Grant Number:	1P01CA306870-01A1 Interpret this number
Primary Investigator:	Tavtigian, Sean
Organization:	Utah State Higher Education System--University Of Utah
Project Title:	Democratizing Detection of Hereditary Cancer Syndromes (2DETECT)
Fiscal Year:	2026

Overall: ABSTRACT (Tavtigian and Del Fiol) Early identification of individuals at high risk of hereditary cancers is critical for personalized cancer prevention and to reduce morbidity and mortality. Despite recommendations from multiple evidence-based guidelines, and increasing availability of low-cost genetic testing, over 85% of individuals who are detected to have a hereditary cancer risk have already developed cancer. Therefore, there is a tremendous missed opportunity to improve cancer prevention at the health system-level. In prior NCI-funded work, we implemented GARDE (Del Fiol & Kawamoto, MPIs), a digital health software platform that uses (i) algorithms to identify individuals meeting criteria for genetic testing of hereditary cancer using family history data in electronic health records; and (ii) automated chatbots (GARDE-Chat) for patient outreach, pre-test education, and genetic test facilitation. While GARDE is highly effective in identifying a pool of patients in need of cancer genetic services, we also identified substantial differences in cancer family history documentation in the electronic health record between demographic groups, leading to similar differences in identification of patients meeting algorithm criteria for genetic evaluation. Here, we aim to address these differences in genetic testing, especially among patients from populations with lower prior utilization of hereditary cancer genetics services. The hypothesis underlying Project 1 is that augmented algorithms, using multiple data sources and novel artificial intelligence approaches, will significantly increase the identification of candidates for genetic testing of hereditary cancer syndromes across all demographic groups. The hypothesis underlying Project 2 is that among non-responders to an automated chatbot intervention, chatbot augmented with proactive navigation will be more effective than chatbot augmented with reactive navigation in terms of (i) completion of missing key family history needed to determine eligibility for genetic testing; and (ii) completion of cancer genetic services. The hypothesis underlying Project 3 is that reclassification of observed sequence variants of uncertain significance (VUS) will become more effective with the addition of calibrated clinical data that are currently not often captured for VUS evaluation, paired with improved calibrations of computational tools and functional assays. Each Project will use of each of our three Cores: Administrative (Core A), Electronic Data Acquisition (Core B), and Economic Evaluation (Core C). 2DETECT is fully aligned with NIH priorities by working on solution-oriented approaches in the implementation of cancer genetics for all populations. Successful completion of these Projects will result in a decisive and generalizable enhancement in cancer genetic predisposition testing. Economics analyses will assess implementation costs of population-based genetic testing programs, which will be central to changing testing guidelines and enabling dissemination of clinical cancer genetic predisposition testing.





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