Bayesian networks established functional differences between breast cancer subtypes

PLoS One. 2020 Jun 11;15(6):e0234752. doi: 10.1371/journal.pone.0234752. eCollection 2020.

Abstract

Breast cancer is a heterogeneous disease. In clinical practice, tumors are classified as hormonal receptor positive, Her2 positive and triple negative tumors. In previous works, our group defined a new hormonal receptor positive subgroup, the TN-like subtype, which had a prognosis and a molecular profile more similar to triple negative tumors. In this study, proteomics and Bayesian networks were used to characterize protein relationships in 96 breast tumor samples. Components obtained by these methods had a clear functional structure. The analysis of these components suggested differences in processes such as mitochondrial function or extracellular matrix between breast cancer subtypes, including our new defined subtype TN-like. In addition, one of the components, mainly related with extracellular matrix processes, had prognostic value in this cohort. Functional approaches allow to build hypotheses about regulatory mechanisms and to establish new relationships among proteins in the breast cancer context.

Publication types

  • Comparative Study
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Bayes Theorem
  • Breast Neoplasms / classification*
  • Breast Neoplasms / diagnosis
  • Breast Neoplasms / metabolism*
  • Breast Neoplasms / pathology
  • Extracellular Matrix / metabolism
  • Gene Ontology
  • Humans
  • Prognosis
  • Proteomics*

Grants and funding

This study was supported by Instituto de Salud Carlos III, Spanish Economy and Competitiveness Ministry, Spain and co-funded by the FEDER program, “Una forma de hacer Europa” (PI07/1302). LT-F is supported by the Spanish Economy and Competitiveness Ministry (DI-15-07614). AZ-M is supported by Consejería de Educación e Investigación de la Comunidad de Madrid (IND2018/BMD-9262). GP-V is supported by Consejería de Educación, Juventud y Deporte of Comunidad de Madrid (IND2017/BMD7783). EL-C is supported by the Spanish Economy and Competitiveness Ministry (PTQ2018-009760). The funders had no role in the study design, data collection and analysis, decision to publish or preparation of the manuscript.