Open AccessIntegrating biomarkers across omic platforms: an approach to improve stratification of patients with indolent and aggressive prostate cancer
Author(s) -
Murphy Keefe,
Murphy Brendan T.,
Boyce Susie,
Flynn Louise,
Gilgunn Sarah,
O'Rourke Colm J.,
Rooney Cathy,
Stöckmann Henning,
Walsh Anna L.,
Finn Stephen,
O'Kennedy Richard J.,
O'Leary John,
Pennington Stephen R.,
Perry Antoinette S.,
Rudd Pauline M.,
Saldova Radka,
Sheils Orla,
Shields Denis C.,
Watson R. William
Publication year - 2018
Publication title -
molecular oncology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.332
H-Index - 88
eISSN - 1878-0261
pISSN - 1574-7891
DOI - 10.1002/1878-0261.12348
Subject(s) - prostate cancer , omics , risk stratification , medicine , biomarker discovery , bioinformatics , oncology , cancer , computational biology , biology , proteomics , biochemistry , gene
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.