CAREER: Algorithms for Comprehensive and Cost-effective Cancer Phylogeny Inference from Multi-omics Single-cell Sequencing Data

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Cancer is the leading cause of premature death in the United States. To lessen the burden of cancer and improve clinical practice, researchers use algorithms to reconstruct the evolutionary history, or phylogeny, of a tumor from DNA sequencing data. Specifically, a tumor phylogeny describes the precise mutational composition of a patient’s tumor, thus enabling precision medicine with improved clinical outcomes. Moreover, comparative analysis of tumor phylogenies from patient cohorts enables the identification of key mechanisms underlying tumor evolution and metastasis, which in turn may lead to new treatment avenues. The next technological frontier in the field is single-cell sequencing (SCS), which holds the potential to precisely reconstruct a tumor's evolutionary history at single cell resolution. While SCS is becoming the de facto standard in cancer genomics, algorithms for this new type of data are still in their infancy and do not yet comprehensively model cancer evolution. This project will address this gap by developing new tools that will enable practitioners to cost-efficiently use SCS to comprehensively study a tumor's evolution, thereby advancing the state of knowledge regarding cancer progression, which in turn may lead to better targeted cancer therapies. Results from this project will be disseminated through open-source software. The integrated research and educational activities include interdisciplinary bioinformatics curriculum development, outreach to high school students and research opportunities for students in underrepresented groups.This project seeks new models, algorithms and practical implementations to accurately and cost-efficiently infer comprehensive cancer phylogenies from SCS data and integrate the proposed research into education and outreach. The project couples a new evolutionary model that incorporates somatic mutations of varying genomic scales with efficient phylogeny inference algorithms that are able to deal with hybrid SCS data obtained using multiple experimental techniques. The aims of this research are: (1) inference of comprehensive cancer phylogenies from hybrid single-cell sequencing data, (2) design of cost-efficient single-cell sequencing experiments that are sufficiently powered to enable accurate phylogeny inference, and (3) application of the developed methods to data obtained from clinical collaborators within integrated, scalable, extensible and user-friendly analysis pipelines. Results, software and additional information will be available at http://el-kebir.net/scp.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.