Deep Learning Modeling of Androgen Receptor Responses to Prostate Cancer Therapies
Abstract
Gain-of-function mutations in human androgen receptor (AR) are among the major causes of drug resistance in prostate cancer (PCa). Identifying mutations that cause resistant phenotype is of critical importance for guiding treatment protocols, as well as for designing drugs that do not elicit adverse responses. However, experimental characterization of these mutations is time consuming and costly; thus, predictive models are needed to anticipate resistant mutations and to guide the drug discovery process. In this work, we leverage experimental data collected on 68 AR mutants, either observed in the clinic or described in the literature, to train a deep neural network (DNN) that predicts the response of these mutants to currently used and experimental anti-androgens and testosterone. We demonstrate that the use of this DNN, with general 2D descriptors, provides a more accurate prediction of the biological outcome (inhibition, activation, no-response, mixed-response) in AR mutant-drug pairs compared to other machine learning approaches. Finally, the developed approach was used to make predictions of AR mutant response to the latest AR inhibitor darolutamide, which were then validated by in-vitro experiments.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Aug 14, 2020
- Source ID
- 10.3390/ijms21165847
Entities
People
- Artem Cherkasov
- Martin Ester
- Nada Lallous
- Oliver Snow
Organizations
- Canadian HIV Trials Network, Canadian Institutes of Health Research
- United States Department of Defense