Studies of germline genetic variation in cancer cases and controls as well as studies of somatic mutation have
transformed our understanding of cancer etiology and lead to the development of life saving cancer
interventions. However, even though tumor progression, evolution, and treatment response are influenced by
both somatic and germline variation, these data have largely been examined in isolation. In this work, we
propose to integrate extensive data collection, novel statistical methods, and cutting-edge functional
validation to discover and characterize somatic-germline interactions in a pan-cancer study. Results
from our work will significantly benefit both cancer researcher and multiple medical research discipline more
broadly. Within the cancer genetics field, identifying somatic-germline interactions will help (i) identify new
classes of drugs targets causally upstream of those identified through somatic driver mutations, (ii) precisely
treat patients by selecting interventions the basis of germline and somatic genetics as well as tumor RNA-
sequencing, (iii) improve risk profiling, especially for tumor recurrence and outcomes, and (iv) develop
hypotheses of the germline risk variants mechanism, especially for non-coding variants.
To accomplish these goals, we will leverage tumor sequencing from the DFCI Profile Project together with
recent innovations in variant imputation to assemble the largest (N>25,000) pan-cancer germline-somatic
cohort to date. We will develop novel statistical and computational methods to maximize the value of these
data. Because over 90% of germline genetic variation associated with cancer risk and outcomes is in non-
coding regions of the genome we especially focus on integration of functional genomic sequencing from both
tumor and normal tissues. Our methods will be capable of modelling proximal germline-somatic interactions as
well as distal effects of germline variation on trans and global somatic changes. Furthermore, by focusing
largely on RNA-sequencing we investigate a gene-centric model that provides specific hypotheses for
mechanism that are readily validated via our experimental follow-up of non-coding variation that is
otherwise difficult to interpret.
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