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.

[1]  Arnoldo Frigessi,et al.  Overrepresentation of transcription factor families in the genesets underlying breast cancer subtypes , 2012, BMC Genomics.

[2]  Clifford A. Meyer,et al.  Model-based Analysis of ChIP-Seq (MACS) , 2008, Genome Biology.

[3]  Kai Li,et al.  Targeted exploration and analysis of large cross-platform human transcriptomic compendia , 2015, Nature Methods.

[4]  D. Zheng,et al.  Pioneer factors govern super-enhancer dynamics in stem cell plasticity and lineage choice , 2015, Nature.

[5]  Sven Bilke,et al.  Transcriptional networks inferred from molecular signatures of breast cancer. , 2008, The American journal of pathology.

[6]  Christian A. Rees,et al.  Molecular portraits of human breast tumours , 2000, Nature.

[7]  T. Sørlie,et al.  Evaluation of MetriGenix custom 4D™ arrays applied for detection of breast cancer subtypes , 2006, BMC Cancer.

[8]  Sampsa Hautaniemi,et al.  Individual and combined effects of DNA methylation and copy number alterations on miRNA expression in breast tumors , 2013, Genome Biology.

[9]  Israel Steinfeld,et al.  BMC Bioinformatics BioMed Central , 2008 .

[10]  Steven J. M. Jones,et al.  Analysis of Normal Human Mammary Epigenomes Reveals Cell-Specific Active Enhancer States and Associated Transcription Factor Networks. , 2016, Cell reports.

[11]  A. Frigessi,et al.  DNA methylation at enhancers identifies distinct breast cancer lineages , 2017, Nature Communications.

[12]  F. Markowetz,et al.  The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups , 2012, Nature.

[13]  Steven J. M. Jones,et al.  Comprehensive molecular portraits of human breast tumors , 2012, Nature.

[14]  A. Børresen-Dale,et al.  The Life History of 21 Breast Cancers , 2012, Cell.

[15]  David Z. Chen,et al.  Architecture of the human regulatory network derived from ENCODE data , 2012, Nature.

[16]  ENCODEConsortium,et al.  An Integrated Encyclopedia of DNA Elements in the Human Genome , 2012, Nature.

[17]  E. Liu,et al.  An Oestrogen Receptor α-bound Human Chromatin Interactome , 2009, Nature.

[18]  Richard S. Sandstrom,et al.  BEDOPS: high-performance genomic feature operations , 2012, Bioinform..

[19]  Gema Moreno-Bueno,et al.  Epithelial-mesenchymal transition in breast cancer relates to the basal-like phenotype. , 2008, Cancer research.

[20]  Arnoldo Frigessi,et al.  Genome-wide DNA methylation profiles in progression to in situ and invasive carcinoma of the breast with impact on gene transcription and prognosis , 2014, Genome Biology.

[21]  Data production leads,et al.  An integrated encyclopedia of DNA elements in the human genome , 2012 .

[22]  A. Nobel,et al.  The molecular portraits of breast tumors are conserved across microarray platforms , 2006, BMC Genomics.

[23]  Steven J. M. Jones,et al.  Comprehensive molecular portraits of human breast tumours , 2013 .

[24]  Michael Q. Zhang,et al.  Integrative analysis of 111 reference human epigenomes , 2015, Nature.

[25]  John D. Storey,et al.  Statistical significance for genomewide studies , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[26]  Olufunmilayo I. Olopade,et al.  Basal-like Breast cancer DNA copy number losses identify genes involved in genomic instability, response to therapy, and patient survival , 2011, Breast Cancer Research and Treatment.

[27]  Stein Aerts,et al.  iRegulon: From a Gene List to a Gene Regulatory Network Using Large Motif and Track Collections , 2014, PLoS Comput. Biol..

[28]  Mark Gerstein,et al.  A comprehensive nuclear receptor network for breast cancer cells. , 2013, Cell reports.

[29]  Philip Machanick,et al.  MEME-ChIP: motif analysis of large DNA datasets , 2011, Bioinform..

[30]  R. Tibshirani,et al.  Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[31]  G. Wahl,et al.  Cell state plasticity, stem cells, EMT, and the generation of intra-tumoral heterogeneity , 2017, npj Breast Cancer.

[32]  Steven L Salzberg,et al.  Fast gapped-read alignment with Bowtie 2 , 2012, Nature Methods.

[33]  Erik W Thompson,et al.  Epithelial to mesenchymal transition and breast cancer , 2009, Breast Cancer Research.

[34]  S. Chin,et al.  BCL11A is a triple-negative breast cancer gene with critical functions in stem and progenitor cells , 2015, Nature Communications.