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

Network-guided prediction of aromatase inhibitor response in breast cancer.

Author information

1
Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America.
2
Women's Cancer Research Center, Department of Pharmacology and Chemical Biology, UPMC Hillman Cancer Center, Magee Womens Research Institute, Pittsburgh, Pennsylvania, United States of America.
3
Machine Learning Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America.

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.

PMID:
30742607
PMCID:
PMC6386390
DOI:
10.1371/journal.pcbi.1006730
[Indexed for MEDLINE]
Free PMC Article

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