|Publication Type||Journal Article|
|Year of Publication||2021|
|Authors||Elmarakeby, HA, Hwang, J, Arafeh, R, Crowdis, J, Gang, S, Liu, D, AlDubayan, SH, Salari, K, Kregel, S, Richter, C, Arnoff, TE, Park, J, Hahn, WC, Van Allen, EM|
|Date Published||2021 Oct|
The determination of molecular features that mediate clinically aggressive phenotypes in prostate cancer remains a major biological and clinical challenge. Recent advances in interpretability of machine learning models as applied to biomedical problems may enable discovery and prediction in clinical cancer genomics. Here we developed P-NET-a biologically informed deep learning model-to stratify patients with prostate cancer by treatment-resistance state and evaluate molecular drivers of treatment resistance for therapeutic targeting through complete model interpretability. We demonstrate that P-NET can predict cancer state using molecular data with a performance that is superior to other modelling approaches. Moreover, the biological interpretability within P-NET revealed established and novel molecularly altered candidates, such as MDM4 and FGFR1, which were implicated in predicting advanced disease and validated in vitro. Broadly, biologically informed fully interpretable neural networks enable preclinical discovery and clinical prediction in prostate cancer and may have general applicability across cancer types.