III: Medium: Scalable Evolutionary Analysis of SNVs and CNAs in Cancer Using Single-Cell DNA Sequencing Data

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Cancer is a disease that is driven by mutations in the genome of an individual. These mutations include single nucleotide variants, or SNVs, which alter a single nucleotide in the genome. They also include copy number aberrations, or CNAs, which result in the deletion or amplification of stretches of DNA in the genome. Detecting these mutations in genomic data obtained from cancer patients allows us to better understand and develop treatments for cancer. This project aims to develop tools for accomplishing this detection task using large amounts of genomic sequences obtained from many individual cells. The project by its nature is interdisciplinary and will help train students at the interface of multiple disciplines as well as provide software for the community at large. The project will result in scalable methods for SNV and CNA detection from single-cell DNA sequencing data. This will be accomplished through four thrusts. In thrust 1, the project will develop methods for simultaneous inference of SNVs and mutation trees. Here, models beyond the infinite-sites assumption will be included. In thrust 2, the project will produce new models and inference methods for genome evolution in the presence of CNAs. In thrust 3, the project will devise novel divide-and-conquer techniques to scale the methods of thrusts 1 and 2 to whole-genome data and data obtained from thousands of cells. In thrust 4, all methods will be evaluated on synthetic and biological data, and open-source implementation will be released publicly.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.