Work

Network-guided prediction of aromatase inhibitor response in breast cancer

Year: 2019

Type: article

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.

Source: PLoS Computational Biology

DOI: 10.1371/journal.pcbi.1006730

Authors: Matthew Ruffalo, Roby Thomas, Jian Chen, Adrian V. Lee, Steffi Oesterreich, Ziv Bar-Joseph

Institutions: Carnegie Mellon University, Magee-Womens Research Institute, UPMC Hillman Cancer Center

Language: English

Topic: Breast Cancer Treatment Studies

Subfield: Cancer Research

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $2,350