MétaCan
Menu
Back to cohort
Record W2623464423 · doi:10.1038/s41523-017-0024-8

The contribution of pathogenic variants in breast cancer susceptibility genes to familial breast cancer risk

2017· article· en· W2623464423 on OpenAlexaff
Thomas P. Slavin, Kara N. Maxwell, Jenna Lilyquist, Joseph Vijai, Susan L. Neuhausen, Steven N. Hart, Vignesh Ravichandran, Tinu Thomas, Ann Maria, Danylo Villano, Kasmintan A. Schrader, Raymond M. Moore, Chunling Hu, Bradley Wubbenhorst, Brandon M. Wenz, Kurt D’Andrea, Mark E. Robson, Paolo Peterlongo, Bernardo Bonanni, James M. Ford, Judy E. Garber, Susan M. Domchek, Csilla I. Szabo, Kenneth Offit, Katherine L. Nathanson, Jeffrey N. Weitzel, Fergus J. Couch

Bibliographic record

Venuenpj Breast Cancer · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBRCA gene mutations in cancer
Canadian institutionsBC Cancer Agency
FundersNational Cancer InstituteNational Institutes of HealthOxnard FoundationPennsylvania Department of HealthStop CancerAvon Foundation for WomenBreast Cancer Research FoundationU.S. Department of Defense
KeywordsBreast cancerCHEK2PALB2Odds ratioOncologyMedicineGermline mutationCancerGeneticsInternal medicineMutationBiologyGene

Abstract

fetched live from OpenAlex

Abstract Understanding the gene-specific risks for development of breast cancer will lead to improved clinical care for those carrying germline mutations in cancer predisposition genes. We sought to detail the spectrum of mutations and refine risk estimates for known and proposed breast cancer susceptibility genes. Targeted massively-parallel sequencing was performed to identify mutations and copy number variants in 26 known or proposed breast cancer susceptibility genes in 2134BRCA1/2-negative women with familial breast cancer (proband with breast cancer and a family history of breast or ovarian cancer) from a largely European–Caucasian multi-institutional cohort. Case–control analysis was performed comparing the frequency of internally classified mutations identified in familial breast cancer women to Exome Aggregation Consortium controls. Mutations were identified in 8.2% of familial breast cancer women, including mutations in high-risk (odds ratio > 5) (1.4%) and moderate-risk genes (2 < odds ratio < 5) (2.9%). The remaining familial breast cancer women had mutations in proposed breast cancer genes (1.7%), Lynch syndrome genes (0.5%), and six cases had two mutations (0.3%). Case–control analysis demonstrated associations with familial breast cancer forATM, PALB2, andTP53mutations (odds ratio > 3.0,p < 10−4),BARD1mutations (odds ratio = 3.2,p = 0.012), andCHEK2truncating mutations (odds ratio = 1.6,p = 0.041). Our results demonstrate that approximately 4.7% ofBRCA1/2negative familial breast cancer women have mutations in genes statistically associated with breast cancer. We classifiedPALB2andTP53as high-risk,ATMandBARD1as moderate risk, andCHEK2truncating mutations as low risk breast cancer predisposition genes. This study demonstrates that large case–control studies are needed to fully evaluate the breast cancer risks associated with mutations in moderate-risk and proposed susceptibility genes.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.009
GPT teacher head0.288
Teacher spread0.279 · 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 source (direct Gemma or distilled Codex), 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

Citations137
Published2017
Admission routes1
Has abstractyes

Explore more

Same venuenpj Breast CancerSame topicBRCA gene mutations in cancerFrench-language works237,207