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Record W4390663732 · doi:10.1038/s41467-023-44300-0

Developmental basis of SHH medulloblastoma heterogeneity

2024· article· en· W4390663732 on OpenAlexafffund
Maxwell P. Gold, Winnie Ong, Andrew M. Masteller, David R. Ghasemi, Julie Galindo, Noel R. Park, Nhan Huynh, Aneesh Donde, Veronika I. Pister, Raul A. Saurez, Maria Vladoiu, Grace H. Hwang, Tanja Eisemann, Laura Donovan, Adam D. Walker, Joseph Benetatos, Christelle Dufour, Livia Garzia, Rosalind A. Segal, Robert J. Wechsler‐Reya, Jill P. Mesirov, Andrey Korshunov, Kristian W. Pajtler, Scott L. Pomeroy, Olivier Ayrault, Shawn M. Davidson, Jennifer Cotter, Michael D. Taylor, Ernest Fraenkel

Bibliographic record

VenueNature Communications · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA modifications and cancer
Canadian institutionsMcGill UniversitySickKids FoundationHospital for Sick ChildrenUniversity of Toronto
FundersNational Institute of Neurological Disorders and StrokeNational Institute of General Medical SciencesCanadian Cancer Society Research InstituteCanadian Institutes of Health ResearchInstitut National Du CancerHospital for Sick ChildrenGovernment of OntarioDeutsche KrebshilfeTerry Fox Research InstituteStudienstiftung des Deutschen VolkesEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentBrain Tumour CharityCancer Research UKStand Up To CancerGenome British ColumbiaKoch Institute for Integrative Cancer Research, Massachusetts Institute of TechnologyNational Cancer InstituteUniversity of TorontoUSC Norris Comprehensive Cancer CenterGenome Canada
KeywordsSonic hedgehogMedulloblastomaBiologyPTCH1PatchedHedgehogCarcinogenesisProgenitor cellComputational biologyCancer researchNeuroscienceGeneStem cellCell biologyGenetics

Abstract

fetched live from OpenAlex

Many genes that drive normal cellular development also contribute to oncogenesis. Medulloblastoma (MB) tumors likely arise from neuronal progenitors in the cerebellum, and we hypothesized that the heterogeneity observed in MBs with sonic hedgehog (SHH) activation could be due to differences in developmental pathways. To investigate this question, here we perform single-nucleus RNA sequencing on highly differentiated SHH MBs with extensively nodular histology and observed malignant cells resembling each stage of canonical granule neuron development. Through innovative computational approaches, we connect these results to published datasets and find that some established molecular subtypes of SHH MB appear arrested at different developmental stages. Additionally, using multiplexed proteomic imaging and MALDI imaging mass spectrometry, we identify distinct histological and metabolic profiles for highly differentiated tumors. Our approaches are applicable to understanding the interplay between heterogeneity and differentiation in other cancers and can provide important insights for the design of targeted therapies.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.671
Threshold uncertainty score0.271

Codex and Gemma teacher scores by category

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

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 head0.018
GPT teacher head0.311
Teacher spread0.293 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations29
Published2024
Admission routes2
Has abstractyes

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