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Recurrent and functional regulatory mutations in breast cancer

  • Esther Rheinbay
  • , Prasanna Parasuraman
  • , Jonna Grimsby
  • , Grace Tiao
  • , Jesse M. Engreitz
  • , Jaegil Kim
  • , Michael S. Lawrence
  • , Amaro Taylor-Weiner
  • , Sergio Rodriguez-Cuevas
  • , Mara Rosenberg
  • , Julian Hess
  • , Chip Stewart
  • , Yosef E. Maruvka
  • , Petar Stojanov
  • , Maria L. Cortes
  • , Sara Seepo
  • , Carrie Cibulskis
  • , Adam Tracy
  • , Trevor J. Pugh
  • , Jesse Lee
  • Zongli Zheng, Leif W. Ellisen, A. John Iafrate, Jesse S. Boehm, Stacey B. Gabriel, Matthew Meyerson, Todd R. Golub, Jose Baselga, Alfredo Hidalgo-Miranda, Toshi Shioda, Andre Bernards, Eric S. Lander, Gad Getz
  • Broad Institute
  • Massachusetts General Hospital
  • Massachusetts Institute of Technology
  • Instituto de Enfermedades de la Mama FUCAM
  • University Health Network
  • Harvard University
  • Dana-Farber Cancer Institute
  • Memorial Sloan-Kettering Cancer Center
  • Instituto Nacional de Medicina Genomica

Research output: Contribution to journalArticlepeer-review

252 Scopus citations

Abstract

Genomic analysis of tumours has led to the identification of hundreds of cancer genes on the basis of the presence of mutations in protein-coding regions. By contrast, much less is known about cancer-causing mutations in non-coding regions. Here we perform deep sequencing in 360 primary breast cancers and develop computational methods to identify significantly mutated promoters. Clear signals are found in the promoters of three genes. FOXA1, a known driver of hormone-receptor positive breast cancer, harbours a mutational hotspot in its promoter leading to overexpression through increased E2F binding. RMRP and NEAT1, two non-coding RNA genes, carry mutations that affect protein binding to their promoters and alter expression levels. Our study shows that promoter regions harbour recurrent mutations in cancer with functional consequences and that the mutations occur at similar frequencies as in coding regions. Power analyses indicate that more such regions remain to be discovered through deep sequencing of adequately sized cohorts of patients.

Original languageEnglish
Pages (from-to)55-60
Number of pages6
JournalNature
Volume547
Issue number7661
DOIs
StatePublished - 6 Jul 2017
Externally publishedYes

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

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