Evidence map›Paper›PMID 25274085›Full record

ArticleBMC medical informatics and decision making2014

Can multiple SNP testing in BRCA2 and BRCA1 female carriers be used to improve risk prediction models in conjunction with clinical assessment?

Mattia C F Prosperi, Sarah L Ingham, Anthony Howell, Fiona Lalloo, Iain E Buchan, Dafydd Gareth Evans

Abstract read
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Article in BMC medical informatics and decision making, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

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3citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

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3 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Mattia C F ProsperiInstitute of Population Health, Centre for Health Informatics, University of Manchester, Manchester, UK. mattia.prosperi@manchester.ac.uk.
Sarah L Ingham
Anthony Howell
Fiona Lalloo
Iain E Buchan
Dafydd Gareth Evans

Funding

Department of Health RP-PG-0707-10031Medical Research Council MC_PC_13042Medical Research Council MR/K006665/1
6 · The paper itself

Abstract

backgroundSeveral single nucleotide polymorphisms (SNPs) at different loci have been associated with breast cancer susceptibility, accounting for around 10% of the familial component. Recent studies have found direct associations between specific SNPs and breast cancer in BRCA1/2 mutation carriers. Our aim was to determine whether validated susceptibility SNP scores improve the predictive ability of risk models in comparison/conjunction to other clinical/demographic information.

methodsFemale BRCA1/2 carriers were identified from the Manchester genetic database, and included in the study regardless of breast cancer status or age. DNA was extracted from blood samples provided by these women and used for gene and SNP profiling. Estimates of survival were examined with Kaplan-Meier curves. Multivariable Cox proportional hazards models were fit in the separate BRCA datasets and in menopausal stages screening different combinations of clinical/demographic/genetic variables. Nonlinear random survival forests were also fit to identify relevant interactions. Models were compared using Harrell's concordance index (1 - c-index).

results548 female BRCA1 mutation carriers and 523 BRCA2 carriers were identified from the database. Median Kaplan-Meier estimate of survival was 46.0 years (44.9-48.1) for BRCA1 carriers and 48.9 (47.3-50.4) for BRCA2. By fitting Cox models and random survival forests, including both a genetic SNP score and clinical/demographic variables, average 1 - c-index values were 0.221 (st.dev. 0.019) for BRCA1 carriers and 0.215 (st.dev. 0.018) for BRCA2 carriers.

conclusionsRandom survival forests did not yield higher performance compared to Cox proportional hazards. We found improvement in prediction performance when coupling the genetic SNP score with clinical/demographic markers, which warrants further investigation.

Indexed as

Survival AnalysisBRCA1 ProteinBRCA2 ProteinBreast NeoplasmsFemaleGenetic TestingHeterozygoteHumansMiddle AgedPolymorphism, Single NucleotideRisk AssessmentBRCA1 ProteinBRCA2 ProteinBRCA2 protein, human

Identifiers

PMID25274085
PMCPMC4197237

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