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Record W3197444260 · doi:10.1186/s12885-021-08718-3

Genetic predisposition to male breast cancer in Poland

2021· article· en· W3197444260 on OpenAlexaff
Marek Szwiec, Joanna Tomiczek‐Szwiec, Wojciech Kluźniak, Dominika Wokołorczyk, Karolina Osowiecka, Robert Sibilski, Małgorzata Wachowiak, Jacek Gronwald, Helena Gronwald, Jan Lubiński, Cezary Cybulski, Steven A. Narod, Tomasz Huzarski

Bibliographic record

VenueBMC Cancer · 2021
Typearticle
Languageen
FieldMedicine
TopicMale Breast Health Studies
Canadian institutionsPublic Health OntarioWomen's College HospitalUniversity of Toronto
FundersNarodowym Centrum Nauki
KeywordsSurgical oncologyBreast cancerMedicineGenetic predispositionOncologyInternal medicineCancerGynecologyDisease

Abstract

fetched live from OpenAlex

BACKGROUND: Breast cancer in men accounts for fewer than 1 % of all breast cancer cases diagnosed in men and women. Genes which predispose to male breast cancer include BRCA1 and BRCA2. The role of other genes is less clear. In Poland, 20 founder mutations in BRCA1, BRCA2, CHEK2, PALB2, NBN, RECQL are responsible for the majority of hereditary breast cancer cases in women, but the utility this genes panel has not been tested in men. METHODS: We estimated the prevalence of 20 alleles in six genes (BRCA1, BRCA2, CHEK2, PALB2, NBN, RECQL) in 165 Polish male breast cancer patients. We compared the frequency of selected variants in male breast cancer cases and controls. RESULTS: One of the 20 mutations was seen in 22 of 165 cases (13.3%). Only one BRCA1 mutation and two BRCA2 mutations were found. We observed statistically significant associations for PALB2 and CHEK2 truncating mutations. A PALB2 mutation was detected in four cases (OR = 11.66; p < 0.001). A CHEK2 truncating mutation was detected in five cases (OR = 2.93;p = 0.02). CONCLUSION: In conclusion, we recommend that a molecular test for BRCA1, BRCA2, PALB2 and CHEK2 recurrent mutations should be offered to male breast cancer patients in Poland.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.998

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)0.0030.000

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 head0.330
Teacher spread0.312 · 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.

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

Citations14
Published2021
Admission routes1
Has abstractyes

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