Over the past two decades, risk assessment for inherited cancers has been driven by syndrome specific
models for the identification of high risk individuals who should undergo genetic testing. Genetic testing results
allow the clinician to ensure strategies for surveillance and/or intervention to prevent cancer in susceptible
individuals. This motivated our previous work in development of the PREMM models, the most recent of which
predicts risk of mutation in 5 genes that cause Lynch syndrome. The PREMM models have been incorporated
into national guidelines for the identification of Lynch syndrome. Parallel syndrome specific models have been
developed for hereditary breast and ovarian cancer (HBOC) based on the prediction of two genes (BRCA1,2).
While these models are well accepted in clinical practice, many individuals with hereditary cancer syndromes
remain unidentified as current models predict risk of a limited number of gene mutations. Evidence from the
use of multigene panel tests has provided an opportunity for the evolution of hereditary risk assessment
models that can lead to increased identification of high risk individuals. These tests have found that an
additional 15+ genes are implicated in both Lynch syndrome and HBOC, and that there is overlap in genetic
profiles across syndromes i.e. BRCA1/2 detected in Lynch patients and mismatch repair gene mutations
detected in HBOC patients. To address these unmet needs and as an extension of our prior work, we propose
to develop a multigene model that will predict risk of mutation in 20+ genes including the genes in current
models for HBOC and Lynch syndromes as well as an additional 15+ cancer susceptibility genes. We will
develop the multigene model in a study of more than 260,000 individuals both affected and unaffected with
cancer, compare its performance to current syndrome specific models, and perform validation in separate
populations. To this end, we propose the following specific aims: 1. To develop a genetic risk assessment tool
that will identify individuals who should undergo multigene panel testing for germline mutations associated with
inherited cancer susceptibility genes, 2. To compare the performance of the multigene risk assessment model
with syndrome specific models, including PREMM1,2,6, MMRpro, and BRCAPRO in a cohort of 2000 ethnically
and racially diverse patients, and 3. To externally validate the multigene risk-assessment model in (A) a clinic-
based population of patients without a personal history of cancer referred for hereditary cancer risk
assessment due to a family history of cancer and (B) unselected clinic-based populations of patients with
colorectal, breast, and pancreatic cancer. This work will lead to streamlined and comprehensive genetic risk
assessment of personal and family cancer histories and the first prediction model that can be used by
clinicians to determine who should undergo multigene panel genetic testing. Systematic application of this
model in clinical practice will lead to increased identification of individuals who carry mutations in cancer
susceptibility genes while reducing the number of low-risk individuals undergoing genetic testing.
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