Joint CMU-Pitt Ph.D. Program in Computational Biology and Department of Computational and Systems Biology Seminar

  • Assistant Professor of Medicine
  • Division of Medical Genetics
  • University of California San Diego

Interaction Landscape of Inherited Polymorphisms with Somatic Events in Cancer

Genome-wide association studies (GWAS) have linked hundreds of common germline variants to inherited predisposition for specific cancers. However, determining the precise biological mechanism by which these loci lead to cancer susceptibility has proven challenging. More recently, there have been reports of specific germline haplotypes that increase the probability that a tumor acquires a specific mutation, but few cancer GWAS thus far have collected both germline and tumor genomes. Using matched germline and tumor genomic data for nearly 6000 The Cancer Genome Atlas (TCGA) patients, it was possible to systematically screen for and validate 412 associations between germline loci and tumor site as well as for a subset of common tumor genotypes involving known cancer genes. By this approach, we sought to evaluate the extent to which the germline influences where and how tumors develop. Among germline-somatic interactions, we found germline variants in RBFOX1 that increase incidence of SF3B1 somatic mutation by eight-fold via functional alterations in RNA splicing. Similarly, 19p13.3 variants were associated with a four-fold increased likelihood of somatic mutations in PTEN. In support of this association, we found that PTEN knock-down sensitized the MTOR pathway to high expression of the 19p13.3 gene GNA11. Finally, we observed that stratifying patients by germline polymorphisms exposes distinct somatic mutation landscapes, implicating new cancer genes. These associations, obtained by comparing similar tumors with distinct genomic characteristics, provide a new perspective on cancer risk by tying the germline locus to a specific event in the tumor. The identified interactions suggest much more specific hypotheses about how a particular germline locus contributes to disease, thereby providing new clues to unravel the biology underlying inherited cancer risk. Our work contributes to accumulating evidence that the germline biases the emergence of specific tumor genotypes suggests that it may be possible to predict how an individual’s tumor will develop, potentially allowing a shift from reactionary approaches toward more proactive approaches for planning therapeutic strategies.

The Carter Lab is a bioinformatics and computational biology lab focused on developing strategies to 1) model the impact of somatic mutations on intracellular biological processes, 2) identify genetic variants that contribute to disease predisposition, 3) quantify the influence of germline polymorphism on somatic tumor phenotypes, 4) investigate the biological networks by which cancer cells transduce information about their environment and 5) inform precision cancer therapy from genomic data.

Host: Anne-Ruxandra Carvunis

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