Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

Convergence of dispersed regulatory mutations predicts driver genes in prostate cancer

Richard C. Sallari, Nicholas A. Sinnott-Armstrong, Juliet D. French, Ken J. Kron, Jason Ho, Jason H. Moore, Vuk Stambolic, Stacey L. Edwards, Mathieu Lupien, Manolis Kellis
doi: https://doi.org/10.1101/097451
Richard C. Sallari
1Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts 02139, USA.
2The Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Nicholas A. Sinnott-Armstrong
3Department of Genetics, Stanford University School of Medicine, Stanford, California 94305, USA.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Juliet D. French
4Cancer Division, QIMR Berghofer Medical Research Institute, Brisbane, Queensland 4029, Australia.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Ken J. Kron
5The Princess Margaret Cancer Centre — University Health Network, Toronto, Ontario M5G 1L7, Canada.
6Department of Medical Biophysics, University of Toronto, Toronto, Ontario M5G 1L7, Canada.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Jason Ho
5The Princess Margaret Cancer Centre — University Health Network, Toronto, Ontario M5G 1L7, Canada.
6Department of Medical Biophysics, University of Toronto, Toronto, Ontario M5G 1L7, Canada.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Jason H. Moore
7Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Vuk Stambolic
5The Princess Margaret Cancer Centre — University Health Network, Toronto, Ontario M5G 1L7, Canada.
6Department of Medical Biophysics, University of Toronto, Toronto, Ontario M5G 1L7, Canada.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Stacey L. Edwards
4Cancer Division, QIMR Berghofer Medical Research Institute, Brisbane, Queensland 4029, Australia.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Mathieu Lupien
5The Princess Margaret Cancer Centre — University Health Network, Toronto, Ontario M5G 1L7, Canada.
6Department of Medical Biophysics, University of Toronto, Toronto, Ontario M5G 1L7, Canada.
8Ontario Institute for Cancer Research, Toronto, Ontario M5G 0A3, Canada.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Manolis Kellis
1Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts 02139, USA.
2The Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Supplementary material
  • Preview PDF
Loading

Abstract

Cancer sequencing predicts driver genes using recurrent protein-altering mutations, but detecting recurrence for non-coding mutations remains unsolved. Here, we present a convergence framework for recurrence analysis of non-coding mutations, using three-dimensional co-localization of epigenomically-defined regions. We define the regulatory plexus of each gene as its cell-type-specific three-dimensional gene-regulatory neighborhood, inferred using Hi-C chromosomal interactions and chromatin state annotations. Using 16 matched tumor-normal prostate transcriptomes, we predict tumor-upregulated genes, and find enriched plexus mutations in distal regulatory regions normally repressed in prostate, suggesting out-of-context de-repression. Using 55 matched tumor-normal prostate genomes, we predict 15 driver genes by convergence of dispersed, low-frequency mutations into high-frequency dysregulatory events along prostate-specific plexi, controlling for mutational heterogeneity across regions, chromatin states, and patients. These play roles in growth signaling, immune evasion, mitochondrial function, and vascularization, suggesting higher-order pathway-level convergence. We experimentally validate the PLCB4 plexus and its ability to affect the canonical PI3K cancer pathway.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
Back to top
PreviousNext
Posted December 30, 2016.
Download PDF

Supplementary Material

Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
Convergence of dispersed regulatory mutations predicts driver genes in prostate cancer
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
Convergence of dispersed regulatory mutations predicts driver genes in prostate cancer
Richard C. Sallari, Nicholas A. Sinnott-Armstrong, Juliet D. French, Ken J. Kron, Jason Ho, Jason H. Moore, Vuk Stambolic, Stacey L. Edwards, Mathieu Lupien, Manolis Kellis
bioRxiv 097451; doi: https://doi.org/10.1101/097451
Digg logo Reddit logo Twitter logo Facebook logo Google logo LinkedIn logo Mendeley logo
Citation Tools
Convergence of dispersed regulatory mutations predicts driver genes in prostate cancer
Richard C. Sallari, Nicholas A. Sinnott-Armstrong, Juliet D. French, Ken J. Kron, Jason Ho, Jason H. Moore, Vuk Stambolic, Stacey L. Edwards, Mathieu Lupien, Manolis Kellis
bioRxiv 097451; doi: https://doi.org/10.1101/097451

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Genomics
Subject Areas
All Articles
  • Animal Behavior and Cognition (3589)
  • Biochemistry (7553)
  • Bioengineering (5498)
  • Bioinformatics (20742)
  • Biophysics (10305)
  • Cancer Biology (7962)
  • Cell Biology (11624)
  • Clinical Trials (138)
  • Developmental Biology (6596)
  • Ecology (10175)
  • Epidemiology (2065)
  • Evolutionary Biology (13586)
  • Genetics (9525)
  • Genomics (12824)
  • Immunology (7911)
  • Microbiology (19518)
  • Molecular Biology (7647)
  • Neuroscience (42014)
  • Paleontology (307)
  • Pathology (1254)
  • Pharmacology and Toxicology (2195)
  • Physiology (3260)
  • Plant Biology (7027)
  • Scientific Communication and Education (1294)
  • Synthetic Biology (1948)
  • Systems Biology (5420)
  • Zoology (1113)