There is a growing trend towards exploring the use of a minimally invasive “liquid biopsy” to identify biomarkers in a number of cancers, including urologic malignancies. Multiple aspects can be assessed in circulating cell-free DNA, including cell-free DNA levels, integrity, methylation and mutations. Other prospective liquid biopsy markers include circulating tumor cells, circulating RNAs (miRNA, lncRNAs and mRNAs), cell-free proteins, peptides and exosomes have also emerged as non-invasive cancer biomarkers. These circulating molecules can be detected in various biological fluids, including blood, urine, saliva and seminal plasma. Liquid biopsies hold great promise for personalized medicine due to their ability to provide multiple non-invasive global snapshots of the primary and metastatic tumors. Molecular profiling of circulating molecules has been a stepping-stone to the successful introduction of several non-invasive multi-marker tests into the clinic. In this review, we provide an overview of the current state of cell-free DNA-based kidney, prostate and bladder cancer biomarker research and discuss the potential utility other circulating molecules. We will also discuss the challenges and limitations facing non-invasive cancer biomarker discovery and the benefits of this growing area of translational research.
On-patent and off-patent drugs with previously unrecognized anticancer activity could be rapidly repurposed for this new indication given their prior toxicity testing. To identify such compounds, we con-
Urological malignancies are a major cause of morbidity and mortality worldwide. Advances in early detection, diagnosis, prognosis and prediction of treatment response can significantly improve patient care. Proteomic and peptidomic profiling studies are at the centre of kidney, prostate and bladder cancer biomarker discovery and have shown great promise for improved clinical assessment. Mass spectrometry (MS) is the most widely employed method for proteomic and peptidomic analyses. A number of MS platforms have been developed to facilitate accurate identification of clinically relevant markers in various complex biological samples including tissue, urine and blood. Furthermore, protein profiling studies have been instrumental in the successful introduction of several diagnostic multimarker tests into the clinic. In this review, we will provide a brief overview of high-throughput technologies for protein and peptide based biomarker discovery. We will also examine the current state of kidney, prostate and bladder cancer biomarker research as well as review the journey toward successful clinical implementation.
We utilized a highly sensitive proteomics approach that enabled us to identify one of the largest sets of protein identifications documented in normal human urine. The raw proteomics and peptidomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD003595.
Clear cell renal cell carcinoma (ccRCC) is an aggressive disease with unpredictable behaviour. Clinical parameters are not always accurate for prognosis prediction. The integration of molecular markers to prognostic models can significantly improve prognostic assessment and consequently patient management. We assessed the expression of alpha-enolase (ENO1) protein by immunohistochemistry in 360 patients with primary ccRCC and correlated its expression with multiple clinicopathological parameters including stage, grade, tumor size, disease-free and overall survival. Cox proportional hazard regression models adjusted for clinicopathological factors were used to test for a link between ENO1 expression and both disease-free and overall survival. We correlated ENO1 mRNA expression with overall survival in an independent set of 428 ccRCC cases from The Cancer Genome Atlas. ENO1 showed cytoplasmic, membranous and nuclear staining patterns. There is a statistically significant negative correlation between ENO1 expression, tumor stage, and grade. ENO1 expression also shows a statistically significant direct correlation with disease-free survival (p = 0.011) and overall survival (p = 0.030) in ccRCC. Patients with higher ENO1 expression had lower hazard ratio of recurrence, although this was not statistically significant (HR = 0.330, p = 0.060). These findings were validated at the mRNA level in an independent set of 428 ccRCC cases which also showed that low ENO1 expression is associated with significantly shorter overall survival. Down-regulation of ENO1 can be a predictor of poor prognosis in ccRCC, and it can be a potential prognostic marker.
Renal cell carcinoma (RCC) is frequently diagnosed incidentally as an early‐stage small renal mass (SRM; pT1a, ≤4 cm). Overtreatment of patients with benign or clinically indolent SRMs is increasingly common and has resulted in a recent shift in treatment recommendations. There are currently no available biomarkers that can accurately predict clinical behavior. Therefore, we set out to identify early biomarkers of RCC progression. We employed a quantitative label‐free liquid chromatography coupled to tandem mass spectrometry (LC‐MS/MS) proteomics approach and targeted parallel‐reaction monitoring to identify and validate early, noninvasive urinary biomarkers for RCC‐SRMs. In total, we evaluated 115 urine samples, including 33 renal oncocytoma (≤4 cm) cases, 30 progressive and 26 nonprogressive clear cell RCC (ccRCC)‐SRM cases, in addition to 26 healthy controls. We identified six proteins, which displayed significantly elevated expression in clear cell RCC‐SRMs (ccRCC‐SRMs) relative to healthy controls. Proteins C12ORF49 and EHD4 showed significantly elevated expression in ccRCC‐SRMs compared to renal oncocytoma (≤4 cm). Additionally, proteins EPS8L2, CHMP2A, PDCD6IP, CNDP2 and CEACAM1 displayed significantly elevated expression in progressive relative to nonprogressive ccRCC‐SRMs. A two‐protein signature (EPS8L2 and CCT6A) showed significant discriminatory ability (areas under the curve: 0.81, 95% CI: 0.70–0.93) in distinguishing progressive from nonprogressive ccRCC‐SRMs. Patients (Stage I–IV) with EPS8L2 and CCT6A mRNA alterations showed significantly shorter overall survival (p = 1.407 × 10−6) compared to patients with no alterations. Our in‐depth proteomic analysis identified novel biomarkers for early‐stage RCC‐SRMs. Pretreatment characterization of urinary proteins may provide insight into early RCC progression and could potentially help assign patients to appropriate management strategies.
Renal cell carcinoma (RCC) constitutes an array of morphologically and genetically distinct tumors the most prevalent of which are clear cell, papillary, and chromophobe RCC. Accurate distinction between the typically benign-behaving renal oncocytoma and RCC subtypes is a frequent challenge for pathologists. This is critical for clinical decision making. Subtypes also have different survival outcomes and responses to therapy. We extracted RNA from ninety formalin-fixed paraffin-embedded (FFPE) tissues (27 clear cell, 29 papillary, 19 chromophobe, 4 unclassified RCC and 11 oncocytomas). We quantified the expression of six miRNAs (miR-221, miR-222, miR-126, miR-182, miR-200b and miR-200c) by qRT-PCR, and by in situ hybridization in an independent set of tumors. We developed a two-step classifier. In the first step, it uses expression of either miR-221 or miR-222 to distinguish the clear cell and papillary subtypes from chromophobe RCC and oncocytoma (miR-221 AUC: 0.96, 95% CI: 0.9132–1.014, p < 0.0001 and miR-222 AUC: 0.91, 95% CI: 0.8478–0.9772, p < 0.0001). In the second step, it uses miR-126 to discriminate clear cell from papillary RCC (AUC: 1, p < 0.0001) and miR-200b to discriminate chromophobe RCC from oncocytoma (AUC: 0.95, 95% CI: 0.8933–1.021, p < 0.0001). In situ hybridization showed a nuclear staining pattern. miR-126, miR-222 and miR-200b were significantly differentially expressed between the subtypes by in situ hybridization. miRNA expression could distinguish RCC subtypes and oncocytoma. miRNA expression assessed by either PCR or in situ hybridization can be a clinically useful diagnostic tool to complement morphologic renal tumor classification, improving diagnosis and patient management.
Henry Rodriguez: Following the initial draft of the human genome in late 2000 (genomics), there was much excitement to immediately move into its scientific counterpart (proteomics). The basis was 3-fold: first, basic biology had already shown that proteins are the workhorses of a cell, the machinery that provides most of the cell's functionality and makes up most of the structures of the cell, and thereby mediators of phenotype characteristics; second, researchers had for the first time a blueprint (the human genome) to infer the possible gene products derived from a genome; and third, biotech/pharma were developing drugs that either act by targeting proteins or are proteins themselves. So coupling these items with the technological breakthroughs at the time in identifying vast amounts of proteins and their posttranslational modifications
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