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Background Multi-omic data integration in translational research has immense potential for driving comprehensive biological insights. High-parameter flow cytometry, which provides proteomic and phenotypic cellular data, is a cornerstone technology ideally suited to complement other proteome modalities to obtain a comprehensive view of extracellular and intracellular processes. Here, we apply a universal omics approach to identify correlates of non-responsiveness to immune checkpoint inhibitors (ICIs) in lung cancer patients. Currently, ICIs are the most effective treatment for late-stage lung cancer; however, most patients fail to mount a durable response, and the mechanisms underlying nonresponsiveness remain elusive.Methods 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.Results Stabl identified key predictive features from both CyTOF and SomaScan technology, providing insight into immune deficits present in nonresponsive patients. The combined model demonstrated a stronger capability to predict nonresponse 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.Conclusions 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 nonresponse and highlights the potential for better treatment options in lung cancer.Efforts are underway to expand this study by implementing asynchronous sample collection and staining, compatible with current hospital laboratories that typically run on unpredictable schedules, to remote settings. This is uniquely enabled by using a dried-down cocktail of CyTOF antibodies that are stable, simple to use and yield reproducible staining while minimizing sample required. This workflow provides access to underserved communities, facilitating for the first time equity and scalability of immune phenotyping studies to harness truly geographically dispersed clinical centers.For Research Use Only. Not for use in diagnostic procedures.