Abstract C056: Insights into immunotherapy response, irAEs, and pre-treatment conditions impacting patient outcomes from the largest plasma proteomics study of patients receiving immune checkpoint inhibitor therapy

Abstract Proteomics holds great promise for cancer immunotherapy, with intensive efforts exerted for early disease identification, patients selection, and adverse event prediction. Despite this potential, high cost and low throughput of existing tools to profile circulating proteins render such studies prohibitively slow and costly, limiting wide-spread application. As a result, proteomics studies in the field have been constrained to sample sizes in 10s and 100s , restricting the power to discover key biomarkers. Here, we leverage a novel proteomics tool, the nELISA, to quantify ∼600 circulating proteins across ∼3000 samples from the RADIOHEAD (Resistance Drivers for Immuno-Oncology Patients Interrogated by Harmonized Molecular Datasets) cohort, a prospective study of 1070 immunotherapy naive pan-tumor patients on standard of care immune checkpoint inhibitor (ICI) therapy regimens from community oncology clinics. The Nomic platform, a highly multiplexed immunoassay technology, enables the profiling of hundreds of proteins at significantly reduced costs. The method miniaturizes sandwich immunoassays by placing antibody pairs on the surface of color-coded microparticles, which can then be analyzed via high-throughput flow cytometry. RADIOHEAD is a prospective study of 1070 immunotherapy naive pan-tumor patients on standard of care immune checkpoint inhibitor (ICI) therapy regimens from community oncology clinics. Longitudinal samples were collected pre- and post-ICI, as well as following irAEs. We previously reported the identification of 200 proteins associated with response to ICI and 150 proteins associated with the development of irAEs. Our preliminary data identified several markers of response to treatment; for example, PD-1 inhibitors result in increased circulating levels of soluble PD-1, and ICIs increase levels of several chemokines including CXCL9 and CXCL10, as seen in several other small-scale studies. We also identified several markers potentially predicting response to treatment and irAEs, which required larger datasets for validation. The scalable nature of the nELISA platform now allows us to validate these findings in such a larger longitudinal cohort, providing the power needed for such a broad biomarker discovery effort. Specifically, analysis of clinical data identified factors impacting response to ICI, including age, smoking, chemotherapy, radiotherapy, systemic corticosteroids, opioids, etc. We will present biomarkers associated with these factors, and their impact on response to ICI. Pairing nELISA protein profiling of these longitudinal samples with associated demographic metadata and clinical outcomes provides an opportunity to identify clinically actionable mechanisms for ICI resistance and adverse events, discover targets for combination therapies and post-ICI treatment, and inform system biology approaches to elucidate disease pathways. Here, we highlight biomarkers and protein signatures related to patient outcomes, to reveal additional insights and further accelerate research in the field of cancer immunotherapy. Citation Format: Nathaniel Robichaud, Samantha Liang, Enjun Yang, Jens Eberlein, Alistaire Sherman, Amy Johnson, Erroll Rueckert, John E. Connolly, Milad Dagher. Insights into immunotherapy response, irAEs, and pre-treatment conditions impacting patient outcomes from the largest plasma proteomics study of patients receiving immune checkpoint inhibitor therapy abstract. In: Proceedings of the AACR Immuno-Oncology Conference (AACR IO): Discovery and Innovation in Cancer Immunology: Revolutionizing Treatment through Immunotherapy; 2026 Feb 18-21; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Immunol Res 2026;14(2 Suppl):Abstract nr C056.