Abstract A039: Detection of immune cell glycosylation as an indicator of metabolic activity in the tumor tissue microenvironment using multimodal mass spectrometry imaging
Notice bibliographique
Résumé
Abstract Glycosylation on the cell surface is a major target and mediator of the immune response to cancers. It is expected that any immune cells present in these tissues will be actively engaged in responding to the presence of the tumor, however, multiple escape mechanisms used by the targets are known to suppress these immune responses. It follows that in this tumor microenvironment, any response, or non-response, to a cancer immunotherapeutic will also be mediated by glycans present in the target tissues and immune cells interacting with them. From a data archive of over 500 FFPE human tumor tissues assessed for N-glycan imaging MS analysis, a subset of tissues (prostate, colon, pancreas, lung) with notable intra- and peri-tumor immune cell clusters were re-evaluated for detection of N-glycans that co-localize to these regions. The metabolic premise for this is that the Warburg metabolites glucose and glutamine are both required for N-glycan biosynthesis and therefore active immune cells would have detectable glycan signatures. In most tumor types evaluated, there was minimal to no detection of N-glycans. This would be consistent with a tumor microenvironment deficient in metabolites due to the presence of the tumor, or other immunosuppressive mechanisms. A subset of the tissues did have glycan signatures associated with immune cell clusters, and the glycan structures present in each cluster were recorded. The same tissues were assessed by multiplexed MALDI-immunohistochemistry (IHC) to identify the immune cell types present in each cluster. Established method workflows were used for the N-glycan MALDI imaging and MALDI-IHC analyses. Previously characterized N-glycans and immune cell clusters (CD4, CD8, CD11b, CD163) in SARS-CoV2 infected autopsy lung tissues were used as positive controls for an active immune microenvironment. Distinct N-glycan species are associated with each immune cell type in these tissues. Detected N-glycans included high mannose structures, and a series of tri- and tetra-antennary structures with one fucose and a bisecting N-acetylglucosamine (GlcNAc). While the high mannose glycans were detected in other areas of the tissues, the bisecting branched N-glycans were distinctly enriched in the immune cell clusters. In these selected tissue subsets, immune cell clusters distal to tumor regions had readily detected high mannose N-glycans and tri-and tetra-antennary bisecting GlcNAc structure. In general, the immune cell clusters adjacent to the tumor region had minimal to no glycan expression relative to the more distal regions. We hypothesize that when N-glycans are detected by imaging mass spectrometry in tissue immune cell clusters that this represents tumors with more active immune functional states, and the lack of detection represents immune-suppressed tumors. Citation Format: Richard R Drake, Kameisha Radford, Caroline Kittrell, Kristin Wallace, Peggi M Angel. Detection of immune cell glycosylation as an indicator of metabolic activity in the tumor tissue microenvironment using multimodal mass spectrometry imaging [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 A039.
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 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 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 ».