A major focus of clinical proteomics is on improving disease detection, characterization, and management, ultimately enabling support personalised medicine. Particularly in the case of cancer, high disease heterogeneity5 requires a comprehensive assessment of molecular disease-associated changes to improve patient management. For that purpose, high-resolution approaches involving -omics datasets represent a promising solution. A specific mass spectrometry-based technology, capillary electrophoresis coupled to mass-spectrometry (CE-MS), has been developed and applied for the assessment of peptides and low molecular weight proteins (<20 kDa) in body fluids, targeting to improve diagnosis and consequently treatment through a) introduction of non-invasive, multi-marker panels for early disease diagnosis, monitoring of disease progression6, b) guidance of the therapy-decision making using tools to predict response to treatment7, c) selection of the appropriate pre-clinical model system, as closest possible to the human disease at the molecular level, to test the new drugs8 and d) assessment of the efficacy of the new drugs in pre-clinical models9. Several studies involving CE-MS have demonstrated that urinary peptide biomarkers enable diagnosis with accuracy superior to the current state-of-the-art when combined to multi-marker models10,11. Results were published also for several specific tumours (e.g. bladder, prostate, pancreatic, renal cell carcinoma, cholangiocarcinoma etc.12,16). Moreover, in the context of chronic kidney disease, the US-FDA issued a “Letter of Support” encouraging to use the CE-MS to identify patients with early-stage disease who may be more likely to progress (http://www.fda.gov/downloads/Drugs/DevelopmentApprovalProcess/UCM508790.pdf).
The study by Belczacka and collaborators17 rests on the observation that tumor growth is associated with general molecular events like systemic inflammation that is triggered by tissue invasion and metastasis. Based on the hypothesis that at least in part, these changes are detectable in urine, the authors compared urinary profiling data of more than 1,400 patients with five types of different tumors (bladder cancer, prostate cancer, pancreatic cancer, cholangiocarcinoma and renal cell carcinoma) and individuals with non-malignant diseases. As a result, 193 urinary peptides could be identified that were significantly associated with tumor occurrence. These potentially tumor-specific urinary peptides were combined into a multi-marker model to establish a non-invasive test to monitor cancer progression and metastasis (Figure 1). Independent validation of this general tumor marker model based on 193 peptides demonstrated a highly significant association with tumor presence in 635 patients (293 cancer cases and 342 controls), with an area under the curve value (AUC) of 0.82.
Good specificity of the test was shown, based on the discrimination of the cancer patients from patients with autoinflammatory diseases like hypersensitivity vasculitis (HV; n=58) and systemic lupus erythematosus (SLE; n=34), with specificity values from the 71% to 88%.
The authors now aim to assess the performance of this test in detecting tumor relapse and/or metastasis after the primary tumor has been removed in the context of a prospective investigation. If validated, such a biomarker pattern would have potential not only in monitoring cancer recurrence but also a response to the treatment, e.g. to chemotherapy. Therefore, such a non-invasive test could be applied in the clinical practice to support patients’ management as well as in the context of clinical trials to support patients’ stratification and improve thus cancer drug development.
Figure 1. Schematic overview of the analytical pipeline followed for the establishment of a 193 peptide multi-marker panel by the application of CE-MS platform17
Written by: Iwona Belczacka1,2, Agnieszka Latosinska1, Maria Frantzi1 and Harald Mischak1,3
2. University Hospital RWTH Aachen, Institute for Molecular Cardiovascular Research (IMCAR), Aachen, Germany.
3. The University of Glasgow, Institute of Cardiovascular and Medical Sciences, Glasgow, United Kingdom.
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