Abstract B108: nELISA high-throughput proteomics enables scalable biomarker discovery: identification of IL-1 pathway intermediates as novel CRC biomarkers
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».