Abstract B001: Leveraging peripheral leukocyte recruitment to improve efficacy and mitigate toxicities following checkpoint blockade
Notice bibliographique
Résumé
Abstract CD8 T cells are effector cells of the adaptive immune system responsible for mediating protective immune responses in cancer. However, CD8 T cells can also be critical drivers of pathologic responses including autoimmunity. A key feature of these cells is that they must infiltrate tissues to execute their primary function of cell killing. It is well established that naive T cell responses do not originate in these tissues. Rather, prior to antigen-driven activation, naïve T cells are restricted to lymph nodes, spleen, and blood. T cell activation by cognate antigen drives expression of multiple immune checkpoint inhibitors, including programmed cell death protein-1 (PD-1) and/or cytotoxic T lymphocyte associated protein-4 (CTLA-4), as well as migration to nonlymphoid tissues. Work over the last two decades has harnessed T cell effector activities by blocking PD-1 and/or CTLA-4, which has revolutionized cancer care and demonstrated remarkable efficacy in diverse cancer types. However, many patients do not respond to checkpoint blockade therapy, and worse, other patients develop autoimmune-like immune-related adverse events (irAEs), limiting the utility of this approach. To maximize the therapeutic benefit of checkpoint blockade without accelerated irAE development, targeted approaches are needed to increase recruitment of protective CD8 T cells to tumors while preventing pathogenic T cell entry into sensitive tissues. However, an inability to granularly measure recruitment into non-lymphoid tissues has created a significant gap in knowledge in: 1) how checkpoint inhibitors impact T cell entry rates into tissues, 2) whether these entry rates are different for tumors and lesional irAE tissues, 3) where recruited cells localize, and 4) the precise functional contributions of recruited versus resident T cells in driving tumor regression or autoimmunity. To fill this gap in knowledge, we are utilizing an intravenous antibody labeling approach to mark CD8 T cells in the blood prior to entry into tissues, an approach which enables a 72-hour window to track tissue entry. Using this approach in the preclinical setting, we are interrogating how PD-1 inhibitors impact CD8 T cell recruitment to tumors as well as the pancreas in the context of Type 1 Diabetes. In our tumor studies, we have found that PD-1 inhibitors induce an increase in the number of recently recruited CD8 T cells, and that these recruited T cells have a less exhausted phenotype than the T cells that have been in the tumor for longer. In our diabetes studies, we have found that PD-1 inhibitors impact CD8 T cells both in the periphery and the pancreas, and in some mice cause an increase in migration to the pancreas. These studies support a model where peripheral CD8 T cell recruitment can positively contribute to productive anti-tumor immunity but can also exacerbate autoimmunity. Thus, our studies emphasize the doubled-edged nature of T cell recruitment, and our future studies will examine ways to both improve recruitment to tumors and mitigate recruitment to lesional irAE tissues. Citation Format: Oluwatoyosi Adewunmi, Arielle G. Dessens, Aloukick K. Singh, Autumn M. Sunderland, Sanjana Ananthula, Rachel M. Morris, Jason M. Schenkel, Kristen E. Pauken. Leveraging peripheral leukocyte recruitment to improve efficacy and mitigate toxicities following checkpoint blockade [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 B001.
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) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 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 ».