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Open Access Research article

Collectives of diagnostic biomarkers identify high-risk subpopulations of hematuria patients: exploiting heterogeneity in large-scale biomarker data

Frank Emmert-Streib1, Funso Abogunrin1, Ricardo de Matos Simoes1, Brian Duggan2, Mark W Ruddock3, Cherith N Reid3, Owen Roddy1, Lisa White1, Hugh F O'Kane2, Declan O'Rourke4, Neil H Anderson5, Thiagarajan Nambirajan2 and Kate E Williamson1*

Author affiliations

1 Centre for Cancer Research & Cell Biology, Queens University Belfast, 97 Lisburn Road, Belfast, BT9 7BL, Northern Ireland

2 Department of Urology, Belfast City Hospital, 75 Lisburn Road, Belfast, BT9 7AB, Northern Ireland

3 Molecular Biology, Randox Laboratories Ltd, Diamond Road, Crumlin, BT29 4QY, Northern Ireland

4 Department of Pathology, Belfast City Hospital, 75 Lisburn Road, Belfast, BT9 7AB, Northern Ireland

5 Department of Pathology, Royal Victoria Hospital, 274 Grosvenor Road, Belfast, BT12 6AB, Northern Ireland

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Citation and License

BMC Medicine 2013, 11:12  doi:10.1186/1741-7015-11-12

Published: 17 January 2013

Abstract

Background

Ineffective risk stratification can delay diagnosis of serious disease in patients with hematuria. We applied a systems biology approach to analyze clinical, demographic and biomarker measurements (n = 29) collected from 157 hematuric patients: 80 urothelial cancer (UC) and 77 controls with confounding pathologies.

Methods

On the basis of biomarkers, we conducted agglomerative hierarchical clustering to identify patient and biomarker clusters. We then explored the relationship between the patient clusters and clinical characteristics using Chi-square analyses. We determined classification errors and areas under the receiver operating curve of Random Forest Classifiers (RFC) for patient subpopulations using the biomarker clusters to reduce the dimensionality of the data.

Results

Agglomerative clustering identified five patient clusters and seven biomarker clusters. Final diagnoses categories were non-randomly distributed across the five patient clusters. In addition, two of the patient clusters were enriched with patients with 'low cancer-risk' characteristics. The biomarkers which contributed to the diagnostic classifiers for these two patient clusters were similar. In contrast, three of the patient clusters were significantly enriched with patients harboring 'high cancer-risk" characteristics including proteinuria, aggressive pathological stage and grade, and malignant cytology. Patients in these three clusters included controls, that is, patients with other serious disease and patients with cancers other than UC. Biomarkers which contributed to the diagnostic classifiers for the largest 'high cancer- risk' cluster were different than those contributing to the classifiers for the 'low cancer-risk' clusters. Biomarkers which contributed to subpopulations that were split according to smoking status, gender and medication were different.

Conclusions

The systems biology approach applied in this study allowed the hematuric patients to cluster naturally on the basis of the heterogeneity within their biomarker data, into five distinct risk subpopulations. Our findings highlight an approach with the promise to unlock the potential of biomarkers. This will be especially valuable in the field of diagnostic bladder cancer where biomarkers are urgently required. Clinicians could interpret risk classification scores in the context of clinical parameters at the time of triage. This could reduce cystoscopies and enable priority diagnosis of aggressive diseases, leading to improved patient outcomes at reduced costs.

Keywords:
hematuria; biomarker; risk stratification; Random Forests Classifier; hierarchical clustering; feature selection; urothelial cancer; proteinuria