Leveraging Tumor Mutation Profiles to Forecast Immune Checkpoint Blockade Resistance in Melanoma, Lung, Head and Neck, Bladder and Renal Cancers - Guillaume Mestrallet (Onco, 2024)
Immune checkpoint blockade (ICB), radiotherapy, chemotherapy and surgery are currently used as therapeutic strategies against melanoma, lung, bladder and renal cancers, but their efficacy is limited. Thus, I need to predict treatment response and resistance to address this challenge. In this study, I analyzed 350 lung cancer, 320 melanoma, 215 bladder cancer, 139 head and neck cancer and 151 renal carcinoma patients treated with ICB to identify tumor mutations associated with response and resistance to treatment. I identified several tumor mutations linked with a difference in survival outcomes following ICB. In lung cancer, missense mutations in ABL1, ASXL1, EPHA3, EPHA5, ERBB4, MET, MRE11A, MSH2, NOTCH1, PAK7, PAX5, PGR, ZFHX3, PIK3C3 and REL genes were indicative of favorable responses to ICB. Conversely, mutations in TGFBR2, ARID5B, CDKN2C, HIST1H3I, RICTOR, SMAD2, SMAD4 and TP53 genes were associated with shorter overall survival post-ICB treatment. In melanoma, mutations in FBXW7, CDK12, CREBBP, CTNNB1, NOTCH1 and RB1 genes predict resistance to ICB, whereas missense mutations in FAM46C and RHOA genes are associated with extended overall survival. In bladder cancer, mutations in HRAS genes predict resistance to ICB, whereas missense mutations in ERBB2, GNAS, ATM, CDKN2A and LATS1 genes, as well as nonsense mutations in NCOR1 and TP53 genes, are associated with extended overall survival. In head and neck cancer, mutations in genes like PIK3CA and KRAS correlated with longer survival, while mutations in genes like TERT and TP53 were linked to shorter survival. In renal carcinoma, mutations such as EPHA5, MGA, PIK3R1, PMS1, TSC1 and VHL were linked to prolonged overall survival, while others, including total splice mutations and mutations in B2M, BCOR, JUN, FH, IGF1R and MYCN genes were associated with shorter overall survival following ICB. Then, I developed predictive survival models by machine learning that correctly forecasted cancer patient survival following ICB within an error between 5 and 8 months based on their distinct tumor mutational attributes. In conclusion, this study advocates for personalized immunotherapy approaches in cancer patients.