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Explore cancer genetics through a revolutionary functional and evolutionary framework that challenges traditional statistical approaches by integrating germline predisposition with somatic tumor evolution. Learn how proteome-wide association studies (PWAS) reveal unexpected protective effects in cancer genetics, where damaging genetic variants paradoxically reduce cancer risk across ten major cancer types using UK Biobank data. Discover how 46% of significant cancer associations operate through recessive inheritance patterns that standard GWAS models typically miss, and examine 145 cancer-associated loci including 51 previously unreported regions. Investigate the FABRIC framework for quantifying gene-level selection in cancer by analyzing over 10,000 tumors to identify approximately 600 genes under significant evolutionary pressure, including 180 previously overlooked coding genes. Delve into breast cancer case studies that combine population-scale PWAS with family-based exome sequencing to uncover context-specific and lineage-dependent cancer susceptibility patterns. Examine ongoing research on antigen-presenting machinery (APM) and its role in determining success or failure in modern cancer immunotherapy through alternative evolutionary pathways. Gain insights into how this integrative approach expands the catalog of cancer-relevant genes and opens new possibilities for early diagnosis, genetic counseling, and personalized cancer risk assessment by embracing functional impact, recessive inheritance, protective effects, and evolutionary selection principles.
