Abstract 100: Blood-based integration of epigenomic profiles, TMB, and MSI to predict immune checkpoint inhibitor response in advanced non-small cell lung cancer (aNSCLC).

Abstract Introduction. Immune checkpoint inhibitors (ICIs) have transformed cancer treatment, yet identifying patients most likely to benefit remains challenging. Established biomarkers such as tumor mutational burden (TMB) and microsatellite instability (MSI) have performance limitations. We hypothesized that combining MSI and TMB with epigenomic features of tumor-intrinsic immune regulation and the tumor microenvironment from pretreatment plasma would improve prediction. We developed and validated a multimodal immunotherapy response score (MIRS-Score) that integrates MSI, TMB, and epigenomic signatures (Guardant360 Liquid) to identify patients likely to respond to ICI alone or in combination with chemotherapy. Methods. From the de-identified GuardantINFORM database we identified 695 advanced NSCLC (aNSCLC) patients treated with first- or second-line ICI monotherapy or ICI+chemotherapy and randomly split them into training (n=483) and test (n=212) sets. A literature-curated, data-driven epigenomic signature associated with real-world time to treatment discontinuation (rwTTD) was combined with MSI and TMB to train the multimodal model. Patients ≥80th MIRS percentile were labeled MIRS-High. Cox proportional hazards models adjusted for sex, age, therapy type (mono vs combo), line of therapy, and baseline tumor fraction provided adjusted hazard ratios (aHR); median rwTTD was estimated by Kaplan-Meier. Results. In the independent test set (n=212), MIRS-High patients had longer rwTTD (median 8.7 vs 5.1 months; aHR 0.61, 95% CI 0.41-0.93, p=0.02) and improved overall survival (OS) (aHR 0.33, 95% CI 0.16-0.68, p0.005). In the ICI monotherapy subgroup (n=69), MIRS-High showed median rwTTD 11.0 vs 4.9 months (aHR 0.31, 95% CI 0.13-0.75, p=0.01) and longer OS (aHR 0.18, 95% CI 0.04-0.79, p=0.023). MIRS-High was not associated with rwTTD in patients treated with chemotherapy alone (aHR 1.16, 95% CI 0.94-1.44, p=0.18). Conclusions. A pretreatment plasma-based score combining MSI, TMB, and epigenomic signatures identifies aNSCLC patients with superior outcomes on ICI and outperforms MSI or TMB alone. The ICI treatment-specific stratification and strong association with both rwTTD and OS support the clinical utility of this multimodal approach, warranting further evaluation to assess the potential for guiding ICI treatment decisions. Citation Format: Sean Gordon, Jing Wang, Shile Zhang, Marisa Juntilla, Tingting Jiang, Matthew Ellis, Vishnu Ramani, Reagan Barnett, Bernard Herrman, Justin Odegaard, Darya Chudova. Blood-based integration of epigenomic profiles, TMB, and MSI to predict immune checkpoint inhibitor response in advanced non-small cell lung cancer (aNSCLC) 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 100.