Abstract B023: A scalable, proteome-wide protein profiling platform with absolute quantification of 1000 proteins
Notice bibliographique
Résumé
Abstract Background: High-plex proteomics is critical to enabling cancer research through comprehensive profiling of immune and tumor-derived signals, facilitating early detection, biomarker discovery, irAE prediction, and real-time monitoring of therapeutic responses. To date, proteomics has been constrained by flexibility, high costs, and inconsistent quantification. Here, we present Omni 1000, a quantitative 1000-plex solution powered by nELISA technology; designed for broad, reproducible, and cost-effective protein measurement. Omni 1000 delivers 0.1 pg/mL sensitivity, 99.99% specificity, and dynamic range spanning 3-6 logs. Method: Omni 1000 content was developed through rigorous, data-driven strategy to achieve comprehensive proteome-wide coverage while retaining high-value markers. The foundation is built from two sources: (1) a curated set of Most Valuable Proteins (MVPs)—biomarkers selected heuristically based on prevalence in key signaling pathways, translational research, and validated endpoints; and (2) large-scale, high-plex proteomic datasets with disease-association. To optimize, we iteratively applied Minimum Redundancy Maximum Relevance (mRMR) to reduce overlap, reconstruction loss minimization to preserve signal from high-dimensional datasets, and prioritized MVPs. Each iteration was validated against key biological ontologies—achieving 92% MVP coverage, 100% Reactome level 0, >80% Reactome level 1, 100% pharma-relevant KEGG signaling pathways, and 100% MeSH disease classes. In addition, we determined the disease prediction power of Omni 1000 content at >95% equivalent to a 3000+ panel, evaluated on the UK Biobank cohort. Results: For biomarker discovery, Omni 1000 makes large-scale and clinically relevant studies achievable through rapid readout with flow cytometry. We leveraged Omni 1000’s capabilities in a high-thoughput drug screening platform structured on patient-derived tumor organoids. Use of Omni 1000 demonstrated insights on baseline donor heterogeneity and drug compound responses and resistances specific to patient tissue profiles. Of interest for immunotherapy applications were compounds inducing cell death while promoting pro-inflammatory immune environments. We observed cytotoxicity with 2 CDK9 inhibitors in organoids across donors, through increased levels of intracellular proteins in culture supernatant and broad decreases in most other protein levels. They simultaneously resulted in increased secretion of chemokines CXCL2, CXCL3, CXCL5, and maintained CCL2 and IL-8 expression, possibly promoting additional immune involvement parallel to direct cell killing. These findings underscore Omni 1000’s capacity to profile functional heterogeneity in tumor immune microenvironments and support development of precision medicine with immunotherapeutic potential. Conclusions: Together, we demonstrate a novel 1000-plex solution, Omni 1000, with content balancing critical targets and biological breadth, and demonstrated real-world utility in early detection of disease and high throughput cancer drug development. Citation Format: Narges Rashidi, Kiran Edwardson, Nathaniel Robichaud, Alyssa Rosenbloom, Grant Ongo, Milad Dagher. A scalable, proteome-wide protein profiling platform with absolute quantification of 1000 proteins [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 B023.
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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 | 0,001 |
| 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 ».