Combinatorial Algorithms for Tumor Phylogenetics

Cancer is a genetic disease, where cell division, mutation and selection produce a heterogeneous tumor composed of multiple subpopulations of cells with different sets of mutations. During later stages of cancer progression, cancerous cells from the primary tumor migrate and seed metastases at distant anatomical sites. The cell division and mutation history of an individual tumor can be represented by a phylogenetic tree, which helps guide patient-specific treatments. In this talk, I will introduce combinatorial algorithms for reconstructing tumor phylogenies from bulk DNA sequencing data, where the measurements are a mixture of thousands of cells. These algorithms are based on a combinatorial characterization of phylogenetic trees as a restricted class of spanning trees in a graph, a characterization that also demonstrates the computational complexity of the problem. In addition, I will introduce a novel framework for analyzing the history of cellular migrations between anatomical sites in metastatic cancers. Finally, I will discuss algorithmic challenges in tumor phylogeny reconstruction from single-cell DNA sequencing data.

Bio:

El-Kebir received his PhD in Computer Science at VU University Amsterdam and Centrum Wiskunde & Informatica (2015) under the direction of Jaap Heringa and Gunnar Klau. He did postdoctoral training with Ben Raphael at Brown University and Princeton University (2014-2017). In 2018, he joined the University of Illinois at Urbana-Champaign as an Assistant Professor of Computer Science. El-Kebir has affiliate faculty appointments in Electrical and Computer Engineering, the Institute of Genomic Biology and the National Center for Supercomputing Applications. He received the National Science Foundation CISE Research Initiation Initiative (CRII) Award in 2019 and the CAREER Award in 2021.

El-Kebir's main research is in combinatorial optimization algorithms for problems in computational biology, with a particular focus on cancer genomics. Among his major contributions are advances in the theoretical foundations of cancer phylogenetics (e.g., hardness proofs for phylogeny estimation problems from mixture data), methods for the estimation of cancer phylogenies from sequencing data of tumors, and new mathematical models for studying cancer evolution and metastasis.

El-Kebir's current focus is on developing methods that enable the estimation of cancer phylogenies from single-cell sequencing data. A specific challenge he is addressing is the integration of data obtained from the same tumor using distinct single-cell technologies. Another focus is the development of comprehensive evolutionary models for somatic mutations that occur at varying genomic scales. More generally, El-Kebir is developing novel problem statements and corresponding methods to analyze omics data in novel ways, thereby improving scientific discovery.