Abstract B032: Generative modeling (AI) for the analysis of Single-Cell RNA-seq data of radioresistant malignant pediatric brain tumors
Notice bibliographique
Résumé
Abstract Malignant pediatric brain tumors (MPBTs) are aggressive tumors that are the leading cause of mortality in pediatric neuro-oncology. Since surgical resection is often limited by tumor location, radiotherapy frequently serves as initial treatment. However, MPBTs might have spontaneously or can develop radioresistance (RR) where the cellular mechanisms underlying this resistance remain poorly understood. Single-Cell RNA sequencing (scRNA-seq), which characterizes tumor heterogeneity at the cellular level, might distinguish intrinsic and extrinsic specific transcriptomic signatures associated with RR before and after irradiation exposure. To investigate those relevant transcriptomic changes induced by irradiation, patient-derived tumoroids established from patient-derived xenograft models were exposed to a repetitive irradiation of 5x4Gys. scRNA-seq approach was performed on naïve tumoroids and 7 days after the end of irradiation. Based on this experimentation, we aim to identify gene expression changes occurring in response to irradiation. For this comparative purpose, we implemented a generative AI model based on Disentangled Variational Auto-Encoders (D-VAE) to learn irradiation-invariant representations of the transcriptome. These representations are then concatenated with controllable treatment attributes for data reconstruction. This model is generating realistic transcriptomic data of irradiated cells from both non-irradiated or irradiated tumor cells just by controlling the irradiation attribute sent to the generator of the model. The exploration of generated synthetic data through this model might be leveraged to understand the effects of irradiation treatments on gene expression profiles. From the generated transcriptomic data, our initial validation confirms that the D-VAE model accurately reproduces the radiation treatment effects and preserve cell clustering patterns. We are now exploring the opportunities provided by the ability to reconstruct "counterfactual" data to understand the fate of different cell clusters in response to irradiation. This process will allow to identify key genes and pathways that are consistently altered following irradiation or associated to RR. In particular, we are focusing on whether subset of cells display pre-existing RR biomarkers prior to treatment. Together, this work is demonstrating the utility of AI-driven approaches in modeling complex cellular responses to irradiation and is offering new insights of the molecular determinants of RR in MPBTs. Citation Format: Chinar Salmanli, Marlene Deschuyter, Natacha Entz-Werle, Julien Godet. Generative modeling (AI) for the analysis of Single-Cell RNA-seq data of radioresistant malignant pediatric brain tumors [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 B032.
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 | 0,003 | 0,001 |
| 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 | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| 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 ».