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Record W2980082240 · doi:10.1002/gcc.22812

A molecular study of synovial chondromatosis

2019· article· en· W2980082240 on OpenAlexaff
Narasimhan P. Agaram, Lei Zhang, Brendan C. Dickson, David Swanson, Yun‐Shao Sung, David M. Panicek, Meera Hameed, John H. Healey, Cristina R. Antonescu

Bibliographic record

VenueGenes Chromosomes and Cancer · 2019
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal synovial abnormalities and treatments
Canadian institutionsMount Sinai Hospital
FundersNational Cancer Institute
KeywordsBiologySynovial chondromatosisComputational biologyPathology

Abstract

fetched live from OpenAlex

Synovial chondromatosis (SC) is a rare benign cartilaginous neoplasm in which recurrent fibronectin 1 (FN1) and activin receptor 2A (ACVR2A) gene rearrangements have been recently reported. Triggered by a case of malignant transformation in SC (synovial chondrosarcoma) showing a novel KMT2A-BCOR gene fusion by targeted RNA sequencing, we sought to evaluate the molecular abnormalities in a cohort of 27 SC cases using a combined methodology of fluorescence in situ hybridization (FISH) and/or targeted RNA sequencing. Results showed that FN1 and /or ACVR2A gene rearrangements were noted in 18 cases (67%), with an FN1-ACVR2A fusion being confirmed in 15 (56%) cases. Two cases showed only FN1 gene rearrangement, without other abnormalities. A novel FN1-NFATc2 gene fusion was noted in one case by RNA sequencing. The remaining nine cases showed no abnormalities in FN1 and ACVR2A genes. No additional cases showed BCOR gene alterations. In conclusion, this study confirms that FN1-ACVR2A fusion is the leading pathogenetic event in SC, at even higher frequency than previously reported. FISH methodology emerges as an appropriate tool in the identification of FN1 and ACVR2A gene abnormalities, which can be used in challenging cases. Further studies are needed to determine the recurrent potential of BCOR abnormalities in this disease.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.053
Threshold uncertainty score0.761

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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.008
GPT teacher head0.265
Teacher spread0.257 · 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 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

Citations56
Published2019
Admission routes1
Has abstractyes

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