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Enregistrement W7132845012 · doi:10.1093/noajnl/vdaf164

The evolving landscape of single-cell genomics in CNS and PNS oncology

2025· article· en· W7132845012 sur OpenAlexaffabout
Yosef Ellenbogen, Mario L Suva, Itay Tirosh

Notice bibliographique

RevueNeuro-Oncology Advances · 2025
Typearticle
Langueen
DomaineBiochemistry, Genetics and Molecular Biology
ThématiqueSingle-cell and spatial transcriptomics
Établissements canadiensPrincess Margaret Cancer CentreUniversity of TorontoUniversity Health Network
Organismes subventionnairesnon disponible
Mots-clésGenomicsGenomeDiseaseMEDLINEPrecision medicine

Résumé

récupéré en direct d'OpenAlex

Single-cell genomics has transformed neuro-oncology, revealing the cellular and molecular diversity of tumors across the central and peripheral nervous systems (CNS and PNS).1–5 These approaches have highlighted intratumoral heterogeneity, identified the molecular programs driving malignancy and refined our understanding of the tumor microenvironment.2 In recent years, this technology has been increasingly applied to tumors of the CNS and PNS, revealing complexities that bulk genomic method cannot capture. This special issue of Neuro-Oncology Advances explores the transformative impact of single-cell technologies on the characterization of CNS and PNS tumors. It provides a comprehensive overview of current advancements, showcasing how single-cell genomics is reshaping our understanding of brain, spine, and peripheral nerve tumors. The goal of this special issue is to consolidate existing knowledge, foster collaborations, and guide future research that integrates single-cell analyses into neuro-oncology. Below is a summary of the articles included in this special issue, organized by category and highlighting their significance. Gonzalez Castro et al.: Redefining the immune microenvironment of gliomas in the era of single-cell genomics.6 Porter et al.: Single-cell advances in the investigation of the pathogenesis and treatment of brain metastasis.7 Ellenbogen et al.: Current landscape of single-cell genomics in meningioma.8 Gliomas are the most common malignant primary brain tumors and are a major focus of single-cell genomics in neuro-oncology. Glioblastoma demonstrates significant intratumoral heterogeneity across malignant and non-malignant populations and is best characterized by single-cell and spatial methods that capture cellular diversity within the tumour and microenvironmental compratments. Non-malignant cells within the tumor microenvironment represent a large portion of the tumor, and single-cell genomics has improved our understanding of its complex cellular composition and interactions. In the first article of this supplement, Gonzalez Castro et al. synthesize recent findings, emphasizing the evolving understanding of glioma immune landscapes and their implications for immunotherapy.6 Similarly, single-cell technologies have expanded our understanding of brain metastases, which remain a major cause of morbidity and mortality in cancer patients. Like glioblastoma, brain metastases exhibit significant cellular heterogeneity and complex interactions with the surrounding microenvironment. In the second article, Porter et al. review how single-cell RNA-seq (scRNA-seq) and related modalities dissect tumor, immune, and stromal populations within metastases and clarify mechanisms of progression and treatment response.7 These insights offer potential avenues for novel therapeutic strategies. Meningiomas are the most common primary CNS tumors.9 These tumors present substantial clinical heterogeneity, with some exhibiting aggressive recurrence despite surgical resection and radiotherapy. Single-cell and spatial transcriptomics have refined molecular subgrouping and resolved intratumoral heterogeneity beyond the resolution of bulk profiling. The third article of this supplement summarizes single-cell and spatial transcriptomic studies of meninigioma and higlights the unique cellular populations, regulatory networks, and microenvironmental interactions that drive meningioma behavior.8 These advances deepen biological understanding and identify candidate therapeutic targets tailored to the molecular diversity of these tumors. Pari et al.: Single-cell multiomic techniques highlight the diverse composition and intercellular interactions of the vestibular schwannoma tumor microenvironment.10 Gui et al.: Single-cell transcriptomic profiling of malignant peripheral nerve sheath tumors.11 Vestibular schwannomas (VS) are benign tumors originating from Schwann cells of the vestibulocochlear nerve and were historically considered molecularly homogeneous.12 However, recent single-cell and multiomic analyses have challenged this notion, revealing substantial cellular diversity within these tumors.13–15 Pari et al. highlight how single-cell transcriptomic and epigenetic profiling identify distinct Schwann cell subpopulations and complex interactions within the tumor microenvironment.10 Their work reveals 2 molecularly distinct groups of VS: one characterized by an “injury-like” Schwann cell phenotype that is associatd with immune cell recruitment, and another composed of quiescent Schwann cells with reduced immune infiltration. These insights reshape the understanding of VS pathogenesis and may inform future therapies targeting specific microenvironmental interactions. Malignant periphal nerve sheath tumors (MPNSTs) are aggressive sarcomas that exhibit significant molecular and cellular heterogeneity. Gui et al. explore how single-cell profiling deepens understanding of MPNST biology, revealing diverse cellular populations including neoplastic Schwann cell-like, malignant neural crest-like, immune, and stromal cells.11 Comparative single-nucleus analyses of MPNSTs and their benign precursors highlight Schwann cell dedifferentiation into mesenchymal, stem-like state that underlies malignant transformation. This article synthesizes these findings, highlighting cellular plasticity within MPNSTs and pointing to potential therapeutic targets designed to halt or reverse malignant progression. Tirosh: Pitfalls in analysis and interpretation of single-cell RNA-seq data in cancer16 scRNA-seq and single-nucleus RNA-seq (snRNA-seq) have become essential tools in cancer research, enabling high-resolution analyses of cellular heterogeneity and the tumor microenvironment. The widespread adoption of these technologies has been supported by the development of specialized computational tools tailored for sc/snRNA-seq data. However, proper analysis and interpretation require significant expertise, as limitations in computational methods can lead to misleading conclusions. This article reviews common pitfalls encountered in cancer sc/snRNA-seq analysis and interpretation, emphasizing recognition of data limitations, understanding the assumptions behind common analytical methods, and employing rigorous statistical approaches.16 It discusses potential sources of error, including inaccuracies in statistical modeling, challenges in trajectory analysis, and the misapplication of single-cell RNA-seq signatures to bulk data. It also addresses pitfalls in inferring chromosomal aberrations such as somatic copy number variations and large-scale structural rearrangements from sc/snRNA-seq data, and the challenges these pose for accurately defining complex cell populations. By outlining these challenges and providing practical strategies to mitigate them, the review aims to guide researchers toward more robust, reproducitve analyses, ultimately promoting more reliable interpretations of sc/snRNA-seq data in cancer research. This special issue underscores the transformative impact of single-cell genomics in CNS and PNS oncology. As these technologies and analytical method continue to improve, the knowledge gained from single-cell analyses is shaping neuro-oncology, informing precision medicine approaches and the development of targeted therapies. M.L.S. is an equity holder, scientific co-founder, and advisory board member of Immunitas Therapeutics. I.T. is an advisory board member of Immunitas Therapeutics. This article appears as part of the supplement “Single-Cell Technologies,” sponsored by the Princess Margaret Cancer Research Centre and the Toronto Western Hospital Division of Neurosurgery.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,652
Score d'incertitude au seuil0,485

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,010
Tête enseignante GPT0,254
Écart entre enseignants0,245 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeExpérimental (laboratoire)
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2025
Routes d'admission2
Résumé présentoui

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