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Record W2113952766 · doi:10.1186/bcr2822

Contribution of large genomic BRCA1 alterations to early-onset breast cancer selected for family history and tumour morphology: a report from The Breast Cancer Family Registry

2011· article· en· W2113952766 on OpenAlexafffund
Letitia Smith, Andrea Tesoriero, Ee Ming Wong, Susan J. Ramus, Frances P. O’Malley, Anna Marie Mulligan, Mary Beth Terry, Ruby T. Senie, Regina M. Santella, Esther M. John, Irene L. Andrulis, Hilmi Özçelik, Mary B. Daly, Andrew K. Godwin, Saundra S. Buys, Stephen B. Fox, David E. Goldgar, Graham G. Giles, John L. Hopper, Melissa C. Southey

Bibliographic record

VenueBreast Cancer Research · 2011
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBRCA gene mutations in cancer
Canadian institutionsSt. Michael's HospitalCanada Research ChairsUniversity of TorontoMount Sinai Hospital
FundersNational Health and Medical Research CouncilNational Institutes of HealthHuntsman Cancer InstituteNational Cancer InstituteCancer Care Ontario
KeywordsBreast cancerFamily historyOvarian cancerSurgical oncologyMedicineGermline mutationGenetic testingOncologyPopulationCancerInternal medicineGermlineMutationGynecologyGeneticsBiologyGene

Abstract

fetched live from OpenAlex

INTRODUCTION: Selecting women affected with breast cancer who are most likely to carry a germline mutation in BRCA1 and applying the most appropriate test methodology remains challenging for cancer genetics services. We sought to test the value of selecting women for BRCA1 mutation testing on the basis of family history and/or breast tumour morphology criteria as well as the value of testing for large genomic alterations in BRCA1. METHODS: We studied women participating in the Breast Cancer Family Registry (BCFR), recruited via population-based sampling, who had been diagnosed with breast cancer before the age of 40 years who had a strong family history of breast or ovarian cancer (n = 187) and/or a first primary breast tumour with morphological features consistent with carrying a BRCA1 germline mutation (n = 133; 37 met both criteria). An additional 184 women diagnosed before the age of 40 years who had a strong family history of breast or ovarian cancer and who were not known to carry a germline BRCA1 mutation were selected from among women who had been recruited into the BCFR from clinical genetics services. These 467 women had been screened for BRCA1 germline mutations, and we expanded this testing to include a screen for large genomic BRCA1 alterations using Multiplex Ligation-dependent Probe Amplification. RESULTS: Twelve large genomic BRCA1 alterations were identified, including 10 (4%) of the 283 women selected from among the population-based sample. In total, 18 (12%), 18 (19%) and 16 (43%) BRCA1 mutations were identified in the population-based groups selected on the basis of family history only (n = 150), the group selected on the basis of tumour morphology only (n = 96) and meeting both criteria (n = 37), respectively. CONCLUSIONS: Large genomic alterations accounted for 19% of all BRCA1 mutations identified. This study emphasises the value of combining information about family history, age at diagnosis and tumour morphology when selecting women for germline BRCA1 mutation testing as well as including a screen for large genomic alterations.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.842
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
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 head0.039
GPT teacher head0.334
Teacher spread0.295 · 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

Citations10
Published2011
Admission routes2
Has abstractyes

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