"SU2C" Stand Up To Cancer

"SU2C" Stand Up To Cancer Stand Up to Cancer The development of cancer requires the accumulation of multiple genetic events. Whereas key driver mutations occur commonly in major tumor suppressors/oncogenes, genetic alterations also occur in modifier pathways (co-driver mutations) that presumably contribute to one or more of the hallmarks of cancer. Thus, unique combinations of somatic mutations, genomic variants, and epigenetic changes result in genetic fingerprints that evoke the clinically identifiable patterns of tumor histology, grade, invasion, response to therapy and perhaps even molecular subtype of disease. To add to this complexity, different mutations in the same gene may lead to different functions and phenotypes. It is important to understand how modifier pathways collaborate with mutations in oncogenes to evoke specific cancer phenotypes. Identifying critical modifier pathways will lead to new therapeutic targets. This will require sophisticated bioinformatics approaches to identify candidate co-driver mutations from existing genomic and other databases, and a systematic, high-throughput (HT) approach to test candidates and measure their individual and combined impact on cancer specific behaviors. We will execute a systematic study of candidate genetic drivers and co-drivers identified by the members of our program to determine if they result in the phenotypic changes of the hallmarks of cancer. The strategy we will take is: (1) assemble genetic perturbagens that correspond to these drivers and co-drivers (including siRNA and shRNA to knock down function and full length cDNA to ectopically express it) and introduce these perturbagens into relevant melanoma cell lines, which are already available in the Trent lab at TGen; (2) the perturbagens will be tested in the different cellular backgrounds with 4 different phenotypic assays cell proliferation, escape from apoptosis, escape from anoikis, and epithelial mesenchymal transition (EMT), (3) perturbagens that lead to cancer phenotypes in these contexts will be noted as hits, validated in follow up assays and used to inform the bioinformatics analysis to improve predictions of candidate drivers and co-drivers and to map out these relationships.