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.
The results of this effort will lead to the development and validation of a comprehensive risk assessment model that will predict the likelihood of carrying a germline cancer susceptibility mutation on multigene panel testing. It builds upon the investigative team's previous work on colorectal and breast-ovarian cancer syndrome-specific models which are limited to the prediction of mutation in 2-5 genes, expanding it in scope to include data on an additional 15+ cancer susceptibility genes generated from multigene panel testing. The result of this work will be a comprehensive, streamlined model that will predict mutation in a broad range of genes, increasing the identification of those at risk for a number of inherited cancers.
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