Patent Application
20140154681
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.
Current approaches to treating early breast cancer, including adjuvant therapy, have indeed improved survival and reduced recurrence. However, the risk of recurrence may be underestimated in some patients, but overestimated in others.
While the risk of recurrence does diminish somewhat over time, ongoing risk has been observed in many studies, some of them involving tens of thousands of patients with breast cancer. In fact, some of the patients who experienced recurrence after five years in these studies had previously been considered “low risk”—for example, their cancer had not spread to the lymph nodes at the time of their initial diagnosis, or their estrogen receptor status was positive. In one of these studies, a substantial number of recurrences occurred more than five years post-treatment. Thus, there is a need in the art to determine risk of recurrence and determine therapies which reduce that risk and improve overall survival.
The present invention provides methods of predicting outcome in a subject having breast cancer including: providing a tumor sample from the subject; determining the gene expression profile of the tumor sample wherein the gene expression profile is based on the expression of a subset of the genes listed in Table 1A or 1B; correlating the gene expression profile of the tumor sample to the average gene expression profile (centroid) of a normal non-tumor subtype; and determining whether the subject has a more favorable outcome or a less favorable outcome based on a correlation value. The subset of genes is at least 4 of the genes listed in Table 1A or 1B.
The expression of the members of the gene list in Tables 1A or 1B, or a subset thereof, can determined using the nanoreporter code system (nCounter® Analysis system) or other gene expression technologies well known in the art.
The average gene expression profile (centroid) of a normal non-tumor subtype can be constructed from more than one non-cancerous breast tissue sample. Preferably, the non-cancerous breast tissue sample is a breast reduction mammoplasty sample.
A correlation value closer to +1 indicates a lower risk of recurrence. A correlation value closer to −1 indicates a higher risk of recurrence.
The sample can be a sampling of cells or tissues. The tissue can be obtained from a biopsy. The sample can be a sampling of bodily fluids. The bodily fluid can be blood, lymph, urine, saliva or nipple aspirate.
While the disclosure has been described in conjunction with the detailed description thereof, the foregoing description is intended to illustrate and not limit the scope of the disclosure, which is defined by the scope of the appended claims. Other aspects, advantages, and modifications are within the scope of the following claims.
The patent and scientific literature referred to herein establishes the knowledge that is available to those with skill in the art. All United States patents and published or unpublished United States patent applications cited herein are incorporated by reference. All published foreign patents and patent applications cited herein are hereby incorporated by reference. Genbank and NCBI submissions indicated by accession number cited herein are hereby incorporated by reference. All other published references, documents, manuscripts and scientific literature cited herein are hereby incorporated by reference.
While this disclosure has been particularly shown and described with references to preferred embodiments thereof, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the scope of the disclosure encompassed by the appended claims.
The present invention provides methods of predicting outcome in a subject having breast cancer including: providing a tumor sample from the subject; determining the gene expression profile of the tumor sample wherein the gene expression profile is based on the expression of a subset of the genes listed in Tables 1A or 1B; correlating the gene expression profile of the tumor sample to the average gene expression profile (centroid) of a normal non-tumor subtype; and determining whether the subject has a more favorable outcome or a less favorable outcome based on a correlation value. The correlation to the normal centroid can also be calculated in combination with other clinicopathological factors to provide a highly effective estimate of risk.
The method can further include determining at least one of, a combination of, or each of, the following clinical variables: tumor stage, tumor grade, tumor ploidy, nodal status, estrogen receptor expression, progesterone receptor expression, and HER2/ERBB2 expression. The breast cancer can be hormone receptor positive. The breast cancer can be ER positive or HER2 negative. The breast cancer can be early stage breast cancer. The breast cancer can be invasive or non-invasive (i.e., ductal carcinoma in situ).
The method provides an estimate of risk for specific outcomes. The outcome can be breast cancer specific survival, cancer recurrence, event-free survival or response to therapy.
The tumor sample is a breast tumor sample, e.g., obtained from a biopsy. For example, the tumor sample is a formalin fixed-paraffin embedded (FFPE) sample.
The subject has or is suspected of having breast cancer. For example, the subject is diagnosed with or has early-stage hormone receptor positive breast cancer. Hormone receptors include, for example, estrogen receptor (ER), human epidermal growth factor receptor 2 (also known as Her2, Neu, or ErbB-2) and progesterone receptor. In some cases, the subject may be Her2 negative. In some cases, the subject may be ER positive. In some cases, the subject may be ER positive and Her2 negative.
The genes listed in Table 1A and 1B are genes 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 (Perou et al. (2000) Nature 406:747-752). In some embodiments, the gene expression profile is determined from a subset of the genes listed in Table 1A or 1B.
The subset of the genes is at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 11, at least 12, at least 13, at least 14, at least 15, at least 16, at least 17, at least 18, at least 19, at least 20, at least 21, at least 22, at least 23, at least 24, at least 25, at least 26, at least 27, at least 28, at least 29, at least 30, at least 31, at least 32, at least 33, at least 34, at least 35, at least 36, at least 37, at least 38, at least 39, at least 40, at least 41, at least 42, at least 43, at least 44, at least 45, at least 46, at least 47, at least 48, at least 49, at least 50, at least 51, at least 52, at least 53, at least 54, at least 55, at least 56, at least 57, at least 58, at least 59, at least 60, at least 61, at least 62, at least 63, at least 64, at least 65, at least 66, at least 67, at least 68, at least 69, at least 70, at least 71, at least 72, at least 73, at least 74, at least 75, at least 76, at least 77, at least 78, at least 79, at least 80, at least 81, at least 82, at least 83, at least 84, at least 85, at least 86, at least 87, at least 88, at least 89, at least 90, at least 91, at least 92, at least 93, at least 94, at least 95, at least 96, at least 97, at least 98, at least 99, at least 100, at least 101, at least 102, at least 103, at least 104, at least 105, at least 106, at least 107, at least 108, at least 109, at least 110, at least 111, at least 112, at least 113, at least 114, at least 115, at least 116, at least 117, at least 118, at least 119, at least 120, at least 121, at least 122, at least 123, at least 124, at least 125, at least 126, at least 127, at least 128, at least 129, at least 130, at least 131, at least 132, at least 133, at least 134, at least 135, at least 136, at least 137, at least 138, at least 139, at least 140, at least 141, at least 142, at least 143, at least 144, at least 145, at least 146, at least 147, at least 148, at least 150, at least 151, at least 152, at least 153, at least 154, at least 155, at least 156, at least 157, at least 158, at least 159, at least 160, at least 161, at least 162, at least 163, at least 164, at least 165, at least 166, at least 167, at least 168, at least 169, at least 170, at least 171, at least 172, at least 173, at least 174, at least 175, at least 176, at least 177, at least 178, at least 179, at least 180, at least 181, at least 182, at least 183, at least 184, at least 185, at least 186, at least 187, at least 188, at least 189, at least 190, at least 191, at least 192, at least 193, at least 194, at least 195, at least 196, at least 197, at least 198, at least 199, at least 200, at least 201, at least 202, at least 203, at least 204, at least 205, at least 206, at least 207, at least 208, at least 209, at least 210, at least 211, or at least 212 of the genes listed in Table 1A.
Preferably, the subset of genes detected from Table 1A are the genes listed in Table 1B.
The subset of the genes is at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 11, at least 12, at least 13, at least 14, at least 15, at least 16, at least 17, at least 18, at least 19, at least 20, at least 21, at least 22, at least 23, at least 24, at least 25, at least 26, at least 27, at least 28, at least 29, at least 30, at least 31, at least 32, at least 33, at least 34, at least 35, at least 36, at least 37, at least 38, at least 39, at least 40, at least 41, at least 42, at least 43, at least 44, at least 45, at least 46, at least 47, at least 48, at least 49 or all 50 of the genes listed in Table 1B.
The average gene expression profile (also referred to herein as a centroid) of a normal non-tumor subtype were pre-defined from a training set of normal non-tumor breast samples using hierarchical clustering analysis of gene expression data. A normal non-tumor breast sample is a sample obtained from a subject that has not been diagnosed with breast cancer or previously treated for breast cancer. For example, the non-tumor breast samples may be breast reduction mammoplasty samples. A heatmap of the prototypical gene expression profile of the normal subtype (as well as the subtypes Basal-like, Her2-enriched, Luminal A and Luminal B) is shown in
One embodiment of the method of the present invention is summarized in
The normal tissue risk score is on a −1 to +1 scale. A score value closer to −1 indicates a less favorable outcome while a score value closer to +1 indicates a more favorable outcome. For example, a score value closer to −1 indicates a higher risk of recurrence. A score value closer to +1 indicates a lower risk of recurrence. In some instances, the scores can also be adjusted by various algorithms to a risk score on a 0-100 scale, where 0 indicates a lower risk of recurrence and more favorable outcome and a score of 100 indicates a higher risk of recurrence and less favorable outcome. The scores can be used to further stratify subjects into sub-categories, such as subjects with high, intermediate, or low risk of recurrence groups.
Classifying Tumor Subtypes
The present invention also provides a method that further includes determining or classifying the cancer or tumor subtype of the tumor sample, wherein the cancer or tumor subtype is selected from the group consisting of at least Basal-like, Luminal A, Luminal B and HER2-enriched.
The genes listed in Tables 1A and 1B, as described in Perou et al. (2000) Nature 406:747-752, are statistically selected to have low variation in expression between biological sample replicates from the same individual and high variation in expression across samples from different individuals. Thus, the genes listed in Table 1A or Table 1B are used as classifier genes for breast cancer classification. Although clinical information was not used to derive the breast cancer subtypes, this classification has proved to have prognostic significance. Gene screening as described herein can be used to classify breast cancers into various subtypes. The major subtypes of breast cancer are referred to as Luminal A (LumA), Luminal B (LumB), HER2-enriched (Her-2-E), Basal-like, and Normal-like.
The PAM50 gene expression assay (Parker et al. J Clin Oncol., 27(8):1160-7 (2009) and International Publication No. WO 2009/158143 and U.S. Patent Publication No. 2011/0145176, incorporated herein by reference, in their entireties) is able to identify a cancer subtype from standard formalin fixed paraffin embedded tumor tissue. The methods utilize a supervised algorithm to classify subject samples according to breast cancer subtype. This algorithm, referred to herein as the PAM50 classification model, is based on the gene expression profile of a defined subset of genes that has been identified herein as superior for classifying breast cancer subtypes, exemplified in Table 1B. The prototypical gene expression profiles (i.e. centroid) of the four subtypes were pre-defined from a training set of FFPE breast tumor samples using hierarchical clustering analysis of gene expression data. A heatmap of the prototypical gene expression profiles of these four subtypes is shown in
After performing the Breast Cancer Subtyping test with a test breast cancer tumor sample, a computational algorithm based on a Pearson's correlation compares the normalized and scaled gene expression profile of the gene set in Table 1B of the test sample to the prototypical expression signatures of the four breast cancer subtypes. The tumor sample is assigned the subtype with the largest positive correlation to the sample. Kaplan Meier survival curves generated from a training set of untreated breast cancer patients demonstrate that the cancer subtypes are a prognostic indicator of recurrence free survival (RFS) in this test population, which includes both estrogen receptor positive/negative and HER2 positive/negative patients.
Description of Breast Cancer 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.
Determining Risk of Recurrence (ROR-PT) Score
The present invention also provides a method that further includes determining a proliferation score based on the expression of a subset of proliferation genes listed in Table 1A or 1B. In one embodiment, the ROR or ROR-PT score is also determined.
The training set of FFPE breast tumor samples from the Breast Cancer Subtyping test, 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 cancer subtype, a proliferation score (mean gene expression of a subset of the genes in Table 1A or 1B), and tumor size, Table 3.
The test variables in Table 3 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 21-gene subset of the genes of Table 1A or 1B selected from ANLN, BIRC5, CCNB1, CCNE1, CDC20, CDC6, CDCA1, CENPF, CEP55, EXO1, KIF2C, KNTC2, MELK, MKI67, MYBL2, ORC6L, PTTG1, RRM2, TYMS, UBE2C and/or UBE2T. The methods of the present invention can include determining the expression of at least one of, a combination of, or each of, a 19-gene subset of the genes of Table 1A or 1B selected from ANLN, CCNB1, CCNE1, CDC20, CDC6, CDCA1, CENPF, CEP55, EXO1, KIF2C, KNTC2, MELK, MKI67, ORC6L, PTTG1, RRM2, TYMS, UBE2C and/or UBE2T. 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 genes of Table 1A or 1B selected from ANLN, CCNE1, CDC20, CDC6, CDCA1, CENPF, CEP55, EXO1, KIF2C, KNTC2, MELK, MKI67, ORC6L, PTTG1, RRM2, TYMS, UBE2C and/or UBE2T. The methods of the present invention can include determining the expression of at least one of, a combination of, or each of, an 11-gene subset of the genes of Table 1A or 1B selected from BIRC5, CCNB1, CDC20, CDCA1/NUF2, CEP55, KNTC2/NDC80, MKI67, PTTG1, RRM2, TYMS and/or UBE2C. The methods of the present invention can include determining the expression of at least one of, a combination of, or each of, a 10-gene subset of the genes of Table 1A or 1B selected from ANLN, CCNB1, CDC20, CENPF, CEP55, KIF2C, MKI67, MYBL2, RRM2 and/or UBE2C. 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).
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 genes listed in Table 1B. In addition to providing a subtype assignment, the PAM50/NANO46 bioinformatics model provides a measurement of the similarity of a test sample to all four subtypes which is translated into a Risk of Recurrence (ROR) score that can be used in any patient population regardless of disease status and treatment options. The cancer 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 recurrence (ROR) model is used to predict outcome. Using these risk models, subjects can be stratified into low, medium, and high risk of recurrence 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/NANO46-defined cancer 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., 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/NANO46 classification model described herein can be trained for risk of recurrence 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 cancer 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).
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 cancer subtype distances alone using the following equation:
ROR-S=0.05*Basal+0.12*Her2+−0.34*LumA+0.0.23*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 cancer 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 4 supra.
Clinical Variables
The normal tissue risk score 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, normal tissue risk score is provided for a subject diagnosed with or suspected of having breast cancer. For example, the normal tissue risk score can be used 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).
Another clinical variable that may be used to calculate the normal tissue risk score is a proliferation score. The gene set of Table 1A and 1B contain many genes that are known markers for proliferation. For example, the subset of proliferation genes can be weighted together to calculated a proliferation score. A proliferation score can be calculated from at least one proliferation gene from Table 1A and 1B. For example, the proliferation genes can include, but are not limited to ANLN, BIRC5, CCNB1, CCNE1, CDC20, CDC6, CDCA1, CENPF, CEP55, EXO1, KIF2C, KNTC2, MELK, MKI67, MYBL2, ORC6L, PTTG1, RRM2, TYMS, UBE2C and/or UBE2T. Additionally, non-proliferation genes can be normalized, scaled, and weighted individually.
Clinical variable scores, such as the proliferation score, can be used in a regression model, i.e., a Cox proportional hazard model, to calculate the relative risk or hazard ratio of each variable. Weighting coefficients can then be determined to calculate a new normal tissue risk score, which incorporates the information from each clinical variable and thereby increases the prognostic value of the risk score. The new risk score is compared to an optimized cutpoint risk score which can be used to stratify the sample into a group, i.e. a higher risk group from a lower risk group.
Sample Source
In one embodiment of the present disclosure, the normal subtype or similarity to the normal subtype is assessed through the evaluation of expression patterns, or profiles, of the genes listed in Table 1 in one or more subject samples. 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. 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. A normal non-tumor breast sample is a sample obtained from a subject that has not been diagnosed with breast cancer. Preferably, a normal non-tumor breast sample is a breast reduction mammoplasty sample from a healthy subject, not diagnosed or previously treated for breast cancer.
In particular embodiments, the methods for predicting outcome 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 a 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 prognosticating or monitoring breast cancer in subjects. In some embodiments, the present invention further provides methods for classifying breast cancer in subjects. In this embodiment, data obtained from analysis of 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 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 gene expression data is used to construct a statistical model that predicts correctly the “subtype” of each sample, such as the normal subtype. 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 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 normal tissue risk score test described herein is based on the gene expression profile for a plurality of subject samples using the genes listed in Tables 1A or 1B. The PAM50/NANO46 classification model described herein is based on the gene expression profile for a plurality of subject samples using the genes listed in Tables 1A or 1B. 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 gene set according to the methods disclosed in International Patent Publication WO 2007/061876 and U.S. Patent Publication No. 2009/0299640, each of 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 gene set, or a subset of the genes, described in Tables 1A and 1B.
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 Gene Expression
Any methods available in the art for detecting expression of the genes listed in Tables 1A and 1B are encompassed herein. By “detecting expression” is intended determining the quantity or presence of an RNA transcript or its expression product of an gene. Methods for detecting expression of the 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 genes listed in Tables 1A and 1B. 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 a 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 gene of the present disclosure, or any derivative DNA or RNA. Hybridization of an mRNA with the probe indicates that the 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 genes of the present disclosure.
An alternative method for determining the level of 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, gene expression is 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 genes listed ins Table 1A or 1B. 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 gene expression. The basis of the nCounter® Analysis system is the unique code assigned to each nucleic acid target to be assayed (International Patent 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 nucleic acid probes is designed for each DNA or RNA target of Table 1A or 1B, a 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. Briefly, sequence-specific DNA oligonucleotide probes are attached to code-specific reporter molecules. Capture probes are made by ligating a second sequence-specific DNA oligonucleotide for each target to a universal oligonucleotide containing biotin, or another suitable moiety. Reporter and capture probes are all pooled into a single hybridization mixture, the “probe library”.
The relative abundance of each target is measured in a single multiplexed hybridization reaction. The sample is combined with the probe library, and hybridization occurs in solution. After hybridization, the tripartite hybridized complexes 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, Inc.).
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, Inc.). 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 Nos. WO 07/076,129 and WO 07/076,132 and U.S. Patent Publication Nos. 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 U.S. Pat. No. 8,519,115, 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.
“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 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 scatter plot 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 the normal tissue risk score is calculated as 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 Outcome
Provided herein are methods for predicting breast cancer outcome within the context of the normal 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.
Detection of Subtypes
Immunohistochemistry for estrogen (ER), progesterone (PgR), HER2, and Ki67 was performed concurrently on serial sections with the standard streptavidin-biotin 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 were considered positive for ER or PR if immunostaining was observed in more than 1% of tumor nuclei, as described previously. Tumors were considered positive for HER2 if immunostaining was 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 was used to segregate immunohistochemistry equivocal tumors (scored as 2+) (Yaziji, et al., JAMA, 291(16):1972-1977 (2004)). Ki67 was visually scored for percentage of tumor cell nuclei with positive immunostaining above the background level by two pathologists.
Other methods can also be used to detect subtypes. These techniques include ELISA, Western blots, Northern blots, or FACS analysis.
Kits
The present disclosure also describes kits useful for providing prognostic information to identify subjects that have an increased or decreased risk of recurrence. These kits comprise a set of reporter probes, capture probes and/or primers specific for the genes listed in Tables 1A or 1B, or a subset thereof. 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 a 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 the genes, or a subset thereof, listed in Tables 1A or 1B.
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 genes listed in Tables 1A or 1B, or a subset thereof. 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 Tables 1A or 1B, or a subset thereof.
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.
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) 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
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.
FFPE Tissue Review/Procurement and RNA Extraction: The Normal Tissue Risk Test will use RNA extracted from Formalin-fixed, Paraffin-embedded (FFPE) tissue that has been diagnosed as invasive carcinoma of the breast. A Pathologist reviews an H & E stained slide to identify the tissue area containing sufficient tumor tissue content for the test. Unstained slide mounted tissue sections are processed by macro-dissecting the identified tumor area on each slide to remove any adjacent normal tissue. RNA is then isolated from the tumor tissue, and DNA is removed from the sample.
Assay Setup and Initiation of Hybridization: For each batch of up to 10 RNA samples isolated from a breast tumor, the user will set up a run using the nCounter Analysis x5 system software, which tracks sample processing, reagent lots, and results for each sample. To initiate the assay, the user will pipette the specified amount of RNA into separate tubes within a 12 reaction strip tube and add the CodeSet and hybridization buffer. A reference sample is pipetted into the remaining two tubes with CodeSet and hybridization buffer. The CodeSet consists of probes for each gene that is targeted, additional probes for endogenous “housekeeping” normalization genes and positive and negative controls that are spiked into the assay. The reference sample consists of in vitro transcribed RNA for the targeted genes and housekeeping genes. Once the hybridization reagents are added to the respective tubes, the user transfers the strip tube into a heated-lid heatblock for a specified period of time at a set temperature.
Purification and Binding on the Prep Station: Upon completing hybridization, the user will transfer the strip tube containing the set of 10 assays and 2 reference samples onto the nCounter Prep Station along with the required prepackaged reagents and disposables. An automated purification process then removes excess capture and reporter probe through two successive hybridization-driven magnetic bead capture steps. The nCounter Prep Station then transfers the purified target/probe complexes into an nCounter cartridge for capture to a glass slide. Following completion of the run, the user removes the cartridge from the Prep Station and seals it with an adhesive film.
Imaging and Analysis on the Digital Analyzer: The cartridge is then sealed and inserted into the nCounter Digital Analyzer which counts the number of probes captured on the slide for each gene, which corresponds to the amount of target in solution. Automated software will then check thresholds for the housekeeping genes, reference sample, and positive and negative controls to qualify each assay and ensure that the procedure was performed correctly. The signals of each sample are next normalized using the housekeeping genes to control for input sample quality. The signals are then normalized to the reference sample within each run to control for run-to-run variations. The resulting normalized data is entered in the Normal Tissue Risk algorithm to determine a normal tissue risk score.
Gene expression profiles from RNA from formalin fixed-paraffin embedded (FFPE) breast tumors from women with early-stage, hormone receptor positive breast cancer were compared (using a Pearson's correlation) to the Normal non-tumor tissue centroid generated from our Algorithm training efforts. The normal tissue centroid was trained on RNA isolated from FFPE breast reduction mammoplasty samples. Very high concordance (R-squared>0.95) was observed when these correlation values (on a −1 to 1 scale) are compared to the ROR scores (on a 0 to 100 scale) that were generated from the same ER+ and Her2− patient samples in two separate patient cohorts (BC noAST and BC-TAM) (
This application claims priority to, and the benefit of, U.S. Provisional Application No. 61/725,079, filed Nov. 12, 2012, the contents of which are incorporated herein by reference in its entirety.
| Number | Date | Country | |
|---|---|---|---|
| 61725079 | Nov 2012 | US |
