Abstract B050: Spatially aware transcriptomic topic modeling reveals novel signals of spatial organization in glioblastoma
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Résumé
Abstract Background: Spatial transcriptomics (ST) enables high-resolution mapping of gene expression across tissue architecture and can reveal the organization of cellular states in complex tissues, such as the tumor microenvironment. Using ST data from glioblastoma (GBM) patients, a large recent study1 identified robust gene programs in a spatially oblivious manner and used them to assign cell states to spatial spots and assess cell-state spatial organization. The results characterized GBM tumors as generally spatially heterogeneous, with some hypoxia-associated structured regions. However, recent computational methods support analyses that more tightly integrate spatial context and gene expression to identify continuous gradients and rare niches in ST data. We hypothesized that applying such an approach may reveal additional key aspects of the spatial organization of transcriptional programs in GBM tumors. Methods: Based on our previous successes with traditional topic modeling in transcriptomic analysis, we focused on the recently published method Spatial Transcriptomics Analysis with topic Modeling to uncover spatial Patterns (STAMP)2. STAMP uses a deep generative model to perform spatially aware topic modeling that can account for replicates. Using STAMP, we reanalyzed the 26 ST samples (10x Visium, 60,000+ spots) from 17 patients in the GBM dataset1 to infer spatial topics or gene programs, which we then interpreted in the context of previous studies. We also extended the analysis framework to enable quantification and statistical assessment of the spatial coherence of continuous gene program weights. Results: Consistent with previous studies, we identified gene programs associated with a hypoxic niche, as well as with precursor and differentiated endothelial, oligodendrocyte and neuronal cell types. However, our study also revealed a number of new gene programs with strongly spatially organized patterns of expression occurring in multiple patient samples. These findings indicate additional spatial axes that may capture important variation in and across GBM tumor microenvironments. Conclusion: Complementing a previous hypoxia-associated model of GBM spatial organization, our study characterized additional, spatially resolved functional processes, which may be clinically and therapeutically relevant. The results suggest that a spatially aware, non-categorical approach to ST analysis may drive discovery of previously hidden, spatially coherent, recurrent gene programs in the tumor microenvironment. This framework is broadly applicable for dissecting the transcriptionally governed spatial architecture of solid tumors. References: 1Greenwald, A.C. et al. Integrative spatial analysis reveals a multi-layered organization of glioblastoma. Cell 187, 2485–2501.e26 (2024). 2ZHong, C., Ang, K.S. & Chen, J. Interpretable spatially aware dimension reduction of spatial transcriptomics with STAMP. Nat. Methods 21, 2072–2083 (2024). Citation Format: Joseph J. Sifakis, Sophia Madejski, Samantha J. Riesenfeld. Spatially aware transcriptomic topic modeling reveals novel signals of spatial organization in glioblastoma [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Artificial Intelligence and Machine Learning; 2025 Jul 10-12; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(13_Suppl):Abstract nr B050.
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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 | 0,004 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 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 découle