Abstract B108: nELISA high-throughput proteomics enables scalable biomarker discovery: identification of IL-1 pathway intermediates as novel CRC biomarkers
Bibliographic record
Abstract
Abstract Proteomics holds great promise for cancer immunotherapy, with intensive efforts being exerted for the early identification of disease, selection of patients likely to respond, and prediction of adverse events. Despite this potential, the high cost and low throughput of existing tools to profile circulating proteins render such studies prohibitively slow and costly, and limit their wide-spread application. Here, we present proof-of-concept results leveraging a novel proteomics tool, the nELISA, to identify cancer biomarkers and inflammatory indicators in a low-cost, high-throughput manner. The nELISA is a highly multiplexed immunoassay platform capable of profiling hundreds of proteins in 1536 samples per instrument per day at a fraction of the cost of other platforms. This is achieved by miniaturizing the sandwich immunoassay, whereby antibody pairs are pre-packaged at the surface of novel color-coded microparticles that can be readout by high-throughput flow cytometry. We leveraged the nELISA to profile circulating protein abundance of 275 proteins in 110 plasma samples across 5 diseases (colorectal cancer, chronic lymphocytic leukemia, type 2 diabetes, cirrhosis, congestive heart failure) and healthy controls. Pathways associated with each disease were identified; for example, CLL was associated with markers of IL-4 and IL-13 pathways and the TNF superfamily (including soluble PD-1 and 4-1BB); T2D was associated with TGFbeta signaling, and CRC was associated with proteases and the IL-1 pathway, which may reflect disruption of the mucosal barrier by cancer cells and an innate response to infiltrating bacteria. Of note, we report decreased levels of soluble IL-1RAcP as a novel biomarker of CRC. We compared our results with the Olink’s Explore 384 Inflammation panel. Protein concentrations in pg/mL correlated well with NPX values from Olink for proteins detected on both platforms (median Spearman correlation 0.76). While absolute protein levels could not be compared due to the relative quantification of the Olink platform, there was 100% agreement on the direction of change in circulating protein levels between healthy and disease states for biomarkers identified on both platforms. Importantly, while the two platforms yielded similar results, the nELISA achieved this at only 7% the cost. Thus, the nELISA is an attractive new tool that renders plasma proteomics accessible to an increasing number of immunotherapy studies. We discuss its application to high-throughput screens and biomarker discovery studies to predict responses and adverse events to immunotherapy. Citation Format: Nathaniel Robichaud, Kiran Edwards, JiaMin Huang, Grant Ongo, Milad Dagher. nELISA high-throughput proteomics enables scalable biomarker discovery: identification of IL-1 pathway intermediates as novel CRC biomarkers [abstract]. In: Proceedings of the AACR IO Conference: Discovery and Innovation in Cancer Immunology: Revolutionizing Treatment through Immunotherapy; 2025 Feb 23-26; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Immunol Res 2025;13(2 Suppl):Abstract nr B108.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".