Abstract 5252: Computational modeling of comprehensive genomic profiling to predict chemo-immunotherapy benefit in early stage NSCLC

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Abstract Background: Immune checkpoint inhibition (ICI), alone and in combination with chemotherapy (ICI+C), has transformed the treatment landscape for non-small cell lung cancer (NSCLC). We have previously reported results from the myCare-040 study[1], where we validated an algorithm capable of distinguishing advanced NSCLC patients with favorable ICI+C benefit from those with no benefit. To understand whether the underlying molecular mechanisms used by the algorithm are conserved across disease stages, we have evaluated the algorithm in a cohort of patients with early stage NSCLC receiving adjuvant ICI or ICI + C. Design: The ΔTRI algorithm uses Cellworks’ computational model of a patient’s tumor genomics to predict biomarker changes related to disease progression and potential benefit from ICI+C therapy. The previously validated ΔTRI and clinical threshold (16) were evaluated in 51 non-squamous, early stage NSCLC patients (Stage I=20, Stage II=12, Stage IIIA=19) receiving adjuvant ICI or ICI+C ,with complete clinical and genomic information (Foundation One CDx) derived from the nationwide (US-based) de-identified ConcertAI Genomics360 database. Results: Patients in the ΔTRI High Benefit Group (ΔTRI ≥ 16, n = 11), had an incremental benefit in median OS of 19.4 months with the addition of chemotherapy to ICI (logrank p = 0.057, median OS ICI = 7 months vs ICI+C = 26.6 months). In contrast, patients in the ΔTRI No Benefit Group (ΔTRI < 16, n = 40) showed no improvement in OS when receiving ICI+C (logrank p = 0.84, median OS ICI = 13 months vs ICI+C = 9 months). A likelihood ratio test of interaction between the linear ΔTRI and treatment (ICI versus ICI+C) was significant (LR p = 0.038). Cut-point optimization for the early stage population (ΔTRI= 9) improved the logrank statistics in the High Benefit Group (ΔTRI ≥ 9; logrank p = 0.003). Conclusions: Although developed and validated in patients with advanced NSCLC, the ΔTRI also predicted incremental chemotherapy benefit in an real-world cohort of patients with early stage NSCLC receiving adjuvant ICI or ICI+C. Further work is needed to understand how these observations could be translated into clinical use. 1 Aggarawal et al, WCLC 2025 Citation Format: Prashant Nair, Kishor Promod, Ansu Kumar, Swati Khandelwal, Ambreen Ambreen, Susheel George, Mamatha Patil, Deepak Lala, Ashokraja Bala, Veena Balakrishnan, Shweta Kapoor, Drew Watson, James Wingrove, Tejas Patil. Computational modeling of comprehensive genomic profiling to predict chemo-immunotherapy benefit in early stage NSCLC [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 5252.

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