Evidence map›Paper›PMID 37398422›Full record

ArticlemedRxiv : the preprint server for health sciences2024

Meta-Analysis of Breast Cancer Risk for Individuals with PALB2 Pathogenic Variants.

Thanthirige Lakshika M Ruberu, Danielle Braun, Giovanni Parmigiani, Swati Biswas

Open access · greenAbstract readPreprint
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
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

0 citing papers in PubMed, 1 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors at 1 institution in 1 country.

Thanthirige Lakshika M RuberuDepartment of Mathematical Sciences, University of Texas at Dallas.
Danielle BraunDepartment of Biostatistics, Harvard T.H. Chan School of Public Health.
Giovanni ParmigianiDepartment of Data Science, Dana Farber Cancer Institute.
Swati BiswasDepartment of Mathematical Sciences, University of Texas at Dallas.
Harvard University · US

Funding

A Bayesian Meta-Analysis Approach for Estimation of Penetrance and its Application to PALB2 gene for Breast Cancer RiskR03CA242562 · NCI · UNIVERSITY OF TEXAS DALLAS · PI BISWAS, SWATI · 2020 to 2021
$164k
NCI NIH HHS R03 CA242562
6 · The paper itself

Abstract

Background: Pathogenic variants in cancer susceptibility genes can now be tested efficiently and economically with the wide availability of multi-gene panel testing. This has resulted in an unprecedented rate of identifying individuals carrying pathogenic variants. These carriers need to be counselled about their future cancer risk conferred by the specific gene mutation. An important cancer susceptibility gene is PALB2. Several studies reported risk estimates for breast cancer (BC) associated with pathogenic variants in PALB2. Because of the variety of modalities (age specific risk, odds ratio, relative risk, and standardized incidence ratio) and effect sizes of these risk estimates, a meta-analysis of all of these estimates of BC risk is necessary to provide accurate counseling of patients with pathogenic variants in PALB2. The challenge, though, in combining these estimates is the heterogeneity of studies in terms of study design and risk measure. Methods: We utilized a recently proposed novel Bayesian random-effects meta-analysis method that can synthesize and combine information from such heterogeneous studies. We applied this method to combine estimates from twelve different studies on BC risk for carriers of pathogenic PALB2 mutations, out of which two report age-specific penetrance, one reports relative risk, and nine report odds ratios. Results: The estimated overall (meta-analysis based) risk of BC is 12.80% by age 50 (6.11% Conclusion: Pathogenic mutations in PALB2 makes women more susceptible to BC. Our risk estimates can help clinically manage patients carrying pathogenic variants in PALB2.

Indexed as

Bayesian modelOdds ratioPathogenic variantsPenetranceRelative risk

Identifiers

PMID37398422
PMCPMC10312825
OpenAlexW4379346125

What Socratic holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

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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 Socratic graph.