Abstract B65: Mathematical modeling studies on spatial profiles of tumor-infiltrating T cells
Notice bibliographique
Résumé
Abstract Activated cytotoxic T lymphocytes have been demonstrated to be able to kill antigen-specific cancer cells via various mechanisms. Not surprisingly, stronger infiltration of cytotoxic T cells into tumor/tumor-cell clusters generally associates with better prognosis, which has been demonstrated in various cancers. There have been efforts on quantifying the distribution of cytotoxic T cells on the whole tumor level. On the other hand, a solid tumor is usually composed of many tumor-cell clusters as well as stroma in gaps between those clusters. It has been noticed that T cells can be mostly localized in the stromal regions of a solid tumor. Therefore, it is also important to quantify the spatial pattern of T cells on the tumor-cell cluster level and further investigate the mechanism underlying the observed limited infiltration. In our work, we quantified the spatial distribution of CD8+ T cells with respect to their distance to the boundary of individual tumor-cell clusters. Generally, we observed that: i) patients differ in infiltration at the cluster level; ii) for most samples, T cells mainly accumulate outside the tumor-cell clusters; iii) for some samples T cells can effectively infiltrate the tumor-cell clusters; and iv) for other samples, the T-cell infiltration profile is intermediate. This last possibility reveals a non-monotonic distribution of T cells, i.e., a drop of T-cell density at the boundary coupled with a second accumulation of T cells at the center of tumor-cell clusters. Based on the quantified CD8+ T-cell profiles on the tumor-cell cluster level, we constructed mathematical models to test two hypothesized contributors affecting the spatial distribution of CD8+ T cells: a physical motility barrier set up by the ECM fibers in the stroma and a biochemical inhibitor ultimately due to the cancer cells inside the tumor-cell clusters. Mathematical models that only include physical barrier effects can qualitatively capture some (but not all) spatial features of the T-cell profiles. However, there is one significant shortcoming: the physical barrier scenario predicts that the profiles observed should be transient and hence eventually T cells should infiltrate all tumors. This appears inconsistent with simple time-scale estimates. A biochemical model focusing on T-cell repulsion can give rise to the observed spatial profiles as steady-state solutions; the different patterns correspond to different properties of cancer cells in different patients. We therefore favor this modeling framework. Of course, a definitive test would require experiments that would enable us to study the infiltration of T cells in a time-dependent manner or alternatively perturb possible mechanisms with drugs to directly test their effects on the infiltration pattern of T cells. Citation Format: Tina Gruosso, Morag Park, Herbert Levine, Xuefei Li. Mathematical modeling studies on spatial profiles of tumor-infiltrating T cells [abstract]. In: Proceedings of the AACR Special Conference on Tumor Immunology and Immunotherapy; 2018 Nov 27-30; Miami Beach, FL. Philadelphia (PA): AACR; Cancer Immunol Res 2020;8(4 Suppl):Abstract nr B65.
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 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| 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) | 0,003 | 0,001 |
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; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».