Hossein Khiabanian, Cancer Institute of New Jersey

Abstract

Recent advances in the use of clinical sequencing platforms in precision oncology settings have resulted in unprecedented access to the genomes of individual tumors. These assays aim to reliably identify and annotate somatic alterations specific to cancer cells for accurate diagnosis and treatment. However, due to the common lack of patient-matched controls, there is a need for a systematic effort to interpret detected variants in tumor-only sequencing data and to accurately describe the genomic landscape of a single tumor. In this talk, I will present a set of integrated, information-theoretic approaches that permit selecting the most consistent mutational model, distinguishing alterations in the tumor from those present in all cells (germline), while accounting for biases inherent to DNA sequencing and sample purity estimation. Using simulations and large, independent clinical datasets, we demonstrate the accuracy and precision of our methods. We will also discuss cases for which these analyses provide a model for tumor evolution, demonstrating that additional inference of mutational signatures and dissection of heterogeneity in tumor microenvironment can generate diagnostic hypotheses that may lead to improved prognostication and treatment design.

Bio: Hossein Khiabanian is an Associate Professor of Pathology in Medical Informatics at Rutgers Cancer Institute of New Jersey. He trained in physics and systems biology, and has developed statistical approaches for analyzing high-throughput data to study hematologic and solid tumors. At Rutgers, he has focused on problems in computational biology and cancer genomics, based on the idea that studying complexity, dynamics, and stochastic patterns in biological data is critical for understanding how disease states initiate and evolve.

SPECIAL NOTE: This seminar is presented online only.

You can join via Webex

Meeting number (access code):  120 379 9698

Meeting password: 1234

Presented in association with the DATA-INSPIRE TRIPODS Institute.