Open Access Highly Accessed Research article

Evaluation of protein biomarkers of prostate cancer aggressiveness

Anthony E Rizzardi12, Nikolaus K Rosener2, Joseph S Koopmeiners34, Rachel Isaksson Vogel3, Gregory J Metzger5, Colleen L Forster6, Lauren O Marston2, Jessica R Tiffany2, James B McCarthy2, Eva A Turley78, Christopher A Warlick9, Jonathan C Henriksen126 and Stephen C Schmechel126*

Author Affiliations

1 Department of Pathology, University of Washington, Mailcode 359791, 908 Jefferson St, Seattle, WA 98104, USA

2 Department of Laboratory Medicine and Pathology and Masonic Cancer Center, University of Minnesota, Minneapolis, MN, USA

3 Biostatistics and Bioinformatics Core, Masonic Cancer Center, University of Minnesota, Minneapolis, MN, USA

4 Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN, USA

5 Department of Radiology, University of Minnesota, Minneapolis, MN, USA

6 BioNet, Academic Health Center, University of Minnesota, Minneapolis, MN, USA

7 Department of Biochemistry, London Health Sciences Center, University of Western Ontario, London, Ontario, Canada

8 Department of Oncology, London Health Sciences Center, University of Western Ontario, London, Ontario, Canada

9 Department of Urology, University of Minnesota, Minneapolis, MN, USA

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BMC Cancer 2014, 14:244  doi:10.1186/1471-2407-14-244

Published: 5 April 2014

Abstract

Background

Prognostic multibiomarker signatures in prostate cancer (PCa) may improve patient management and provide a bridge for developing novel therapeutics and imaging methods. Our objective was to evaluate the association between expression of 33 candidate protein biomarkers and time to biochemical failure (BF) after prostatectomy.

Methods

PCa tissue microarrays were constructed representing 160 patients for whom clinicopathologic features and follow-up data after surgery were available. Immunohistochemistry for each of 33 proteins was quantified using automated digital pathology techniques. Relationships between clinicopathologic features, staining intensity, and time to BF were assessed. Predictive modeling using multiple imputed datasets was performed to identify the top biomarker candidates.

Results

In univariate analyses, lymph node positivity, surgical margin positivity, non-localized tumor, age at prostatectomy, and biomarkers CCND1, HMMR, IGF1, MKI67, SIAH2, and SMAD4 in malignant epithelium were significantly associated with time to BF. HMMR, IGF1, and SMAD4 remained significantly associated with BF after adjusting for clinicopathologic features while additional associations were observed for HOXC6 and MAP4K4 following adjustment. In multibiomarker predictive models, 3 proteins including HMMR, SIAH2, and SMAD4 were consistently represented among the top 2, 3, 4, and 5 most predictive biomarkers, and a signature comprised of these proteins best predicted BF at 3 and 5 years.

Conclusions

This study provides rationale for investigation of HMMR, HOXC6, IGF1, MAP4K4, SIAH2, and SMAD4 as biomarkers of PCa aggressiveness in larger cohorts.

Keywords:
Prostate cancer; Aggressiveness; Biomarker; Signature