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Record W1541382664 · doi:10.1111/cge.12524

Mutations predisposing to breast cancer in 12 candidate genes in breast cancer patients from Poland

2014· article· en· W1541382664 on OpenAlexaff
Cezary Cybulski, Jan Lubiński, Dominika Wokołorczyk, W. Kuźniak, Aniruddh Kashyap, Victoria Sopik, Tomasz Huzarski, Jacek Gronwald, Tomasz Byrski, Marek Szwiec, Anna Jakubowska, Bohdan Górski, Tadeusz Dębniak, Steven A. Narod, Mohammad R. Akbari

Bibliographic record

VenueClinical Genetics · 2014
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBRCA gene mutations in cancer
Canadian institutionsPublic Health OntarioWomen's College HospitalUniversity of Toronto
Fundersnot available
KeywordsPALB2CHEK2Breast cancerMutationGeneticsFounder effectBRCA2 ProteinCancerExome sequencingGermline mutationGenetic testingBiologyMedicineGeneHaplotypeGenotype

Abstract

fetched live from OpenAlex

A number of genes other than BRCA1 and BRCA2 have been associated with breast cancer predisposition, and extended genetic testing panels have been proposed. It is of interest to establish the full spectrum of deleterious mutations in women with familial breast cancer.We performed whole-exome sequencing of 144 women with familial breast cancer and negative for 11 Polish founder mutations in BRCA1, CHEK2 and NBS1, and we evaluated the sequences of 12 known breast cancer susceptibility genes. A truncating mutation in a breast cancer gene was detected in 24 of 144 women (17%) with familial breast cancer. A BRCA2 mutation was detected in 12 cases, a (non-founder) BRCA1 mutation was detected in 5 cases, a PALB2 mutation was detected in 4 cases and an ATM mutation was detected in 2 cases. Polish women with familial breast cancer who are negative for founder mutations in BRCA1, CHEK2 and NBS1 should be fully screened for mutations in BRCA1, BRCA2 and PALB2. The PALB2 founder mutation c.509_519delGA should be included in the panel of Polish founder mutations.

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 score0.064
Threshold uncertainty score0.900

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)0.0000.000

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 head0.016
GPT teacher head0.336
Teacher spread0.320 · 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

Citations45
Published2014
Admission routes1
Has abstractyes

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