Talks

Computational Foundations of Tumor Evolution and Intratumor Heterogeneity Inference

S. Cenk Sahinalp

IRB 2107 or Zoom https://umd.zoom.us/j/91267609044?pwd=g4uLkhpH4T8d2Gln0HXg6s4qaxzpbj.1

Friday, October 9, 2026, 11:00 am-12:00 pm

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Abstract

Cancer progression is fundamentally an evolutionary process: subpopulations of cells accumulate distinct mutations and are shaped by selection, giving rise to extensive intratumor heterogeneity (ITH) that drives treatment resistance and relapse. Reconstructing this evolutionary history from sequencing data is a rich source of computational problems, blending combinatorial optimization, probabilistic modeling, and statistical learning.

This talk will give an overview of the computational foundations of tumor evolution, with the aim of three key questions. First, how can mixed sequencing signals, from bulk sequenced tissue or low-coverage single cells, be deconvolved into distinct clonal populations? Second, how can phylogenetic trees relating these clonal cell populations be reconstructed, subject to biologically motivated constraints? Third, how can discordant evolutionary signals across multiple data modalities, including single-nucleotide and structural alterations, be reconciled into a single consistent model of tumor history?

The talk will present algorithmic methods to address these questions and will discuss how they extend to emerging single-cell and long-read sequencing technologies, where the volume and structure of the data open new opportunities and new computational bottlenecks. It will also touch on the statistical challenges of uncertainty in this setting, where ground truth is rarely available and model misspecification is the norm rather than the exception. Throughout, the talk will highlight open problems at the interface of combinatorics, statistics, and cancer biology, making the case that tumor evolution is not just an application domain for existing techniques but a source of new computational questions.

Bio

S. Cenk Sahinalp is the Acting Co-Chief and a Senior Investigator in the Cancer Data Science Laboratory at the National Cancer Institute, NIH, where his research focuses on developing computational methods for analyzing and managing biomolecular cancer sequencing data, with a long-standing emphasis on the discovery and interpretation of large-scale genomic and transcriptomic alterations in tumor cells. He received his B.Sc. in Electrical Engineering from Bilkent University in Ankara, and his Ph.D. in Computer Science from the University of Maryland, College Park, where his doctoral research introduced the first work-optimal parallel algorithm for suffix tree construction and the first linear-time algorithm for approximate pattern matching. Over the past two decades, his lab has developed numerous widely used algorithmic methods for harnessing high-throughput sequencing data to characterize the structure, evolution, and heterogeneity of cancer genomes. He has also been an active contributor to the computational biology community, having organized the RECOMB conference, founded the RECOMB-Seq meeting series in 2011, chaired the RECOMB program committee in 2017, and currently serving on the RECOMB steering committee. Sahinalp is a Fellow of the International Society for Computational Biology (ISCB) and was named a University of Maryland Department of Computer Science Distinguished Alumnus.

This talk is organized by Samuel Malede Zewdu