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Record W4403456234 · doi:10.1093/jnci/djae255

Polygenic risk scores stratify breast cancer risk among women with benign breast disease

2024· article· en· W4403456234 on OpenAlexafffund
Mark E. Sherman, Stacey J. Winham, Robert A. Vierkant, Bryan M. McCauley, Christopher G. Scott, Sarah E. Schrup, Mia M. Gaudet, Melissa A. Troester, Sandhya Pruthi, Derek C. Radisky, Amy C. Degnim, Fergus J. Couch, Manjeet K. Bolla, Joe Dennis, Kyriaki Michailidou, Pascal Guénel, Thérèse Truong, Jenny Chang‐Claude, Nadia Obi, Kristan J. Aronson, Rachel A. Murphy, Montserrat García‐Closas, Stephen J. Chanock, Thomas U. Ahearn, Alison M. Dunning, Nasim Mavaddat, Paul D.P. Pharoah, Douglas F. Easton, Celine M Vachon

Bibliographic record

VenueJNCI Journal of the National Cancer Institute · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBRCA gene mutations in cancer
Canadian institutionsUniversity of British ColumbiaQueen's University
FundersDivision of Cancer Epidemiology and Genetics, National Cancer InstituteNational Cancer InstituteNIHR Cambridge Biomedical Research CentreBreast Cancer CampaignCanadian Institutes of Health ResearchInstitut National Du CancerDeutsche KrebshilfeAgence Nationale de la RechercheAgence Nationale de Sécurité Sanitaire de l’Alimentation, de l’Environnement et du TravailDepartment of Health and Social CareNational Institute for Health and Care ResearchDivision of Cancer Prevention, National Cancer InstituteNational Institutes of HealthHamburger KrebsgesellschaftFondation de FranceDeutsches KrebsforschungszentrumLigue Contre le CancerBundesministerium für Bildung und ForschungGenome CanadaU.S. Department of Health and Human ServicesOvarian Cancer Research FundNational Institute on Handicapped ResearchEuropean CommissionBreast Cancer Research FoundationCancer Research UKGovernment of Canada
KeywordsMedicineBreast cancerBreast diseaseOdds ratioAtypical hyperplasiaCase-control studyInternal medicineRisk factorLogistic regressionObstetricsGynecologyHyperplasiaCancer

Abstract

fetched live from OpenAlex

BACKGROUND: Most breast biopsies are diagnosed as benign breast disease, with 1.5- to 4-fold increased breast cancer risk. Apart from pathologic diagnoses of atypical hyperplasia, few factors aid in breast cancer risk assessment of these patients. We assessed whether a 313-single nucleotide variation (formerly single-nucleotide polymorphism) polygenic risk score stratifies risk of benign breast disease patients. METHODS: We pooled data from 5 Breast Cancer Association Consortium case-control studies (mean age = 59.9 years), including 6706 participants with breast cancer and 8488 participants without breast cancer. Using logistic regression, we estimated breast cancer risk associations by self-reported benign breast disease history and strata of polygenic risk score, with median polygenic risk score category among women without benign breast disease as the referent. We assessed interactions and mediation of benign breast disease and polygenic risk score with breast cancer risk. RESULTS: Benign breast disease history was associated with increased breast cancer risk (odds ratio [OR] = 1.48, 95% confidence interval [CI] = 1.37 to 1.60; P < .001). Polygenic risk score increased breast cancer risk, irrespective of benign breast disease history (Pinteraction = .48), with minimal evidence of mediation of either factor by the other. Women with benign breast disease and polygenic risk score in the highest tertile had more than twofold increased odds of breast cancer (OR = 2.73, 95% CI = 2.41 to 3.09), and those with benign breast disease and polygenic risk score in the lowest tertile experienced reduced breast cancer risk (OR = 0.79, 95% CI = 0.70 to 0.91) compared with the referent group. Women with benign breast disease and polygenic risk score in the highest decile had a 3.7-fold increase (95% CI = 3.00 to 4.61) compared with those with median polygenic risk score without benign breast disease. CONCLUSION: Breast cancer risks are elevated among women with benign breast disease and increase progressively with polygenic risk score, suggesting that optimal combinations of these factors may improve risk stratification.

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)0.002
metaresearch head score (Gemma)0.005
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 score0.005
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.011
GPT teacher head0.285
Teacher spread0.274 · 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

Citations4
Published2024
Admission routes2
Has abstractyes

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