Abstract Background: The PACIFIC trial established consolidation durvalumab as standard of care for unresectable stage III non-small cell lung cancer (NSCLC) following concurrent chemoradiotherapy (CRT). Real-world studies such as PACIFIC-R confirmed these benefits; however, comprehensive characterization of molecular features in this setting remains limited. Advances in AI enable integration of heterogeneous genomic and transcriptomic data, opening new opportunities for biomarker discovery. Our study applies a multimodal AI framework combining contrastive learning and foundational-model representations to identify distinct molecular features associated with survival outcomes from pre-treatment tumor data. Methods: Stage III NSCLC EGFR/ALK-negative patients with unresectable disease who received CRT with subsequent durvalumab (CRT+D, N = 281) or CRT alone (N = 72) were identified from the Tempus AI multimodal real-world database. Molecular characterization included assessment of radiation and immunotherapy (IO)-related biomarkers to evaluate the impact on real-world progression-free survival (rwPFS). To identify potential DNA and RNA biomarkers, a multimodal AI framework was developed via integration of two recently published models: 1) COMPASS, a foundation model encoding RNA profiles into 43 interpretable immune-related concept scores, and 2) Predictive Biomarker Mapping Framework, a deep learning model mapping molecular features to treatment outcomes in CRT+D when compared to CRT-alone. Results: In the CRT+D group, patients with high levels of biomarkers linked to IO response, such as PD-L1 ≥50% (p-value = 0.01) and a high tumor mutational burden (p-value = 0.02), showed improved survival rates. Patients exhibiting low STK11 gene expression signature had shortened rwPFS (p-value = 0.03). Tumors classified as radioresistant using the radiosensitivity index also showed diminished rwPFS benefit (p-value = 0.02). Our multimodal AI framework revealed additional markers associated with improved rwPFS to CRT+D therapy, including tumors with adenocarcinoma histology, mutations in LRP1B, NF1, KRAS, and CDKN2A, and enrichment in expression of immune activation pathways (cytotoxic T-cell, IFN-γ, tertiary lymphoid structure, and immune-checkpoint signatures). In contrast, tumors with mutations in TP53, RB1, NOTCH1, CUX1, and STK11 as well as those with elevated expression of regulatory and stromal signals (Tregs, exhaustion, stroma) were linked to worsened outcomes in patients treated with CRT+D. Cross-validation confirmed model reproducibility and feature stability. Conclusions: Our study demonstrates the utility of a multi-omics approach to characterize molecular landscape of tumors treated with consolidation durvalumab and to drive biomarker discovery using an AI-driven, foundational-model-based framework. Citation Format: Katherina C. Chua, Yun-Ching Chen, Ariel Chen, Stewart Bates, Mehdi Pirooznia, Assieh Saadatpour. Integrative multi-omics characterization and AI-driven biomarker discovery for NSCLC stage III outcomes abstract. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 1056.
Chua et al. (Fri,) studied this question.