Integrating biomarkers across omic platforms: an approach to improve stratification of patients with indolent and aggressive prostate cancer

Molecular Oncology

Orla Sheils¹  Stephen Finn¹  Louise Flynn¹  John O'Leary¹  Pauline M. Rudd²  Radka Saldova²  Henning Stöckmann²  Antoinette S. Perry³  Anna L. Walsh³  Colm J. O'Rourke³  Richard J. O'Kennedy⁴  Sarah Gilgunn⁴  Keefe Murphy⁵  Susie Boyce⁵  Brendan T. Murphy⁵  Stephen R. Pennington⁶  Denis C. Shields⁶  Cathy Rooney⁶  R. William Watson⁶ 

[1] Department of Histopathology Central Pathology Laboratory Trinity College St James Hospital University of Dublin Ireland;NIBRT GlycoScience Group National Institute for Bioprocessing Research and Training Dublin Ireland;Prostate Molecular Oncology Institute of Molecular Medicine Trinity College Dublin St James Hospital Dublin Ireland;School of Biotechnology Dublin City University Ireland;UCD School of Mathematics and Statistics University College Dublin Ireland;UCD School of Medicine Conway Institute of Biomolecular and Biomedical Research University College Dublin Ireland;

关键词: biomarkers;    indolent;    integration;    LASSO;    omics;    prostate cancer;   

DOI  :  10.1002/1878-0261.12348

来源: DOAJ

【 摘 要 】

Classifying indolent prostate cancer represents a significant clinical challenge. We investigated whether integrating data from different omic platforms could identify a biomarker panel with improved performance compared to individual platforms alone. DNA methylation, transcripts, protein and glycosylation biomarkers were assessed in a single cohort of patients treated by radical prostatectomy. Novel multiblock statistical data integration approaches were used to deal with missing data and modelled via stepwise multinomial logistic regression, or LASSO. After applying leave‐one‐out cross‐validation to each model, the probabilistic predictions of disease type for each individual panel were aggregated to improve prediction accuracy using all available information for a given patient. Through assessment of three performance parameters of area under the curve (AUC) values, calibration and decision curve analysis, the study identified an integrated biomarker panel which predicts disease type with a high level of accuracy, with Multi AUC value of 0.91 (0.89, 0.94) and Ordinal C‐Index (ORC) value of 0.94 (0.91, 0.96), which was significantly improved compared to the values for the clinical panel alone of 0.67 (0.62, 0.72) Multi AUC and 0.72 (0.67, 0.78) ORC. Biomarker integration across different omic platforms significantly improves prediction accuracy. We provide a novel multiplatform approach for the analysis, determination and performance assessment of novel panels which can be applied to other diseases. With further refinement and validation, this panel could form a tool to help inform appropriate treatment strategies impacting on patient outcome in early stage prostate cancer.

【 授权许可】

Unknown