nELISA high-throughput protein profiling applied to the RADIOHEAD cohort: Insights from the largest plasma proteomics study of patients receiving checkpoint inhibitor therapy

Background Proteomics holds great promise for cancer immunotherapy, with intensive efforts being exerted for early disease identification, patients selection, and adverse event prediction. Despite this potential, the high cost and low throughput of existing tools to profile circulating proteins render such studies prohibitively slow and costly, limiting their wide-spread application. As a result, proteomics studies in the field have been constrained to sample sizes in the 10s and 100s, restricting the power to discover key biomarkers. Methods The Nomic platform is a highly multiplexed immunoassay technology that enables the profiling of hundreds of proteins across 1536 samples per instrument daily, 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. Results We previously reported leveraging the nELISA protein profiling platform to quantify 600 circulating proteins across 3000 samples from the RADIOHEAD cohort, resulting in the identification of greater than 200 proteins associated with response to ICI and greater than150 proteins associated with the development of irAEs. Here, we further dissect the dataset to capture treatment-specific biomarkers. 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 each of these factors, and their impact on response to ICI. Conclusions Pairing nELISA protein profiling of these longitudinal samples with associated demographic metadata and clinical outcomes provides an opportunity to identify clinically actionable mechanisms to guide ICI therapeutic approaches. 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.