Abstract B052: Single-cell analysis of multiple cancers in the upper gastrointestinal tract uncovers immune characteristics of tumor microenvironments linked to the predictive biomarkers for immunotherapy in esophageal cancers
Notice bibliographique
Résumé
Abstract Esophageal cancer is mainly composed of two subtypes – esophageal squamous cell carcinoma (ESCC) and esophageal adenocarcinoma (EAC) – with distinct risk factors and cancer phenotypes despite arising from the same organ. The recent study by the Cancer Genome Atlas (TCGA) has revealed their distinct genomic characteristics and similarities to subsets of other nearby cancers such as head and neck squamous neck carcinoma (HNSCC) and gastric adenocarcinoma (GAC) for ESCC and EAC, respectively. Furthermore, recent clinical trials with anti-PD-1 monotherapy and combination treatment with anti-PD-1 and anti-CTLA-4 reported varying degrees of responses to immunotherapy for those cancers. Here we performed single-cell RNA sequencing of patients with ESCC, EAC, and HNSCC, and collected additional public datasets for comparative analysis of four cancer types in the upper gastrointestinal tract, especially in connection to responses to immunotherapy. In total, we integrated 35 patient samples from 4 different cohorts to comprehensively analyze malignant cells, stromal/endothelial cells, and immune cells. With the integrative analysis, we confirmed the similarities and differences among those cancer types in the upper gastrointestinal tract and expanded understanding of immune mechanisms at single-cell resolution. For malignant cells, we utilized matrix factorization analysis to identify underlying malignant cell programs related to each cancer type. We confirmed clear separation between malignant cells of squamous epithelial cell origins and glandular epithelial cell origins. We further identified the malignant cell programs related to both cancer cell origins and molecular mechanisms of cancer cells. For stromal and endothelial cells, we identified compositional differences in their cellular subtypes. With biological pathway and signature analyses, we revealed that their distinct cellular subtypes might be connected to distinct immune mechanisms in tumor microenvironments. For immune cells, despite having less compositional difference in cellular subtypes, we identified their underlying immune mechanisms that could explain key differences in responses to immunotherapy. With comprehensive analyses of various immune cell-types and their interactions, we identified several T cell populations and related tumor-associated macrophages that could serve as predictive markers of responses to cancer immunotherapy. In order to validate our findings, we utilized both bulk and single-cell sequencing datasets of various cancer types with treatments of immune checkpoint inhibitors (ICI) from previous studies and confirmed significance of those immune signatures and cellular compositions in responses to the ICI treatment. Citation Format: Seungbyn Baek, Gamin Kim, Sang Jun Ha, Hye Ryun Kim, Seong Yong Park, Insuk Lee. Single-cell analysis of multiple cancers in the upper gastrointestinal tract uncovers immune characteristics of tumor microenvironments linked to the predictive biomarkers for immunotherapy in esophageal cancers [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 B052.
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) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,003 |
| 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) | 0,000 | 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 ».