Abstract PO-012: Spatial transcriptomic analysis of HPV-related and HPV-unrelated head and neck squamous cell carcinoma
Notice bibliographique
Résumé
Abstract Background: Recent work characterizing head and neck squamous cell carcinoma (HNSCC) using single-cell RNA-sequencing (scRNA-seq) has highlighted intra- and inter-tumoral heterogeneity in both HPV-related and HPV-unrelated disease. These studies have identified previously undescribed gene expression programs associated with poor survival and adverse features. Unfortunately, these approaches necessarily forego the spatial localization of diverse cell types in the tumor microenvironment (TME). Spatial transcriptomics (ST) is a novel platform that allows for the unbiased detection of thousands of genes with near single-cell resolution, while retaining the physical, geographic source of these mRNA transcripts. Interactions between malignant cell populations and the surrounding TME influence tumor behavior, yet no study to date has characterized HNSCC with ST. Methods: We characterized 27 HNSCC patient tumors (19 HPV-unrelated and 8 HPV-related tumors) with the 10X Visium ST platform, 11 of which had paired scRNA-seq data. Cell phenotypes were identified by defining consensus meta-programs through a combined use of Leiden clustering and non-negative matrix factorization (NMF). Malignant regions were computationally defined through adaptation of previously employed inferred copy number alteration (CNA) algorithms. Relationships between cell types were characterized using distance-based metrics. Using consensus meta-program assignments and CNA scores, we defined 3 zones: tumor stroma, tumor-stroma interface (TSI), and tumor nests. Within each of these zones, we characterized enrichment for cell types and cell-to-cell interactions. Results: After quality control filtering, we retained 77,604 capture spots with a mean depth of 83,519 reads/spot and a median of 3,729 genes/spot for downstream analysis. In total, 13 consensus meta-programs were defined, including immune cell populations (e.g. T cells, macrophages), stromal cells (e.g. fibroblasts, endothelial cells), and multiple epithelial cell populations. Inferred CNA analysis suggested that these epithelial populations consisted of both normal epithelium and multiple malignant cell populations, including cells expressing markers typical of the previously described partial epithelial mesenchymal transition (p-EMT) gene signature as well as a hypoxia signature. The p-EMT signature was enriched at the TSI and the hypoxia signature was enriched in the center of the tumor nests in HPV-unrelated tumors, while HPV-related tumors had distinct gene expression at the TSI. Conclusions: Our study represents the first unbiased ST analysis of the HNSCC TME. Our results suggest that heterogeneity of intra-tumoral malignant cell states are consistent across multiple lesions and strongly associated with HPV etiology, particularly at the TSI. These findings suggest that HPV-related and HPV-unrelated tumors may have unique modes of local invasion. Citation Format: Thomas F. Barrett, Dor Simkin, Alissa R. Greenwald, Anuraag Parikh, Hiram Gay, Anthony Apicelli, Douglas Adkins, Wade Thorstad, Jason T. Rich, Randal C. Paniello, Paul Zolkind, Patrik Pipkorn, Ryan S. Jackson, Rebecca Chernock, Itay Tirosh, Sidharth V. Puram. Spatial transcriptomic analysis of HPV-related and HPV-unrelated head and neck squamous cell carcinoma [abstract]. In: Proceedings of the AACR-AHNS Head and Neck Cancer Conference: Innovating through Basic, Clinical, and Translational Research; 2023 Jul 7-8; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2023;29(18_Suppl):Abstract nr PO-012.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 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) | 0,001 | 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 source (Gemma direct ou Codex distillé), 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 ».