FAT1 Mutation as a Predictive Biomarker of Non-Durable Response to Immune Checkpoint Blockade in Non-Small Cell Lung Cancer

Immune checkpoint inhibitors (ICIs) have revolutionized the treatment of non-small cell lung cancer (NSCLC), offering durable benefits in a subset of patients. Nevertheless, only about one-fifth of individuals respond effectively, leaving resistance to ICI therapy as a critical barrier to improved outcomes. To explore the genomic factors underlying limited therapeutic benefit, four publicly available NSCLC cohorts (n = 2,986) were analyzed. From the Rizvi cohort, 158 patients exhibiting no durable clinical benefit (NDB) following ICI treatment were identified. Potential NDB-associated mutations were detected through univariate and multivariate Cox regression analyses. In addition, PD-L1 expression, tumor mutation burden (TMB), neoantigen load, tumor-infiltrating lymphocytes, and immune-related gene profiles were examined to clarify immune microenvironmental differences. A composite predictive model was subsequently constructed to estimate ICI treatment efficacy. Mutations in FAT1 and KEAP1 were enriched in NDB cases. Further analyses revealed that FAT1 mutation was specifically linked to diminished response to ICI therapy, whereas KEAP1 mutation functioned primarily as a prognostic rather than predictive factor. FAT1-mutant tumors displayed elevated TMB yet reduced immune infiltration, particularly of CD8⁺ T cells. Incorporating PD-L1 expression, TMB, smoking history, treatment regimen, therapy type, and FAT1 mutation status, the resulting prognostic model achieved high predictive accuracy (AUC = 0.763 for 6-month survival; AUC = 0.871 for 12-month survival). The presence of FAT1 mutation may signify resistance to ICI therapy and serve as a predictive biomarker in NSCLC. The proposed FAT1-based predictive framework may aid in identifying patients more likely to benefit from immunotherapy, contributing to precision treatment approaches.