Abstract A029: Treatment-Specific Immune Phenotypes Identified by nELISA High-Throughput Proteomics Reveal Actionable Insights for Drug Discovery
Notice bibliographique
Résumé
Abstract Understanding tumor immunology requires high content tools that can capture the complex microenvironment, as well as high-throughput cell-based assays to rapidly screen compounds, antibodies, or cell therapies. Unfortunately, proteomics tools to investigate interactions between cancer and immune cells compromise either content or cost, limiting access to phenotypic data. To overcome this issue, we developed the nELISA: a high-throughput miniaturized ELISA quantifying 191 cytokines, chemokines, proteases and growth factors, at 10x-reduced cost compared to previous tools, and applied it to cell based models to demonstrate its ability to characterize immune phenotypes in co-culture systems. We ran the largest PBMC secretome screen to date, in which ~10,000 PBMC samples were treated with various inflammatory stimuli, and were further perturbed with a selected library of 80 recombinant protein “perturbagens”. 191 secreted proteins were profiled in all samples, resulting in ~2M datapoints. The nELISA profiles were able to capture phenotypes associated with specific stimulation conditions, individual donors, and potent cytokine perturbagens. By compensating for stimulation and donor differences, we clustered perturbagens according to their effects on PBMC secretomes. As expected, perturbagens such as IFN gamma and IL-4 led to well-established Th1 or Th2 responses, respectively, and clustered with perturbagens involved in these phenotypes. Novel phenotypic effects were also identified, such as distinct responses to the near identical CXCL12 alpha and beta isoforms. Interestingly, we observed important similarities between PBMC responses to the cytokine drugs IFN beta and IL-1 Receptor antagonist, supporting the use of the latter as a replacement for the former in certain indications. These findings highlight the ability of the nELISA to capture actionable insights from high-throughput screens, and demonstrate its applicability to SAR studies and drug repurposing screens. Thus, the nELISA is a powerful tool for immunotherapy drug discovery, and we will expand upon its use for target identification, in vitro pharmacology, predicting patient response to therapy, as well as characterizing the potential of iPSC- or donor-derived material for cell therapy. Citation Format: Nathaniel Robichaud, Grant Ongo, Ivan Teahulos, Woojong Rho, Milad Dagher. Treatment-Specific Immune Phenotypes Identified by nELISA High-Throughput Proteomics Reveal Actionable Insights for Drug Discovery [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Tumor Immunology and Immunotherapy; 2023 Oct 1-4; Toronto, Ontario, Canada. Philadelphia (PA): AACR; Cancer Immunol Res 2023;11(12 Suppl):Abstract nr A029.
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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 | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 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 ».