Matthew Myers will present his General Exam on Tuesday, May 21, 2019 at 2pm in CS 401. The members of his committee are as follows: Ben Raphael (adviser), Mona Singh, and Olga Troyanskaya. Everyone is invited to attend his talk, and those faculty wishing to remain for the oral exam following are welcome to do so. His abstract and reading list follow below. Abstract: Cancer is a complex disease, and treatment of cancer is complicated by its heterogeneity: mutations and other characteristics vary greatly not just between cancer types, but also between cancers, and even between cells within the same individual. Longitudinal DNA sequencing of cancer patients yields insight into how tumors evolve over time or in response to treatment. However, sequencing data from bulk tumor samples often has considerable ambiguity in clonal composition, complicating the inference of ancestral relationships between clones. We introduce CALDER (Cancer Analysis of Longitudinal Data through Evolutionary Reconstruction), an algorithm to infer phylogenetic trees from longitudinal bulk DNA sequencing data. CALDER explicitly models a longitudinally-observed phylogeny incorporating constraints that longitudinal sampling imposes on phylogeny reconstruction. We show on simulated bulk tumor data that longitudinal constraints substantially reduce ambiguity in phylogeny reconstruction and that CALDER outperforms existing methods that do not leverage longitudinal information. On real data from two chronic lymphocytic leukemia patients, we find that CALDER reconstructs more plausible and parsimonious phylogenies than existing methods, with CALDER phylogenies containing fewer tumor clones per sample. CALDER’s use of longitudinal information will be advantageous in further studies of tumor heterogeneity and evolution. Reading list: Main textbook: Compeau & Pevzner - Bioinformatics Algorithms: an Active Learning Approach (2nd edition vol. I-II) Papers: El-Kebir et al., 2015, “Reconstruction of clonal trees and tumor composition from multi-sample sequencing data” (AncesTree) Popic et al., 2015, “Fast and scalable inference of multi-sample cancer lineages” (LICHeE) El-Kebir et al., 2018, “Inferring parsimonious migration histories for metastatic cancers” (MACHINA) Deshwar et al., 2015, “PhyloWGS: Reconstructing subclonal composition and evolution from whole-genome sequencing of tumors” Caravagna et al, 2018, “Detecting repeated cancer evolution from multi-region tumor sequencing data” (REVOLVER) Jahn et al., 2016, “Tree inference for single-cell data” (SCITE) Turajlic et al., 2019, Resolving genetic heterogeneity in cancer Schwartz et al, 2017, “The evolution of tumour phylogenetics: principles and practice“ Chapter 2 from Gusfield - ReCombinatorics: The Algorithmics of Ancestral Recombination Graphs and Explicit Phylogenetic Networks Gabow and Myers, 1978, “Finding all spanning trees of directed and undirected graphs.”