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Record W2508524924 · doi:10.1097/md.0000000000004756

First case report of an adrenocortical carcinoma caused by a BRCA2 mutation

2016· article· en· W2508524924 on OpenAlexafffund
Nada El Ghorayeb, Solange Grunenwald, Serge Nolet, Vanessa Primeau, Stéphanie Côté, Christine M. Maugard, André Lacroix, Louis Gaboury, Isabelle Bourdeau

Bibliographic record

VenueMedicine · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic and Clinical Aspects of Sex Determination and Chromosomal Abnormalities
Canadian institutionsHôtel-Dieu de MontréalCentre Hospitalier de l’Université de Montréal
FundersFonds de Recherche du Québec - Santé
KeywordsFrameshift mutationLoss of heterozygosityMedicineAdrenocortical carcinomaGermline mutationMutationGermlineCancer researchGeneticsAlleleInternal medicineBiologyGene

Abstract

fetched live from OpenAlex

BACKGROUND: Adrenocortical carcinoma (ACC) may rarely be a component of inherited cancer syndromes such as Li-Fraumeni syndrome and Beckwith-Wiedemann syndrome. ACC caused by a BRCA2 mutation has never been reported. METHODS: Nucleotide sequencing of BRCA2 in lymphocyte and tumoral DNA of a 50-year-old male who presented with an androgen-secreting ACC and a strong family history of breast, ovarian, and pancreatic cancers. RESULTS: A germline BRCA2 2 bp heterozygous deletion at nucleotide 8765 (8765delAG) leading to a frameshift mutation (p.Glu2846GlyfsX23) was detected. Only the BRCA2 deleted allele was retained in the ACC tumoral DNA compared with the control DNA supporting a loss of heterozygosity in the tumor. CONCLUSION: This is the first reported case of a patient with ACC associated with a BRCA2 germline mutation. Loss of heterozygosity in ACC DNA suggests a causal link with the BRCA2 8765delAG mutation.

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)0.003
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: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.001
Science and technology studies0.0030.002
Scholarly communication0.0020.002
Open science0.0020.002
Research integrity0.0060.003
Insufficient payload (model declined to judge)0.0060.003

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 head0.013
GPT teacher head0.277
Teacher spread0.263 · 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

Citations21
Published2016
Admission routes2
Has abstractyes

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