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

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 multi-block 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 by 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 multi-platform 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.

Molecular oncology. 2018 Jun 21 [Epub ahead of print]

Keefe Murphy, Brendan T Murphy, Susie Boyce, Louise Flynn, Sarah Gilgunn, Colm J O'Rourke, Cathy Rooney, Henning Stöckmann, Anna L Walsh, Stephen Finn, Richard J O'Kennedy, John O'Leary, Stephen R Pennington, Antoinette S Perry, Pauline M Rudd, Radka Saldova, Orla Sheils, Denis C Shields, R William Watson

UCD School of Mathematics and Statistics, University College Dublin, Belfield, Dublin, 4, Ireland., Department of Histopathology, Central Pathology Laboratory, University of Dublin, Trinity College, St James Hospital, Dublin, 8, Ireland., School of Biotechnology, Dublin City University, Dublin , 9, Ireland., Prostate Molecular Oncology, Institute of Molecular Medicine, Trinity College Dublin, St James Hospital, Dublin, 8, Ireland., UCD School of Medicine, Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Belfield, Dublin, 4, Ireland., NIBRT GlycoScience Group, National Institute for Bioprocessing Research and Training, Fosters Avenue, Mount Merrion, Blackrock, Dublin, 4, Ireland.