Exploring the role of co-driver mutations in cancer: Implications for precision medicine

Abstract

Cancer develops through driver mutations, genetic alterations that promote tumor initiation and growth. However, additional co-driver mutations can profoundly modify tumor behavior, metabolism, and therapy response. This thesis investigates the role of co-driver mutations in cancer and their impact on tumor biology, progression, and metabolic vulnerabilities. Using genetically engineered mouse models, patient-derived xenografts, and human cancer datasets, the studies examine how specific combinations of genetic alterations shape tumor behavior in lung adenocarcinoma, thyroid squamous cell carcinoma, and melanoma. Paper I showed that p53 deficiency is required for KEAP1-mediated metabolic rewiring and glutaminase dependency in KRAS-driven lung adenocarcinoma. Paper II demonstrated that physiological aging promotes lung cancer metastasis in KRAS-p53 mutated lung cancer through altered tumor metabolism. Paper III identified metabolic vulnerabilities in thyroid squamous cell carcinoma with NRF2 activation, showing that glutaminase inhibition suppresses tumor growth and induces squamous differentiation. Paper IV reveals that partial versus complete Keap1 loss differentially cooperates with BRAFV600E in lung adenocarcinoma, but not melanoma, emphasizing tissue- and dose-dependent effects of Keap1 disruption. Paper V shows that allelic imbalance of BRAFV600E amplifies MAPK signaling and tumor aggressiveness in lung adenocarcinoma, an effect further enhanced by p53 loss, identifying allelic imbalance as a key determinant of tumor progression.

Collectively, these studies demonstrate that cancer evolution and therapy response cannot be understood by single mutations alone. Co-driver mutations, allelic imbalances, and physiological factors such as aging profoundly influence cancer progression, metabolic state, and therapeutic vulnerabilities. These findings provide mechanistic insight into tumor heterogeneity and highlight the importance of integrating primary oncogenic drivers with co-occurring mutations to inform precision medicine strategies.