Abstract 5004: Application of a comprehensive multi-omic immune profiling strategy achieves superior checkpoint immunotherapy response prediction in lung cancer

Abstract Immune checkpoint inhibitors (ICIs) are currently the most effective treatment for late-stage lung cancer. However, most patients fail to mount a durable response, and the mechanisms underlying non-responsiveness remain elusive. Here, we apply a universal omics approach to identify correlates of non-responsiveness to ICIs in lung cancer. In-depth characterization of the plasma proteome was performed using the SomaScan™ platform on pretreatment and longitudinal blood samples from 40 ICI-treated patients, resulting in quantification of 10, 000 unique proteins. In parallel, comprehensive immune phenotyping was achieved with matched pretreatment PBMC for 90% of patients (36/40) through CyTOF™ technology, with a 38-plex panel describing over 150 immune populations in circulation. To enhance clinical outcome predictability of cellular and plasma-based immune relationships, cross-platform data integration was achieved with Stabl, a sparse, reliable omic biomarkers analysis strategy. Stabl identified key predictive features from both CyTOF and SomaScan technology, providing insight into immune deficits present in non-responsive patients. The combined model demonstrated a stronger capability to predict non-response from a pretreatment blood sample (AUROC = 0.79, p-value = 1.7e-2, Mann-Whitney non-parametric test) compared with individual platforms alone. Additionally, this multi-omic strategy is compatible with Imaging Mass Cytometry™ technology, unlocking another layer of clinical investigation via spatial biology. This study ultimately demonstrates both the clinical impact and utility of blood- and imaging-based CyTOF technology and the benefit of combining SomaScan and multi-omic-appropriate analysis approaches. Overall, robust treatment prediction, attributed to biomarker features, provides insights into mechanisms for non-response and highlights the potential for better treatment options in lung cancer. Citation Format: Helen M. McGuire, Natalie Smith, Michael Cohen, Julien Hedou, Grégoire Bellan, Xavier Durand, Erika L. Smith-Mahoney, Julie Alipaz, Jennifer Snyder-Cappione, Brice Gaudilliere, Christina Loh, David King, Michael Hinterberg, Clare Paterson, Barbara Fazekas de St Groth. Application of a comprehensive multi-omic immune profiling strategy achieves superior checkpoint immunotherapy response prediction in lung cancer [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 5004.