preferably, the taxanes are paclitaxel or docetaxel.
The biological sample can be a cell, a tissue or a bodily fluid. The tissue can be sampled from a biopsy or smear. The sample can also be a sampling of bodily fluids. These bodily fluids can include blood, lymph, urine, saliva, nipple aspirates and gynecological fluids. The biological sample can be a formalin-fixed, paraffin-embedded sample.
The present invention also provides a method of screening for the likelihood of the effectiveness of a breast cancer treatment including gemcitabine in a subject in need thereof. This method includes the steps of providing a biological sample from the subject and assaying the biological sample to determine whether the biological sample is classified as a basal-like subtype. If the biological sample is classified as a basal-like subtype, the breast cancer treatment including the gemcitabine is more likely to be effective in the subject. The breast cancer can be primary breast cancer, locally advanced breast cancer or metastatic breast cancer.
The assaying of the biological sample to determine whether the biological sample is classified as a basal-like subtype is performed using RNA expression profiling. The assaying the biological sample to determine whether the biological sample is classified as a basal-like subtype is performed by detecting at least 10, at least 15, at least 20, at least 25, at least 40, 41, 42, 43, 44, 45, 46 47, 48, 49 or all 50 of the intrinsic genes listed in Table 1. Preferably, detection is of all 50 of the intrinsic genes listed in Table 1. The expression of the members of the intrinsic gene list of Table 1 can be determined using and nanoreporter and the nanoreporter code system (nCounter® Analysis system).
The breast cancer treatment that includes gemcitabine can also include anthracycline, cyclophosphamide, fluorouracil (or 5-fluorouracil or 5-FU), methotrexate, thiotepa, carboplatin, cisplatin, taxanes, paclitaxel, protein-bound paclitaxel, docetaxel, vinorelbine, tamoxifen, raloxifene, toremifene, fulvestrant, 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. Preferably, the treatment that includes gemcitabine also includes one or more anti-cancer taxanes. More preferably, the taxanes are paclitaxel or docetaxel.
The biological sample can be a cell, a tissue or a bodily fluid. The tissues can be sampled from a tumor biopsy or surgical specimen. The sample can also be a sampling of bodily fluids. These bodily fluids can include blood, lymph, urine, saliva and nipple aspirates. The biological sample can be a formalin-fixed, paraffin-embedded sample.
The present invention also provides a kit for screening for the likelihood of the effectiveness of a breast cancer treatment including reagents sufficient for the detection of at least 10, at least 15, at least 20, at least 25, at least 40, 41, 42, 43, 44, 45, 46 47, 48, 49 or all 50 of the intrinsic genes and sufficient to determine a basal-like subtype. Preferably, the kit includes reagents sufficient for the detection of all 50 of the intrinsic genes listed in Table 1. The reagent sufficient for the detection of the at least 10, at least 15, at least 20, at least 25, at least 40, 41, 42, 43, 44, 45, 46 47, 48, 49 or all 50 of the intrinsic genes listed in Table 1 can include a microarray. Preferably, the reagents include a reporter probe and capture probe for the detection of at least 10, at least 15, at least 20, at least 25, at least 40, 41, 42, 43, 44, 45, 46 47, 48, 49 or all 50 of the intrinsic genes listed in Table 1. Preferably, there is only one reporter probe/capture probe pair for any one gene of Table 1 to be detected. Preferably, the kit includes instructions for utilizing the reagents and for performing any of the methods provided in the instant invention. Preferably, the instructions are for screening for the likelihood of the effectiveness of a breast cancer treatment.
Unless otherwise defined, 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. In the specification, the singular forms also include the plural unless the context clearly dictates otherwise. Although methods and materials similar or equivalent to those described herein can be used in the practice or testing of the present invention, suitable methods and materials are described below. All publications, patent applications, patents, and other references mentioned herein are incorporated by reference in their entirety. The references cited herein are not admitted to be prior art to the claimed invention. In the case of conflict, the present specification, including definitions, will control. In addition, the materials, methods, and examples are illustrative only and are not intended to be limiting. Other features and advantages of the invention will be apparent from the following detailed description and claim
The present invention provides a method of determining whether a breast cancer treatment comprising gemcitabine is optimal for administration to a patient suffering from breast cancer. Determining whether a breast cancer patient should receive a treatment including gemcitabine includes determining the subtype of the breast cancer using an intrinsic gene expression set and determining the basal-like subtype of the breast cancer by using immunohistochemistry (IHC). The disclosure also provides a method of treating breast cancer by determining whether a breast cancer patient should receive a treatment including gemcitabine and then administering the optimal breast cancer treatment to the patient based on that determination.
Intrinsic genes 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. Thus, intrinsic genes are used as classifier genes for breast cancer classification. Although clinical information was not used to derive the breast cancer intrinsic subtypes, this classification has proved to have prognostic significance. Intrinsic gene screening can be used to classify breast cancers into various subtypes. The major intrinsic subtypes of breast cancer are referred to as 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 PAM50 gene expression assay, as described herein, is able to identify intrinsic subtype from standard formalin fixed paraffin embedded tumor tissue (also see, Parker et al. J Clin Oncol., 27(8):1160-7 (2009) and U.S. Patent Application Publication No. 2011/0145176). The methods utilize a supervised algorithm to classify subject samples according to breast cancer intrinsic subtype. This algorithm, referred to herein as the PAM50 classification model, is based on the gene expression profile of a defined subset of intrinsic genes that has been identified herein as superior for classifying breast cancer intrinsic subtypes. The subset of genes, along with primers specific for their detection, is provided in Table 1. The target specific probe sequences are merely representative and not meant to limit the invention. The skilled artisan can utilize any target sequence-specific probe for detecting any of (or each of) the genes in Table 1.
Table 2 provides select sequences for the PAM50 genes of Table 1.
At least 10, at least 15, at least 20, at least 25, at least 40, at least 41, at least 42, at least 43, at least 44, at least 46, at least 47, at least 48, at least 49 or all 50 of the genes in Table 1 can be utilized in the methods of the present invention. Preferably, the expression of each of the 50 genes is determined in a biological sample. The prototypical gene expression profiles (i.e. centroid) of the four intrinsic subtypes were pre-defined from a training set of FFPE breast tumor samples using hierarchical clustering analysis of gene expression data. Table 3 shows the actual values of the prototypical gene expression profiles (i.e. centroids) of these four subtypes.
After performing the Breast Cancer Intrinsic Subtyping test with a test breast cancer tumor sample and the reference sample provided as part of the test kit, a computational algorithm based on a Pearson's correlation compares the normalized and scaled gene expression profile of the PAM50 intrinsic gene set of the test sample to the prototypical expression signatures of the four breast cancer intrinsic subtypes. The intrinsic subtype analysis is determined by determining the expression of a PAM50 set of genes and the risk of recurrence (“ROR”) is determined using the NANO46 set of genes (which is determining the expression of all 50 genes in Table 1 with the exception of determining the expression of MYBL2, BIRCS, GRB7 and CCNB1). Specifically, the intrinsic subtype is identified by comparing the expression of the PAM50 set of genes in the biological sample with the expected expression profiles for the four intrinsic subtypes. The subtype with the most similar expression profile is assigned to the biological sample. The ROR score is an integer value on a 0-100 scale that is related to an individual patient's probability of distant recurrence within 10 years for the defined intended use population. The ROR score is calculated by comparing the expression profiles of the NANO46 genes in the biological sample with the expected profiles for the four intrinsic subtypes, as described above, to calculate four different correlation values. These correlation values are then combined with a proliferation score (and optionally one or more clinicopathological variables, such as tumor size) to calculate the ROR score. Preferably, the ROR score is calculated by comparing only the expression profiles of the NANO46 genes.
The training set of FFPE breast tumor samples, which had well defined clinical characteristics and clinical outcome data, were used to establish a continuous Risk of Recurrence (ROR) score. The score is calculated using coefficients from a Cox model that includes correlation to each intrinsic subtype, a proliferation score (mean gene expression of a subset of 18 of the 46 genes), and tumor size, Table 4.
The test variables in Table 4 are multiplied by the corresponding coefficients and summed to produce a risk score (“ROR-PT”).
ROR-PT equation=−0.0067*A+0.4317*B+−0.3172*C+0.4894*D+0.1981*E+0.1133*F
In previous studies, the ROR score provided a continuous estimate of the risk of recurrence for ER-positive, node-negative patients who were treated with tamoxifen for 5 years (Nielsen et al. Clin. Cancer Res., 16(21):5222-5232 (2009)). The ROR score also exhibited a statistically significant improvement over a clinical model based in determining RFS within this test population providing further evidence of the improved accuracy of this decision making tool when compared to traditional clinicopathological measures (Nielsen et al. Clin. Cancer Res., 16(21):5222-5232 (2009)).
The gene set contains many genes that are known markers for proliferation. The methods of the present invention provide for the determination of subsets of genes that provide a proliferation signature. The methods of the present invention can include determining the expression of at least one of, a combination of, or each of, a 18-gene subset of the intrinsic genes of Table 1 selected from ANLN, CCNE1, CDC20, CDC6, CDCA1, CENPF, CEP55, EXO1, KIF2C, KNTC2, MELK, MKI67, ORC6L, PTTG1, RRM2, TYMS, UBE2C and/or UBE2T. Preferably, the expression of each of the 18-gene subset of the gene set of Table 1 is determined to provide a proliferation score. The expression of one or more of these genes may be determined and a proliferation signature index can be generated by averaging the normalized expression estimates of one or more of these genes in a sample. The sample can be assigned a high proliferation signature, a moderate/intermediate proliferation signature, a low proliferation signature or an ultra-low proliferation signature. Methods of determining a proliferation signature from a biological sample are as described in Nielsen et al. Clin. Cancer Res., 16(21):5222-5232 (2009) and supplemental online material (these documents are incorporated herein, by reference, in their entireties).
Breast Cancer
Subjects with breast cancer tumors that fit in the basal-like subtype, classified by intrinsic gene analysis, were surprisingly found to have a better prognosis on average when treated with a breast cancer treatment that included gemcitabine. Also surprisingly, breast cancer tumors that fit in the HER2-enriched subtype were found to have a poorer prognosis on average when treated with a breast cancer treatment that included gemcitabine.
Differentiating the clinical outcome in breast cancer patients demonstrating the basal-like subtype from those demonstrating non-basal-like subtypes administered a breast cancer treatment including gemcitabine when this treatment would not provide increased therapeutic efficacy and be accompanied by worse side effects, improves the clinical outcome and quality of life of thousands of patients.
For the purposes of the present disclosure, “breast cancer” includes, for example, those conditions classified by biopsy or histology as malignant pathology. The clinical delineation of breast cancer diagnoses is well known in the medical arts. One of skill in the art will appreciate that breast cancer refers to any malignancy of the breast tissue, including, for example, carcinomas and sarcomas. Particular embodiments of breast cancer include ductal carcinoma in situ (DCIS), lobular carcinoma in situ (LCIS), or mucinous carcinoma. Breast cancer also refers to infiltrating ductal (IDC), lobular neoplasia or infiltrating lobular carcinoma (ILC). In most embodiments of the disclosure, the subject of interest is a human patient suspected of or actually diagnosed with breast cancer.
Breast cancer includes all forms of cancer of the breast. Breast cancer can include primary epithelial breast cancers. Breast cancer can include cancers in which the breast is involved by other tumors such as lymphoma, sarcoma or melanoma. Breast cancer can include carcinoma of the breast, ductal carcinoma of the breast, lobular carcinoma of the breast, undifferentiated carcinoma of the breast, cystosarcoma phyllodes of the breast, angiosarcoma of the breast, and primary lymphoma of the breast. Breast cancer can include Stage I, II, IIIA, IIIB, IIIC and IV breast cancer. Ductal carcinoma of the breast can include invasive carcinoma, invasive carcinoma in situ with predominant intraductal component, inflammatory breast cancer, and a ductal carcinoma of the breast with a histologic type selected from the group consisting of comedo, mucinous (colloid), medullary, medullary with lymphcytic infiltrate, papillary, scirrhous, and tubular. Lobular carcinoma of the breast can include invasive lobular carcinoma with predominant in situ component, invasive lobular carcinoma, and infiltrating lobular carcinoma. Breast cancer can include Paget's disease, Paget's disease with intraductal carcinoma, and Paget's disease with invasive ductal carcinoma. Breast cancer can include breast neoplasms having histologic and ultrastructual heterogeneity (e.g., mixed cell types).
A breast cancer that is to be treated can include familial breast cancer. A breast cancer that is to be treated can include sporadic breast cancer. A breast cancer that is to be treated can arise in a male subject. A breast cancer that is to be treated can arise in a female subject. A breast cancer that is to be treated can arise in a premenopausal female subject or a postmenopausal female subject.
A breast cancer that is to be treated can include a localized tumor of the breast. A breast cancer that is to be treated can include a tumor of the breast that is associated with a negative sentinel lymph node (SLN) biopsy. A breast cancer that is to be treated can include a tumor of the breast that is associated with a positive sentinel lymph node (SLN) biopsy. A breast cancer that is to be treated can include a tumor of the breast that is associated with one or more positive axillary lymph nodes, where the axillary lymph nodes have been staged by any applicable method. A breast cancer that is to be treated can include a tumor of the breast that has been typed as having nodal negative status (e.g., node-negative) or nodal positive status (e.g., node-positive). A breast cancer that is to be treated can include a tumor of the breast that has metastasized to other locations in the body. A breast cancer that is to be treated can be classified as having metastasized to a location selected from the group consisting of bone, lung, liver, or brain. A breast cancer that is to be treated can be classified according to a characteristic selected from the group consisting of metastatic, localized, regional, local-regional, locally advanced, distant, multicentric, bilateral, ipsilateral, contralateral, newly diagnosed, recurrent, and inoperable.
For the purposes of the present disclosure, “a breast cancer treatment comprising gemcitabine” is a breast cancer treatment that includes gemcitabine. A “breast cancer treatment comprising gemcitabine” can also be a breast cancer treatment that includes an analog or derivative of gemcitabine or another nucleoside anti-tumor agent. These treatments can also include other anti-cancer or chemotherapeutic agents.
For the purposes of the present disclosure, “a breast cancer treatment not comprising gemcitabine” is a breast cancer treatment that does not include any gemcitabine. These treatments contain other anti-cancer or chemotherapeutic agents.
Classes of anti-cancer or chemotherapeutic agents can include anthracycline agents, alkylating agents, platinum agents, taxanes, vinca agents, anti-estrogen drugs, aromatase inhibitors, ovarian suppression agents, endocrine/hormonal agents, bisphosphonate therapy agents and targeted biological therapy agents.
Specific anti-cancer or chemotherapeutic agents can include anthracyclines, cyclophosphamide, fluorouracil (or 5-fluorouracil or 5-FU), methotrexate, thiotepa, carboplatin, cisplatin, taxanes, paclitaxel, protein-bound paclitaxel, docetaxel, vinorelbine, tamoxifen, raloxifene, toremifene, fulvestrant, 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.
Combinational anti-cancer or chemotherapeutic therapies can include AT: Adriamycin® (Doxorubicin) and Taxotere® (Docetaxel); AC: Adriamycin®, Cytoxan® (Cyclophosphamide); AC+Taxol®; AC+Taxotere®; CMF: Cytoxan®, Methotrexate, 5-fluorouracil; CEF: Cytoxan®Ellence® (Epirubicin), and fluorouracil; EC: Ellence®, Cytoxan®; FAC: 5-fluorouracil, Adriamycin®, and Cytoxan®; GET: Gemzar® (Gemcitabine), Ellence®, and Taxol®; TC: Taxotere®, Cytoxan®; TC: Taxotere®, Paraplatin® (Carboplatin); TAC: Taxotere®, Adriamycin®, Cytoxan® or TCH: Taxotere®, Herceptin® (Trastuzumab), and Paraplatin®. Additional combination chemotherapeutic therapies for metastatic breast cancer can include: Taxol® and Xeloda® (Capecitabine); Taxotere® and Xeloda®; Taxotere® and Paraplatin®; Taxol® and Paraplatin®; Taxol® and Gemzar®; Abraxane® (Protein-bound Paclitaxel) and Xeloda®; Abraxane® and Paraplatin®; Camptosor® (Irinotecan) and Temodar® (Temozolomide); Gemzar® and Paraplatin® or Ixempra® (Ixabepilone) and Xeloda®
Preferably, the anti-cancer or chemotherapeutic agents include one or more taxanes. More preferably, the taxanes are paclitaxel or docetaxel.
Preferably gemcitabine is administered intravenously, but can be administered by any method known in the art. In certain embodiments, a subject or patient receives gemcitabine, administered at about 2500 mg/m2 to about 50 mg/m2, once daily. In certain embodiments, gemcitabine is administered at a decreased dose to reduce toxicity. For example, gemcitabine is administered at 1500 mg/m2, 1250 mg/m2, 1000 mg/m2, 750 mg/m2, 500 mg/m2, 250 mg/m2, 100 mg/m2, or 50 mg/m2 once daily.
The taxane agents may be administered in any manner found appropriate by a clinician in generally accepted efficacious dose ranges such as those described in the Physician Desk Reference, 53th Ed. (1999), Publisher Edward R. Barnhart, New Jersey (“PDR”). Preferably taxanes are administered intravenously, but can be administered by any method known in the art. In general, paclitaxel is administered at dosages from about 135 to about 300 mg/m2, preferably from about 135 to about 175 mg/m2, and most preferably about 175 mg/m2 daily. In general, docetaxel is administered at dosages from about 60 to about 100 mg/m2, and most preferably about 75 mg/m2 daily.
The article “a” and “an” are used herein to refer to one or more than one (i.e., to at least one) of the grammatical object of the article. By way of example, “an element” means one or more element.
Throughout the specification the word “comprising,” or variations such as “comprises” or “comprising,” will be understood to imply the inclusion of a stated element, integer or step, or group of elements, integers or steps, but not the exclusion of any other element, integer or step, or group of elements, integers or steps.
Description of Intrinsic Subtype Biology
Luminal subtypes: The most common subtypes of breast cancer are the luminal subtypes, Luminal A and Luminal B. Prior studies suggest that luminal A comprises approximately 30% to 40% and luminal B approximately 20% of all breast cancers, but they represent over 90% of hormone receptor positive breast cancers (Nielsen et al. Clin. Cancer Res., 16(21):5222-5232 (2009)). The gene expression pattern of these subtypes resembles the luminal epithelial component of the breast. These tumors are characterized by high expression of estrogen receptor (ER), progesterone receptor (PR), and genes associated with ER activation, such as LIV1, GATA3, and cyclin D1, as well as expression of luminal cytokeratins 8 and 18 (Lisa Carey & Charles Perou (2009). Gene Arrays, Prognosis, and Therapeutic Interventions. Jay R. Harris et al. (4th ed.), Diseases of the breast (pp. 458-472). Philadelphia, Pa.: Lippincott Williams & Wilkins).
Luminal A: Luminal A (LumA) breast cancers exhibit low expression of genes associated with cell cycle activation and the ERBB2 cluster resulting in a better prognosis than Luminal B. The Luminal A subgroup has the most favorable prognosis of all subtypes and is enriched for endocrine therapy-responsive tumors.
Luminal B: Luminal B (LumB) breast cancers also express ER and ER-associated genes. Genes associated with cell cycle activation are highly expressed and this tumor type can be HER2(+) (˜20%) or HER2(−). The prognosis is unfavorable (despite ER expression) and endocrine therapy responsiveness is generally diminished relative to LumA.
HER2-enriched: The HER2-enriched subtype is generally ER-negative and is HER2-positive in the majority of cases with high expression of the ERBB2 cluster, including ERBB2 and GRB7. Genes associated with cell cycle activation are highly expressed and these tumors have a poor outcome.
Basal-like: The Basal-like subtype is generally ER-negative, is almost always clinically HER2-negative and expresses a suite of “basal” biomarkers including the basal epithelial cytokeratins (CK) and epidermal growth factor receptor (EGFR). Genes associated with cell cycle activation are highly expressed.
Clinical Variables
The PAM50 classification model described herein may be further combined with information on clinical variables to generate a continuous risk of relapse (ROR) predictor. As described herein, a number of clinical and prognostic breast cancer factors are known in the art and are used to predict treatment outcome and the likelihood of disease recurrence. Such factors include, for example, lymph node involvement, tumor size, histologic grade, estrogen and progesterone hormone receptor status, HER-2 levels, and tumor ploidy. In one embodiment, risk of relapse (ROR) score is provided for a subject diagnosed with or suspected of having breast cancer. This score uses the PAM50 classification model in combination with clinical factors of lymph node status (N) and tumor size (T). Assessment of clinical variables is based on the American Joint Committee on Cancer (AJCC) standardized system for breast cancer staging. In this system, primary tumor size is categorized on a scale of 0-4 (TO: no evidence of primary tumor; T1: <2 cm; T2: >2 cm-<5 cm; T3: >5 cm; T4: tumor of any size with direct spread to chest wall or skin). Lymph node status is classified as N0-N3 (NO: regional lymph nodes are free of metastasis; N1: metastasis to movable, same-side axillary lymph node(s); N2: metastasis to same-side lymph node(s) fixed to one another or to other structures; N3: metastasis to same-side lymph nodes beneath the breastbone). Methods of identifying breast cancer patients and staging the disease are well known and may include manual examination, biopsy, review of patient's and/or family history, and imaging techniques, such as mammography, magnetic resonance imaging (MRI), and positron emission tomography (PET).
Sample Source
In one embodiment of the present disclosure, breast cancer subtype is assessed through the evaluation of expression patterns, or profiles, of the intrinsic genes listed in Table 1 in one or more subject samples and/or FISH analysis or IHC performed to ascertain the Her-2 status of the cancer. For the purpose of discussion, the term subject, or subject sample, refers to an individual regardless of health and/or disease status. A subject can be a subject, a study participant, a control subject, a screening subject, or any other class of individual from whom a sample is obtained and assessed in the context of the disclosure. Accordingly, a subject can be diagnosed with breast cancer, can present with one or more symptoms of breast cancer, or a predisposing factor, such as a family (genetic) or medical history (medical) factor, for breast cancer, can be undergoing treatment or therapy for breast cancer, or the like. As such, the subject is a subject in need of treatment for breast cancer or detection of breast cancer. Alternatively, a subject can be healthy with respect to any of the aforementioned factors or criteria. It will be appreciated that the term “healthy” as used herein, is relative to breast cancer status, as the term “healthy” cannot be defined to correspond to any absolute evaluation or status. Thus, an individual defined as healthy with reference to any specified disease or disease criterion, can in fact be diagnosed with any other one or more diseases, or exhibit any other one or more disease criterion, including one or more cancers other than breast cancer. However, the healthy controls are preferably free of any cancer.
As used herein, a “subject in need thereof” is a subject having breast cancer or presenting with one or more symptoms of breast cancer, or a subject having an increased risk of developing breast cancer relative to the population at large. Preferably, a subject in need thereof has breast cancer. The breast cancer can be primary breast cancer, locally advanced breast cancer or metastatic breast cancer. A “subject” includes a mammal. The mammal can be e.g., any mammal, e.g., a human, primate, bird, mouse, rat, fowl, dog, cat, cow, horse, goat, camel, sheep or a pig. Preferably, the mammal is a human.
In particular embodiments, the methods for predicting breast cancer intrinsic subtypes or Her-2 status include collecting a biological sample comprising a cancer cell or tissue, such as a breast tissue sample or a primary breast tumor tissue sample. 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, 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, particularly 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. Fixative and staining solutions may be applied to the cells or tissues for preserving the specimen and for facilitating examination. Biological samples, particularly breast tissue samples, may be transferred to a glass slide for viewing under magnification. 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.
Expression Profiling
In various embodiments, the present disclosure provides methods for classifying, prognosticating, or monitoring breast cancer in subjects. In this embodiment, data obtained from analysis of intrinsic gene expression is evaluated using one or more pattern recognition algorithms. Such analysis methods may be used to form a predictive model, which can be used to classify test data. For example, one convenient and particularly effective method of classification employs multivariate statistical analysis modeling, first to form a model (a “predictive mathematical model”) using data (“modeling data”) from samples of known subtype (e.g., from subjects known to have a particular breast cancer intrinsic subtype: LumA, LumB, Basal-like, HER2-enriched, or normal-like), and second to classify an unknown sample (e.g., “test sample”) according to subtype. Pattern recognition methods have been used widely to characterize many different types of problems ranging, for example, over linguistics, fingerprinting, chemistry and psychology. In the context of the methods described herein, pattern recognition is the use of multivariate statistics, both parametric and non-parametric, to analyze data, and hence to classify samples and to predict the value of some dependent variable based on a range of observed measurements. There are two main approaches. One set of methods is termed “unsupervised” and these simply reduce data complexity in a rational way and also produce display plots which can be interpreted by the human eye. However, this type of approach may not be suitable for developing a clinical assay that can be used to classify samples derived from subjects independent of the initial sample population used to train the prediction algorithm.
The other approach is termed “supervised” whereby a training set of samples with known class or outcome is used to produce a mathematical model which is then evaluated with independent validation data sets. Here, a “training set” of intrinsic gene expression data is used to construct a statistical model that predicts correctly the “subtype” of each sample. This training set is then tested with independent data (referred to as a test or validation set) to determine the robustness of the computer-based model. These models are sometimes termed “expert systems,” but may be based on a range of different mathematical procedures. Supervised methods can use a data set with reduced dimensionality (for example, the first few principal components), but typically use unreduced data, with all dimensionality. In all cases the methods allow the quantitative description of the multivariate boundaries that characterize and separate each subtype in terms of its intrinsic gene expression profile. It is also possible to obtain confidence limits on any predictions, for example, a level of probability to be placed on the goodness of fit. The robustness of the predictive models can also be checked using cross-validation, by leaving out selected samples from the analysis.
The PAM50 classification model described herein is based on the gene expression profile for a plurality of subject samples using the intrinsic genes listed in Table 1. The plurality of samples includes a sufficient number of samples derived from subjects belonging to each subtype class. By “sufficient samples” or “representative number” in this context is intended a quantity of samples derived from each subtype that is sufficient for building a classification model that can reliably distinguish each subtype from all others in the group. A supervised prediction algorithm is 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 International Patent Publication WO 2007/061876 and U.S. Patent Publication No. 2009/0299640, which is herein incorporated by reference in its entirety. Alternatively, the samples can be subtyped according to any known assay for classifying breast cancer subtypes. After stratifying the training samples according to subtype, a centroid-based prediction algorithm is used to construct centroids based on the expression profile of the intrinsic gene set described in Table 1.
In one embodiment, the prediction algorithm is the nearest centroid methodology related to that described in Narashiman and Chu (2002) PNAS 99:6567-6572, which is herein incorporated by reference in its entirety. In the present disclosure, the method computes a standardized centroid for each subtype. This 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, and assigning a sample to a subtype based on the nearest centroid.
Detection of Intrinsic Gene Expression
Any methods available in the art for detecting expression of the intrinsic genes listed in Table 1 are encompassed herein. By “detecting expression” is intended determining the quantity or presence of an RNA transcript or its expression product of an intrinsic gene. Methods for detecting expression of the intrinsic genes of the disclosure, that is, gene expression profiling, include 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. In preferred embodiments, 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 (Schena et al., Science 270:467-70, 1995) are used. By “microarray” is intended an ordered arrangement of hybridizable array elements, such as, for example, polynucleotide probes, on a substrate. The term “probe” refers to any molecule that is capable of selectively binding to a specifically intended target biomolecule, for example, a nucleotide transcript or a protein encoded by or corresponding to an intrinsic gene. Probes can be synthesized by one of skill in the art, or derived from appropriate biological preparations. Probes may be specifically designed to be labeled. Examples of molecules that can be utilized as probes include, but are not limited to, RNA, DNA, proteins, antibodies, and organic molecules.
Many expression detection methods use isolated RNA. The starting material is typically total RNA isolated from a biological sample, such as a tumor or tumor cell line, and corresponding normal tissue or cell line, respectively. 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).
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). In particular, RNA isolation can be performed using a purification kit, a buffer set and protease from commercial manufacturers, such as Qiagen (Valencia, Calif.), according to the manufacturer's instructions. For example, total RNA from cells in culture can be isolated using Qiagen RNeasy mini-columns. Other commercially available RNA isolation kits include MASTERPURE™ Complete DNA and RNA Purification Kit (Epicentre, Madison, Wis.) and Paraffin Block RNA Isolation Kit (Ambion, Austin, Tex.). Total RNA from tissue samples can be isolated, for example, using RNA Stat-60 (Tel-Test, Friendswood, Tex.). RNA prepared from a tumor can be isolated, for example, by cesium chloride density gradient centrifugation. Additionally, large numbers of tissue samples can readily be processed using techniques well known to those of skill in the art, such as, for example, the single-step RNA isolation process of Chomczynski (U.S. Pat. No. 4,843,155).
Isolated RNA can be used in hybridization or amplification assays that include, but are not limited to, PCR analyses and probe arrays. One method for the detection of RNA levels involves contacting the isolated RNA with a nucleic acid molecule (probe) that can hybridize to the mRNA encoded by the gene being detected. The nucleic acid probe can be, for example, a full-length cDNA, or a portion thereof, such as an oligonucleotide of at least 7, 15, 30, 60, 100, 250, or 500 nucleotides in length and sufficient to specifically hybridize under stringent conditions to an intrinsic gene of the present disclosure, or any derivative DNA or RNA. Hybridization of an mRNA with the probe indicates that the intrinsic gene in question is being expressed.
In one embodiment, the mRNA is immobilized on a solid surface and contacted with a probe, for example by running the isolated mRNA on an agarose gel and transferring the mRNA from the gel to a membrane, such as nitrocellulose. In an alternative embodiment, the probes are immobilized on a solid surface and the mRNA is contacted with the probes, for example, in an Agilent gene chip array. A skilled artisan can readily adapt known mRNA detection methods for use in detecting the level of expression of the intrinsic genes of the present disclosure.
An alternative method for determining the level of intrinsic gene expression product in a sample involves the process of nucleic acid amplification, for example, by RT-PCR (U.S. Pat. No. 4,683,202), ligase chain reaction (Barany, PNAS USA 88: 189-93, (1991)), self sustained sequence replication (Guatelli et al., Proc. Natl. Acad. Sci. USA 87: 1874-78, (1990)), transcriptional amplification system (Kwoh et al., Proc. Natl. Acad. ScL USA 86: 1173-77, (1989)), Q-Beta Replicase (Lizardi et al., Bio/Technology 6:1197, (1988)), rolling circle replication (U.S. Pat. No. 5,854,033), or any other nucleic acid amplification method, followed by the detection of the amplified molecules using techniques well known to those of skill in the art. These detection schemes are especially useful for the detection of nucleic acid molecules if such molecules are present in very low numbers.
In particular aspects of the disclosure, intrinsic gene expression can assessed by quantitative RT-PCR. Numerous different PCR or QPCR protocols are known in the art and exemplified herein below and can be directly applied or adapted for use using the presently-described compositions for the detection and/or quantification of the intrinsic genes listed in Table 1. Generally, in PCR, a target polynucleotide sequence is amplified by reaction with at least one oligonucleotide primer or pair of oligonucleotide primers. The primer(s) hybridize to a complementary region of the target nucleic acid and a DNA polymerase extends the primer(s) to amplify the target sequence. Under conditions sufficient to provide polymerase-based nucleic acid amplification products, a nucleic acid fragment of one size dominates the reaction products (the target polynucleotide sequence which is the amplification product). The amplification cycle is repeated to increase the concentration of the single target polynucleotide sequence. The reaction can be performed in any thermocycler commonly used for PCR. However, preferred are cyclers with real time fluorescence measurement capabilities, for example, SMARTCYCLER® (Cepheid, Sunnyvale, Calif.), ABI PRISM 7700® (Applied Biosystems, Foster City, Calif.), ROTOR-GENE™ (Corbett Research, Sydney, Australia), LIGHTCYCLER® (Roche Diagnostics Corp, Indianapolis, Ind.), ICYCLER® (Biorad Laboratories, Hercules, Calif.) and MX4000® (Stratagene, La Jolla, Calif.).
In another embodiment of the disclosure, microarrays are used for expression profiling. Microarrays are particularly well suited for this purpose because of the reproducibility between different experiments. DNA microarrays provide one method for the simultaneous measurement of the expression levels of large numbers of genes. Each array consists of 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.
In a preferred embodiment, the nCounter® Analysis system is used to detect intrinsic gene expression. The basis of the nCounter® Analysis system is the unique code assigned to each nucleic acid target to be assayed (International Patent Application Publication No. WO 08/124,847, U.S. Pat. No. 8,415,102 and Geiss et al. Nature Biotechnology. 2008. 26(3): 317-325; the contents of which are each incorporated herein by reference in their entireties). The code is composed of an ordered series of colored fluorescent spots which create a unique barcode for each target to be assayed. A pair of probes is designed for each DNA or RNA target, a biotinylated capture probe and a reporter probe carrying the fluorescent barcode. This system is also referred to, herein, as the nanoreporter code system.
Specific reporter and capture probes are synthesized for each target. The reporter probe can comprise at a least a first label attachment region to which are attached one or more label monomers that emit light constituting a first signal; at least a second label attachment region, which is non-over-lapping with the first label attachment region, to which are attached one or more label monomers that emit light constituting a second signal; and a first target-specific sequence. Preferably, each sequence specific reporter probe comprises a target specific sequence capable of hybridizing to no more than one PAM50 gene of Table 1 and optionally comprises at least three, or at least four label attachment regions, said attachment regions comprising one or more label monomers that emit light, constituting at least a third signal, or at least a fourth signal, respectively. The capture probe can comprise a second target-specific sequence; and a first affinity tag. In some embodiments, the capture probe can also comprise one or more label attachment regions. Preferably, the first target-specific sequence of the reporter probe and the second target-specific sequence of the capture probe hybridize to different regions of the same gene of Table 1 to be detected. Reporter and capture probes are all pooled into a single hybridization mixture, the “probe library”. Preferably, the probe library comprises a probe pair (a capture probe and reporter) for each of the PAM50 genes in Table 1.
The relative abundance of each target is measured in a single multiplexed hybridization reaction. The method comprises contacting a biological sample with a probe library, the library comprising a probe pair for the PAM50 genes in Table 1, such that the presence of the target in the sample creates a probe pair—target complex. The complex is then purified. More specifically, the sample is combined with the probe library, and hybridization occurs in solution. After hybridization, the tripartite hybridized complexes (probe pairs and target) are purified in a two-step procedure using magnetic beads linked to oligonucleotides complementary to universal sequences present on the capture and reporter probes. This dual purification process allows the hybridization reaction to be driven to completion with a large excess of target-specific probes, as they are ultimately removed, and, thus, do not interfere with binding and imaging of the sample. All post hybridization steps are handled robotically on a custom liquid-handling robot (Prep Station, NanoString Technologies).
Purified reactions are deposited by the Prep Station into individual flow cells of a sample cartridge, bound to a streptavidin-coated surface via the capture probe, electrophoresed to elongate the reporter probes, and immobilized. After processing, the sample cartridge is transferred to a fully automated imaging and data collection device (Digital Analyzer, NanoString Technologies). The expression level of a target is measured by imaging each sample and counting the number of times the code for that target is detected. For each sample, typically 600 fields-of-view (FOV) are imaged (1376×1024 pixels) representing approximately 10 mm2 of the binding surface. Typical imaging density is 100-1200 counted reporters per field of view depending on the degree of multiplexing, the amount of sample input, and overall target abundance. Data is output in simple spreadsheet format listing the number of counts per target, per sample.
This system can be used along with nanoreporters. Additional disclosure regarding nanoreporters can be found in International Publication No. WO 07/076,129 and WO 07/076,132, and US Patent Publication No. 2010/0015607 and 2010/0261026, the contents of which are incorporated herein in their entireties. Further, the term nucleic acid probes and nanoreporters can include the rationally designed (e.g. synthetic sequences) described in International Publication No. WO 2010/019826 and US Patent Publication No. 2010/0047924, incorporated herein by reference in its entirety.
Data Processing
It is often useful to pre-process gene expression data, for example, by addressing missing data, translation, scaling, normalization, weighting, etc. Multivariate projection methods, such as principal component analysis (PCA) and partial least squares analysis (PLS), are so-called scaling sensitive methods. By using prior knowledge and experience about the type of data studied, the quality of the data prior to multivariate modeling can be enhanced by scaling and/or weighting. Adequate scaling and/or weighting can reveal important and interesting variation hidden within the data, and therefore make subsequent multivariate modeling more efficient. Scaling and weighting may be used to place the data in the correct metric, based on knowledge and experience of the studied system, and therefore reveal patterns already inherently present in the data.
If possible, missing data, for example gaps in column values, should be avoided. However, if necessary, such missing data may be replaced or “filled” with, for example, the mean value of a column (“mean fill”); a random value (“random fill”); or a value based on a principal component analysis (“principal component fill”).
“Translation” of the descriptor coordinate axes can be useful. Examples of such translation include normalization and mean centering. “Normalization” may be used to remove sample-to-sample variation. For microarray data, the process of normalization aims to remove systematic errors by balancing the fluorescence intensities of the two labeling dyes. The dye bias can come from various sources including differences in dye labeling efficiencies, heat and light sensitivities, as well as scanner settings for scanning two channels. Some commonly used methods for calculating normalization factor include: (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)). In one embodiment, the intrinsic genes disclosed herein can be normalized to control housekeeping genes. For example, the housekeeping genes described in U.S. Patent Publication 2008/0032293, which is herein incorporated by reference in its entirety, can be used for normalization. Exemplary housekeeping genes include MRPL19, PSMC4, SF3A1, PUM1, ACTB, GAPD, GUSB, RPLPO, and TFRC. It will be understood by one of skill in the art that the methods disclosed herein are not bound by normalization to any particular housekeeping genes, and that any suitable housekeeping gene(s) known in the art can be used.
Many normalization approaches are possible, and they can often be applied at any of several points in the analysis. In one embodiment, microarray data is normalized using the LOWESS method, which is a global locally weighted scatterplot smoothing normalization function. In another embodiment, qPCR data is normalized to the geometric mean of set of multiple housekeeping genes.
“Mean centering” may also be used to simplify interpretation. Usually, for each descriptor, the average value of that descriptor for all samples is subtracted. In this way, the mean of a descriptor coincides with the origin, and all descriptors are “centered” at zero. In “unit variance scaling,” data can be scaled to equal variance. Usually, the value of each descriptor is scaled by 1/StDev, where StDev is the standard deviation for that descriptor for all samples. “Pareto scaling” is, in some sense, intermediate between mean centering and unit variance scaling. In pareto scaling, the value of each descriptor is scaled by 1/sqrt(StDev), where StDev is the standard deviation for that descriptor for all samples. In this way, each descriptor has a variance numerically equal to its initial standard deviation. The pareto scaling may be performed, for example, on raw data or mean centered data.
“Logarithmic scaling” may be used to assist interpretation when data have a positive skew and/or when data spans a large range, e.g., several orders of magnitude. Usually, for each descriptor, the value is replaced by the logarithm of that value. In “equal range scaling,” each descriptor is divided by the range of that descriptor for all samples. In this way, all descriptors have the same range, that is, 1. However, this method is sensitive to presence of outlier points. In “autoscaling,” each data vector is mean centered and unit variance scaled. This technique is a very useful because each descriptor is then weighted equally, and large and small values are treated with equal emphasis. This can be important for genes expressed at very low, but still detectable, levels.
In one embodiment, data is collected for one or more test samples and classified using the PAM50 classification model described herein. When comparing data from multiple analyses (e.g., comparing expression profiles for one or more test samples to the centroids constructed from samples collected and analyzed in an independent study), it will be necessary to normalize data across these data sets. In one embodiment, Distance Weighted Discrimination (DWD) is used to combine these data sets together (Benito et al. (2004) Bioinformatics 20(1): 105-114, incorporated by reference herein in its entirety). DWD is a multivariate analysis tool that is able to identify systematic biases present in separate data sets and then make a global adjustment to compensate for these biases; in essence, each separate data set is a multi-dimensional cloud of data points, and DWD takes two points clouds and shifts one such that it more optimally overlaps the other.
The methods described herein may be implemented and/or the results recorded using any device capable of implementing the methods and/or recording the results. Examples of devices that may be used include but are not limited to electronic computational devices, including computers of all types. When the methods described herein are implemented and/or recorded in a computer, the computer program that may be used to configure the computer to carry out the steps of the methods may be contained in any computer readable medium capable of containing the computer program. Examples of computer readable medium that may be used include but are not limited to diskettes, CD-ROMs, DVDs, ROM, RAM, and other memory and computer storage devices. The computer program that may be used to configure the computer to carry out the steps of the methods and/or record the results may also be provided over an electronic network, for example, over the internet, an intranet, or other network.
Calculation of Risk of Relapse
Provided herein are methods for predicting breast cancer outcome within the context of the intrinsic subtype and optionally other clinical variables. Outcome may refer to overall or disease-specific survival, event-free survival, or outcome in response to a particular treatment or therapy. In particular, the methods may be used to predict the likelihood of long-term, disease-free survival. “Predicting the likelihood of survival of a breast cancer patient” is intended to assess the risk that a patient will die as a result of the underlying breast cancer. “Long-term, disease-free survival” is intended to mean that the patient does not die from or suffer a recurrence of the underlying breast cancer within a period of at least five years, or at least ten or more years, following initial diagnosis or treatment.
In one embodiment, outcome is predicted based on classification of a subject according to subtype. This classification is based on expression profiling using the list of intrinsic genes listed in Table 1. In addition to providing a subtype assignment, the PAM50 bioinformatics model provides a measurement of the similarity of a test sample to all four subtypes which is translated into a Risk of Relapse (ROR) score that can be used in any patient population regardless of disease status and treatment options. The intrinsic subtypes and ROR also have value in the prediction of pathological complete response in women treated with, for example, neoadjuvant taxane and anthracycline chemotherapy (Rouzier et al., J Clin Oncol 23:8331-9 (2005), incorporated herein by reference in its entirety). Thus, in various embodiments of the present disclosure, a risk of relapse (ROR) model is used to predict outcome. Using these risk models, subjects can be stratified into low, medium, and high risk of relapse groups. Calculation of ROR can provide prognostic information to guide treatment decisions and/or monitor response to therapy.
In some embodiments described herein, the prognostic performance of the PAM50-defined intrinsic subtypes and/or other clinical parameters is assessed utilizing a Cox Proportional Hazards Model Analysis, which is a regression method for survival data that provides an estimate of the hazard ratio and its confidence interval. The Cox model is a well-recognized statistical technique for exploring the relationship between the survival of a patient and particular variables. This statistical method permits estimation of the hazard (i.e., risk) of individuals given their prognostic variables (e.g., intrinsic gene expression profile with or without additional clinical factors, as described herein). The “hazard ratio” is the risk of death at any given time point for patients displaying particular prognostic variables. See generally Spruance et al., Antimicrob. Agents & Chemo. 48:2787-92 (2004).
The PAM50 classification model described herein can be trained for risk of relapse using subtype distances (or correlations) alone, or using subtype distances with clinical variables as discussed supra. In one embodiment, the risk score for a test sample is calculated using intrinsic subtype distances alone using the following equation:
ROR=0.05*Basal+0.11*Her2+−0.25*LumA+0.07*LumB+−0.11*Normal,
where the variables “Basal,” “Her2,” “LumA,” “LumB,” and “Normal” are the distances to the centroid for each respective classifier when the expression profile from a test sample is compared to centroids constructed using the gene expression data deposited with the Gene Expression Omnibus (GEO) as accession number GSE2845.
Risk score can also be calculated using a combination of breast cancer subtype and the clinical variables tumor size (T) and lymph nodes status (N) using the following equation: ROR (full)=0.05*Basal+0.1*Her2+-0.19*LumA+0.05*LumB+−0.09*Normal+0.16*T+0.08*N, again when comparing test expression profiles to centroids constructed using the gene expression data deposited with GEO as accession number GSE2845.
In yet another embodiment, risk score for a test sample is calculated using intrinsic subtype distances alone using the following equation:
ROR-S=0.05*Basal+0.12*Her2+−0.34*LumA+0.23*LumB,
where the variables “Basal,” “Her2,” “LumA,” and “LumB” are as described supra and the test expression profiles are compared to centroids constructed using the gene expression data deposited with GEO as accession number GSE2845. In yet another embodiment, risk score can also be calculated using a combination of breast cancer subtype and the clinical variable tumor size (T) using the following equation (where the variables are as described supra):
ROR-C=0.05*Basal+0.11*Her2+−0.23*LumA+0.09*LumB+0.17*T.
In yet another embodiment, risk score for a test sample is calculated using intrinsic subtype distances in combination with the proliferation signature (“Prolif”) using the following equation:
ROR-P=−0.001*Basal+0.7*Her2+−0.95*LumA+0.49*LumB+0.34*Prolif,
where the variables “Basal,” “Her2,” “LumA,” “LumB” and “Prolif” are as described supra and the test expression profiles are compared to centroids constructed using the gene expression data deposited with GEO as accession number GSE2845.
In yet another embodiment, risk score can also be calculated using a combination of breast cancer subtype, proliferation signature and the clinical variable tumor size (T) using the ROR-PT described in conjunction with Table 3, supra.
Detection of Subtypes
Immunohistochemistry for estrogen (ER), progesterone (PgR), HER2, and Ki67 can be performed concurrently on serial sections with the standard streptavidinbiotin complex method with 3,3′-diaminobenzidine as the chromogen. Staining for ER, PgR, and HER2 interpretation can be performed as described previously (Cheang et al., Clin Cancer Res. 2008; 14(5):1368-1376.), however any method known in the art may be used.
For example, a Ki67 antibody (clone SP6; ThermoScientific, Fremont, Calif.) can be applied at a 1:200 dilution for 32 minutes, by following the Ventana Benchmark automated immunostainer (Ventana, Tucson Ariz.) standard Cell Conditioner 1 (CC1, a proprietary buffer) protocol at 98° C. for 30 minutes. An ER antibody (clone SP1; ThermoFisher Scientific, Fremont Calif.) can be used at 1:250 dilution with 10-minute incubation, after an 8-minute microwave antigen retrieval in 10 mM sodium citrate (pH 6.0). Ready-to-use PR antibody (clone 1E2; Ventana) can be used by following the CC1 protocol as above. HER2 staining can be done with a SP3 antibody (ThermoFisher Scientific) at a 1:100 dilution after antigen retrieval in 0.05 M Tris buffer (pH 10.0) with heating to 95° C. in a steamer for 30 minutes. For HER2 fluorescent in situ hybridization (FISH) assay, slides can be hybridized with probes to LSI (locus-specific identifier) HER2/neu and to centromere 17 by use of the PathVysion HER-2 DNA Probe kit (Abbott Molecular, Abbott Park, Ill.) according to manufacturer's instructions, with modifications to pretreatment and hybridization as previously described (Brown L A, Irving J, Parker R, et al. Amplification of EMSY, a novel oncogene on 11q13, in high grade ovarian surface epithelial carcinomas. Gynecol Oncol. 2006; 100(2):264-270). Slides can then be counterstained with 4′,6-diamidino-2-phenylindole, stained material was visualized on a Zeiss Axioplan epifluorescent microscope, and signals were analyzed with a Metafer image acquisition system (Metasystems, Altlussheim, Germany). Biomarker expression from immunohistochemistry assays can then be scored by two pathologists, who were blinded to the clinicopathological characteristics and outcome and who used previously established and published criteria for biomarker expression levels that had been developed on other breast cancer cohorts.
Tumors are considered positive for ER or PR if immunostaining is observed in more than 1% of tumor nuclei, as described previously. Tumors are considered positive for HER2 if immunostaining is scored as 3+ according to HercepTest criteria, with an amplification ratio for fluorescent in situ hybridization of 2.0 or more being the cut point that can be used to segregate immunohistochemistry equivocal tumors (scored as 2+) (Yaziji, et al., JAMA, 291(16):1972-1977 (2004)). Ki67 can be visually scored for percentage of tumor cell nuclei with positive immunostaining above the background level.
Other methods can also be used to detect the Her2+ subtype. These techniques include ELISA, Western blots, Northern blots, or FACS analysis.
Kits
The present disclosure also describes kits useful for classifying breast cancer intrinsic subtypes and/or providing prognostic information to identify breast cancers that are more responsive to gemcitabine. These kits comprise a set of capture probes and/or primers specific for the intrinsic genes listed in Table 1 and can further include instructions for detecting the genes in Table 1 and classifying breast cancer intrinsic subtypes and/or providing prognostic information to identify breast cancers that are more responsive to gemcitabine. The kits may also contain reagents sufficient to facilitate detection and/or quantitation of Her2, in order to classify cells as Her2+. Preferably, the kit comprises a set of capture probes and/or primers specific for at least 10, at least 15, at least 20, at least 25 of the intrinsic genes or all 50 intrinsic genes listed in Table 1. The kit may further comprise a computer readable medium.
In one embodiment of the present disclosure, the capture probes are immobilized on an array. By “array” is intended a solid support or a substrate with peptide or nucleic acid probes attached to the support or substrate. Arrays typically comprise a plurality of different capture probes that are coupled to a surface of a substrate in different, known locations. The arrays of the disclosure comprise a substrate having a plurality of capture probes that can specifically bind an intrinsic gene expression product. The number of capture probes on the substrate varies with the purpose for which the array is intended. The arrays may be low-density arrays or high-density arrays and may contain 4 or more, 8 or more, 12 or more, 16 or more, 32 or more addresses, but will minimally comprise capture probes for at least 10, at least 15, at least 20, at least 25 of the intrinsic genes or all 50 intrinsic genes listed in Table 1.
Techniques for the synthesis of these arrays using mechanical synthesis methods are described in, e.g., U.S. Pat. No. 5,384,261, incorporated herein by reference in its entirety for all purposes. The array may be fabricated on a surface of virtually any shape or even a multiplicity of surfaces. Arrays may be probes (e.g., nucleic-acid binding probes) on beads, gels, polymeric surfaces, fibers such as fiber optics, glass or any other appropriate substrate, see U.S. Pat. Nos. 5,770,358, 5,789,162, 5,708,153, 6,040,193 and 5,800,992, each of which is hereby incorporated in its entirety for all purposes. Arrays may be packaged in such a manner as to allow for diagnostics or other manipulation on the device. See, for example, U.S. Pat. Nos. 5,856,174 and 5,922,591 herein incorporated by reference.
In another embodiment, the kit comprises a set of oligonucleotide primers sufficient for the detection and/or quantitation of each of the intrinsic genes listed in Table 1. Preferably, the kit comprises a set of oligonucleotide primers sufficient for the detection and/or quantitation of at least 10, at least 15, at least 20, at least 25 of the intrinsic genes or all 50 intrinsic genes listed in Table 1. The oligonucleotide primers may be provided in a lyophilized or reconstituted form, or may be provided as a set of nucleotide sequences. In one embodiment, the primers are provided in a microplate format, where each primer set occupies a well (or multiple wells, as in the case of replicates) in the microplate. The microplate may further comprise primers sufficient for the detection of one or more housekeeping genes as discussed infra. The kit may further comprise reagents and instructions sufficient for the amplification of expression products from the genes listed in Table 1.
In order to facilitate ready access, e.g., for comparison, review, recovery, and/or modification, the molecular signatures/expression profiles are typically recorded in a database. Most typically, the database is a relational database accessible by a computational device, although other formats, e.g., manually accessible indexed files of expression profiles as photographs, analogue or digital imaging readouts, spreadsheets, etc. can be used. Regardless of whether the expression patterns initially recorded are analog or digital in nature, the expression patterns, expression profiles (collective expression patterns), and molecular signatures (correlated expression patterns) are stored digitally and accessed via a database. Typically, the database is compiled and maintained at a central facility, with access being available locally and/or remotely.
In certain embodiments, the kit also includes a substance that is used to find the expression level of Her-2. This substance can be an antibody or a nucleic acid probe. These substances can be used to detect Her-2 using FISH, IHC, ELISA, Western blots, Northern blots, or FACS analysis. Optionally, the kit also includes reagents that allows for the detection of the detecting substance and the quantitation of Her-2 expression in a sample.
The Patient Study Cohort
The current study is based upon a patient cohort enrolled in a randomized trial comparing the efficacy of single agent docetaxel (D) versus gemcitabine plus docetaxel (GD) in 337 women with locally advanced or metastatic disease (3). Patients were randomly assigned to docetaxel (100 mg/m2) day 1, every 21 days or gemcitabine (1000 mg/m2) days 1 and 8 plus docetaxel (75 mg/m2) day 8. Patients were either previously untreated, had prior anthracycline-based (neo)adjuvant chemotherapy or had received a single prior anthracycline-based chemotherapy regimen for metastatic breast cancer. The Danish Breast Cancer Cooperative Group (DBCG) prepared the original protocol as well as the biomarker supplement, and the Danish National Committee on Biomedical Research Ethics has approved the original protocol as well as the add-on (KF 02-045-01 and H-KF-02-045-01) before their activation.
Macro-Dissection and RNA Isolation
Hematoxylin and eosin stained sections from archival formalin-fixed, paraffin-embedded (FFPE) primary breast tumor tissue were reviewed by a biologist (CLTJ) under supervision of a pathologist (TON). Areas containing representative invasive breast carcinoma were outlined on the slide. Depending on the tumor surface area, 1-6 unstained tissue sections of 10-15 μm thickness were mounted on positively charged glass microscope slides and baked overnight at 45° C. The unstained tissue sections were deparaffinized with CitroSolv, rinsed in ethanol and left to dry. The tissue was rehydrated with 3% glycerol, before manual macro-dissection to remove the surrounding normal tissue outside the outlined area.
Total RNA was extracted using the High Pure RNA Paraffin Kit (Roche Applied Science, Indianapolis Ind., cat #03270289001), according to the manufacturer's protocol. RNA yield and purity were assessed using the NanoDrop ND-1000 Spectrophotometer (NanoDrop Technologies, Rockland, Del.). RNA samples used in downstream analysis met prespecified quality criteria of an initial concentration of total RNA≧12.5 ng/1.11, a minimum total yield of 250 ng, and a purity ratio in the range 1.7-2.5.
The PAM50 nCounter System Assay
Gene expression was measured on the NanoString nCounter Analysis System which delivers direct, multiplexed measurements through digital readouts of the relative abundance of hundreds of mRNA transcripts. In brief, the expression of the fifty target genes of Table 1 (PAM50) as well as normalizing “housekeeping” genes (for example MRPL19, PSMC4, SF3A1, PUM1, ACTB, GAPDH, GUSB, RPLPO, and TFRC) was measured in a single hybridization reaction without the use of any enzymatic reactions. An nCounter CodeSet with gene-specific probe-pairs to the PAM50 targets as well as exogenous positive and negative controls was hybridized in solution to 125-500 ng total RNA (nominally 250 ng). After overnight hybridization, the samples were processed using the NanoString nCounter Prep Station and Digital Analyzer according to the instructions and kits provided by NanoString Technologies. Data from each sample were qualified using prospectively defined quality control metrics for the positive and negative controls included in each reaction.
Intrinsic subtype classification of qualified patient samples was based upon the PAM50 gene expression signature. Reporter-code-count files, containing the digital abundance or “counts” of each target mRNA molecule for every sample, were sent to NanoString Technologies for PAM50 subtype calling using a prospectively defined and locked proprietary algorithm. Assignment of subtypes was performed in a blinded fashion, by researchers with no access to information regarding the clinical parameters or outcomes.
Results
The original trial of GD versus D recruited 337 participants; archival tumor tissue was available from 273 (81%) patients (CONSORT diagram,
The assessable 270 patients differed from the 67 non-assessable patients (P<0.05) with regard to prior (neo)adjuvant chemotherapy, adjuvant hormonal therapy, and adjuvant radiotherapy, but not for other assessed parameters (Table 5). These differences are considered reflections of a higher number of locally advanced cases in the excluded cohort. Primary tumor samples from locally advanced patients were in general more often either unavailable or had insufficient tissue for subtype analysis (i.e. needle biopsy only).
Sufficient high quality RNA was obtained from the 270 patients allowing accurate estimation of the PAM50 algorithm. Based on the nearest PAM50 centroid algorithm, intrinsic breast cancer subtypes were assigned using gene expression as follows: 84 samples (31.1%) were luminal A, 97 samples (35.9%) luminal B, 43 (15.9%) basal-like, and 46 (17.1%) HER2-enriched. Patient and baseline characteristics of the 270 cases according to intrinsic subtypes are summarized in Table 6.
Statistical Considerations
The association between PAM50 subtypes and prognostic and demographic variables of the main study was assessed (Nielsen et al., JCO 2011; 29:4748-4754). Associations between PAM50 subtypes and categorical variables (regimen, hormone receptor status, HER2 status, type of metastatic site, stage of disease, and previous chemo-, hormonal-, and radio-therapy) were evaluated by Fisher's exact test, while associations between PAM50 subtypes and ordinal and interval variables (WHO performance status, age at randomization, number of metastatic sites, and disease-free interval) were evaluated by the Kruskal-Wallis test.
Time to progression (TTP) was the primary endpoint for the original trial as well as this biomarker sub-study (Nielsen et al., JCO 2011; 29:4748-4754). Overall survival (OS) and response rate (RR) were secondary endpoints. TTP was measured from random assignment to date of documented progression with censoring at date of last visit or of death. OS was calculated from date of random assignment to date of death with censoring for surviving patients at last visit date. Time-to-event endpoints (TTP and OS) were estimated by the Kaplan-Meier method, and PAM50 subtypes were compared using the log-rank test. Analyses of PAM50 subtypes were done unadjusted and adjusted for preselected covariates in multivariate Cox proportional hazards models. The preselected covariates were those found to be significant in the previous analysis of the main study (Nielsen et al., JCO 2011; 29:4748-4754): regimen, disease type, and stage of disease, or were included due to their molecular association with PAM50 subtypes: hormone receptor status (positive/unknown vs. negative) and HER2 status (amplified vs. normal/deleted/unknown). The adjusted model was further stratified for previous chemotherapy (Nielsen et al., JCO 2011; 29:4748-4754). The assumption of proportional hazards was assessed by Schoenfeld residuals.
Analyses were done to assess whether treatment effects on TTP and OS varied according to PAM50 subtypes or the levels of preselected variables. The multivariate Cox proportional hazards model was extended by one interaction term at a time. The interaction terms were tested using the Wald test and results were given in a Forest plot. RR was evaluated for patients with measurable disease. The overall RR was defined as a complete or partial response according to RECIST criteria, version 1.0. RRs were compared by using Fisher's exact test.
Statistical analyses were conducted using the SAS System (version 9.2). All statistical tests are two sided, and P<0.05 was considered statistically significant. Results of this study are presented according to reporting recommendations for tumor marker prognostic studies (McShane et al., Breast Cancer Res Treat 2006; 100:229-235). The design of the study is prospective-retrospective as described in Simon et al (JNCI commentaries 2009; 101:1446-1452).
Results
Recurrence patterns were significantly different between molecular subtypes. Visceral metastasis was more common in luminal B and HER2-enriched subtypes, and non-visceral metastasis more frequent in luminal A and basal-like subtypes. The luminal B and HER2-enriched showed a roughly similar pattern in terms of preferred sites for systemic relapse, however, luminal cases presented more often with bone metastases compared to both basal-like and HER2-enriched subtypes. Less frequently the luminal A subtype metastasized to lung, whereas metastases in the liver were less observed in the basal-like patients, however not statistically significant.
Median disease-free (MDF) interval (time interval from diagnosis of primary cancer to recurrence) differed significantly between subtypes (P<0.001), with the luminal A and B subtypes demonstrating the longest MDF interval (45 and 37 months respectively), compared to the HER2-enriched and basal-like groups who had significantly shorter MDF intervals (20 and 15 months respectively).
Intrinsic Subtypes and Univariate Analysis
In Kaplan-Meier analyses, the intrinsic biological subtypes were significantly associated with TTP (P=0.0006) and OS (P=0.0083) (
The Cox univariate proportional hazards model for TTP and OS (Table 7) confirmed this result (TTP, P=0.0008; OS, P=0.009).
Furthermore, a significant difference in outcome was evident when comparing the luminal A subtype versus non-luminal A subtypes (TTP, HR, 0.56; 95% CI, 0.40-0.79; P=0.001; OS, HR, 0.71; 95% CI, 0.54-0.94; P=0.02), and the basal-like versus the non-basal-like subtypes (TTP, HR, 1.80; 95% CI, 1.23-2.64; P=0.003; OS, HR, 1.65; 95% CI, 1.18-2.31; P=0.004).
Multivariate Analysis
To test the independent value of PAM50 subtyping against standard clinical and pathologic factors multivariable Cox models were constructed. The intrinsic biological subtype remained a significant independent prognostic factor for both TTP and OS (Table 8).
The treatment effect was similar to the effect observed in the original study (HR=0.68 for TTP, HR=0.94 for OS) (3), with an HR favoring GD for TTP (adjusted HR 0.57, P=0.0007) but not for OS (adjusted HR 0.81, P=0.13).
Interaction Tests for Treatment Effect on TTP and OS
In multivariate Cox regression analyses, heterogeneity of treatment according to HER2 status and PAM50 intrinsic subtype was further examined. TTP seemed equally improved in PAM50 intrinsic subtypes (
Kaplan-Meier estimates revealed a gain in median overall survival of 10 months for the basal-like patients in the doublet arm compared to the monotherapy arm, hence reaching the same level of median overall survival as the non-basal-like patients (
Intrinsic Subtypes and Response Rate
Overall RR (complete response plus partial response) among patients with measurable disease (n=168) did not differ significantly among the four subtypes (luminal A 37.5%, luminal B 42.0%, basal-like 24.1%, HER2-enriched 43.3%; P=0.36; Table 9), nor between the basal-like versus non-basal-like (P=0.10) nor luminal A versus non-luminal A (P=1.00) pre-specified subtype groupings.
Discussion
Disease segmentation into breast cancer intrinsic subtypes can offer insight into personalized treatment. Thus, to test the hypothesis that molecular subtypes differ in their response to therapeutic agents, the relationship between molecular subtypes classified by the PAM50 assay and the effect of gemcitabine was evaluated, in patients with available tumor blocks enrolled in a randomized trial of docetaxel alone versus gemcitabine and docetaxel doublet for advanced breast cancer. Although the clinical trial, when analyzed as a whole, failed to demonstrate any clinically meaningful difference between the docetaxel versus gemcitabine plus docetaxel arms, the present invention demonstrates that when assessed by subtype, wider differences in TTP and OS between the two treatment arms are found. By PAM50 intrinsic subtype classification, in patients with a basal-like subtype, a 73% relative reduction in mortality from the addition of gemcitabine to docetaxel compared to docetaxel alone was demonstrated. In contrast, patients with non-basal-like subtypes had no significant incremental survival benefit from gemcitabine plus docetaxel compared with docetaxel monotherapy. The test for interaction between basal-like subtype and addition of gemcitabine was highly significant for OS (Pinteraction=0.0004). A similar trend was observed for TTP with a relative 63% reduction for patients with basal-like and a 37% reduction for patients with other subtypes, although this difference was not statistically significant (Pinteraction=0.19). No support was found for a more general benefit from adding gemcitabine to docetaxel in patients with highly proliferative subtypes (non-luminal A). An unexpected finding among patients with HER2 amplified tumors was a higher risk of TTP events (Pinteraction=0.0019) and mortalit Y (Pinteraction=0.019) in the doublet arm compared to single agent docetaxel. A similar trend was noticed for patients with a HER2-enriched subtype by PAM50.
This study furthermore ascertains intrinsic molecular subtypes among primary tumors from patients who went on to have advanced breast cancer. All subtypes were represented and as expected luminal subtypes were the most frequent (67%), though in contrast to most published literature the luminal B subtype was more common than luminal A (33-36). Luminal B subtype is associated with an higher risk of recurrence compared to luminal A and this may explain a higher frequency of luminal B in patients with advanced breast cancer compared to other published series of patients with early breast cancer. Nevertheless a significant proportion of patients with recurrent disease had a luminal A subtype in their primary tumor.
In agreement with previous studies PAM50 intrinsic subtypes were associated with significant differences in the timing of distant recurrences. Recent studies described site-specific recurrence patterns according to subtypes supporting previous publications suggesting distinct patterns of metastatic spread and survival. This study supports a distinct metastatic pattern by PAM50 intrinsic subtypes as well as supports that subtype in addition influences survival after relapse.
In summary, this retrospective subtype analysis applied to a prospective clinical trial demonstrates that subtype classification reveals predictive capacity not evident in an unselected patient cohort. A more substantial reduction in mortality was demonstrated by gemcitabine and docetaxel compared to docetaxel in patients with basal-like tumors. However, a similar significant reduction in TTP events was not evident.
bTime interval from diagnosis of primary cancer to recurrence.
cUnknown values excluded from analysis.
bChiSq = 11.51, df = 3, P = 0.009.
bTotal responses, Fishers exact test P = .36.
cTotal responses, Luminal A v non-Luminal A, Fishers exact test P = 1.00.
dTotal responses, Basal-like v non-Basal-like, Fishers exact test P = .10.
This application claims priority to, and the benefit of, U.S. Provisional Application No. 61/666,355, filed Jun. 29, 2012 and U.S. Provisional Application No. 61/733,545, filed Dec. 5, 2012. The contents of each of these applications are incorporated herein by reference in their entireties.
| Number | Date | Country | |
|---|---|---|---|
| 61666355 | Jun 2012 | US | |
| 61733545 | Dec 2012 | US |
