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.

journal_name

Int J Mol Sci

authors

Snow O,Lallous N,Ester M,Cherkasov A

doi

10.3390/ijms21165847

subject

Has Abstract

pub_date

2020-08-14 00:00:00

issue

16

issn

1422-0067

pii

ijms21165847

journal_volume

21

pub_type

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