MétaCan
Menu
Back to cohort
Record W3049687009 · doi:10.3390/cancers12082321

Prevalence of Recurrent Mutations Predisposing to Breast Cancer in Early-Onset Breast Cancer Patients from Poland

2020· article· en· W3049687009 on OpenAlexaff
Emilia Rogoża-Janiszewska, Karolina Malińska, Cezary Cybulski, Anna Jakubowska, Jacek Gronwald, Tomasz Huzarski, Marcin Lener, Bohdan Górski, Wojciech Kluźniak, Helena Rudnicka, Mohammad R. Akbari, Aniruddh Kashyap, Steven A. Narod, Jan Lubiński, Tadeusz Dębniak

Bibliographic record

VenueCancers · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBRCA gene mutations in cancer
Canadian institutionsPublic Health OntarioWomen's College HospitalUniversity of Toronto
FundersNarodowym Centrum Nauki
KeywordsPALB2Breast cancerCHEK2MedicineGermline mutationOncologyFamily historyAge of onsetGenetic testingInternal medicineCancerMutationGeneticsGeneBiologyDisease

Abstract

fetched live from OpenAlex

There are twenty recurrent mutations in six breast-cancer-predisposing genes in Poland (BRCA1, BRCA2, CHEK2, PALB2, NBN, and RECQL). The frequencies of the twenty alleles have not been measured in a large series of early-onset breast cancer patients from Poland unselected for family history. We genotyped 2464 women with breast cancer diagnosed below age 41 years for twenty recurrent germline mutations in six genes, including BRCA1, BRCA2 CHEK2, PALB2, NBN, and RECQL. A mutation in one of the six genes was identified in 419 of the 2464 early-onset breast cancer cases (17%), including 22.4% of those cases diagnosed below age 31. The mutation frequency was 18.8% for familial breast cancer cases and 6% for non-familial cases. Among women with breast cancer below age 31, the mutation frequency was 23.6% for familial cases and 17.4% in non-familial cases. The majority of mutations (76.2%) were seen in BRCA1 and BRCA2. In Poland, a panel of twenty recurrent mutations in six genes can identify a genetic basis for a high percentage of early-onset cases and testing is recommended for all women with breast cancer at age 40 or below.

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 score0.043
Threshold uncertainty score0.741

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 head0.010
GPT teacher head0.271
Teacher spread0.260 · 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

Citations16
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueCancersSame topicBRCA gene mutations in cancerFrench-language works237,207