CD74 and CD44 Expression on CTCs in Cancer Patients with Brain Metastasis.

Up to 40% of advance lung, melanoma and breast cancer patients suffer from brain metastases (BM) with increasing incidence. Here, we assessed whether circulating tumor cells (CTCs) in peripheral blood can serve as a disease surrogate, focusing on CD44 and CD74 expression as prognostic markers for BM. We show that a size-based microfluidic approach in combination with a semi-automated cell recognition system are well suited for CTC detection in BM patients and allow further characterization of tumor cells potentially derived from BM. CTCs were found in 50% (7/14) of breast cancer, 50% (9/18) of non-small cell lung cancer (NSCLC) and 36% (4/11) of melanoma patients. The next-generation sequencing (NGS) analysis of nine single CTCs from one breast cancer patient revealed three different CNV profile groups as well as a resistance causing ERS1 mutation. CD44 and CD74 were expressed on most CTCs and their expression was strongly correlated, whereas matched breast cancer BM tissues were much less frequently expressing CD44 and CD74 (negative in 46% and 54%, respectively). Thus, plasticity of CD44 and CD74 expression during trafficking of CTCs in the circulation might be the result of adaptation strategies.

Proteomics biomarkers for solid tumors: Current status and future prospects.

Cancer is a heterogeneous multifactorial disease, which continues to be one of the main causes of death worldwide. Despite the extensive efforts for establishing accurate diagnostic assays and efficient therapeutic schemes, disease prevalence is on the rise, in part, however, also due to improved early detection. For years, studies were focused on genomics and transcriptomics, aiming at the discovery of new tests with diagnostic or prognostic potential. However, cancer phenotypic characteristics seem most likely to be a direct reflection of changes in protein metabolism and function, which are also the targets of most drugs. Investigations at the protein level are therefore advantageous particularly in the case of in-depth characterization of tumor progression and invasiveness. Innovative high-throughput proteomic technologies are available to accurately evaluate cancer formation and progression and to investigate the functional role of key proteins in cancer. Employing these new highly sensitive proteomic technologies, cancer biomarkers may be detectable that contribute to diagnosis and guide curative treatment when still possible. In this review, the recent advances in proteomic biomarker research in cancer are outlined, with special emphasis placed on the identification of diagnostic and prognostic biomarkers for solid tumors. In view of the increasing number of screening programs and clinical trials investigating new treatment options, we discuss the molecular connections of the biomarkers as well as their potential as clinically useful tools for diagnosis, risk stratification and therapy monitoring of solid tumors.

Urinary proteomic biomarkers in oncology: ready for implementation?

Introduction: Biomarkers are expected to improve the management of cancer patients by enabling early detection and prediction of therapeutic response. Proteins reflect a molecular phenotype, have high potential as biomarkers, and also are key targets for intervention. Given the ease of collection and proximity to certain tumors, the urinary proteome is a rich source of biomarkers and several proteins have been already implemented. Areas covered: We examined the literature on urine proteins and proteome analysis in oncology from reports published during the last 5 years to generate an overview on the status of urine protein and peptide biomarkers, with emphasis on their actual clinical value. Expert commentary: A few studies report on biomarkers that are ready to be implemented in patient management, among others in bladder cancer and cholangiocarcinoma. These reports are based on multi-marker approaches. A high number of biomarkers, though, has been described in studies with low statistical power. In fact, several of them have been consistently reported across different studies. The latter should be the focus of attention and be tested in properly designed confirmatory and ultimately, prospective investigations. It is expected that multi-marker classifiers for a specific context-of-use, will be the preferred path toward clinical implementation.

Author Correction: Urinary CE-MS peptide marker pattern for detection of solid tumors.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Proteomics in Kidney Allograft Transplantation-Application of Molecular Pathway Analysis for Kidney Allograft Disease Phenotypic Biomarker Selection.

There is a need for accurate, robust, non-invasive methods to provide early diagnosis of graft lesions after kidney transplantation. A multitude of proteomic biomarkers for the major kidney allograft disease phenotypes defined by the BANFF classification criteria have been described in literature. None of these biomarkers have been established in the clinic. A key reason for this is the lack of clinical validation which is difficult, as even the gold standard of diagnosis, kidney biopsy, is often ambiguous. The semantic clustering by ReviGO on top of transcriptomic pathway analysis is evaluated to connect histological and transcriptomic kidney allograft disease characteristics with proteomic biomarker qualification. By using public data generated in microarray studies of kidney allograft tissue, biological processes and key molecules specifically associated with the different kidney allograft disease phenotypes are identified. Semantic clustering holds the promise to guide adaptation of proteomic marker panels to molecular pathology. This can support the development of noninvasive tests (e.g. in urine, by capillary electrophoresis mass spectrometry) that simultaneously detect diverse kidney allograft phenotypes with high accuracy and sensitivity.

Urinary Glycopeptide Analysis for the Investigation of Novel Biomarkers.

PURPOSE: Urine is a rich source of potential biomarkers, including glycoproteins. Glycoproteomic analysis remains difficult due to the high heterogeneity of glycans. Nevertheless, recent advances in glycoproteomics software solutions facilitate glycopeptide identification and characterization. The aim is to investigate intact glycopeptides in the urinary peptide profiles of normal subjects using a novel PTM-centric software-Byonic. EXPERIMENTAL DESIGN: The urinary peptide profiles of 238 normal subjects, previously analyzed using CE-MS and CE-MS/MS and/or LC-MS/MS, are subjected to glycopeptide analysis. Additionally, glycopeptide distribution is assessed in a set of 969 patients with five different cancer types: bladder, prostate and pancreatic cancer, cholangiocarcinoma, and renal cell carcinoma. RESULTS: A total of 37 intact O-glycopeptides and 23 intact N-glycopeptides are identified in the urinary profiles of 238 normal subjects. Among the most commonly identified O-glycoproteins are Apolipoprotein C-III and insulin-like growth factor II, while titin among the N-glycoproteins. Further statistical analysis reveals that three O-glycopeptides and five N-glycopeptides differed significantly in their abundance among the different cancer types, comparing to normal subjects. CONCLUSIONS AND CLINICAL RELEVANCE: Through the established glycoproteomics workflow, intact O- and N-glycopeptides in human urine are identified and characterized, providing novel insights for further exploration of the glycoproteome with respect to specific diseases.

Urinary peptide panel for prognostic assessment of bladder cancer relapse.

Non-invasive tools stratifying bladder cancer (BC) patients according to the risk of relapse are urgently needed to guide clinical intervention. As a follow-up to the previously published study on CE-MS-based urinary biomarkers for BC detection and recurrence monitoring, we expanded the investigation towards BC patients with longitudinal data. Profiling datasets of BC patients with follow-up information regarding the relapse status were investigated. The peptidomics dataset (n = 98) was split into training and test set. Cox regression was utilized for feature selection in the training set. Investigation of the entire training set at the single peptide level revealed 36 peptides being strong independent prognostic markers of disease relapse. Those features were further integrated into a Random Forest-based model evaluating the risk of relapse for BC patients. Performance of the model was assessed in the test cohort, showing high significance in BC relapse prognosis [HR = 5.76, p-value = 0.0001, c-index = 0.64]. Urinary peptide profiles integrated into a prognostic model allow for quantitative risk assessment of BC relapse highlighting the need for its incorporation in prospective studies to establish its value in the clinical management of BC.

Urinary proteome signature of Renal Cysts and Diabetes syndrome in children.

Renal Cysts and Diabetes Syndrome (RCAD) is an autosomal dominant disorder caused by mutations in the HNF1B gene encoding for the transcriptional factor hepatocyte nuclear factor-1B. RCAD is characterized as a multi-organ disease, with a broad spectrum of symptoms including kidney abnormalities (renal cysts, renal hypodysplasia, single kidney, horseshoe kidneys, hydronephrosis), early-onset diabetes mellitus, abnormal liver function, pancreatic hypoplasia and genital tract malformations. In the present study, using capillary electrophoresis coupled to mass spectrometry (CE-MS), we investigated the urinary proteome of a pediatric cohort of RCAD patients and different controls to identify peptide biomarkers and obtain further insights into the pathophysiology of this disorder. As a result, 146 peptides were found to be associated with RCAD in 22 pediatric patients when compared to 22 healthy age-matched controls. A classifier based on these peptides was generated and further tested on an independent cohort, clearly discriminating RCAD patients from different groups of controls. This study demonstrates that the urinary proteome of pediatric RCAD patients differs from autosomal dominant polycystic kidney disease (PKD1, PKD2), congenital nephrotic syndrome (NPHS1, NPHS2, NPHS4, NPHS9) as well as from chronic kidney disease conditions, suggesting differences between the pathophysiology behind these disorders.

Urinary CE-MS peptide marker pattern for detection of solid tumors.

Urinary profiling datasets, previously acquired by capillary electrophoresis coupled to mass-spectrometry were investigated to identify a general urinary marker pattern for detection of solid tumors by targeting common systemic events associated with tumor-related inflammation. A total of 2,055 urinary profiles were analyzed, derived from a) a cancer group of patients (n = 969) with bladder, prostate, and pancreatic cancers, renal cell carcinoma, and cholangiocarcinoma and b) a control group of patients with benign diseases (n = 556), inflammatory diseases (n = 199) and healthy individuals (n = 331). Statistical analysis was conducted in a discovery set of 676 cancer cases and 744 controls. 193 peptides differing at statistically significant levels between cases and controls were selected and combined to a multi-dimensional marker pattern using support vector machine algorithms. Independent validation in a set of 635 patients (293 cancer cases and 342 controls) showed an AUC of 0.82. Inclusion of age as independent variable, significantly increased the AUC value to 0.85. Among the identified peptides were mucins, fibrinogen and collagen fragments. Further studies are planned to assess the pattern value to monitor patients for tumor recurrence. In this proof-of-concept study, a general tumor marker pattern was developed to detect cancer based on shared biomarkers, likely indicative of cancer-related features.

Proteomics and Metabolomics for AKI Diagnosis.

Acute kidney injury (AKI) is a severe and frequent condition in hospitalized patients. Currently, no efficient therapy of AKI is available. Therefore, efforts focus on early prevention and potentially early initiation of renal replacement therapy to improve the outcome in AKI. The detection of AKI in hospitalized patients implies the need for early, accurate, robust, and easily accessible biomarkers of AKI evolution and outcome prediction because only a narrow window exists to implement the earlier-described measures. Even more challenging is the multifactorial origin of AKI and the fact that the changes of molecular expression induced by AKI are difficult to distinguish from those of the diseases associated or causing AKI as shock or sepsis. During the past decade, a considerable number of protein biomarkers for AKI have been described and we expect from recent advances in the field of omics technologies that this number will increase further in the future and be extended to other sorts of biomolecules, such as RNAs, lipids, and metabolites. However, most of these biomarkers are poorly defined by their AKI-associated molecular context. In this review, we describe the state-of-the-art tissue and biofluid proteomic and metabolomic technologies and new bioinformatics approaches for proteomic and metabolomic pathway and molecular interaction analysis. In the second part of the review, we focus on AKI-associated proteomic and metabolomic biomarkers and briefly outline their pathophysiological context in AKI.