Quantitative Analysis of Seven New Prostate Cancer Biomarkers and the Potential Future of the ‘Biomarker Laboratory’

Abstract

Prostate cancer is the third highest cause of male mortality in the developed world, with the burden of the disease increasing dramatically with demographic change. There are significant limitations to the current diagnostic regimens and no established effective screening modality. To this end, research has discovered hundreds of potential ‘biomarkers’ that may one day be of use in screening, diagnosis or prognostication. However, the barriers to bringing biomarkers to clinical evaluation and eventually into clinical usage have yet to be realised. This is an operational challenge that requires some new thinking and development of paradigms to increase the efficiency of the laboratory process and add ‘value’ to the clinician. Value comes in various forms, whether it be a process that is seamlessly integrated into the hospital laboratory environment or one that can provide additional ‘information’ for the clinical pathologist in terms of risk profiling. We describe, herein, an efficient and tissue-conserving pipeline that uses Tissue Microarrays in a semi-automated process that could, one day, be integrated into the hospital laboratory domain, using seven putative prostate cancer biomarkers for illustration.

The potential of tumour microenvironment markers to stratify the risk of recurrence in prostate cancer patients

“…There were a myriad of PCa biomarker studies focusing on the TME over the last 10-15 years, and many of them have focused on its potential prognostic value. Hence, clinical recurrence (CLR) in PCa was linked to changes in the TME in several studies [8][9][10][11][12], including reports on CLR-dependent alterations in expression profiles of steroid hormone receptors (SHR) [13][14][15][16], cancer activated fibroblast (CAF) markers [17,18] and vascular markers. Interestingly, most commercially available prognostic biomarker-panels have also several stromal cell markers in their gene panels [2].…”

Equine penile squamous cell carcinoma: expression of biomarker proteins and EcPV2

“…In analysing cancer related proteins, putatively associated with the Wnt pathway, in EpSCC and their association with EcPV2 infection we may find dual benefit in creating an animal model of human disease as well as improving our understanding of an equine condition that causes significant morbidity. Using a cohort of EpSCC tissue, we conducted medium throughput protein staining and imaging and a quantitative approach previously established in our laboratory 18,[28][29][30][31] to investigate the hypothesis that viral-driven alterations in inflammation may play a role in EpSCC development.…”

A method for semi-automated image analysis of HLA class I tumour epithelium expression in rectal cancer

“…Many different techniques have been reported to evaluate biomarkers on tumour tissue. [17][18][19][20][21][22] Some studies, using MATLAB software, could give insights in the relocation of tumour biomarkers between a cell's membrane, cytoplasm and nucleus using RGB unmixing of images of conventional IHC-stained sections (DAB and haematoxylin signals). 20,21 Additionally, MATLAB software (MIAQuant code) was recently reported as novel computational method for the quantification of IHC stained tissue sections.…”

“…Other studies reported that the software platform ImageJ can be used to evaluate the expression of biomarkers based on its immunohistochemical staining intensity on a TMA in a high throughput manner. [17][18][19] Using this method, the tumour-epithelium specific expression of biomarkers could be analysed since the biomarker expression signal on epithelium was higher compared to non-epithelial tissue. Hence, the threshold for positive staining was set in a way that the relatively low signal in stromal tissue was scored as negative.…”