Subtype-specific transcriptional regulators in breast tumors subjected to genetic and epigenetic alterations

Abstract

Motivation: Breast cancer consists of multiple distinct tumor subtypes, and results from epigenetic and genetic aberrations that give rise to distinct transcriptional profiles. Despite previous efforts to understand transcriptional deregulation through transcription factor networks, the transcriptional mechanisms leading to subtypes of the disease remain poorly understood.

Results: We used a sophisticated computational search of thousands of expression datasets to define extended signatures of distinct breast cancer subtypes. Using ENCODE ChIP-seq data of surrogate cell lines and motif analysis we observed that these subtypes are determined by a distinct repertoire of lineage-specific transcription factors. Furthermore, specific pattern and abundance of copy number and DNA methylation changes at these TFs and targets, compared to other genes and to normal cells were observed. Overall, distinct transcriptional profiles are linked to genetic and epigenetic alterations at lineage-specific transcriptional regulators in breast cancer subtypes.

Availability and implementation: The analysis code and data are deposited at https://bitbucket.org/qzhu/breast.cancer.tf/.

Supplementary information: Supplementary data are available at Bioinformatics online.

Fig. 1.

ChIP-seq experiments ranked highest in binding signals at (a) luminal A and (b) basal extended signature genes (ESGs). Each column indicates one of five ESG sets being interrogated for strength of binding in the ChIP-seq experiments (rows). As expected, lumA-relevant ChIP-seq contains significant binding at lumA genes, but not at basal genes (a)

Fig. 2.

Luminal A (a) and basal (b) TF-TF network. A directed edge from TF A to TF B means that A binds to B according to ChIP-seq experiment of A in ENCODE database. Blue nodes: ChIP’ed TFs. Red nodes: the TF subset of the subtype’s ESGs which are targeted by the ChIP’ed TFs. TF target genes (red nodes) are partitioned into groups based on different upstream binding factors (blue nodes), and are involved in distinct biological processes (circled and process names in red). In addition to this network, we show an extended TF-target network as a heatmap in Supplementary Figures S3 and S4