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

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.

Methods

This study takes advantage of a unique population-level data resource, the Utah Population Database (UPDB), containing vast genealogy and statewide cancer data. Familial risk is measured using standardized incidence risk (SIR) ratios that account for sex, age, birth cohort, and person-years of the pedigree

SUBMITTER: Hanson HA 

PROVIDER: S-EPMC7196496 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature