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Proc Natl Acad Sci U S A. 2015 Jun 23;112(25):7731-6. doi: 10.1073/pnas.1424272112. Epub 2015 Jun 8.

Inference of transcriptional regulation in cancers.

Author information

1
Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Harvard T.H. Chan School of Public Health, Boston, MA 02215;
2
Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215; Center for Cancer Genome Discovery, Dana-Farber Cancer Institute, Boston, MA 02215; Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142;
3
Department of Statistics, Harvard University, Cambridge, MA 02138.
4
Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Harvard T.H. Chan School of Public Health, Boston, MA 02215; xsliu@jimmy.harvard.edu.

Abstract

Despite the rapid accumulation of tumor-profiling data and transcription factor (TF) ChIP-seq profiles, efforts integrating TF binding with the tumor-profiling data to understand how TFs regulate tumor gene expression are still limited. To systematically search for cancer-associated TFs, we comprehensively integrated 686 ENCODE ChIP-seq profiles representing 150 TFs with 7484 TCGA tumor data in 18 cancer types. For efficient and accurate inference on gene regulatory rules across a large number and variety of datasets, we developed an algorithm, RABIT (regression analysis with background integration). In each tumor sample, RABIT tests whether the TF target genes from ChIP-seq show strong differential regulation after controlling for background effect from copy number alteration and DNA methylation. When multiple ChIP-seq profiles are available for a TF, RABIT prioritizes the most relevant ChIP-seq profile in each tumor. In each cancer type, RABIT further tests whether the TF expression and somatic mutation variations are correlated with differential expression patterns of its target genes across tumors. Our predicted TF impact on tumor gene expression is highly consistent with the knowledge from cancer-related gene databases and reveals many previously unidentified aspects of transcriptional regulation in tumor progression. We also applied RABIT on RNA-binding protein motifs and found that some alternative splicing factors could affect tumor-specific gene expression by binding to target gene 3'UTR regions. Thus, RABIT (rabit.dfci.harvard.edu) is a general platform for predicting the oncogenic role of gene expression regulators.

KEYWORDS:

RNA-binding protein; regulatory inference; transcription factor; tumor profiling

PMID:
26056275
PMCID:
PMC4485084
DOI:
10.1073/pnas.1424272112
[Indexed for MEDLINE]
Free PMC Article

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