Abstract 7481: Genomic aberration-derived signature in lung adenocarcinoma improves patient prognosis and therapy stratification

Abstract Background: Lung cancer is the leading cause of cancer-related mortality worldwide, with a poor prognosis due to late-stage diagnoses. While advances in targeted therapies and immune checkpoint blockade therapies (ICBT) have improved survival outcomes for subsets of non-small cell lung cancer (NSCLC) patients, the clinical heterogeneity and variable response rates to these therapies remain significant challenges in precision medicine. Identifying robust biomarkers for stratifying patients and tailoring personalized treatments remains a critical unmet need. Methods: We developed the Genomic Aberration-derived Signature for Patient Stratification (GASPS), a novel statistical framework integrating genomic and transcriptomic data. GASPS models the effects of driver genomic aberrations—mutations, amplifications, and deletions—on gene expression to define actionable gene signatures. Using lung adenocarcinoma (LUAD) data from The Cancer Genome Atlas (TCGA), we identified and validated driver gene signatures across independent NSCLC datasets, including TRACERx. The clinical relevance of these signatures was assessed for their ability to predict prognosis and treatment response. Results: GASPS defined 38 driver genomic aberration signatures, which consistently outperformed genomic aberration status in predicting patient outcomes. Hierarchical clustering of signature scores stratified patients into subgroups with distinct survival patterns. Notably, the EGFR-Mut and EGFR-Amp signatures demonstrated contrasting prognostic and predictive associations: EGFR-Mut was linked to favorable prognosis and chemotherapy sensitivity, whereas EGFR-Amp was associated with poor prognosis and increased tumor proliferation. The STK11-Mut signature emerged as a robust biomarker for resistance to ICBT, correlating with reduced immune infiltration and a "cold" tumor microenvironment. Validation across multiple datasets confirmed the generalizability of these findings. Conclusion: The GASPS framework provides a powerful tool for integrating genomic and transcriptomic data to derive clinically actionable gene signatures. These signatures offer superior prognostic and predictive capabilities, enabling better patient stratification and advancing personalized treatment strategies for NSCLC. While demonstrated in LUAD, this framework is adaptable to other cancer types, paving the way for broader applications in precision oncology. Citation Format: Xiang Wang, Zijian Zhang, Jian-Rong Li, Jianjun Zhang, Chao Cheng. Genomic aberration-derived signature in lung adenocarcinoma improves patient prognosis and therapy stratification [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 7481.