Abstract A001: nELISA high-throughput protein profiling applied to the RADIOHEAD cohort: insights from the largest plasma proteomics study of patients receiving checkpoint inhibitor therapy
Notice bibliographique
Résumé
Abstract 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. 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. 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. Results: Using this technology, 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 will require much larger datasets for validation. The scalable nature of the nELISA platform now allows us to validate these findings in a large longitudinal cohort, providing the power needed for such a broad biomarker discovery effort. In this presentation, we share the results of applying nELISA to the RADIOHEAD blood serum samples from pretreatment, early on-treatment, 6-month, and 12-month timepoints. For participants who experienced immune-related adverse events, additional samples were collected upon presentation and in follow-up visits - these samples were also analyzed in this study. Conclusions: 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: Eric Miller, Nathaniel Robichaud, Grant Ongo, Samantha I. Liang, EnJun Yang, John E. Connolly, Kiran Edwardson, Narges Rashidi, Andy Lee, JiaMin Huang, Jeffery Munzar, Milad Dagher. nELISA high-throughput protein profiling applied to the RADIOHEAD cohort: insights from the largest plasma proteomics study of patients receiving checkpoint inhibitor therapy [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Mechanisms of Cancer Immunity and Cancer-related Autoimmunity; 2025 Sep 24-27; Montreal, QC, Canada. Philadelphia (PA): AACR; Cancer Immunol Res 2025;13(9 Suppl):Abstract nr A001.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,002 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,002 |
| 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 ».