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Transcriptional profiling reveals a subset of human breast tumors that retain wt TP53 but display mutant p53-associated features

  • Gal Benor
  • , Garold Fuks
  • , Suet Feung Chin
  • , Oscar M. Rueda
  • , Saptaparna Mukherjee
  • , Sharathchandra Arandkar
  • , Yael Aylon
  • , Carlos Caldas
  • , Eytan Domany*
  • , Moshe Oren*
  • *Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

13 Scopus citations

Abstract

TP53 gene mutations are very common in human cancer. While such mutations abrogate the tumor suppressive activities of the wild-type (wt) p53 protein, some of them also endow the mutant (mut) protein with oncogenic gain of function (GOF), facilitating cancer progression. Yet, p53 may acquire altered functionality even without being mutated; in particular, experiments with cultured cells revealed that wtp53 can be rewired to adopt mut-like features in response to growth factors or cancer-mimicking genetic manipulations. To assess whether such rewiring also occurs in human tumors, we interrogated gene expression profiles and pathway deregulation patterns in the METABRIC breast cancer (BC) dataset as a function of TP53 gene mutation status. Harnessing the power of machine learning, we optimized a gene expression classifier for ER+Her2- patients that distinguishes tumors carrying TP53 mutations from those retaining wt TP53. Interestingly, a small subset of wt TP53 tumors displayed gene expression and pathway deregulation patterns markedly similar to those of TP53-mutated tumors. Moreover, similar to TP53-mutated tumors, these ‘pseudomutant’ cases displayed a signature for enhanced proliferation and had worse prognosis than typical wtp53 tumors. Notably, these tumors revealed upregulation of genes which, in BC cell lines, were reported to be positively regulated by p53 GOF mutants. Thus, such tumors may benefit from mut p53-associated activities without having to accrue TP53 mutations.

Original languageEnglish
Pages (from-to)1640-1652
Number of pages13
JournalMolecular Oncology
Volume14
Issue number8
DOIs
StatePublished - 1 Aug 2020
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2020 The Authors. Published by FEBS Press and John Wiley & Sons Ltd.

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

Keywords

  • METABRIC
  • breast cancer
  • machine learning
  • p53 gain of function
  • pseudomutant p53

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