Bilkent University
Department of Computer Engineering
SEMINAR
Trisicell: a computational toolkit for investigating mutational intratumor heterogeneity via single cell full-length transcriptome sequencing
Prof.Süleyman Cenk Şahinalp
National Cancer Institute, NIH, United States
Abstract: Tumor evolution and the resulting intratumor heterogeneity (ITH) are among the primary causes of treatment failure in cancers. Recently single-cell RNA sequencing (scRNAseq) has advanced our understanding of ITH from a gene expression point of view, but there has been limited progress in modeling tumor evolution through mutations observed in scRNAseq data due to low coverage and high levels of noise. In this talk we will introduce Trisicell, a computational toolkit to investigate progression history of a tumor. Trisicell robustly reconstructs the progression history and the resulting ITH on matched bulk DNA and single cell RNA sequencing data from mouse models and human tumor samples. On scRNAseq data from mouse melanoma tumors that have been subject to immune checkpoint blockade (ICB) therapy, Trisicell identified subclonal-seeding mutations associated with specific developmental states and revealed that neoantigens depleted by ICB were predominantly expressed in minor subclones, suggesting that post-treatment cancer recurrence is driven by immunoediting.
Bio: S. Cenk Sahinalp received his B.Sc. in Electrical Engineering at Bilkent University, Ankara, Turkey and his Ph.D. in Computer Science from the University of Maryland, College Park. His Ph.D. thesis introduced the first work optimal parallel algorithm for suffix tree construction and the first linear time algorithm for pattern matching. After a brief postdoctoral fellowship at Bell Labs, Murray Hill, he has worked as a Computer Science professor, most recently at Indiana University, Bloomington. Sahinalp’s research has focused on combinatorial algorithms and data structures, primarily for strings/sequences, and their applications to biomolecular sequence analysis, especially in the context of cancer. In the past decade, his lab has developed several algorithmic methods for efficient and effective use of high-throughput sequencing data for better characterization of the structure, evolution and heterogeneity of cancer genomes. He has (co)trained more than two dozen Ph.D. students and postdocs - many of them now hold independent academic and research positions in the U.S., Canada and Europe. He is also actively engaged in the computational biology community, having organized RECOMB 2011 in Vancouver, BC, chairing the program committee of RECOMB 2017 in Hong Kong, and founding the RECOMB-Seq meeting series.
DATE: April 24, Monday @ 13:30
Place: EA 409