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Record W2292454252 · doi:10.1101/mcs.a000729

Putative <i>BRAF</i> activating fusion in a medullary thyroid cancer

2015· article· en· W2292454252 on OpenAlexafffund
Sam M. Wiseman, Blair Walker, Jacqueline E. Schein, Martin Hirst, Richard A. Moore, Andrew J. Mungall, Marco A. Marra, Steven J.M. Jones

Bibliographic record

VenueMolecular Case Studies · 2015
Typearticle
Languageen
FieldMedicine
TopicThyroid Cancer Diagnosis and Treatment
Canadian institutionsSimon Fraser UniversityUniversity of British ColumbiaSt. Paul's HospitalCanada's Michael Smith Genome Sciences Centre
FundersCanadian Cancer Society Research InstituteCanadian Institutes of Health ResearchCanada's Michael Smith Genome Sciences CentreConquer Cancer Foundation
KeywordsVandetanibMedullary thyroid cancerCabozantinibMedicineCancer researchThyroid cancerCalcitoninThyroidCancerProto-Oncogene Proteins c-retMalignancyTargeted therapyFusion geneInternal medicineOncologyBiologySorafenibGeneReceptorGenetics

Abstract

fetched live from OpenAlex

Medullary thyroid cancer (MTC) is a malignancy of the calcitonin-producing parafollicular cells of the thyroid gland. Surgery is the only curative treatment for this cancer. External beam radiation therapy is reserved for adjuvant treatment of MTC with aggressive features. Targeted therapeutics vandetanib and cabozantinib are approved for the treatment of aggressive and metastatic tumors that are not amenable to surgery. The use of these multikinase inhibitors are supported by the observed overactivation of the RET oncoprotein in a large subpopulation of MTCs. However, not all patients carry oncogenic alterations of this kinase. Hence, there is still a need for comprehensive molecular characterization of MTC utilizing whole-genome and transcriptome-sequencing methodologies with the aim of identifying targetable mutations. Here, we describe the genomic profiles of two medullary thyroid cancers and report the presence of a putative oncogenic BRAF fusion in one. Such alterations, previously observed in other malignancies and known targets of available drugs, can benefit patients who currently have no treatment options.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.061
GPT teacher head0.378
Teacher spread0.317 · 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 designCase report
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

Citations16
Published2015
Admission routes2
Has abstractyes

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