CCB Seminar Series

Learn the Latest Developments in Computational Biomedicine

Seminar Topics: Subject areas include single-cell, spatial, and multi-omics approaches, image analysis, knowledge representation and ontologies, and computational aspects underlying the analysis of large and hard-to-manage volumes of biomedical data. CCB invites HMS-affiliated researchers and external speakers to present their work on these topics with the goal of fostering exchange and stimulating discussions between researchers, experimentalists, computational biologists, data scientists, and software developers. We welcome suggestions for future speakers! Please submit these requests via email to Ludwig Geistlinger for review.

Attendees: Graduate and medical students, postdocs, research staff and faculty interested in getting up to speed with current breakthrough innovations for obtaining and analyzing biomedical data. If interested in staying up-to-date on upcoming seminars, submit a request to be added to the seminar mailing list.

Past CCB Seminars: Descriptions and recordings of select past seminars can be found here.

May 2024 Seminar

Speaker: Dr. Alexander (Sasha) Gusev, HMS Professor

When: Monday, May 20, 11:00 AM ET

Talk Title: Learning germline influences on clinical outcomes from thousands of sequenced tumors

Hybrid: Countway L1-032 or Zoom Link

Abstract: A comprehensive analysis of clinical and polygenic germline influences on somatic mutational burden. Tumor mutational burden (TMB), the total number of somatic mutations in the tumor, and copy number burden (CNB), the corresponding measure of aneuploidy, are established fundamental somatic features and emerging biomarkers for immunotherapy. However, the genetic and non-genetic influences on TMB/CNB and, critically, the manner by which they influence patient outcomes remain poorly understood. Here, we present a large germline-somatic study of TMB/CNB with >23,000 individuals across 17 cancer types, of which 12,000 also have extensive clinical, treatment, and overall survival (OS) measurements available. We report dozens of clinical associations with TMB/CNB, observing older age and male sex to have a strong effect on TMB and weaker impact on CNB. We additionally identified significant germline influences on TMB/CNB, including fine-scale European ancestry and germline polygenic risk scores (PRSs) for smoking, tanning, white blood cell counts, and educational attainment. We quantify the causal effect of exposures on somatic mutational processes using Mendelian randomization. Many of the identified features associated with TMB/CNB were additionally associated with OS for individuals treated at a single tertiary cancer center. For individuals receiving immunotherapy, we observed a complex relationship between PRSs for educational attainment, self-reported college attainment, TMB, and survival, suggesting that the influence of this biomarker may be substantially modified by socioeconomic status. While the accumulation of somatic alterations is a stochastic process, our work demonstrates that it can be shaped by host characteristics including germline genetics.

Bio: Alexander (Sasha) Gusev, PhD is a computational biologist and an Assistant Professor of Medicine at Harvard Medical School. His research focuses on deciphering biological mechanisms and trait biology from genome-wide association studies; germline influences of cancer and response to therapy; and population genetics of large clinical cohorts. Dr. Gusev has developed computational methods that use genetic data to decipher disease mechanisms. For example, he has identified 34 new genes associated with increased risk of earliest-stage ovarian cancer. He has developed computational methods that integrate molecular data to facilitate functional interpretation of findings from genome-wide association studies. He has contributed to the development of the transcriptome-wide association study approach to mapping disease-associated genes. In addition, he studies the interactions between germline (host) and somatic events (tumor), and their effects on cancer progression and treatment response to advance precision oncology.

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