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

Molecular analysis reveals heterogeneity of mouse mammary tumors conditionally mutant for Brca1

<p>Abstract</p> <p>Background</p> <p>Development of therapies for patients with BRCA1 mutations has been hampered by lack of readily available <it>in vitro </it>and <it>in vivo </it>models. We recently showed that transplantation of transgenic mammary tumors as cell suspensions into naïve recipients generates reproducible tumors with remarkable stability of gene expression profile. We examined the expression profiles of original and serially transplanted mammary tumors from <it>Brca1 </it>deficient mice, and tumor derived cell lines to validate their use for preclinical testing and studies of tumor biology.</p> <p>Methods</p> <p>Original tumors, serially transplanted and multiple cell lines derived from <it>Brca1 </it>mammary tumors were characterized by morphology, gene and protein expression, and cell surface markers.</p> <p>Results</p> <p>Gene expression among <it>Brca1 </it>tumors showed more heterogeneity than among previously characterized tumors from MMTV-<it>PyMT </it>and -<it>Wnt1 </it>models. Gene expression data segregated <it>Brca1 </it>tumors into 3 distinct types: basal, mixed luminal, and tumors with epithelial-to-mesenchymal transition (EMT). Serial transplantation of individual tumors and multiple cell lines derived from the original tumors recapitulated the molecular characteristics of each tumor of origin. One tumor had distinct features of EMT and gave rise to cell lines that contained a distinct CD44⁺/CD24^-/lowpopulation that may correlate with human breast cancer stem cells.</p> <p>Conclusion</p> <p>Although individual tumors expanded by transplantation maintain the genomic profile of the original tumors, the heterogeneity among <it>Brca1 </it>tumors limits the extent of their use for preclinical testing. However, cell lines offer a robust material for understanding tumor biology and response to therapies driven by BRCA1 deficiency.</p

SigFuge: Single gene clustering of RNA-seq reveals differential isoform usage among cancer samples

High-throughput sequencing technologies, including RNA-seq, have made it possible to move beyond gene expression analysis to study transcriptional events including alternative splicing and gene fusions. Furthermore, recent studies in cancer have suggested the importance of identifying transcriptionally altered loci as biomarkers for improved prognosis and therapy. While many statistical methods have been proposed for identifying novel transcriptional events with RNA-seq, nearly all rely on contrasting known classes of samples, such as tumor and normal. Few tools exist for the unsupervised discovery of such events without class labels. In this paper, we present SigFuge for identifying genomic loci exhibiting differential transcription patterns across many RNA-seq samples. SigFuge combines clustering with hypothesis testing to identify genes exhibiting alternative splicing, or differences in isoform expression. We apply SigFuge to RNA-seq cohorts of 177 lung and 279 head and neck squamous cell carcinoma samples from the Cancer Genome Atlas, and identify several cases of differential isoform usage including CDKN2A, a tumor suppressor gene known to be inactivated in a majority of lung squamous cell tumors. By not restricting attention to known sample stratifications, SigFuge offers a novel approach to unsupervised screening of genetic loci across RNA-seq cohorts. SigFuge is available as an R package through Bioconductor

BlackOPs: Increasing confidence in variant detection through mappability filtering

Identifying variants using high-throughput sequen-cing data is currently a challenge because true biological variants can be indistinguishable from technical artifacts. One source of technical arti-fact results from incorrectly aligning experimen-tally observed sequences to their true genomic origin (‘mismapping’) and inferring differences in mismapped sequences to be true variants. We de-veloped BlackOPs, an open-source tool that simu-lates experimental RNA-seq and DNA whole exome sequences derived from the reference genome, aligns these sequences by custom parameters, detects variants and outputs a blacklist of positions and alleles caused by mismapping. Blacklist

SWISS MADE: Standardized WithIn Class Sum of Squares to Evaluate Methodologies and Dataset Elements

Contemporary high dimensional biological assays, such as mRNA expression microarrays, regularly involve multiple data processing steps, such as experimental processing, computational processing, sample selection, or feature selection (i.e. gene selection), prior to deriving any biological conclusions. These steps can dramatically change the interpretation of an experiment. Evaluation of processing steps has received limited attention in the literature. It is not straightforward to evaluate different processing methods and investigators are often unsure of the best method. We present a simple statistical tool, Standardized WithIn class Sum of Squares (SWISS), that allows investigators to compare alternate data processing methods, such as different experimental methods, normalizations, or technologies, on a dataset in terms of how well they cluster a priori biological classes. SWISS uses Euclidean distance to determine which method does a better job of clustering the data elements based on a priori classifications. We apply SWISS to three different gene expression applications. The first application uses four different datasets to compare different experimental methods, normalizations, and gene sets. The second application, using data from the MicroArray Quality Control (MAQC) project, compares different microarray platforms. The third application compares different technologies: a single Agilent two-color microarray versus one lane of RNA-Seq. These applications give an indication of the variety of problems that SWISS can be helpful in solving. The SWISS analysis of one-color versus two-color microarrays provides investigators who use two-color arrays the opportunity to review their results in light of a single-channel analysis, with all of the associated benefits offered by this design. Analysis of the MACQ data shows differential intersite reproducibility by array platform. SWISS also shows that one lane of RNA-Seq clusters data by biological phenotypes as well as a single Agilent two-color microarray

Integrated RNA and DNA sequencing improves mutation detection in low purity tumors

Identifying somatic mutations is critical for cancer genome characterization and for prioritizing patient treatment. DNA whole exome sequencing (DNA-WES) is currently the most popular technology; however, this yields low sensitivity in low purity tumors. RNA sequencing (RNA-seq) covers the expressed exome with depth proportional to expression. We hypothesized that integrating DNA-WES and RNA-seq would enable superior mutation detection versus DNA-WES alone. We developed a first-of-its-kind method, called UNCeqR, that detects somatic mutations by integrating patient-matched RNA-seq and DNA-WES. In simulation, the integrated DNA and RNA model outperformed the DNA-WES only model. Validation by patient-matched whole genome sequencing demonstrated superior performance of the integrated model over DNA-WES only models, including a published method and published mutation profiles. Genome-wide mutational analysis of breast and lung cancer cohorts (n = 871) revealed remarkable tumor genomics properties. Low purity tumors experienced the largest gains in mutation detection by integrating RNA-seq and DNA-WES. RNA provided greater mutation signal than DNA in expressed mutations. Compared to earlier studies on this cohort, UNCeqR increased mutation rates of driver and therapeutically targeted genes (e.g. PIK3CA, ERBB2 and FGFR2). In summary, integrating RNA-seq with DNA-WES increases mutation detection performance, especially for low purity tumors

Basal keratin expression in breast cancer by quantification of mRNA and by immunohistochemistry

Definitions of basal-like breast cancer phenotype vary, and microarray-based expression profiling analysis remains the gold standard for the identification of these tumors. Immunohistochemical identification of basal-like carcinomas is hindered with a fact, that on microarray level not all of them express basal-type cytokeratin 5/6, 14 and 17. We compared expression of cytokeratin 5, 14 and 17 in 115 patients with operable breast cancer estimated by real-time RT-PCR and immunohistochemistry

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

EGFR associated expression profiles vary with breast tumor subtype

<p>Abstract</p> <p>Background</p> <p>The epidermal growth factor receptor (EGFR/HER1) and its downstream signaling events are important for regulating cell growth and behavior in many epithelial tumors types. In breast cancer, the role of EGFR is complex and appears to vary relative to important clinical features including estrogen receptor (ER) status. To investigate EGFR-signaling using a genomics approach, several breast basal-like and luminal epithelial cell lines were examined for sensitivity to EGFR inhibitors. An EGFR-associated gene expression signature was identified in the basal-like SUM102 cell line and was used to classify a diverse set of sporadic breast tumors.</p> <p>Results</p> <p><it>In vitro</it>, breast basal-like cell lines were more sensitive to EGFR inhibitors compared to luminal cell lines. The basal-like tumor derived lines were also the most sensitive to carboplatin, which acted synergistically with cetuximab. An EGFR-associated signature was developed <it>in vitro</it>, evaluated on 241 primary breast tumors; three distinct clusters of genes were evident <it>in vivo</it>, two of which were predictive of poor patient outcomes. These EGFR-associated poor prognostic signatures were highly expressed in almost all basal-like tumors and many of the HER2+/ER- and Luminal B tumors.</p> <p>Conclusion</p> <p>These results suggest that breast basal-like cell lines are sensitive to EGFR inhibitors and carboplatin, and this combination may also be synergistic. <it>In vivo</it>, the EGFR-signatures were of prognostic value, were associated with tumor subtype, and were uniquely associated with the high expression of distinct EGFR-RAS-MEK pathway genes.</p