Evidence map›Paper›PMID 41781474›Full record

ArticleScientific reports2026

Evaluating mammographic density polygenic risk score for contralateral breast cancer risk prediction.

Elnaz Naderi, Gordon P Watt, Julia A Knight, Kathleen E Malone, Charles F Lynch, Esther M John, Xiang Shu, Tuong L Nguyen, Jung Hun Oh, Meghan Woods and 4 more

Abstract read
In one paragraph

Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

14 authors.

Elnaz NaderiDepartment of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
Gordon P WattDivision of Psychosocial Research and Epidemiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands.
Julia A KnightProsserman Centre for Population Health Research, Lunenfeld-Tanenbaum Research Institute, Sinai Health, Toronto, ON, Canada.
Kathleen E MaloneEpidemiology Program, Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
Charles F LynchDepartment of Epidemiology, University of Iowa College of Public Health, Iowa City, IA, USA.
Esther M JohnDepartment of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, CA, USA.
Xiang ShuDepartment of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
Tuong L NguyenMelbourne School of Population and Global Health, University of Melbourne, Parkville, VIC, Australia.
Jung Hun OhDepartment of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
Meghan WoodsDepartment of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
Xiaolin LiangDepartment of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
Andriy DerkachDepartment of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
Malcolm C PikeDepartment of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
Jonine L BernsteinDepartment of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA. BernsteJ@mskcc.org.

Funding

X-RAY CRYSTALLOGRAPHYP30CA008748 · NCI · SLOAN-KETTERING INSTITUTE FOR CANCER RES · PI SELWYN M VICKERS · 1985 to 2026
$347.4M
NCI NIH HHS P30 CA008748
6 · The paper itself

Abstract

Survivors of breast cancer face a substantially increased risk of developing contralateral breast cancer (CBC). We assessed whether risk prediction models for CBC are improved by integrating mammographic density (MD) and polygenic risk scores (PRS). We analyzed data from 399 European-ancestry breast cancer survivors in the WECARE Study, an international, population-based case-control study. Cases were women who developed CBC, and controls were women with unilateral breast cancer (UBC). All participants had genome-wide genotyping and MD measurements at three intensity levels (Cumulus, Altocumulus, and Cirrocumulus) using the CUMULUS software. A weighted PRS was constructed comprised of 64 previously identified genome-wide significant single nucleotide polymorphisms (SNPs) associated with MD (PRS_MD). Linear and logistic regression models were used to assess the associations between PRS_MD, MD measurements, and CBC risk, adjusting for potential confounders. PRS_MD was significantly associated with Cumulus and Altocumulus densities, but not Cirrocumulus. In multivariable-adjusted predictive models, the inclusion of PRS_MD improved adjusted R-squared values for Cumulus (from 20.6% to 22.8%) and Altocumulus (22.7% to 24.7%). However, for Cirrocumulus the PRS_MD was not a significant predictor of CBC risk, with an effect estimate of 0.27 (95% CI: -0.9,1.4; P = 0.69). PRS_MD was not independently associated with CBC risk and adding it to MD models resulted in only small, non‑significant gains in AUC. Exploratory interaction analyses did not indicate that PRS_MD modified the association between MD and CBC risk. MD remains a robust independent predictor of CBC risk. Although PRS_MD captures inherited predisposition to MD, the current PRS explains only a small fraction of MD variance and does not enhance CBC risk prediction beyond measured MD. Further research is needed to elucidate the genetic underpinnings of MD and their relevance to CBC susceptibility.

Indexed as

Breast DensityBreast NeoplasmsAgedCase-Control StudiesFemaleGenetic Predisposition to DiseaseGenetic Risk ScoreGenome-Wide Association StudyHumansMammographyMiddle AgedPolymorphism, Single NucleotideRisk FactorsBreast cancer survivorsContralateral breast cancerMammographic densityPolygenic risk scoreRisk prediction

Identifiers

PMID41781474
PMCPMC13077059

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LicenceCC BY-NC-ND
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.