Nature Communications (Apr 2019)

Breast cancer quantitative proteome and proteogenomic landscape

  • Henrik J. Johansson,
  • Fabio Socciarelli,
  • Nathaniel M. Vacanti,
  • Mads H. Haugen,
  • Yafeng Zhu,
  • Ioannis Siavelis,
  • Alejandro Fernandez-Woodbridge,
  • Miriam R. Aure,
  • Bengt Sennblad,
  • Mattias Vesterlund,
  • Rui M. Branca,
  • Lukas M. Orre,
  • Mikael Huss,
  • Erik Fredlund,
  • Elsa Beraki,
  • Øystein Garred,
  • Jorrit Boekel,
  • Torill Sauer,
  • Wei Zhao,
  • Silje Nord,
  • Elen K. Höglander,
  • Daniel C. Jans,
  • Hjalmar Brismar,
  • Tonje H. Haukaas,
  • Tone F. Bathen,
  • Ellen Schlichting,
  • Bjørn Naume,
  • Consortia Oslo Breast Cancer Research Consortium (OSBREAC),
  • Torben Luders,
  • Elin Borgen,
  • Vessela N. Kristensen,
  • Hege G. Russnes,
  • Ole Christian Lingjærde,
  • Gordon B. Mills,
  • Kristine K. Sahlberg,
  • Anne-Lise Børresen-Dale,
  • Janne Lehtiö

DOI
https://doi.org/10.1038/s41467-019-09018-y
Journal volume & issue
Vol. 10, no. 1
pp. 1 – 14

Abstract

Read online

Gene expression profiles can classify breast cancer into five clinically relevant subtypes. Here, the authors perform an in-depth quantitative profiling of the proteome of 45 breast tumors, and show they can recapitulate the transcriptome-based classifications and identify many potentially antigenic tumour-specific peptides.