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

Background The clinical course of prostate cancer (PCa) measured by biochemical failure (BF) after prostatectomy remains unpredictable in many patients, particularly in intermediate Gleason score (GS) 7 tumors, suggesting that identification of molecular mechanisms associated with aggressive PCa biology may be exploited for improved prognostication or therapy. Hyaluronan (HA) is a high molecular weight polyanionic carbohydrate produced by synthases (HAS1-3) and fragmented by oxidative/nitrosative stress and hyaluronidases (HYAL1-4, SPAM1) common in PCa microenvironments. HA and HA fragments interact with receptors CD44 and HMMR resulting in increased tumor aggressiveness in experimental PCa models. We evaluated the association of HA-related molecules with BF after prostatectomy in GS7 tumors. Methods Tissue microarrays were constructed from a 96-patient cohort. HA histochemistry and HAS2, HYAL1, CD44, CD44v6, and HMMR immunohistochemistry were quantified using digital pathology techniques. Results HA in tumor-associated stroma and HMMR in malignant epithelium were significantly and marginally significantly associated with time to BF in univariate analysis, respectively. After adjusting for clinicopathologic features, both HA in tumor-associated stroma and HMMR in malignant epithelium were significantly associated with time to BF. Although not significantly associated with BF, HAS2 and HYAL1 positively correlated with HMMR in malignant epithelium. Cell culture assays demonstrated that HMMR bound native and fragmented HA, promoted HA uptake, and was required for a pro-migratory response to fragmented HA. Conclusions HA and HMMR are factors associated with time to BF in GS7 tumors, suggesting that increased HA synthesis and fragmentation within the tumor microenvironment stimulates aggressive PCa behavior through HA-HMMR signaling.

Molecular classification of diseases based on multigene expression signatures is increasingly used for diagnosis, prognosis, and prediction of response to therapy. Immunohistochemistry (IHC) is an optimal method for validating expression signatures obtained using high-throughput genomics techniques since IHC allows a pathologist to examine gene expression at the protein level within the context of histologically interpretable tissue sections. Additionally, validated IHC assays may be readily implemented as clinical tests since IHC is performed on routinely processed clinical tissue samples. However, methods have not been available for automated n-gene expression profiling at the protein level using IHC data. We have developed methods to compute expression level maps (signature maps) of multiple genes from IHC data digitized on a commercial whole slide imaging system. Areas of cancer for these expression level maps are defined by a pathologist on adjacent, co-registered H&E slides, allowing assessment of IHC statistics and heterogeneity within the diseased tissue. This novel way of representing multiple IHC assays as signature maps will allow the development of n-gene expression profiling databases in three dimensions throughout virtual whole organ reconstructions.

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