Evidence map›Paper›PMID 24134749›Full record

ArticleBMC cancer2013

MC1R genotype as a predictor of early-onset melanoma, compared with self-reported and physician-measured traditional risk factors: an Australian case-control-family study.

Anne E Cust, Chris Goumas, Kylie Vuong, John R Davies, Jennifer H Barrett, Elizabeth A Holland, Helen Schmid, Chantelle Agha-Hamilton, Bruce K Armstrong, Richard F Kefford and 7 more

Open access · goldAbstract readComparative Study
In one paragraph

Article in BMC cancer, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed, 2 pooled it
2.2field-weighted citation impact, top 12% 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

17 citing papers in PubMed, 2 syntheses or guidelines pooled it, 34 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Unique Features of Melanoma Risk and Diagnosis in Red-Haired Populations.International journal of molecular sciences · 2026
    Review
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  11. Cancer management and research · 2018
    Article
  12. Review
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  14. Public preferences for communicating personal genomic risk information: a focus group study.Health expectations : an international journal of public participation in health care and health policy · 2016
    Article
  15. Article
  16. Clinical applications of melanoma genetics.Current treatment options in oncology · 2014
    Review
  17. Article
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

17 authors at 7 institutions in 2 countries.

Anne E Cust
Chris Goumas
Kylie Vuong
John R Davies
Jennifer H Barrett
Elizabeth A Holland
Helen Schmid
Chantelle Agha-Hamilton
Bruce K Armstrong
Richard F Kefford
Joanne F Aitken
Graham G Giles
D Bishop
Julia A Newton-Bishop
John L Hopper
Graham J Mann
Mark A Jenkins
University of Sydney · AUUniversity of Leeds · GBMelanoma Institute Australia · AUUniversity of Melbourne · AUCancer Council Queensland · AUCancer Council Victoria · AUWestmead Institute · AU

Funding

Genetic Epidermiology of MelanomaR01CA083115 · NCI · UNIVERSITY OF PENNSYLVANIA · PI BISHOP, DAVID TIMOTHY, ELDER, DAVID ERIC · 2001 to 2017
$13.0M
Cancer Research UK 10589NCI NIH HHS R01 CA-83115-01A2
6 · The paper itself

Abstract

backgroundMelanocortin-1 receptor (MC1R) gene variants are very common and are associated with melanoma risk, but their contribution to melanoma risk prediction compared with traditional risk factors is unknown. We aimed to 1) evaluate the separate and incremental contribution of MC1R genotype to prediction of early-onset melanoma, and compare this with the contributions of physician-measured and self-reported traditional risk factors, and 2) develop risk prediction models that include MC1R, and externally validate these models using an independent dataset from a genetically similar melanoma population.

methodsUsing data from an Australian population-based, case-control-family study, we included 413 case and 263 control participants with sequenced MC1R genotype, clinical skin examination and detailed questionnaire. We used unconditional logistic regression to estimate predicted probabilities of melanoma. Results were externally validated using data from a similar study in England.

resultsWhen added to a base multivariate model containing only demographic factors, MC1R genotype improved the area under the receiver operating characteristic curve (AUC) by 6% (from 0.67 to 0.73; P < 0.001) and improved the quartile classification by a net 26% of participants. In a more extensive multivariate model, the factors that contributed significantly to the AUC were MC1R genotype, number of nevi and previous non-melanoma skin cancer; the AUC was 0.78 (95% CI 0.75-0.82) for the model with self-reported nevi and 0.83 (95% CI 0.80-0.86) for the model with physician-counted nevi. Factors that did not further contribute were sun and sunbed exposure and pigmentation characteristics. Adding MC1R to a model containing pigmentation characteristics and other self-reported risk factors increased the AUC by 2.1% (P = 0.01) and improved the quartile classification by a net 10% (95% CI 1-18%, P = 0.03).

conclusionsAlthough MC1R genotype is strongly associated with skin and hair phenotype, it was a better predictor of early-onset melanoma than was pigmentation characteristics. Physician-measured nevi and previous non-melanoma skin cancer were also strong predictors. There might be modest benefit to measuring MC1R genotype for risk prediction even if information about traditional self-reported or clinically measured pigmentation characteristics and nevi is already available.

Indexed as

PhysiciansSelf ReportAdolescentAdultAge of OnsetAustraliaCase-Control StudiesFamilyFemaleFollow-Up StudiesGenotypeHumansMaleMelanomaModels, StatisticalNeoplasm StagingReceptor, Melanocortin, Type 1

Identifiers

PMID24134749
PMCPMC3766240
OpenAlexW2038503100

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.