Biologically informed deep neural network for prostate cancer discovery

Haitham A. Elmarakeby, Justin Hwang, Rand Arafeh, Jett Crowdis, Sydney Gang, David Liu, Saud H. AlDubayan, Keyan Salari, Steven Kregel, Camden Richter, Taylor E. Arnoff, Jihye Park, William C. Hahn and Eliezer M. Van Allen ()
Additional contact information
Haitham A. Elmarakeby: Dana-Farber Cancer Institute
Justin Hwang: University of Minnesota, Division of Hematology, Oncology and Transplantation
Rand Arafeh: Dana-Farber Cancer Institute
Jett Crowdis: Dana-Farber Cancer Institute
Sydney Gang: Dana-Farber Cancer Institute
David Liu: Dana-Farber Cancer Institute
Saud H. AlDubayan: Dana-Farber Cancer Institute
Keyan Salari: Dana-Farber Cancer Institute
Steven Kregel: University of Illinois at Chicago
Camden Richter: Dana-Farber Cancer Institute
Taylor E. Arnoff: Dana-Farber Cancer Institute
Jihye Park: Dana-Farber Cancer Institute
William C. Hahn: Dana-Farber Cancer Institute
Eliezer M. Van Allen: Dana-Farber Cancer Institute

Nature, 2021, vol. 598, issue 7880, 348-352

Abstract: Abstract The determination of molecular features that mediate clinically aggressive phenotypes in prostate cancer remains a major biological and clinical challenge1,2. Recent advances in interpretability of machine learning models as applied to biomedical problems may enable discovery and prediction in clinical cancer genomics3–5. 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.

Date: 2021
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DOI: 10.1038/s41586-021-03922-4

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