Harnessing Population Pedigree Data and Machine Learning Methods to Identify Patterns of Familial Bladder Cancer Risk.

Journal: Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology
PMID: 32098890

Abstract

BACKGROUND: Relatives of patients with bladder cancer have been shown to be at increased risk for kidney, lung, thyroid, and cervical cancer after correcting for smoking-related behaviors that may concentrate in some families. We demonstrate a novel approach to simultaneously assess risks for multiple cancers to identify distinct multicancer configurations (multiple different cancer types that cluster in relatives) surrounding patients with familial bladder cancer.

Authors

  • Heidi A Hanson
    Advanced Computing for Health Sciences, Oak Ridge National Laboratory, Oak Ridge, TN 37830, United States.
  • Claire L Leiser
    Population Sciences, Huntsman Cancer Institute at the University of Utah, Salt Lake City, Utah.
  • Brock O'Neil
    Division of Urology, Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah.
  • Christopher Martin
    Division of Urology, Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah.
  • Sumati Gupta
    Division of Oncology, Department of Medicine, Huntsman Cancer Institute at the University of Utah, Salt Lake City, Utah.
  • Ken R Smith
    Population Sciences, Huntsman Cancer Institute at the University of Utah, Salt Lake City, Utah.
  • Christopher Dechet
    Division of Urology, Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah.
  • William T Lowrance
    Division of Urology, Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah.
  • Michael J Madsen
    Population Sciences, Huntsman Cancer Institute at the University of Utah, Salt Lake City, Utah.
  • Nicola J Camp
    Population Sciences, Huntsman Cancer Institute at the University of Utah, Salt Lake City, Utah.

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