Patent Application
20130345161
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.
Human breast cancers are classifiable into five molecular distinct intrinsic subtypes, Her2-enriched, Basal-like, Luminal A, Luminal B and normal-like (Perou et al. Nature, 406(6797):747-52 (2000); Sorlie et al. PNAS, 98(19):10869-74 (2001)). Although differences in prognosis and molecular biology have been established, to date, there exists less evidence demonstrating a variation in chemosensitivity among the intrinsic subtypes.
Taxane therapy has proven to be effective against many types of tumors. However, side effects are associated with taxane therapy, including nausea and vomiting, loss of appetite, change in taste, thinned or brittle hair, pain in the joints of the arms or legs lasting two to three days, changes in the color of the nails, and tingling in the hands or toes. More serious side effects such include bruising or bleeding, pain/redness/swelling at the injection site, change in normal bowel habits for more than two days, fever, chills, cough, sore throat, difficulty swallowing, dizziness, shortness of breath, severe exhaustion, skin rash, facial flushing, female infertility by ovarian damage and chest pain. Based on these side-effects of taxane based therapy, there is a need in the art to determine types of cancer that respond best to taxane based therapy and which types of cancer would be better to treat with non-taxane based therapy.
The present invention provides methods of treating breast cancer in a subject in need thereof comprising: providing a sample from the subject; determining the expression of at least one gene from Table 1 in the sample; determining a proliferation signature based on the expression of said at least one gene in the sample; and administering a breast cancer treatment to the subject, wherein if the sample is classified as having a low proliferation signature, the subject is administered a breast cancer treatment comprising a taxane or taxane derivative and wherein if the sample is classified as not having a low proliferation signature, the subject is administered a breast cancer treatment not comprising a taxane or taxane derivative, thereby treating breast cancer in the subject.
The present invention also provides methods of screening for the likelihood of the effectiveness of a breast cancer treatment comprising a taxane or a taxane derivative in a subject in need thereof comprising: providing a sample from the subject; determining the expression of at least one gene from Table 1 in the sample; and determining a proliferation signature based on the expression of said at least one gene in the sample; wherein if the sample is classified as having a low proliferation signature, the breast cancer treatment comprising the taxane or taxane derivative is more likely to be effective in the subject.
The present invention also provides methods of treating breast cancer in a subject in need thereof comprising: providing a sample from the subject; determining the expression of at least one gene of Table 1 in said sample; determining the average gene expression of said at least one gene in said sample to obtain a proliferation signature; comparing the proliferation signature of the sample to a reference sample set; and administering a breast cancer treatment to the subject, wherein if the proliferation signature of the sample is within the lowest sub-range of the reference sample set, the proliferation signature of the sample is a low proliferation signature, and wherein if the sample is classified as having a low proliferation signature, the subject is subjected to a breast cancer treatment comprising a taxane or taxane derivative, administered weekly, and wherein if the sample is classified as not having a low proliferation signature, the subject is subjected to a breast cancer treatment not comprising a taxane or taxane derivative, administered weekly, thereby treating breast cancer in the subject.
The present invention also provides methods of screening for the likelihood of the effectiveness of a breast cancer treatment comprising a taxane or a taxane derivative, administered weekly, in a subject in need thereof comprising: providing a sample from the subject; determining the expression of at least one gene of Table 1 in said sample; determining the average gene expression of said at least one gene in said sample to obtain a proliferation signature; and comparing the proliferation signature of the sample to a reference sample set; wherein if the proliferation signature of the sample is within the lowest sub-range of the reference sample set, the proliferation signature of the sample is a low proliferation signature, and wherein if the sample is classified as having a low proliferation signature, the breast cancer treatment comprising the taxane or taxane derivative, administered weekly, is more likely to be effective in the subject.
The methods of the present invention can include determining the expression of at least one of, a combination of, or each of, the genes recited in Table 1. Preferably, the at least one gene is a proliferation gene. More preferably, the at least one gene is at least 1, at least 2, at least 3, 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, or at least 21 genes listed in Table 1.
In some embodiments, the methods of the present invention can include determining the expression of at least one of, a combination of, or each of, the PAM50 intrinsic genes 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. In some embodiments, the methods of the present invention can include determining the expression of at least one of, a combination of, or each of, the PAM50 intrinsic genes selected from ANLN, CCNB1, CCNE1, CDC20, CDC6, CDCA1, CENPF, CEP55, EXO1, KIF2C, KNTC2, MELK, MKI67, ORC6L, PTTG1, RRM2, TYMS, UBE2C and/or UBE2T. In some embodiments, the methods of the present invention can include determining the expression of at least one of, a combination of, or each of, the PAM50 intrinsic genes selected from BIRC5, CCNB1, CDC20, CDCA1/NUF2, CEP55, KNTC2/NDC80, MKI67, PTTG1, RRM2, TYMS and/or UBE2C. In some embodiments, the methods of the present invention can include determining the expression of at least one of, a combination of, or each of, the PAM50 intrinsic genes selected from ANLN, CCNB1, CDC20, CENPF, CEP55, KIF2C, MKI67, MYBL2, RRM2 and/or UBE2C. The expression of the at least one gene from Table 1 can be determined using the nanoreporter code system (nCounter® Analysis system).
The taxane or taxane derivative can be paclitaxel (Taxol®) or docetaxel (Taxotere®). Preferably, the taxane or taxane derivative is paclitaxel. The taxane or taxane derivative can be administered daily (once every 24 hours), weekly (once every 5-7 days), every two weeks (every 10-14 days) or monthly (once every 30 days). Preferably, the taxane or taxane derivative is administered weekly.
The breast cancer treatment comprising a taxane or taxane derivative can further comprises one or more members, or each of the members of the group consisting of anthracycline, cyclophosphamide and 5-fluorouracil. The anthracycline can be doxorubicin or epirubicin. The taxane or taxane derivative can be administered before, after, or simultaneously with, the administration of the anthracycline, cyclophosphamide and/or 5-fluorouracil. Preferably, the taxane or taxane derivative is administered before or after the administration of the anthracycline, cyclophosphamide and/or 5-fluorouracil.
The methods of the present invention can include determining at least one of, a combination of, or each of, the following: tumor size, tumor grade, nodal status, intrinsic subtype, estrogen receptor expression, progesterone receptor expression, HER2/ERBB2 expression and/or ROR score.
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 disclosure presents a method of determining whether a breast cancer treatment comprising a taxane or taxane derivative is optimal for administration to a patient suffering from breast cancer. Determining whether a breast cancer patient should receive a treatment including a taxane or taxane derivative includes determining the proliferation signature of the breast cancer using an intrinsic gene expression set. The disclosure also provides a method of treating breast cancer by determining whether a breast cancer patient should receive a treatment including a taxane or taxane derivative and then administering the optimal breast cancer treatment to the patient based on that determination.
Intrinsic genes, as described in Perou et al. (2000) Nature 406:747-752, are statistically selected to have low variation in expression between biological sample replicates from the same individual and high variation in expression across samples from different individuals. 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.
The PAM50 gene expression assay (Parker et al. J Clin Oncol., 27(8):1160-7 (2009) and International Publication No. WO 2009/158143, both incorporated herein, by reference, in their entireties) is able to identify intrinsic subtype from standard formalin fixed paraffin embedded tumor tissue. 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.
Table 2 provides select sequences for the genes of Table 1.
The subset of the genes that maybe assayed for expression include at least 1, at least 2, at least 3, 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 2.
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 proliferation gene. Preferably, the at least one proliferation gene is least 1, at least 2, at least 3, 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, or at least 21 genes listed in Table 1 or Table 2.
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 PAM50 intrinsic genes 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 PAM50 intrinsic genes 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 PAM50 intrinsic genes 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, a 11-gene subset of the PAM50 intrinsic genes 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 PAM50 intrinsic genes selected from ANLN, CCNB1, CDC20, CENPF, CEP55, KIF2C, MKI67, MYBL2, RRM2 and/or UBE2C.
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 and in Bastien et al. BMC Medical Genomics, 5:44 (2012) (published online) and supplemental online material (these documents are incorporated herein, by reference, in their entireties).
The present invention provides methods for determining a proliferation signature (also referred to as proliferation score or p-score, these terms are utilized interchangeably herein) of a breast cancer sample from a subject. The expression of one or more of the genes listed in Table 1 may be determined using methods known in the art and described herein, and normalized to control housekeeping genes (i.e., MRPL19, PSMC4, SF3A1, PUM1, ACTB, GAPD, GUSB, RPLPO, and TFRC). Preferably, the one or more genes from Table 1 are a subset of genes known for proliferation (e.g., cell cycle regulated genes see Bastien et al., BMC Medical Genomics 5:44-, 2012), as described herein. Optionally, the gene expression can be also normalized to a control sample by determining the ratio of each gene between the sample and a control sample. While any control sample known in the art may be utilized, one exemplary control sample comprises in vitro transcribed RNA sequences of each gene at a known concentration. The mean of all the log ratios or normalized values of each proliferation gene can be calculated to determine the average proliferation gene expression of the sample. The proliferation signature can be determined by scaling the calculated average gene expression to a range of, for example 1-10 (e.g.,
The reference sample set is a population of breast cancer samples wherein the proliferation signature of each sample has been determined as described supra. The reference sample set must be of sufficient size such that the set can be used to assess various clinical variables, for example response to treatment regimen, estrogen receptor status, and tumor size and the like, with statistical significance. In some embodiments, the reference sample set comprises primary breast cancer tissue from subjects diagnosed with breast cancer and “normal” breast tissue samples from reduction mammoplasties or non-cancerous breast tissue. These samples can be classified to particular breast cancer intrinsic subtypes, for example Luminal A, Luminal B, Basal-like and Her2 using the PAM50 classification model described herein. For example, the reference sample set contains at least 100 samples, at least 200 samples, at least 300 samples, at least 400 samples, at least 500 samples, at least 600 samples, at least 700 samples, at least 800 samples, at least 900 samples, or at least 1000 samples. Preferably, the reference sample set contains at least 500 samples.
The proliferation signatures of each reference sample in the reference sample set can be arranged from lowest to highest, for example 1 to 10. Once arranged by proliferation signature, the reference sample set can then be divided into sub-ranges, wherein each sub-range is a non-overlapping fraction of the reference set. The proliferation signature of the sample can be compared to reference sample set. These sub-ranges are used to determine the cutoff threshold limits for a low proliferation signature. For example, the sub-range can be 50%, 33%, 30%, 25%, 20%, 15%, 10%, or 5% of the proliferation signatures of the arranged reference sample set. Irrespective of the number of sub-ranges, the proliferation signature of the sample is deemed to be a low proliferation signature if it is present within the lowest sub-range of the reference sample set. For example, if the reference sample set is divided into three sub-ranges, the classification of a low proliferation signature is assigned if the proliferation signature of the sample is present within the lowest 33% of proliferation scores of the arranged reference sample set.
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 breast cancer.
For the purposes of the present disclosure, “taxane or taxane derivatives” are diterpenes, a class of drugs used in cancer chemotherapy produced by the plants of the genus Taxus (yews). These drugs are used to treat a wide variety of cancers including breast cancer. However, this class of drugs is extremely toxic and produces significant deleterious side effects. Taxanes and taxane derivatives include paclitaxel (Taxol®) or docetaxel (Taxotere®).
For the purposes of the present disclosure, “a breast cancer treatment comprising taxane or a taxane derivative” is a breast cancer treatment that includes a taxane or a taxane derivative. These treatments can also include other cancer agents. These other agents can include anthracycline, cyclophosphamide or 5-fluorouracil, or a combination thereof.
For the purposes of the present disclosure, “a breast cancer treatment not comprising taxane or a taxane derivative” is a breast cancer treatment that does not include any taxane or a taxane derivative. These treatments contain other anti-cancer agents. These other agents can include anthracycline, cyclophosphamide or 5-fluorouracil, or a combination thereof.
Preferably taxanes and taxane derivatives are administered intravenously, but can be administered by any method known in the art. Taxanes or taxane derivatives can be administered at dosages from about 75 mg/m2 to about 300 mg/m2, preferably from about 75 mg/m2 to about 175 mg/m2, and most preferably about 100 mg/m2. It is preferred that dosages be administered over a time period of about 1 to about 24 hours or weekly (5-7 days). Dosages can be repeated from 1 to about 4 weeks or more, preferably from about 2 to about 3 weeks. Preferably, the dosage schedule is eight 1-week courses of paclitaxel administered via a 60-minute intravenous infusion.
Preferably anthracyclines are administered intravenously, but can be administered by any method known in the art. Anthracyclines can be administered at dosages from 10 mg/m2 to 300 mg/m2 per week. Anthracyclines can be administered at 20-200 mg/m2, 30-100 mg/m2, or 35-75 mg/m2 per week. Preferably, the anthracycline is administered at about 60 mg/m2 per week. Anthracyclines include daunorubicin, doxorubicin, epirubicin, idarubicin, valrubicin and mitoxantrone.
Preferably 5-fluorouracil is administered intravenously, but can be administered by any method known in the art. 5-fluorouracil can be administered at dosages from 25 mg/m2 to 1000 mg/m2 per week. 5-fluorouracil can be administered at 50-900 mg/m2, 100-800 mg/m2, 300-700 mg/m2 or 450-650 mg/m2 per week. Preferably, 5-fluorouracil is administered at about 500 mg/m2 per week.
Preferably cyclophosphamide is administered orally, but can be administered by any method known in the art. Cyclophosphamide can be administered at dosages from 10 mg/m2 to 300 mg/m2 per day. Cyclophosphamide can be administered at 20-200 mg/m2, 30-100 mg/m2, or 40-80 mg/m2 per day. Preferably, cyclophosphamide is administered at about 75 mg/m2 per day.
Methods, schedules and dosages for administering taxanes or taxane derivatives, anthracyclines, 5-fluorouracil and/or cyclophosphamide are described in Martin et al., J Natl Cancer Inst. 100(11):805-14, 2008, which is incorporated herein, by reference, in its entirety.
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.
Clinical Variables
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. 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.
In particular embodiments, the methods for predicting breast cancer intrinsic subtypes 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, 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 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 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 No. PCT/US2008/059959 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. 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. 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).
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 Technlogies). 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, 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, 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 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 Usqrt(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).
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+0.0.23*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+0.1 1*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 following equation (where the variables are as described supra): ROR-PT=−0.001*Basal+0.73*Her2+−0.9*LumA+0.05*LumB+0.13*T+0.33*Prolif.
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 classifying breast cancer intrinsic subtypes and/or providing prognostic information to identify breast cancers that are more responsive to anthracyclines. These kits comprise a set of capture probes and/or primers specific for the 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 the 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. 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.
Patients, Samples and Clinical Data
The GEICAM/9906 trial was a prospective adjuvant multi-center randomized phase III study (n=1,246 subjects) comparing six cycles of Fluorouracil, Epirubicin, and Cyclophosphamide Alone (FEC, control arm) versus four cycles of FEC followed by eight weekly cycles of paclitaxel at 100 mg/m2 (FEC-P, experimental arm) in node-positive breast cancers. The primary endpoint of the GEICAM/9906 clinical trial was disease-free survival. Secondary endpoints were: (a) overall survival; (b) prognostic and predictive value of molecular/genomic markers and (c) safety. The study was performed in accordance with the Declaration of Helsinki, approved by the ethics committees at all participating institutions and the Spanish Health Authority, and it was registered at www.clinicaltrials.gov (identifier code: NCT00129922). All patients provided written informed consent for therapy randomization and molecular analyses. Details of the study design and patients' characteristics have been previously reported (Martin et al., J Natl Cancer Inst 100: 805-814, 2008; Martin et al., Breast Cancer Res Treat 123:149-157, 2010). Formalin-fixed, paraffin-embedded tumor blocks were available on 825 patients. H&E sections from each FFPE tissue block were reviewed by a pathologist at GEICAM's central laboratory. At least two tumor cores were extracted from areas containing representative invasive breast carcinoma using a 1 mm core punch. A detailed protocol of RNA extraction from FFPE tissue, and the RT-qPCR PAM50 assay have been previously described (Parker et al., J Clin Oncol 27:1160-1167, 2009).
PAM50 Subtype Classification
Samples were gene expression profiled using the previously described RT-qPCR assay and analyzed using the clinical algorithm for subtype prediction (Parker et al., J Clin Oncol 27:1160-1167, 2009; Arup Laboratories: PAM50 Breast Cancer Intrinsic Classifier Information. http://www.aruplab.com/Lab-Tests/General-Oncology/PAM50/index jsp). Samples were assigned into the following intrinsic subtype categories: Luminal A, Luminal B, HER2-enriched, Basal-like and Normal-like. Samples classified as Normal-like were excluded from further analyses due to the potential for misclassification resulting from normal breast tissue or stroma contamination within the tumor specimen (Elloumi et al., BMC Med Genomics 4:54, 2011). In addition to the subtype classification, a PAM50 proliferation score was calculated using the previously described 11-gene signature (BIRC5, CCNB1, CDC20, CDCA1, CEP55, KNTC2, MKI67, PTTG1, RRM2, TYMS, UBE2C) (Nielsen et al., Clin Cancer Res 16(21):5222-32, 2010). The significance of proliferation was evaluated using a classification into quartiles, and using the proliferation scores as a continuous variable.
Immunohistochemical (IHC) Ki-67 Quantification
Ki-67 status was assessed in a central laboratory on paraffin sections by an immunohistochemical method using Clone MIB 1 antibody (DakoCytomation, Glostrup, Denmark). Ki67 score was defined as the percentage of total number of tumor cells with nuclear staining.
Statistical Analysis
This analysis has a prospective-retrospective design (retrospective analysis of a randomized prospective trial) with pre-specified study objectives and pre-specified laboratory assays in a predefined population. The primary pre-specified objectives of the study were to determine whether the PAM50 subtypes, and/or the PAM50 proliferation score, were associated with OS and/or predictive of paclitaxel benefit. The Kaplan-Meier method was used to estimate overall survival (OS), and the log-rank test was used to compare OS between groups. Univariate and multivariate Cox proportional hazard models were used to examine the association of each variable with survival, and interaction between treatment and PAM50 subtype and proliferation. The results are presented in accordance with reporting recommendations for tumor marker prognostic studies (REMARK) criteria (McShane et al., J Clin Oncol 23:9067-9072, 2005).
Patient Demographics
Tumor blocks were available for 825 patients, and PAM50 genomic profiling was successful in 820 samples (99.4%) of patients whose signed informed consent was obtained (
Overall Survival Outcomes
With a median follow up of 8.7 years, OS of the FEC-P arm was significantly superior compared to the FEC arm (hazard ratio [HR] for O δ 0.693, 95% confidence interval [CI] 0.693-0.927, p=0.013) with 10-year OS rates in the FEC and FEC-P arms of 70.90% and 78.44%, respectively (
Among the variables evaluated, tumor size, nodal status and proliferation signature were found to be independent predictors of OS in multivariate analysis with treatment arm showing a tendency for significance (p=0.067). (Table 4). Of note, Ki-67 by IHC and histological grade were superseded by the information provided by the proliferation signature.
Effect of Paclitaxel in the PAM50 Subtypes and Proliferation Score
Kaplan-Meier plots for OS comparing treatment with FEC-P vs. FEC were evaluated in each group category defined by the PAM50 assay. The individual PAM50 subtypes were not found predictive of paclitaxel efficacy. On the other hand, a benefit from paclitaxel was observed in patients whose tumors had a low proliferation score (HR=0.23 within the lowest quartile, CI 0.09-0.57, p<0.001), showing an improvement of the 10-year OS from 71.24% to 94.42% (
Relationship of the Proliferation Score and Paclitaxel Treatment Benefit
To test the statistical validity of the relationship between the magnitude of paclitaxel benefit and the proliferation score, a formal test of statistical interaction between proliferation score and paclitaxel treatment was performed. In a multivariate analysis of Cox models containing paclitaxel treatment and proliferation score, the tests for interaction were found to be statistically significant (p=0.006 as a continuous variable; p=0.019 as group categories using quartile expression). In addition, a multivariate model for the interaction between Proliferation Score and paclitaxel treatment that was adjusted for all clinical-pathological variables showed continued significance of the interaction between proliferation score and paclitaxel treatment (Table 5).
To explore the degree of benefit from paclitaxel treatment in relationship to the Proliferation score as a continuous function, the likelihood of OS was fit as a linear function of the Proliferation score for both arms. As expected, the magnitude of paclitaxel benefit appeared to increase continuously as the Proliferation score decreased (
Paclitaxel Benefit and Clinical-Pathological Variables
In order to identify other predictors of response to weekly paclitaxel, the interaction of paclitaxel treatment with clinical-pathological variables (age, menopausal status, histological grade, tumor size, ER [IHC] status, PR [IHC] status, Ki-67 ([IHC] and HER2 status [IHC/CISH]) was also evaluated. No significant interactions between these variables and treatment were found.
Discussion
In the era of personalized medicine, new tools that may be able to provide clinically useful prognostic and predictive information for breast cancer patients are needed. Two genomic assays (OncotypeDX and Mammaprint) provide prognostic information in early breast cancer (Van de Vijver et al., N Engl J Med 347:1999-2009, 2002; Buyse et al., J Natl Cancer Inst 98: 1183-92, 2006; Paik et al., N Engl J Med 351:2817-26, 2004), and OncotypeDX provides predictive information of benefit from adjuvant chemotherapy (cyclophosphamide-methotrexate-fluorouracil (CMF) or cyclophosphamide-doxorubicin(Adriamycin®)-fluorouracil (CAF)) in ER-positive disease (Paik et al., J Clin Oncol 24:3726-34, 2006; Albain et al., Lancet Oncol 11: 55-65, 2010). However, the ability of these and other assays, to predict treatment benefit to modern taxane regimens, and/or the benefit to specific drugs, is unclear.
It has been previously reported that the benefit of adding weekly adjuvant paclitaxel to anthracycline-based chemotherapy is small (De Laurentiis et al., J Clin Oncol 26:44-53, 2008; Nowak et al., Lancet Oncol 5: 372-80, 2004; Tang, Cancer Investigation 27:489-495, 2009; Bria et al., Cancer 106: 2337-2344, 2006). Thus, identification of which patients might benefit the most from this drug and schedule seems justified. Traditional clinical-pathological parameters (i.e. age, tumor size, number of positive nodes, ER status, PR status, and HER2 status) and the PAM50 intrinsic subtypes were not found to be predictive of adjuvant paclitaxel efficacy.
A measure of proliferation is an important component of tests used for prognosis, especially in early stage ER-positive breast cancer. Proliferation is also incorporated into histological grading, either by counting mitotic figures (i.e. modified Nottingham-Bloom-Richardson score) or by developing a mitotic index using a cell cycle regulated biomarker such as Ki-67 (Simpson et al., J Clin Oncol. 18(10):2059-69, 2000; Meyer et al., Mod Pathol. 2005 August; 18(8):1067-78. Erratum in: Mod Pathol 18(12):1649, 2005; Dowsett et al., J Natl Cancer Inst 99(2):167-70, 2007). In this study, it was found that the proliferation score signature, which is the average expression value of 11 proliferation-related genes, was predictive for benefit of weekly paclitaxel in the adjuvant setting. Although no pre-specified cutoffs of this signature were tested in the GEICAM/9906 trial here, the HR for OS in the low quartile group was noteworthy (HR=0.232, p=0.002). In addition, the test of interaction between paclitaxel treatment and Proliferation score was statistically significant, even when all other clinical-pathological variables were considered. Interestingly, the proliferation-related biomarker, Ki-67 by IHC, did not predict paclitaxel benefit despite being evaluated at a central pathology laboratory, while the 11-gene proliferation score was significant.
This application claims priority to, and the benefit of, U.S. Ser. No. 61/565,133, filed Nov. 30, 2011, and U.S. Ser. No. 61/635,048, filed Apr. 18, 2012. The contents of each of these applications are herein incorporated by reference in their entirety.
| Number | Date | Country | |
|---|---|---|---|
| 61565133 | Nov 2011 | US | |
| 61635048 | Apr 2012 | US |
