MétaCan
Menu
Back to cohort
Record W4414792320 · doi:10.1093/neuonc/noaf193.050

OS05.2.A DECIPHERING THE LONGITUDINAL TRAJECTORIES OF GLIOBLASTOMA ECOSYSTEMS BY INTEGRATIVE SINGLE-CELL GENOMICS

2025· article· en· W4414792320 on OpenAlexaff
Alan R. Spitzer, Kevin C. Johnson, Masashi Nomura, Luciano Garofano, Djamel Nehar-Belaid, Noam Galili Darnell, Anthony G. Greenwald, Lillian Bussema, Yeonyee Oh, Frederick S. Varn, Francesca D’Angelo, S Gritsch, Kevin Anderson, S Migliozzi, L Gonzalez Castro, Tamrin Chowdhury, Nicolas Robine, Catherine Reeves, Jongsun Park, A Lipsa, Frank Hertel, Anna Golebiewska, Simone P. Niclou, Labeeba Nusrat, Salomey Kellett, Sunit Das, Hyeong‐Gon Moon, S Paek, Franck Bielle, Alice Laurenge, Anna Luisa Di Stefano, Bertrand Mathon, Alberto Pïcca, Marc Sanson, Shigenori Tanaka, Nobuhito Saito, David M. Ashley, Stephen T. Keir, Keith L. Ligon, J. T. Huse, W.K. Alfred Yung, Anna Lasorella, Antonio Iavarone, Roel G.W. Verhaak, Itay Tirosh, Mario L. Suvà

Bibliographic record

VenueNeuro-Oncology · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicSingle-cell and spatial transcriptomics
Canadian institutionsUniversity of TorontoSt. Michael's Hospital
Fundersnot available
KeywordsGlioblastomaTranscriptomeGenomicsTumor progressionTumor microenvironmentCellGenomeDiseaseGene

Abstract

fetched live from OpenAlex

Abstract BACKGROUND The mechanisms governing the progression of IDH-wildtype glioblastoma (GBM) following standard-of-care therapy remain incompletely understood. While recurrence is nearly universal, the cellular and molecular trajectories that underpin this process - particularly the dynamics of tumor and microenvironmental compartments - have yet to be fully resolved. MATERIAL AND METHODS To dissect the longitudinal evolution of the GBM ecosystem, we analyzed paired primary and recurrent tumor specimens from 59 patients. We employed single-nucleus RNA sequencing to profile transcriptomic states at single-cell resolution and integrated these data with bulk DNA sequencing to assess genomic alterations. This approach enabled a comprehensive interrogation of cellular heterogeneity and malignant state transitions over time. RESULTS Across the cohort, the most consistent feature at recurrence was a decreased proportion of malignant cells, accompanied by a reciprocal expansion of non-malignant glial and neuronal cell populations within the tumor microenvironment (TME). While the dominant malignant cell state often differed between primary and recurrent samples, no state was uniquely associated with a specific disease stage. Moreover, no singular evolutionary trajectory characterized the cohort as a whole. Instead, subsets of patients exhibited enriched and partially convergent state transitions. Notably, shifts in malignant cell states were mirrored by concurrent remodeling of the TME, implicating a tightly interwoven pattern of tumor-microenvironment co-evolution. CONCLUSION These findings reveal diverse and patient-specific evolutionary trajectories in IDH-wildtype GBM, shaped by both therapeutic pressure and microenvironmental context. Our study provides a reference framework for understanding longitudinal GBM dynamics and highlights the importance of ecosystem-level interactions in driving recurrence.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.012
GPT teacher head0.245
Teacher spread0.233 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueNeuro-OncologySame topicSingle-cell and spatial transcriptomicsFrench-language works237,207