Evidence map›Paper›PMID 38174903›Full record

ArticleCancer2024

Estimation of age of onset and progression of breast cancer by absolute risk dependent on polygenic risk score and other risk factors.

Rikesh Bhatt, Ardo van den Hout, Antonis C Antoniou, Mitul Shah, Lorenzo Ficorella, Emily Steggall, Douglas F Easton, Paul D P Pharoah, Nora Pashayan

Open access · hybridAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Cancer, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
4.2field-weighted citation impact, top 5% of its field
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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

10 citing papers in PubMed, 15 citations in OpenAlex.

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4 · The record

Corrections and comments

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

9 authors at 4 institutions in 2 countries.

Rikesh BhattDepartment of Applied Health Research, University College London, London, UK.ORCID 0000-0003-3924-3619
Ardo van den HoutDepartment of Statistical Science, University College London, London, UK.
Antonis C AntoniouDepartment of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
Mitul ShahDepartment of Oncology, University of Cambridge, Cambridge, UK.
Lorenzo FicorellaDepartment of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
Emily SteggallSchool of Medicine, Cardiff University, Cardiff, UK.
Douglas F EastonDepartment of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
Paul D P PharoahDepartment of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
Nora PashayanDepartment of Applied Health Research, University College London, London, UK.ORCID 0000-0003-0843-2468
University of Cambridge · GBUniversity College London · GBCedars-Sinai Medical Center · USUniversities UK · GB

Funding

Epidemiologic StudiesU19CA148065 · NCI · HARVARD SCHOOL OF PUBLIC HEALTH · PI AHSAN, HABIBUL, BRUGGE, JOAN SIEFERT · 2010 to 2014
$10.6M
Breast Cancer Now \#2017NovPR1024Breast Cancer Now 2017NOVPR1024Cancer Research UK C1287/A10710Cancer Research UK C490/A10124Cancer Research UK C490/A16561Cancer Research UK PPRPGM-Nov20\100002NCI NIH HHS U19 CA148065NIH HHS U19CA148065-01Wellcome TrustWellcome Trust v203477/Z/16/Z
6 · The paper itself

Abstract

backgroundGenetic, lifestyle, reproductive, and anthropometric factors are associated with the risk of developing breast cancer. However, it is not yet known whether polygenic risk score (PRS) and absolute risk based on a combination of risk factors are associated with the risk of progression of breast cancer. This study aims to estimate the distribution of sojourn time (pre-clinical screen-detectable period) and mammographic sensitivity by absolute breast cancer risk derived from polygenic profile and the other risk factors.

methodsThe authors used data from a population-based case-control study. Six categories of 10-year absolute risk based on different combinations of risk factors were derived using the Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm. Women were classified into low, medium, and high-risk groups. The authors constructed a continuous-time multistate model. To calculate the sojourn time, they simulated the trajectories of subjects through the disease states.

resultsThere was little difference in sojourn time with a large overlap in the 95% confidence interval (CI) between the risk groups across the six risk categories and PRS studied. However, the age of entry into the screen-detectable state varied by risk category, with the mean age of entry of 53.4 years (95% CI, 52.2-54.1) and 57.0 years (95% CI, 55.1-57.7) in the high-risk and low-risk women, respectively.

conclusionIn risk-stratified breast screening, the age at the start of screening, but not necessarily the frequency of screening, should be tailored to a woman's risk level. The optimal risk-stratified screening strategy that would improve the benefit-to-harm balance and the cost-effectiveness of the screening programs needs to be studied.

Indexed as

Breast NeoplasmsAge of OnsetCase-Control StudiesFemaleGenetic Predisposition to DiseaseGenetic Risk ScoreHumansMiddle AgedRisk AssessmentRisk Factorsabsolute riskbreast cancermultistate modelnatural historypolygenic risk scoresensitivitysojourn time

Identifiers

PMID38174903
PMCPMC7615824
OpenAlexW4390589261

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

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.