Network-guided prediction of aromatase inhibitor response in breast cancer

PLoS Comput Biol. 2019 Feb 11;15(2):e1006730. doi: 10.1371/journal.pcbi.1006730. eCollection 2019 Feb.

Abstract

Prediction of response to specific cancer treatments is complicated by significant heterogeneity between tumors in terms of mutational profiles, gene expression, and clinical measures. Here we focus on the response of Estrogen Receptor (ER)+ post-menopausal breast cancer tumors to aromatase inhibitors (AI). We use a network smoothing algorithm to learn novel features that integrate several types of high throughput data and new cell line experiments. These features greatly improve the ability to predict response to AI when compared to prior methods. For a subset of the patients, for which we obtained more detailed clinical information, we can further predict response to a specific AI drug.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Algorithms
  • Aromatase Inhibitors / pharmacology
  • Breast Neoplasms / genetics
  • Cell Line, Tumor
  • Computational Biology / methods*
  • Drug Resistance, Neoplasm / drug effects
  • Female
  • Gene Expression Regulation, Neoplastic / drug effects
  • Genetic Testing / methods*
  • Humans
  • Neural Networks, Computer
  • Receptors, Estrogen / genetics

Substances

  • Aromatase Inhibitors
  • Receptors, Estrogen

Grant support

This work was supported in part by the National Science Foundation (grant number DBI-1356505 to Z.B.J.), by the U.S. National Institute of Health (grants 1U54HL127624 to Z.B.J. and 1F32CA216937 to M.M.R.) and by the Pennsylvania Department of Health (Health Research Nonformula Grant (CURE) Awards to Z.B.J.). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.