This federal Project Grant award of $505,613 from the National Cancer Institute (CFDA 93.394 - Cancer Detection and Diagnosis Research) will support the development of statistical and computational approaches to leverage genomic data and spatial omics technology for precision cancer prevention.
The key products and services to be delivered include: (1) deep learning-based methods to identify tumor subtypes and risk factors associated with poor outcomes, and (2) statistical methods for assessing the cost-effectiveness of time-varying cancer screening strategies based on improved risk prediction. The awardee, Fred Hutchinson Cancer Center, will apply these novel analytical approaches to data from cancer consortia and cohorts to gain insights into carcinogenesis and risk assessment, with the goal of enabling tailored prevention strategies. The project will also produce open-source software packages and data processing pipelines to make these methodologies broadly available to the research community.
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