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Associations of a breast cancer polygenic risk score with tumor characteristics and survival.

2022· article· en· W4281877857 on OpenAlexaff
Josephine Lopes Cardozo, Irene L. Andrulis, Devilee Peter, Thilo Dörk, Caroline A. Drukker, Peter A. Fasching, Maartje J. Hooning, Renske Keeman, Heli Nevanlinna, Emiel J. Rutgers, Laura van ‘t Veer, Per Hall, Stig E. Bojesen, Easton Douglas, Diana Eccles, Paul D.P. Pharoah, Marjanka K. Schmidt

Bibliographic record

VenueJournal of Clinical Oncology · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBRCA gene mutations in cancer
Canadian institutionsLunenfeld-Tanenbaum Research Institute
Fundersnot available
KeywordsMedicineBreast cancerInternal medicineOncologyHazard ratioProportional hazards modelConfidence intervalPathologicalCancerLogistic regression

Abstract

fetched live from OpenAlex

563 Background: A polygenic risk score (PRS) consisting of 313 single nucleotide polymorphisms (PRS313) is associated with risk of breast cancer and contralateral breast cancer. One of the most promising clinical applications for the use of PRS is to provide a personalized risk assessment to individualize breast cancer screening programs. This study aimed to evaluate the association of the PRS313 with clinical-pathological characteristics and survival of breast cancer. Methods: Women of European ancestry with invasive breast cancer were included, 98,397 women from the Breast Cancer Association Consortium (BCAC) and 683 women from the MINDACT trial. Associations between PRS313 (continuous, per SD) and clinical-pathological characteristics, including the 70-gene signature for patients included in MINDACT, were evaluated with logistic regression analyses. Associations of PRS313 with overall survival (OS), breast cancer-specific survival (BCSS) and distant metastasis-free interval (DMFI) were evaluated with Cox regression analyses, adjusted for clinical-pathological characteristics and treatment. Results: The PRS313 was associated with favorable tumor characteristics. In BCAC, increasing PRS313 was mostly associated with lower grade, hormone receptor-positive tumors, and with smaller tumor size. In MINDACT, PRS313 was associated with a low risk 70-gene signature, but the association was attenuated after adjustment for clinical-pathological characteristics. In BCAC, univariable analyses showed an association of PRS313 with better survival, hazard ratio (HR) per unit SD increase of PRS313 for OS: 0.96 (95% confidence interval (CI): 0.94-0.97), for BCSS HR: 0.96 (95% CI: 0.94-0.98) and for DMFI HR: 0.98 (95% CI: 0.96-1.00). The association in the unadjusted analysis was explained by differences in clinical-pathological characteristics (and treatment) and disappeared after adjustment: OS HR: 1.01 (95% CI: 0.98-1.05), BCSS HR: 1.02 (95% CI: 0.98-1.07) and DMFI HR: 1.03 (95% CI: 0.99-1.07). Conclusions: An increased PRS313 is associated with favorable tumor characteristics but was not independently associated with prognosis. This information is crucial input for modelling effective stratified screening programs, especially in the current era of optimized (systemic) treatments. Given the significant increase in breast cancer risk associated with increasing PRS313, absolute breast cancer mortality will still be higher for women with higher PRS313.

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)0.002
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 score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
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)0.0010.000

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 head0.053
GPT teacher head0.399
Teacher spread0.346 · 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

Citations2
Published2022
Admission routes1
Has abstractyes

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