Justyna Siwy

Bile proteomic profiles differentiate cholangiocarcinoma from primary sclerosing cholangitis and choledocholithiasis

Early detection of malignant biliary tract diseases, especially cholangiocarcinoma (CC) in patients with primary sclerosing cholangitis (PSC), is very difficult and often comes too late to give the patient a therapeutic benefit. We hypothesize that bile proteomic analysis distinguishes CC from nonmalignant lesions. We used capillary electrophoresis mass spectrometry (CE‐MS) to identify disease‐specific peptide patterns in patients with choledocholithiasis (n = 16), PSC (n = 18), and CC (n = 16) in a training set. A model for differentiation of choledocholithiasis from PSC and CC (PSC/CC model) and another model distinguishing CC from PSC (CC model) were subsequently validated in independent cohorts (choledocholithiasis [n = 14], PSC [n = 18] and CC [n = 25]). Peptides were characterized by sequencing. Application of the PSC/CC model in the independent test cohort resulted in correct exclusion of 12/14 bile samples from patients with choledocholithiasis and identification of 40/43 patients with PSC or CC (86% specificity, 93% sensitivity). The corresponding receiver operating characteristic (ROC) analysis revealed an area under the curve (AUC) of 0.93 (95% confidence interval [CI]: 0.82‐0.98, P = 0.0001). The CC model succeeded in an accurate detection of 14/18 bile samples from patients with PSC and 21/25 samples with CC (78% specificity, 84% sensitivity) in the independent cohort, resulting in an AUC value of 0.87 (95% CI: 0.73‐0.95, P = 0.0001) in ROC analysis. Eight out of 10 samples of patients with CC complicating PSC were identified. Conclusion: Bile proteomic analysis discriminates benign conditions from CC accurately. This method may become a diagnostic tool in future as it offers a new possibility to diagnose malignant bile duct disease and thus enables efficient therapy particularly in patients with PSC. (HEPATOLOGY 2010;)

Comprehensive human urine standards for comparability and standardization in clinical proteome analysis

Purpose: Urine proteomics is emerging as a powerful tool for biomarker discovery. The purpose of this study is the development of a well‐characterized “real life” sample that can be used as reference standard in urine clinical proteomics studies.

Urinary Proteomics for Prediction of Preeclampsia

Preeclampsia is a major determinant of fetal and maternal morbidity and mortality. We used a proteomic strategy to identify urinary biomarkers that predict preeclampsia before the onset of disease. We prospectively collected urine samples from women throughout pregnancy. Samples from gestational weeks 12 to 16 (n=45), 20 (n=50), and 28 (n=18) from women who subsequently had preeclampsia develop were matched to controls (n=86, n=49, and n=17, respectively). We performed capillary electrophoresis online coupled to micro-time-of-flight mass spectrometry. Disease-specific peptide patterns were generated using support vector machine-based software. Candidate biomarkers were sequenced by liquid chromatography-tandem mass spectrometry. From comparison with nonpregnant controls, we defined a panel of 284 pregnancy-specific proteomic biomarkers. Subsequently, we developed a model of 50 biomarkers from specimens obtained at week 28 that was associated with future preeclampsia (classification factor in cases, 1.032±0.411 vs controls, −1.038±0.432; P<0.001). Classification factor increased markedly from week 12 to 16 to 28 in women who subsequently had preeclampsia develop (n=16; from −0.392±0.383 to 1.070±0.383; P<0.001) and decreased slightly in controls (n=16; from −0.647±0.437 to −1.024±0.433; P=0.043). Among the biomarkers are fibrinogen alpha chain, collagen alpha chain, and uromodulin fragments. The markers appear to predict preeclampsia at gestational week 28 with good confidence but not reliably at earlier time points (weeks 12–16 and 20). After prospective validation in other cohorts, these markers may contribute to better prediction, monitoring, and accurate diagnosis of preeclampsia.

Diagnosis and Prediction of CKD Progression by Assessment of Urinary Peptides

Progressive CKD is generally detected at a late stage by a sustained decline in eGFR and/or the presence of significant albuminuria. With the aim of early and improved risk stratification of patients with CKD, we studied urinary peptides in a large cross-sectional multicenter cohort of 1990 individuals, including 522 with follow-up data, using proteome analysis. We validated that a previously established multipeptide urinary biomarker classifier performed significantly better in detecting and predicting progression of CKD than the current clinical standard, urinary albumin. The classifier was also more sensitive for identifying patients with rapidly progressing CKD. Compared with the combination of baseline eGFR and albuminuria (area under the curve [AUC]=0.758), the addition of the multipeptide biomarker classifier significantly improved CKD risk prediction (AUC=0.831) as assessed by the net reclassification index (0.303±-0.065; P<0.001) and integrated discrimination improvement (0.058±0.014; P<0.001). Correlation of individual urinary peptides with CKD stage and progression showed that the peptides that associated with CKD, irrespective of CKD stage or CKD progression, were either fragments of the major circulating proteins, suggesting failure of the glomerular filtration barrier sieving properties, or different collagen fragments, suggesting accumulation of intrarenal extracellular matrix. Furthermore, protein fragments associated with progression of CKD originated mostly from proteins related to inflammation and tissue repair. Results of this study suggest that urinary proteome analysis might significantly improve the current state of the art of CKD detection and outcome prediction and that identification of the urinary peptides allows insight into various ongoing pathophysiologic processes in CKD.

Human urinary peptide database for multiple disease biomarker discovery

Purpose: Human urine is an ideal candidate for use in clinical diagnostics. It is easily available, as untrained personnel can collect it. It correlates well with the pathophysiology of a number of diseases, making it a useful source for clinical proteomics.

CKD273, a new proteomics classifier assessing CKD and its prognosis.

National Kidney Foundation CKD staging has allowed uniformity in studies on CKD. However, early diagnosis and predicting progression to end stage renal disease are yet to be improved. Seventy six patients with different levels of CKD, including outpatients and dialysed patients were studied for transcriptome, metabolome and proteome description. High resolution urinary proteome analysis was blindly performed in the 53 non-anuric out of the 76 CKD patients. In addition to routine clinical parameters, CKD273, a urinary proteomics-based classifier and its peptides were quantified. The baseline values were analyzed with regard to the clinical parameters and the occurrence of death or renal death during follow-up (3.6 years) as the main outcome measurements. None of the patients with CKD273<0.55 required dialysis or died while all fifteen patients that reached an endpoint had a CKD273 score >0.55. Unsupervised clustering analysis of the CKD273 peptides separated the patients into two main groups differing in CKD associated parameters. Among the 273 biomarkers, peptides derived from serum proteins were relatively increased in patients with lower glomerular filtration rate, while collagen-derived peptides were relatively decreased (p<0.05; Spearman). CKD273 was different in the groups with different renal function (p<0.003). The CKD273 classifier separated CKD patients according to their renal function and informed on the likelihood of experiencing adverse outcome. Recently defined in a large population, CKD273 is the first proteomic-based classifier successfully tested for prognosis of CKD progression in an independent cohort.

Urine as a source for clinical proteome analysis: from discovery to clinical application.

The success of clinical proteome analysis should be assessed based on the clinical impact following implementation of findings. Although there have been several technological advancements in mass spectrometry in the last years, these have not resulted in similar advancements in clinical proteomics. In addition, application of proteomic biomarkers in clinical diagnostics and practical improvement in the disease management is extremely rare. In this review, we discuss the relevant issues associated with identification of robust biomarkers of clinical value. Urine appears to be an ideal source of biomarkers, for theoretical, methodological, and practical reasons. Therefore, this review is focused on the search for biomarkers in urine within the last decade. Urine can be used for non-invasive assessment of a variety of diseases including those affecting the urogenital tract and also other pathologies such as cardiovascular disease or appendicitis. We also discuss the importance of data validation, an essential step in translating biomarkers into the clinical practice. Furthermore, we examine several examples of apparently successful proteomic biomarker discovery studies and their implications for disease diagnosis, prognosis, and therapy evaluation. We also discuss some current challenges in this field and reflect on future research prospects. This article is part of a Special Issue entitled: Biomarkers: A Proteomic Challenge.

Urinary Proteomic Biomarkers for Diagnosis and Risk Stratification of Autosomal Dominant Polycystic Kidney Disease: A Multicentric Study

Treatment options for autosomal dominant polycystic kidney disease (ADPKD) will likely become available in the near future, hence reliable diagnostic and prognostic biomarkers for the disease are strongly needed. Here, we aimed to define urinary proteomic patterns in ADPKD patients, which aid diagnosis and risk stratification. By capillary electrophoresis online coupled to mass spectrometry (CE-MS), we compared the urinary peptidome of 41 ADPKD patients to 189 healthy controls and identified 657 peptides with significantly altered excretion, of which 209 could be sequenced using tandem mass spectrometry. A support-vector-machine based diagnostic biomarker model based on the 142 most consistent peptide markers achieved a diagnostic sensitivity of 84.5% and specificity of 94.2% in an independent validation cohort, consisting of 251 ADPKD patients from five different centers and 86 healthy controls. The proteomic alterations in ADPKD included, but were not limited to markers previously associated with acute kidney injury (AKI). The diagnostic biomarker model was highly specific for ADPKD when tested in a cohort consisting of 481 patients with a variety of renal and extrarenal diseases, including AKI. Similar to ultrasound, sensitivity and specificity of the diagnostic score depended on patient age and genotype. We were furthermore able to identify biomarkers for disease severity and progression. A proteomic severity score was developed to predict height adjusted total kidney volume (htTKV) based on proteomic analysis of 134 ADPKD patients and showed a correlation of r = 0.415 (p<0.0001) with htTKV in an independent validation cohort consisting of 158 ADPKD patients. In conclusion, the performance of peptidomic biomarker scores is superior to any other biochemical markers of ADPKD and the proteomic biomarker patterns are a promising tool for prognostic evaluation of ADPKD.

A comparison between MALDI-MS and CE-MS data for biomarker assessment in chronic kidney diseases

Non-invasive detection of diseases, based on urinary proteomics, is becoming an increasingly important area of research, especially in the area of chronic kidney disease (CKD). Different platforms have been used in independent studies, mostly capillary-electrophoresis coupled ESI-MS (CE-MS), liquid chromatography coupled mass spectrometry, and matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS). We have compared the performance of CE-MS with MALDI-MS in detecting CKD, based on a cohort of 137 urine samples (62 cases and 75 controls). Data cross-talk between the two platforms was established for the comparison of detected biomarkers. The results demonstrate superior performance of the CE-MS approach in terms of peptide resolution and obtained disease prediction accuracy rates. However, the data also demonstrate the ability of the MALDI-MS approach to separate CKD patients from controls, at slightly reduced accuracy, but expected reduced cost and time. As a consequence, a practical approach can be foreseen where MALDI-MS is employed as an inexpensive, fast, and robust screening tool to detect probable CKD. In a second step, high resolution CE-MS could be used in those patients only that scored negative for CKD in the MALDI-MS analysis, reducing costs and time of such a program.

Fetal Urinary Peptides to Predict Postnatal Outcome of Renal Disease in Fetuses with Posterior Urethral Valves (PUV)

Peptides found in fetal urine predict end-stage renal disease in patients with a congenital abnormality of the kidney and urinary tract. Seeking a Signature of Renal Disease Being able to predict—in utero—whether a fetus will have severe kidney disease would help anxious parents make informed treatment decisions earlier. To this end, Klein and colleagues searched through thousands of peptides in fetal urine to find potential markers of end-stage renal disease (ESRD). The authors obtained urine samples from fetuses with posterior urethral valves (PUV), which is an abnormality of the urinary tract that can lead to ESRD. They knew the outcome of these patients with PUV, that is, whether or not they had ESRD before the age of 2. Out of >4000 peptides, Klein et al. identified a handful that were linked to ESRD, but were not present in the fetal urine of patients who did not progress to ESRD. In an independent set of samples, this peptide-based signature, called 12PUV, was similarly able to predict postnatal renal function, outperforming routine clinical procedures such as ultrasound and biochemical tests. Although many more fetal urine samples are needed for validation, this peptide-based predictor shows promise as a much needed test to help clinicians and families make prenatal treatment decisions for fetuses with PUV. Bilateral congenital abnormalities of the kidney and urinary tract (CAKUT), although are individually rare diseases, remain the main cause of chronic kidney disease in infants worldwide. Bilateral CAKUT display a wide spectrum of pre- and postnatal outcomes ranging from death in utero to normal postnatal renal function. Methods to predict these outcomes in utero are controversial and, in several cases, lead to unjustified termination of pregnancy. Using capillary electrophoresis coupled with mass spectrometry, we have analyzed the urinary proteome of fetuses with posterior urethral valves (PUV), the prototypic bilateral CAKUT, for the presence of biomarkers predicting postnatal renal function. Among more than 4000 fetal urinary peptide candidates, 26 peptides were identified that were specifically associated with PUV in 13 patients with early end-stage renal disease (ESRD) compared to 15 patients with absence of ESRD before the age of 2. A classifier based on these peptides correctly predicted postnatal renal function with 88% sensitivity and 95% specificity in an independent blinded validation cohort of 38 PUV patients, outperforming classical methods, including fetal urine biochemistry and fetal ultrasound. This study demonstrates that fetal urine is an important pool of peptides that can predict postnatal renal function and thus be used to make clinical decisions regarding pregnancy.