Abstract A018: Characterization of macrophage population in head and neck squamous cell carcinoma and renal cell carcinoma and their role in modulating immune checkpoint blockade response
Notice bibliographique
Résumé
Abstract Tumor-associated macrophages (TAMs) and monocytes are an integral part of tumor microenvironment (TME) which modulates disease progression. M1 and M2 are the two well-defined subtypes of TAMs whose polarization influence response to immune checkpoint inhibitors (ICIs). Unavailability of data from complex models poses limitations to extensive characterization of these immune subpopulations. In this study, we have evaluated the role of monocytes and TAMs in modulating response to ICI, using the FarcastTM TruTumor histoculture platform. Two different cancer indications, Head and Neck Squamous Cell Carcinoma (HNSCC) and Renal Cell Carcinoma (RCC) were used in the study. HNSCC (n = 25) and RCC (n = 24) tissue samples were collected along with matched blood from the consented patients, post-surgery. Tissue explants were generated and allotted to arms and cultured for 72 hours. Fifteen samples from both indication were treated with anti-PD1 ICI, Nivolumab at a concentration of 132 µg/ml. Macrophage and monocyte sub-populations were characterized by performing flow cytometry, and cytokine (tumor necrosis factor-α (TNF-α), and interferon gamma (IFN-γ)) analysis. In HNSCC, a higher proportion of monocytes compared to RCC (p = 0.02) was observed. Though total TAM proportions in the two indications exhibited no significant difference, a significantly higher proportion of M1/M2 was observed in RCC (p = 0.006) as compared to HNSCC. In addition, RCC also exhibited higher secretion of TNF-α (p = 0.08) as compared to HNSCC. Eleven out of fifteen HNSCC samples (73%) exhibited more than 1.2-fold increase in IFN-γ secretion as opposed to only 6/13 RCC samples (46%), on treatment with Nivolumab. However, the correlation between fold change, with respect to control, in IFN- γ secretion and tumor content was much stronger in RCC (ρ=-0.82; p=0.0009) as compared to HNSCC (ρ=-0.07; p=0.81). This observation could be explained by a comparatively higher immunosuppressive microenvironment in HNSCC, potentially mediated by a higher monocyte subpopulation. On the other hand, the relatively higher M1/M2 ratio in RCC seemed to potentially enhance Nivolumab treatment efficacy as compared to HNSCC. Spatial orientation of macrophage sub-population could give further insights into the role they play in TME. Thus, FarcastTM TruTumor is a relevant platform to characterize the monocyte and TAM population in TME across different cancer indications and to investigate their role in modulating ICI response. Citation Format: Moumita Nath, Nandini Pal Basak, Kowshik Jaganathan, Oliyarasi M, Rajashekar M, Saurabh Bhargava, Amritha Suresh, Lalitha Laxhmi, Jayaprakash Chandra Reddy, Ganesh Mandakulutur Subramanya, Amritha Prabha, Prakash BV, Biswajit Das, Syamkumar V, Chandan Bhowal, Mouniss M, Dharanidharan M, Ritu Malhotra, Govindraj K, Mohit Malhotra, Satish Sankaran. Characterization of macrophage population in head and neck squamous cell carcinoma and renal cell carcinoma and their role in modulating immune checkpoint blockade response [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 A018.
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 | 0,001 | 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 | 0,001 |
| 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 ».