Format

Send to

Choose Destination
Cell. 2019 Sep 5;178(6):1465-1477.e17. doi: 10.1016/j.cell.2019.08.018.

A Pan-cancer Transcriptome Analysis Reveals Pervasive Regulation through Alternative Promoters.

Author information

1
Computational and Systems Biology, Genome Institute of Singapore, Singapore 138672, Singapore; School of Computing, National University of Singapore, Singapore 117417, Singapore.
2
Computational and Systems Biology, Genome Institute of Singapore, Singapore 138672, Singapore.
3
European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK; Genome Biology Unit, EMBL, Heidelberg, 69117, Germany.
4
European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK; CIBIO/InBIO - Research Center in Biodiversity and Genetic Resources, Universidade do Porto, Vairão 4485-601, Portugal.
5
Department of Computer Science, ETH Zurich, Zurich 8092, Switzerland; Department of Biology, ETH Zurich, Zurich 8093, Switzerland; Computational Biology Center, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; SIB Swiss Institute of Bioinformatics, Lausanne 1015, Switzerland; Biomedical Informatics Research, University Hospital Zurich, Zurich 8091, Switzerland.
6
Department of Computer Science, ETH Zurich, Zurich 8092, Switzerland; Computational Biology Center, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; SIB Swiss Institute of Bioinformatics, Lausanne 1015, Switzerland; Biomedical Informatics Research, University Hospital Zurich, Zurich 8091, Switzerland.
7
European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK; Genome Biology Unit, EMBL, Heidelberg, 69117, Germany; Division of Computational Genomics and Systems Genetics, German Cancer Research Center (DKFZ), Heidelberg 69120, Germany.
8
European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK.
9
Department of Biomolecular Engineering, University of California, Santa Cruz, Santa Cruz, CA 95064, USA.
10
Department of Computer Science, ETH Zurich, Zurich 8092, Switzerland; Department of Biology, ETH Zurich, Zurich 8093, Switzerland; Computational Biology Center, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; SIB Swiss Institute of Bioinformatics, Lausanne 1015, Switzerland; Biomedical Informatics Research, University Hospital Zurich, Zurich 8091, Switzerland; Weill Cornell Medical College, New York, NY 10065, USA.
11
Program in Cancer and Stem Cell Biology, Duke-NUS Medical School, Singapore 169857, Singapore; Cancer Science Institute of Singapore, National University of Singapore, Singapore 117599, Singapore; Cancer Therapeutics and Stratified Oncology, Genome Institute of Singapore, Singapore 138672, Singapore; SingHealth/Duke-NUS Institute of Precision Medicine, National Heart Centre Singapore, Singapore 169856, Singapore; Cellular and Molecular Research, National Cancer Centre, Singapore 169610, Singapore; Singapore Gastric Cancer Consortium, Singapore 119074, Singapore.
12
Computational and Systems Biology, Genome Institute of Singapore, Singapore 138672, Singapore; Cellular and Molecular Research, National Cancer Centre, Singapore 169610, Singapore. Electronic address: gokej@gis.a-star.edu.sg.

Abstract

Most human protein-coding genes are regulated by multiple, distinct promoters, suggesting that the choice of promoter is as important as its level of transcriptional activity. However, while a global change in transcription is recognized as a defining feature of cancer, the contribution of alternative promoters still remains largely unexplored. Here, we infer active promoters using RNA-seq data from 18,468 cancer and normal samples, demonstrating that alternative promoters are a major contributor to context-specific regulation of transcription. We find that promoters are deregulated across tissues, cancer types, and patients, affecting known cancer genes and novel candidates. For genes with independently regulated promoters, we demonstrate that promoter activity provides a more accurate predictor of patient survival than gene expression. Our study suggests that a dynamic landscape of active promoters shapes the cancer transcriptome, opening new diagnostic avenues and opportunities to further explore the interplay of regulatory mechanisms with transcriptional aberrations in cancer.

KEYWORDS:

RNA sequencing; TCGA; alternative promoters; cancer; computational biology; gene expression; genomics; survival analysis; transcriptional regulation; transcriptomics

PMID:
31491388
DOI:
10.1016/j.cell.2019.08.018

Supplemental Content

Full text links

Icon for Elsevier Science
Loading ...
Support Center