Joint and individual analysis of breast cancer histologic images and genomic covariates

A key challenge in modern data analysis is understanding connections between complex and differing modalities of data. For example, two of the main approaches to the study of breast cancer are histopathology (analyzing visual characteristics of tumors) and genetics. While histopathology is the gold standard for diagnostics and there have been many recent breakthroughs in genetics, there is little overlap between these two fields. We aim to bridge this gap by developing methods based on Angle-based Joint and Individual Variation Explained (AJIVE) to directly explore similarities and differences between these two modalities. Our approach exploits Convolutional Neural Networks (CNNs) as a powerful, automatic method for image feature extraction to address some of the challenges presented by statistical analysis of histopathology image data. CNNs raise issues of interpretability that we address by developing novel methods to explore visual modes of variation captured by statistical algorithms (e.g. PCA or AJIVE) applied to CNN features. Our results provide many interpretable connections and contrasts between histopathology and genetics

PAM50 assay and the three-gene model for identifying the major and clinically relevant molecular subtypes of breast cancer

It has recently been proposed that a three-gene model (SCMGENE) that measures ESR1, ERBB2, and AURKA identifies the major breast cancer intrinsic subtypes and provides robust discrimination for clinical use in a manner very similar to a 50-gene subtype predictor (PAM50). However, the clinical relevance of both predictors was not fully explored, which is needed given that a~30% discordance rate between these two predictors was observed. Using the same datasets and subtype calls provided by Haibe-Kains and colleagues, we compared the SCMGENE assignments and the research-based PAM50 assignments in terms of their ability to (1) predict patient outcome, (2) predict pathological complete response (pCR) after anthracycline/taxane-based chemotherapy, and (3) capture the main biological diversity displayed by all genes from a microarray. In terms of survival predictions, both assays provided independent prognostic information from each other and beyond the data provided by standard clinical–pathological variables; however, the amount of prognostic information was found to be significantly greater with the PAM50 assay than the SCMGENE assay. In terms of chemotherapy response, the PAM50 assay was the only assay to provide independent predictive information of pCR in multivariate models. Finally, compared to the SCMGENE predictor, the PAM50 assay explained a significantly greater amount of gene expression diversity as captured by the two main principal components of the breast cancer microarray data. Our results show that classification of the major and clinically relevant molecular subtypes of breast cancer are best captured using larger gene panels.Electronic supplementary materialThe online version of this article (doi:10.1007/s10549-012-2143-0) contains supplementary material, which is available to authorized users

Identification of a stable molecular signature in mammary tumor endothelial cells that persists in vitro

Long-term, in vitro propagation of tumor-specific endothelial cells (TEC) allows for functional studies and genome-wide expression profiling of clonally-derived, well-characterized subpopulations. Using a genetically engineered mouse model (GEMM) of mammary adenocarcinoma, we have optimized an isolation procedure and defined growth conditions for long-term propagation of mammary TEC. The isolated TEC maintain their endothelial specification and phenotype in culture. Furthermore, gene expression profiling of multiple TEC subpopulations revealed striking, persistent overexpression of several candidate genes including Irx2 and Zfp503 (transcription factors), Alcam and Cd133 (cell surface markers), Ccl4 and neurotensin (Nts) (angiocrine factors), and Gpr182 and Cnr2 (G protein-coupled receptors, GPCRs). Taken together, we have developed an effective method for isolating and culture-expanding mammary TEC, and uncovered several new TEC-selective genes whose overexpression persists even after long-term in vitro culture. These results suggest that the tumor microenvironment may induce changes in vascular endothelium in vivo that are stably transmittable in vitro

Molecular Characterization of Basal-Like and Non-Basal-Like Triple-Negative Breast Cancer

Triple-negative (TN) and basal-like (BL) breast cancer definitions have been used interchangeably to identify breast cancers that lack expression of the hormone receptors and overexpression and/or amplification of HER2. However, both classifications show substantial discordance rates when compared to each other. Here, we molecularly characterize TN tumors and BL tumors, comparing and contrasting the results in terms of common patterns and distinct patterns for each. In total, when testing 412 TN and 473 BL tumors, 21.4% and 31.5% were identified as non-BL and non-TN, respectively. TN tumors identified as luminal or HER2-enriched (HER2E) showed undistinguishable overall gene expression profiles when compared versus luminal or HER2E tumors that were not TN. Similar findings were observed within BL tumors regardless of their TN status, which suggests that molecular subtype is preserved regardless of individual marker results. Interestingly, most TN tumors identified as HER2E showed low HER2 expression and lacked HER2 amplification, despite the similar overall gene expression profiles to HER2E tumors that were clinically HER2-positive. Lastly, additional genomic classifications were examined within TN and BL cancers, most of which were highly concordant with tumor intrinsic subtype. These results suggest that future clinical trials focused on TN disease should consider stratifying patients based upon BL versus non-BL gene expression profiles, which appears to be the main biological difference seen in patients with TN breast cancer

Concordance among gene expression-based predictors for ER-positive breast cancer treated with adjuvant tamoxifen

Funding: NCI Breast SPORE program (P50-CA58223-09A1); (RO1-420 CA138255) Breast Cancer Research Foundation, the Sociedad Española de Oncología Médica (SEOM) and the V Foundation for Cancer Research. AP is affiliated to the Medicine PhD program of the Autonomous University of Barcelona, Spain.ER-positive (ER+) breast cancer includes all of the intrinsic molecular subtypes, although the luminal A and B subtypes predominate. In this study, we evaluated the ability of six clinically relevant genomic signatures to predict relapse in patients with ER+ tumors treated with adjuvant tamoxifen only. Four microarray datasets were combined and research-based versions of PAM50 intrinsic subtyping and risk of relapse (PAM50-ROR) score, 21-gene recurrence score (OncotypeDX), Mammaprint, Rotterdam 76 gene, index of sensitivity to endocrine therapy (SET) and an estrogen-induced gene set were evaluated. Distant relapse-free survival (DRFS) was estimated by Kaplan-Meier and log-rank tests, and multivariable analyses were done using Cox regression analysis. Harrell's C-index was also used to estimate performance. All signatures were prognostic in patients with ER+ node-negative tumors, whereas most were prognostic in ER+ node-positive disease. Among the signatures evaluated, PAM50-ROR, OncotypeDX, Mammaprint and SET were consistently found to be independent predictors of relapse. A combination of all signatures significantly increased the performance prediction. Importantly, low-risk tumors (>90% DRFS at 8.5 years) were identified by the majority of signatures only within node-negative disease, and these tumors were mostly luminal A (78%-100%). Most established genomic signatures were successful in outcome predictions in ER+ breast cancer and provided statistically independent information. From a clinical perspective, multiple signatures combined together most accurately predicted outcome, but a common finding was that each signature identified a subset of luminal A patients with node-negative disease who might be considered suitable candidates for adjuvant endocrine therapy alone

Radiation-Induced Gene Signature Predicts Pathologic Complete Response to Neoadjuvant Chemotherapy in Breast Cancer Patients

The identification of biomarkers predictive of neoadjuvant chemotherapy response in breast cancer patients would be an important advancement in personalized cancer therapy. In this study, we hypothesized that due to similarities between radiation- and chemotherapy-induced cellular response mechanisms, radiation-responsive genes may be useful in predicting response to neoadjuvant chemotherapy. Murine p53 null breast cancer cell lines representative of the luminal, basal-like and claudin-low human breast cancer subtypes were irradiated to identify radiation-responsive genes across subtypes. These murine tumor radiation-induced genes were then converted to their human orthologs, and subsequently tested as a predictor of pathologic complete response (pCR), which was validated on two independent published neoadjuvant chemotherapy datasets of genomic data with chemotherapy response. A radiation-induced gene signature consisting of 30 genes was identified on a training set of 337 human primary breast cancer tumor samples that was prognostic for survival. Mean expression of this signature was calculated for individual samples on two independent published datasets and was found to be significantly predictive of pCR. Multivariate logistic regression analysis in both independent datasets showed that this 30 gene signature added significant predictive information independent of that provided by standard clinical predictors and other gene expression-based predictors of pCR. This study provides new information for radiation-induced biology, as well as information regarding response to neoadjuvant chemotherapy and a possible means of improving the prediction of pCR

Expression of miR-200c in claudin-low breast cancer alters stem cell functionality, enhances chemosensitivity and reduces metastatic potential

Claudin-low tumors are a highly aggressive breast cancer subtype with no targeted treatments and a clinically documented resistance to chemotherapy. They are significantly enriched in cancer stem cells (CSCs), which makes claudin-low tumor models particularly attractive for studying CSC behavior and developing novel approaches to minimize CSC therapy resistance. One proposed mechanism by which CSCs arise is via an epithelial-mesenchymal transition (EMT), and reversal of this process may provide a potential therapeutic approach for increasing tumor chemosensitivity. Therefore, we investigated the role of known EMT regulators, miR-200 family of microRNAs in controlling the epithelial state, stem-like properties, and therapeutic response in an in vivo primary, syngeneic p53null claudin-low tumor model that is normally deficient in miR-200 expression. Using an inducible lentiviral approach, we expressed the miR-200c cluster in this model and found that it changed the epithelial state, and consequently, impeded CSC behavior in these mesenchymal tumors. Moreover, these state changes were accompanied by a decrease in proliferation and an increase in the differentiation status. miR-200c expression also forced a significant reorganization of tumor architecture, affecting important cellular processes involved in cell-cell contact, cell adhesion, and motility. Accordingly, induced miR200c expression significantly enhanced the chemosensitivity and decreased the metastatic potential of this p53null claudin-low tumor model. Collectively, our data suggest that miR-200c expression in claudin-low tumors offers a potential therapeutic application to disrupt the EMT program on multiple fronts in this mesenchymal tumor subtype, by altering tumor growth, chemosensitivity, and metastatic potential in vivo

Age-Specific Changes in Intrinsic Breast Cancer Subtypes: A Focus on Older Women

Breast cancer (BC) is a disease of aging and the number of older BC patients in the U.S. is rising. Immunohistochemical data show that with increasing age, the incidence of hormone receptor-positive tumors increases, whereas the incidence of triple-negative tumors decreases. Few data exist on the frequency of molecular subtypes in older women. Here, we characterize the incidence and outcomes of BC patients by molecular subtypes and age

In vitro and in vivo analysis of B-Myb in basal-like breast cancer

A defining feature of basal-like breast cancer, a breast cancer subtype with poor clinical prognosis, is the high expression of “proliferation signature” genes. We identified B-Myb, a MYB family transcription factor that is often amplified and overexpressed in many tumor types, as being highly expressed in the proliferation signature. However, the roles of B-Myb in disease progression, and its mammary-specific transcriptional targets, are poorly understood. Here, we demonstrated that B-Myb expression is a significant predictor of survival and pathological complete response to neoadjuvant chemotherapy in breast cancer patients. We also identified a significant association between the G/G genotype of a nonsynonymous B-Myb germline variant (rs2070235, S427G) and an increased risk of basal-like breast cancer [OR 2.0, 95% CI (1.1-3.8)]. In immortalized, human mammary epithelial cell lines, but not basal-like tumor lines, cells ectopically expressing wild-type B-Myb or the S427G variant showed increased sensitivity to two DNA topoisomerase IIα inhibitors, but not to other chemotherapeutics. In addition, microarray analyses identified many G2/M genes as being induced in B-Myb overexpressing cells. These results confirm that B-Myb is involved in cell cycle control, and that dysregulation of B-Myb may contribute to increased sensitivity to a specific class of chemotherapeutic agents. These data provide insight into the influence of B-Myb in human breast cancer, which is of potential clinical importance for determining disease risk and for guiding treatment