Deconstructing the molecular portraits of breast cancer

Breast cancer is a heterogeneous disease in terms of histology, therapeutic response, dissemination patterns to distant sites, and patient outcomes. Global gene expression analyses using high-throughput technologies have helped to explain much of this heterogeneity and provided important new classifications of cancer patients. In the last decade, genomic studies have established five breast cancer intrinsic subtypes (Luminal A, Luminal B, HER2-enriched, Claudin-low, Basal-like) and a Normal Breast-like group. In this review, we dissect the most recent data on this genomic classification of breast cancer with a special focus on the Claudin-low subtype, which appears enriched for mesenchymal and stem cell features. In addition, we discuss how the combination of standard clinical-pathological markers with the information provided by these genomic entities might help further understand the biological complexity of this disease, increase the efficacy of current and novel therapies, and ultimately improve outcomes for breast cancer patients

A Comparison of Gene Expression Signatures from Breast Tumors and Breast Tissue Derived Cell Lines

Cell lines derived from human tumors have historically served as the primary experimental model system for exploration of tumor cell biology and pharmacology. Cell line studies, however, must be interpreted in the context of artifacts introduced by selection and establishment of cell lines in vitro. This complication has led to difficulty in the extrapolation of biology observed in cell lines to tumor biology in vivo. Modern genomic analysis tool like DNA microarrays and gene expression profiling now provide a platform for the systematic characterization and classification of both cell lines and tumor samples. Studies using clinical samples have begun to identify classes of tumors that appear both biologically and clinically unique as inferred from their distinctive patterns of expressed genes. In this review, we explore the relationships between patterns of gene expression in breast tumor derived cell lines to those from clinical tumor specimens. This analysis demonstrates that cell lines and tumor samples have distinctive gene expression patterns in common and underscores the need for careful assessment of the appropriateness of any given cell line as a model for a given tumor subtype

The Genomic Landscape of Breast Cancer as a Therapeutic Roadmap

The application of high throughput techniques to profile DNA, RNA and protein in breast cancer samples from hundreds of patients has profoundly increased our knowledge of the disease. The etiological events that drive breast cancer are finally coming into focus and should be used to set priorities for clinical trials. In this Research Focus we summarize some of the headline conclusions from six recent breast cancer ‘omics profiling’ papers in Nature, with an emphasis on the implications for systemic therapy

Finding large average submatrices in high dimensional data

The search for sample-variable associations is an important problem in the exploratory analysis of high dimensional data. Biclustering methods search for sample-variable associations in the form of distinguished submatrices of the data matrix. (The rows and columns of a submatrix need not be contiguous.) In this paper we propose and evaluate a statistically motivated biclustering procedure (LAS) that finds large average submatrices within a given real-valued data matrix. The procedure operates in an iterative-residual fashion, and is driven by a Bonferroni-based significance score that effectively trades off between submatrix size and average value. We examine the performance and potential utility of LAS, and compare it with a number of existing methods, through an extensive three-part validation study using two gene expression datasets. The validation study examines quantitative properties of biclusters, biological and clinical assessments using auxiliary information, and classification of disease subtypes using bicluster membership. In addition, we carry out a simulation study to assess the effectiveness and noise sensitivity of the LAS search procedure. These results suggest that LAS is an effective exploratory tool for the discovery of biologically relevant structures in high dimensional data. Software is available at https://genome.unc.edu/las/.Comment: Published in at http://dx.doi.org/10.1214/09-AOAS239 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Systems Biology and Genomics of Breast Cancer

It is now accepted that breast cancer is not a single disease, but instead it is composed of a spectrum of tumor subtypes with distinct cellular origins, somatic changes, and etiologies. Gene expression profiling using DNA microarrays has contributed significantly to our understanding of the molecular heterogeneity of breast tumor formation, progression, and recurrence. For example, at least two clinical diagnostic assays exist (i.e., OncotypeDX RS and Mammaprint®) that are able to predict outcome in patients using patterns of gene expression and predetermined mathematical algorithms. In addition, a new molecular taxonomy based upon the inherent, or “intrinsic,” biology of breast tumors has been developed; this taxonomy is called the “intrinsic subtypes of breast cancer,” which now identifies five distinct tumor types and a normal breast-like group. Importantly, the intrinsic subtypes of breast cancer predict patient relapse, overall survival, and response to endocrine and chemotherapy regimens. Thus, most of the clinical behavior of a breast tumor is already written in its subtype profile. Here, we describe the discovery and basic biology of the intrinsic subtypes of breast cancer, and detail how this interacts with underlying genetic alternations, response to therapy, and the metastatic process

Practical implications of gene-expression-based assays for breast oncologists

Gene-expression profiling has had a considerable impact on our understanding of breast cancer biology, and more recently on clinical care. Two statistical approaches underlie these advancements. Supervised analyses have led to the development of gene-expression signatures designed to predict survival and/or treatment response, which has resulted in the development of new clinical assays. Unsupervised analyses have identified numerous biological signatures including signatures of cell type of origin, signaling pathways, and of cellular proliferation. Included within these biological signatures are the molecular subtypes known as the ‘intrinsic’ subtypes of breast cancer. This classification has expanded our appreciation of the heterogeneity of breast cancer and has provided a way to sub-classify the disease in a manner that might have clinical utility. In this Review, we discuss the clinical utility of gene-expression-based assays and their technical potential as clinical tools vis-a-vis the performance of breast cancer biomarkers that are the current standard of care

Statistical modeling for selecting housekeeper genes

There is a need for statistical methods to identify genes that have minimal variation in expression across a variety of experimental conditions. These 'housekeeper' genes are widely employed as controls for quantification of test genes using gel analysis and real-time RT-PCR. Using real-time quantitative RT-PCR, we analyzed 80 primary breast tumors for variation in expression of six putative housekeeper genes (MRPL19 (mitochondrial ribosomal protein L19), PSMC4 (proteasome (prosome, macropain) 26S subunit, ATPase, 4), SF3A1 (splicing factor 3a, subunit 1, 120 kDa), PUM1 (pumilio homolog 1 (Drosophila)), ACTB (actin, beta) and GAPD (glyceraldehyde-3-phosphate dehydrogenase)). We present appropriate models for selecting the best housekeepers to normalize quantitative data within a given tissue type (for example, breast cancer) and across different types of tissue samples

Potential Tumor Suppressor Role for the c-Myb Oncogene in Luminal Breast Cancer

The transcription factor c-Myb has been well characterized as an oncogene in several human tumor types, and its expression in the hematopoietic stem/progenitor cell population is essential for proper hematopoiesis. However, the role of c-Myb in mammopoeisis and breast tumorigenesis is poorly understood, despite its high expression in the majority of breast cancer cases (60-80%).We find that c-Myb high expression in human breast tumors correlates with the luminal/ER+ phenotype and a good prognosis. Stable RNAi knock-down of endogenous c-Myb in the MCF7 luminal breast tumor cell line increased tumorigenesis both in vitro and in vivo, suggesting a possible tumor suppressor role in luminal breast cancer. We created a mammary-derived c-Myb expression signature, comprised of both direct and indirect c-Myb target genes, and found it to be highly correlated with a published mature luminal mammary cell signature and least correlated with a mammary stem/progenitor lineage gene signature.These data describe, for the first time, a possible tumor suppressor role for the c-Myb proto-oncogene in breast cancer that has implications for the understanding of luminal tumorigenesis and for guiding treatment