Integrating multiple molecular sources into a clinical risk prediction signature by extracting complementary information

BMC Bioinformatics

Research Article

Richard F. Schlenl¹  Lars Bullinger¹  Axel Benner²  Martin Schumacher³  Stefanie Hieke⁴  Harald Binder⁵ 

[1] Department of Internal Medicine III, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081, Ulm, Germany;Division of Biostatistics, German Cancer Research Center, Im Neuenheimer Feld 280, 69120, Heidelberg, Germany;Institute for Medical Biometry and Statistics, Faculty of Medicine and Medical Center - University of Freiburg, Stefan-Meier-Str. 26, 79104, Freiburg, Germany;Institute for Medical Biometry and Statistics, Faculty of Medicine and Medical Center - University of Freiburg, Stefan-Meier-Str. 26, 79104, Freiburg, Germany;Freiburg Center for Data Analysis and Modeling, University Freiburg, Eckerstr. 1, 79104, Freiburg, Germany;Institute of Medical Biostatistics, Epidemiology and Informatics, University Medical Center Johannes Gutenberg University Mainz, Obere Zahlbacher Str. 69, 55131, Mainz, Germany;

关键词: Acute myeloid leukemia;    Multiple genome-wide data sets;    Risk prediction;    Multivariable model;    Boosting;    Time-to-event endpoint;   

DOI  :  10.1186/s12859-016-1183-6

 received in 2015-09-11, accepted in 2016-08-12,  发布年份 2016

来源: Springer

【 摘 要 】

BackgroundHigh-throughput technology allows for genome-wide measurements at different molecular levels for the same patient, e.g. single nucleotide polymorphisms (SNPs) and gene expression. Correspondingly, it might be beneficial to also integrate complementary information from different molecular levels when building multivariable risk prediction models for a clinical endpoint, such as treatment response or survival. Unfortunately, such a high-dimensional modeling task will often be complicated by a limited overlap of molecular measurements at different levels between patients, i.e. measurements from all molecular levels are available only for a smaller proportion of patients.ResultsWe propose a sequential strategy for building clinical risk prediction models that integrate genome-wide measurements from two molecular levels in a complementary way. To deal with partial overlap, we develop an imputation approach that allows us to use all available data. This approach is investigated in two acute myeloid leukemia applications combining gene expression with either SNP or DNA methylation data. After obtaining a sparse risk prediction signature e.g. from SNP data, an automatically selected set of prognostic SNPs, by componentwise likelihood-based boosting, imputation is performed for the corresponding linear predictor by a linking model that incorporates e.g. gene expression measurements. The imputed linear predictor is then used for adjustment when building a prognostic signature from the gene expression data. For evaluation, we consider stability, as quantified by inclusion frequencies across resampling data sets. Despite an extremely small overlap in the application example with gene expression and SNPs, several genes are seen to be more stably identified when taking the (imputed) linear predictor from the SNP data into account. In the application with gene expression and DNA methylation, prediction performance with respect to survival also indicates that the proposed approach might work well.ConclusionsWe consider imputation of linear predictor values to be a feasible and sensible approach for dealing with partial overlap in complementary integrative analysis of molecular measurements at different levels. More generally, these results indicate that a complementary strategy for integrating different molecular levels can result in more stable risk prediction signatures, potentially providing a more reliable insight into the underlying biology.

【 授权许可】

CC BY   
© The Author(s) 2016

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