PD16-03 UNDERSTANDING THE MOLECULAR CHARACTERISTICS AND VULNERABILITIES OF SARCOMATOID/RHABDOID RENAL CELL CARCINOMAS THROUGH INTEGRATIVE HISTOLOGICAL AND SPATIAL GENOMICS APPROACHES
Notice bibliographique
Résumé
You have accessJournal of UrologyKidney Cancer: Basic Research & Pathophysiology I (PD16)1 May 2024PD16-03 UNDERSTANDING THE MOLECULAR CHARACTERISTICS AND VULNERABILITIES OF SARCOMATOID/RHABDOID RENAL CELL CARCINOMAS THROUGH INTEGRATIVE HISTOLOGICAL AND SPATIAL GENOMICS APPROACHES Mustafa Soytas, Tamiko Nishimura, Madeleine Arseneault, Eleonora Scarlata, Kate Glennon, Peixi Liu, Senthilkumar Kailasam, Fadi Brimo, Simon Tanguay, and Yasser Riazalhosseini Mustafa SoytasMustafa Soytas , Tamiko NishimuraTamiko Nishimura , Madeleine ArseneaultMadeleine Arseneault , Eleonora ScarlataEleonora Scarlata , Kate GlennonKate Glennon , Peixi LiuPeixi Liu , Senthilkumar KailasamSenthilkumar Kailasam , Fadi BrimoFadi Brimo , Simon TanguaySimon Tanguay , and Yasser RiazalhosseiniYasser Riazalhosseini View All Author Informationhttps://doi.org/10.1097/01.JU.0001009560.23593.56.03AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Genomic and immune analyses in S/R RCC have been limited to bulk tumor analysis and thus lack cellular resolution and spatial perspective. Herein, we use in situ Whole Transcriptome Profiling (WTP) to define molecular differences between tumor regions with and without S/R features, aiming at identifying molecular markers of S/R tumors that could lead to better diagnosis or treatments. METHODS: All patients who underwent surgical excision of RCC at the MUHC between 2010 and 2020 were screened by a uropathologist, and histologically defined regions of S/R, ccRCC, papillary, chromophobe RCC, and benign kidney were selected to construct tissue microarrays (TMAs). Whole-exome sequencing (WES) and Compartment-Guided Spatial WTP were applied for gene and transcriptome analysis (Figure 1). RESULTS: A cohort of 56 RCC patients and their TMAs, consisting of 403 cores representing patient-matched tumor areas with and without S/R features. For WES, 47 patients were used to identify copy number variations (CNVs) analysis. Four hundred cores of 55 patients were used for WTP and 5 groups of clustered with 2000 highly variable genes (HVGs) were constructed. The most variable genes of each tumor type were identified by using digital spatial transcriptome profiling (Figure 2). Whole-exome sequencing was used to identify mutational patterns of tumor cells using a list of specific genes of interest (Figure 3). CONCLUSIONS: According to current and ongoing results, WES, and compartment-guided WTP should be used to generate an unprecedented resolution to the molecular and genomic characteristics of S/R RCC tumors and tumor microenvironment. Download PPTDownload PPTDownload PPT Source of Funding: The Kidney Foundation of Canada © 2024 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 211Issue 5SMay 2024Page: e366 Advertisement Copyright & Permissions© 2024 by American Urological Association Education and Research, Inc.Metrics Author Information Mustafa Soytas More articles by this author Tamiko Nishimura More articles by this author Madeleine Arseneault More articles by this author Eleonora Scarlata More articles by this author Kate Glennon More articles by this author Peixi Liu More articles by this author Senthilkumar Kailasam More articles by this author Fadi Brimo More articles by this author Simon Tanguay More articles by this author Yasser Riazalhosseini More articles by this author Expand All Advertisement PDF downloadLoading ...
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 | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| 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) | 0,000 | 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 ».