Akhil Jakatdar will present his General Exam "Inferring Phylogenetic Trees from Longitudinal Single-Cell Tumor Samples" on Friday, October 11, 2024 at 3:30 PM in CS 402. Committee Members: Ben Raphael (advisor), Mona Singh, Yuri Pritykin Abstract: Recent advances in high-throughput single-cell sequencing technology provide the opportunity to better understand the tumor evolutionary process through large-scale longitudinal profiling studies. These studies allow for cancer progression to be observed in the same patient across multiple timepoints thus introducing a new modality of temporal information associated with the already collected genomic data. As cancer proliferates through the cell division process, we can model this evolutionary process with phylogenetic trees. We demonstrate that current single-cell tumor phylogenetic inference methods infer trees that are discordant with the additional temporal information collected through longitudinal studies. We model a longitudinal observance process underlying these single-cell longitudinal profiling studies. We integrate this longitudinal observance process model with the Perfect Phylogeny mutation evolutionary model to construct the Longitudinally Observed Perfect Phylogeny (LOPP) model which characterizes the tumor evolutionary process in longitudinal studies. As high-throughput single-cell sequencing technology is error-prone, we formulate the Maximum Likelihood Longitudinal Phylogeny Reconstruction (ML-LPR) problem that asks to reconstruct the highest likelihood phylogeny given observed single-cell sequencing reads under some probabilistic model. We prove that the ML-LPR under the LOPP model is NP-hard and introduce an ILP method, Phyllochron, that solves a variant of the ML-LPR on a fixed mutation tree. We show that Phyllochron produces longitudinal observed phylogenies on simulated data cases where established single-cell phylogeny inference methods that do not explicitly model temporal information cannot. On real data, Phyllochron infers more plausible phylogenies compared with competing methods, suggesting that the LOPP can accurately describe the tumor evolutionary process even in the event of sampling concerns and sequencing errors. Reading List: https://docs.google.com/document/d/1xOMfVhdcSmnmGLZMyhiauNsd_7KXJwIyTha79NZX... Everyone is invited to attend the talk, and those faculty wishing to remain for the oral exam following are welcome to do so.