Abstract SY32-01: Knowledge management and decision support for commonly tested susceptibility mutations

Abstract

Abstract Challenges: Germline testing for inherited genetic susceptibility to cancer has become cheaper and widely used. Multigene panels now routinely test 25 to 123 genes. Health care providers, including clinicians and genetic counselors, must interpret results, assess the risk of various cancers, and advise on clinical management strategies. Aside from a small number of well-known genes, clinicians lack simple and reliable tools to personalize prevention decisions for individuals who test positive. Evidence on the types of cancer associated with susceptibility genes, and on the magnitude of increased risks associated with a mutation, is emerging at a fast rate. However, a large number of combinations of cancer type and gene variant needs to be considered. No comprehensive databases exist, information is dispersed over a vast number of published studies, the quality of the studies is uneven, and the data presented are seldom directly applicable to precision prevention decisions, which require absolute risk estimates. Thus, when assessing cancer risks for mutation carriers, clinical practice can take little advantage of the impressive scientific advancements in this field. Approaches: This presentation describes ongoing work to develop tools that could facilitate the translation into clinical practice of research results that would otherwise be difficult to access, systematize, interpret, and convert into actionable knowledge. The long-term goal should be to develop a clinical decision-support system that allows a clinician to enter important factors about a patient with a mutation and immediately receive patient-specific absolute risk estimates. The foundation for the clinical decision-support system needs to be laid by systematically collecting and curating studies on gene cancer associations and, ideally, developing a public access knowledge base for researchers and health care providers. These steps need computational tools for efficient implementations of this plan and for sustainable long-term updating of the knowledge base and decision-support system. The ask2me.org project and the data science behind it are attempting to achieve some of these steps. This project will be used as an illustration throughout. Impact: Progress in this area will have a direct clinical impact by providing patient-specific absolute risk estimates. These risk estimates will, in turn, lead to more carefully personalized prevention and management for individuals undergoing susceptibility testing, and will help us optimize screening and behavioral interventions to reduce risk. Citation Format: Giovanni Parmigiani, Danielle Braun, Kevin S. Hughes. Knowledge management and decision support for commonly tested susceptibility mutations [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr SY32-01.