Application of comprehensive multi-omic immune profiling strategy achieves superior checkpoint immunotherapy response prediction in lung cancer 3507

Abstract Description 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, achieving quantification of 10,000 unique proteins. 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 strong capability to predict non-response from a pretreatment blood sample (AUROC = 0.79, p-value = 1.7e-2, Mann-Whitney non-parametric test). Overall, robust treatment prediction, attributed to biomarker features, provides insights into mechanisms for non-response and highlights potential better treatment options in lung cancer. Funding Sources Supported by NHMRC Development Grant. Topic Categories Computational and Systems Immunology (COMP)