Abstract 5640: Identifying pathophysiological features of mouse tumors using imaging mass cytometry
Notice bibliographique
Résumé
Abstract Mouse tumor models are widely utilized for cancer studies and preclinical drug development. An obstacle in predicting therapeutic drug efficacy is the ability to quantitatively evaluate the multi-parametric post-treatment response in the tumor microenvironment (TME). Identification of immuno-oncological processes that dictate tumor growth, metastasis, and immune response is essential for selecting promising drug candidates for further clinical evaluation. Imaging Mass Cytometry™ (IMC™) is a vital and proven high-plex imaging technology that enables deep characterization of the complexity and diversity of tumor tissue without disrupting spatial context. The Hyperion™ Imaging System utilizes IMC technology to simultaneously assess 40-plus individual structural and functional markers in tissues, providing unprecedented insight into the organization and function of the TME. We have previously demonstrated the application of IMC in combination with Maxpar® and Maxpar OnDemand™ antibodies to highlight cellular composition of normal mouse tissues. Here, we showcase the Maxpar OnDemand Mouse Immuno-Oncology IMC Panel Kit for application on a variety of mouse tumor tissues. We compiled the antibody panel to quantitatively assess IO-related processes and applied it to a tissue microarray (TMA) containing a large variety of mouse tumors. Antibodies in panel kits were selected from the Maxpar and Maxpar OnDemand catalogs. We digitized high-plex data from mouse tissues using the Hyperion Imaging System and generated images demonstrating the detailed layout of the TME. We further conducted single-cell analysis to identify specific populations of tumor and immune cells in the TME. The Mouse Immuno-Oncology IMC Panel Kit successfully identified pathophysiological processes such as immune cell infiltration and activation, signaling pathway activation, biomarkers of epithelial-to-mesenchymal transition (EMT), metabolic activity, growth, and the tissue architecture of the TME. Single-cell analysis of several highly relevant tumor types separated distinct cellular clusters representing tumor, immune, stromal, and vascular cells. Activation of cellular processes associated with signaling, growth, and metastasis were identified in tumor cells. In addition, cytotoxic and inflammatory activation in lymphoid and myeloid immune cell subtypes were detected. Application of IMC based multiparametric analysis successfully identified the spatial landscape of the TME at single-cell resolution. Quantitative analysis of tumor composition revealed critical insights regarding prognostic parameters such as metastatic and growth potential of tumors, and identification and activation of immune cell infiltrates. Overall, we demonstrate the power of IMC and provide evidence of its successful application in mouse tumor models. For Research Use Only. Not for use in diagnostic procedures. Citation Format: Qanber Raza, Liang Lim, Thomas D. Pfister, Nick Zabinyakov, Christina Loh. Identifying pathophysiological features of mouse tumors using imaging mass cytometry. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5640.
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 | 0,001 | 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 | 0,000 |
| 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 ».