TMIC-25. DISSECTING THE ROLE OF HOST GENETICS IN GLIOMA EVOLUTION USING GENETICALLY-ENGINEERED MOUSE MODELS AND THE COLLABORATIVE CROSS. (5th November 2018) - Skinner, Kasey; Ferris, Martin; Bash, Ryan; Shelton, Abigail; Smithberger, Erin; Angus, Steven; Golitz, Brian; Sciacky, Noah; Simon, Jeremy; Stein, Jason; Matsushima, Glenn; Ostrom, Quinn; et al.
Abstract: Gliomas have fatal outcomes and few effective treatments. Precision medicine promises to target somatic tumor mutations, but the impact of host genetics on glioma biology remains unclear. Germline polymorphisms have been associated with glioma risk, but such studies cannot disambiguate loci affecting cancer cell-autonomous versus tumor microenvironment (TME) phenotypes, since germline variation is shared by both. Here, we implement a novel platform to discover genetic modifiers of both cancer cell autonomous and TME phenotypes using genetically defined non-germline genetically-engineered mouse (nGEM) glioma models and genetically diverse hosts from the Collaborative Cross (CC). We stereotactically injected Nf1;Trp53 -/- oligodendrocyte progenitor-derived tumor cells into syngeneic C57/BL6 control mice and 14 different CC strains. Survival analysis showed 7 strains with significantly prolonged survival relative to controls (P<0.05), suggesting slower tumor growth (Gs, growth slow). The remaining 7 strains survived similarly to controls, suggesting fast growth (Gf, growth fast). Variable tumor growth in CC mice suggests host genetic background influences molecular processes in the TME that potentiate or inhibit tumor growth. To identify such candidates, we performed RNA sequencing on 36 tumors from 3 Gf strains, 4 Gs strains, and controls. We identified differentially expressed genes that segregated Gf, Gs, and control tumors (P<0.05). Gene ontology analyses showed that these genes were significantly associated with immune response or extracellular matrix biology. These results suggest that Gs strains activate immune and TME processes that slow tumor growth. Quantitative trait locus (QTL) analyses of mouse host genetics and tumor data will facilitate identification of genetic variants that influence tumor progression. Future studies using human datasets are planned for validation of candidate host loci identified in mice.