Single nucleotide polymorphism (SNP) microarray data. SNP data underlying the finding in this article. (Rdata 50688 kb

Axel Benner

Harald Binder

Lars Bullinger

Martin Schumacher

Richard F. Schlenl

Stefanie Hieke

BMC Bioinformatics

PubMed

Springer - Publisher Connector

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

BACKGROUND: High-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. RESULTS: We 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. CONCLUSIONS: We 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

Hieke, S.

Benner, A.

Schlenl, R.F.

Schumacher, M.

Bullinger, L.

Binder, H.

MDC Repository

Crossref

R code. This R-file provides the underlying R code to reproduce the results of the first AML application example combining SNP and microarray-based gene expression data. (R 33 kb

Stefanie Hieke (2736115)

Axel Benner (30194)

Richard Schlenl (3402509)

Martin Schumacher (50493)

Lars Bullinger (2736118)

Harald Binder (39439)

The Francis Crick Institute

Additional file 9 of Integrating multiple molecular sources into a clinical risk prediction signature by extracting complementary information

Variability of the.632+ prediction error estimates in the first AML data application example (swapped order). Boxplots of the integrated prediction error curve estimates for the Cox model, the SNP model and the sequential complementary strategy (swapped order). (PDF 35 kb

Additional file 8 of Integrating multiple molecular sources into a clinical risk prediction signature by extracting complementary information

Resampling inclusion frequency algorithm. Resampling inclusion frequency setup investigated to evaluate model stability. (PDF 77 kb

Additional file 5 of Integrating multiple molecular sources into a clinical risk prediction signature by extracting complementary information

Coefficient paths of SNP and GEP measurements (first AML application example). Parameter estimates obtained from componentwise likelihood-based boosting for the SNP microarray data (left panel) and parameter estimates obtained from componentwise likelihood-based boosting for the microarray-based GEP data conditioning on the overlap samples (right panel), plotted against the number of boosting steps. (PDF 64 kb

Additional file 3 of Integrating multiple molecular sources into a clinical risk prediction signature by extracting complementary information

Additional file 1 of Integrating multiple molecular sources into a clinical risk prediction signature by extracting complementary information

file:///data/remote/core/dit/data/Springer-OA/pdf/d70/aHR0cDovL2xpbmsuc3ByaW5nZXIuY29tLzEwLjExODYvczEyODU5LTAxNi0xMTgzLTYucGRm.pdf

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

Abstract

Similar works

Full text

Available Versions

Springer - Publisher Connector

MDC Repository

Crossref

Springer - Publisher Connector

The Francis Crick Institute

The Francis Crick Institute

The Francis Crick Institute

The Francis Crick Institute

The Francis Crick Institute