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Record W2113865865 · doi:10.1186/s13058-014-0474-y

Refined histopathological predictors of BRCA1 and BRCA2mutation status: a large-scale analysis of breast cancer characteristics from the BCAC, CIMBA, and ENIGMA consortia

2014· article· en· W2113865865 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueBreast Cancer Research · 2014
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBRCA gene mutations in cancer
Canadian institutionsJuravinski HospitalMcMaster University Medical CentreCanada Research ChairsUniversity Health NetworkUniversity of New BrunswickUniversity of TorontoMount Sinai HospitalLunenfeld-Tanenbaum Research Institute
FundersUniversitätsklinikum Hamburg-EppendorfInstituto de Salud Carlos IIICancer Council VictoriaMedical Research CouncilCanadian Institutes of Health ResearchSchool of MedicineU.S. Department of DefenseCancer Research UKUniversität UlmNational Health and Medical Research CouncilUniversiti MalayaOulun YliopistoDeutsche KrebshilfeMedizinischen Hochschule HannoverKuopion Yliopistollinen SairaalaKarolinska InstitutetBundesministerium für Bildung und ForschungMinisterio de Economía y CompetitividadDeutsche Gesetzliche UnfallversicherungNederlandse Organisatie voor Wetenschappelijk OnderzoekKommunfullmäktige, Stockholms StadMinistero della SaluteAcademy of FinlandRobert Bosch StiftungCancer Research InstituteAgency for Science, Technology and ResearchNational Breast Cancer FoundationCancer Center, University of KansasCancerfondenFundación Mutua MadrileñaNational Cancer InstituteCancer Institute NSWBreast Cancer CampaignNational Institute for Health and Care ResearchKWF KankerbestrijdingPeter MacCallum Cancer CentreHerlev HospitalNational Institutes of HealthDavid F. and Margaret T. Grohne Family FoundationUniversity of WestminsterDeutsches KrebsforschungszentrumKansas Bioscience AuthoritySundhed og Sygdom, Det Frie ForskningsrådAssociazione Italiana per la Ricerca sul CancroItä-Suomen YliopistoHelsingin ja Uudenmaan SairaanhoitopiiriFox Chase Cancer CenterEberhard Karls Universität TübingenMemorial Sloan-Kettering Cancer CenterSusan G. Komen for the CureRheinische Friedrich-Wilhelms-Universität BonnBeth Israel Deaconess Medical CenterRoyal Marsden NHS Foundation TrustHuntsman Cancer InstituteMayo ClinicBreast Cancer Research Foundation
KeywordsSurgical oncologyBreast cancerMedicineOncologyInternal medicineScale (ratio)Cancer

Abstract

fetched live from OpenAlex

INTRODUCTION: The distribution of histopathological features of invasive breast tumors in BRCA1 or BRCA2 germline mutation carriers differs from that of individuals with no known mutation. Histopathological features thus have utility for mutation prediction, including statistical modeling to assess pathogenicity of BRCA1 or BRCA2 variants of uncertain clinical significance. We analyzed large pathology datasets accrued by the Consortium of Investigators of Modifiers of BRCA1/2 (CIMBA) and the Breast Cancer Association Consortium (BCAC) to reassess histopathological predictors of BRCA1 and BRCA2 mutation status, and provide robust likelihood ratio (LR) estimates for statistical modeling. METHODS: Selection criteria for study/center inclusion were estrogen receptor (ER) status or grade data available for invasive breast cancer diagnosed younger than 70 years. The dataset included 4,477 BRCA1 mutation carriers, 2,565 BRCA2 mutation carriers, and 47,565 BCAC breast cancer cases. Country-stratified estimates of the likelihood of mutation status by histopathological markers were derived using a Mantel-Haenszel approach. RESULTS: ER-positive phenotype negatively predicted BRCA1 mutation status, irrespective of grade (LRs from 0.08 to 0.90). ER-negative grade 3 histopathology was more predictive of positive BRCA1 mutation status in women 50 years or older (LR = 4.13 (3.70 to 4.62)) versus younger than 50 years (LR = 3.16 (2.96 to 3.37)). For BRCA2, ER-positive grade 3 phenotype modestly predicted positive mutation status irrespective of age (LR = 1.7-fold), whereas ER-negative grade 3 features modestly predicted positive mutation status at 50 years or older (LR = 1.54 (1.27 to 1.88)). Triple-negative tumor status was highly predictive of BRCA1 mutation status for women younger than 50 years (LR = 3.73 (3.43 to 4.05)) and 50 years or older (LR = 4.41 (3.86 to 5.04)), and modestly predictive of positive BRCA2 mutation status in women 50 years or older (LR = 1.79 (1.42 to 2.24)). CONCLUSIONS: These results refine likelihood-ratio estimates for predicting BRCA1 and BRCA2 mutation status by using commonly measured histopathological features. Age at diagnosis is an important variable for most analyses, and grade is more informative than ER status for BRCA2 mutation carrier prediction. The estimates will improve BRCA1 and BRCA2 variant classification and inform patient mutation testing and clinical management.

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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.164
Threshold uncertainty score0.477

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 studies0.0000.001
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 head0.019
GPT teacher head0.325
Teacher spread0.306 · 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