The Use of Proteomics in Urological Research

The Relevance of Mass Spectrometry Analysis for Personalized Medicine through Its Successful Application in Cancer "Omics"

Mass spectrometry (MS) is an essential analytical technology on which the emerging omics domains; such as genomics; transcriptomics; proteomics and metabolomics; are based. This quantifiable technique allows for the identification of thousands of proteins from cell culture; bodily fluids or tissue using either global or targeted strategies; or detection of biologically active metabolites in ultra amounts. The routine performance of MS technology in the oncological field provides a better understanding of human diseases in terms of pathophysiology; prevention; diagnosis and treatment; as well as development of new biomarkers; drugs targets and therapies. In this review; we argue that the recent; successful advances in MS technologies towards cancer omics studies provides a strong rationale for its implementation in biomedicine as a whole.

Targeting chemotherapy to advanced bladder cancer patients most likely to benefit

Evaluation of: Vickers AJ, Cronin AM, Kattan MW et al.; The International Bladder Cancer Nomogram Consortium: Clinical benefits of a multivariate prediction model for bladder cancer: a decision analytic approach. Cancer 115(23), 5460-5469 (2009). The prognosis from muscle-invasive bladder cancer is poor. Improvements in survival can be made with the use of chemotherapy. The best results are obtained using multiagent regimens, which increase cure rates by approximately 5%. Thus, few patients benefit when compared with treatment morbidity. This low complete response rate makes powering of clinical trials difficult and may prevent them determining which patients benefit most from chemotherapy. Here, we discuss work by Vickers et al. reporting a decision-based analysis using a nomogram to determine the benefit for individual patients from chemotherapy. This decision aid can reduce the number of patients treated by 0.006, without compromising recurrence. The authors conclude that a nomogram-derived 25% risk threshold produced better targeting of chemotherapy than the current standard criteria (mostly using pathological stage).

iTRAQ-facilitated proteomic analysis of human prostate cancer cells identifies proteins associated with progression

The unpredictable behavior of prostate cancer presents a major clinical challenge during patient management. In order to gain an insight into the molecular mechanisms associated with prostate cancer progression, we employed the shot-gun proteomic approach of isobaric tags for relative and absolute quantitation (iTRAQ), followed by 2D-LC-MS/MS, using the poorly metastatic LNCaP cell line and its highly metastatic variant LNCaP-LN3 cell line as a model. A total number of 280 unique proteins were identified (> or =95% confidence), and relative expression data was obtained for 176 of these. Ten proteins were found to be significantly up-regulated (> or =1.50 fold), while 4 proteins were significantly down-regulated (> or = -1.50 fold), in LNCaP-LN3 cells. Differential expression of brain creatine kinase (CKBB), soluble catechol-O-methyltransferase (S-COMT), tumor rejection antigen (gp96), and glucose regulated protein, 78 kDa (grp78), was confirmed by Western blotting or independent 2D-PAGE analysis. Additionally, iTRAQ analysis identified absence of the lactate dehydrogenase-B (LDH-B) subunit in LNCaP-LN3 cells, confirming our published data. The clinical relevance of gp96 was assessed by immunohistochemistry using prostate tissues from benign ( n = 95), malignant ( n = 66), and metastatic cases ( n = 3). Benign epithelium showed absent/weak gp96 expression in the basal cells, in contrast to the moderate/strong expression seen in malignant epithelium. Furthermore, there was a statistically significant difference in the intensity of gp96 expression between benign and malignant cases ( p < 0.0005, Mann-Whitney U). Our study is the first to report the application of iTRAQ technology and its potential for the global proteomic profiling of prostate cancer cells, including the identification of absent protein expression.