Erica Gianazza

Human urine biomarkers of renal cell carcinoma evaluated by ClinProt

Renal cell carcinoma (RCC) is one of the major causes of cancer death and is radio‐ and chemoresistant. Urine of 29 healthy subjects and 39 clear cell RCC patients were analyzed using the ClinProt technique to search for possible biomarkers for early RCC diagnosis. A cluster of three signals (marker A= at m/z 1827 ± 8 Da, marker B = 1914 ± 8 Da and marker C = 1968 ± 8 Da) was able to discriminate patients from controls. A receiver operating characteristic curve analysis showed values of area under the curve (AUC) higher than 0.9 for marker A and B, corresponding to a sensitivity of 85–90% and a specificity of 90%, while marker C gave a lower AUC (0.84) corresponding to sensitivity of 70% and specificity of 100%. The combination of three markers lead to an improvement in diagnostic efficacy, with specificity and sensitivity of 100% and 95%, respectively, in the training test and of 100% and of 85% in the test experiment. The efficacy of this cluster of signals to distinguish RCC patients grouped by tumor stage showed a sensibility of 100% for patients at the primary tumor 1 stage. One of the signals present in the cluster was identified as a fragment of Tamm‐Horsfall protein.

Detection of high molecular weight proteins by MALDI imaging mass spectrometry

MALDI imaging mass spectrometry (IMS) is a unique technology to explore the spatial distribution of biomolecules directly on tissues. It allows the in situ investigation of a large number of small proteins and peptides. Detection of high molecular weight proteins through MALDI IMS still represents an important challenge, as it would allow the direct investigation of the distribution of more proteins involved in biological processes, such as cytokines, enzymes, neuropeptide precursors and receptors. In this work we compare the traditional method performed with sinapinic acid with a comparable protocol using ferulic acid as the matrix. Data show a remarkable increase of signal acquisition in the mass range of 20k to 150k Th. Moreover, we report molecular images of biomolecules above 70k Th, demonstrating the possibility of expanding the application of this technology both in clinical investigations and basic science.

Alterations of the serum peptidome in renal cell carcinoma discriminating benign and malignant kidney tumors

Renal cell carcinoma (RCC) is typically asymptomatic and surgery usually increases patients life only for early stage tumors. However, some cystic and solid renal lesions cannot be confidently differentiated from clear-cell-RCC. Therefore possible markers for early detection and to distinguish malignant kidney tumors are needed. To this aim, we applied MALDI-TOF and LC-MS/MS analysis to RPC18 MB purified serum of ccRCC, non-ccRCC patients and controls. A cluster of five signals differentiate malignant tumors from benign renal masses and healthy subjects. Moreover, a combination of six ions showed the highest specificity and sensitivity to distinguish ccRCC from controls. Healthy subjects were also differentiated from non-ccRCC by three features. Peptide ratios obtained by MALDI-TOF were compared with those from label-free LC-ESI and no statistical difference was found (p>0.05). ESI-results were linked with MALDI profiles by both TOF/TOF sequencing and MALDI FT-ICR accurate mass measurements. About 200 unique endogenous peptides, originating from 32 proteins, were identified. Among them, SDPR and ZYX were found down-expressed, while SRGN and TMSL3 were up-expressed. In conclusion, our results suggest the possibility to discriminate malignant kidney tumors based on a cluster of serum peptides. Moreover, label-free approach may represent a valid method to verify results obtained by MALDI-TOF. This article is part of a Special Issue entitled: Integrated omics.

Serum biomarkers of Renal Cell Carcinoma assessed using a protein profiling approach based on ClinProt technique

OBJECTIVES To investigate the possibility of using the ClinProt technique to find serum cancer related diagnostic markers that are able to better discriminate healthy subjects from patients affected by renal cell carcinoma (ccRCC). Renal cell carcinoma is the most common malignancy of the kidney. Biomarkers for early detection, prognosis, follow-up, and differential diagnosis of ccRCC from benign renal lesions are needed in daily clinical practice when imaging is not helpful. METHODS Serum of 29 healthy subjects and 33 ccRCC patients was analyzed by the ClinProt/MALDI-ToF technique. RESULTS A cluster of 3 peptides (A = m/z 1083 +/- 8 Da, B = m/z 1445 +/- 8 Da and C = m/z 6879 +/- 8 Da) was able to discriminate patients from control subjects. Cross-validation analysis using the whole casistic showed 88% and 96% of sensitivity and specificity, respectively. Moreover, the cluster showed 100% sensitivity for the identification of patients at pT2 (n = 5) and pT3 (n = 8) and 85% for pT1 patients (n = 20). The intensity of peaks A and C continuously decreased from pT1 to pT3, whereas peak B increased in pT1 and pT2. CONCLUSIONS These results may be useful to set up new diagnostic or prognostic tools.

Biomarkers discovery by peptide and protein profiling in biological fluids based on functionalized magnetic beads purification and mass spectrometry

Proteomics aims for the full identification and quantification of all expressed proteins in any organism. This is however an extremely tedious task since one gene often accounts for multiple proteins due to gene splicing and processing of proteins, such as the addition of post-translational modifications. Moreover, the concentration range of occurring proteins varies more than a factor of one million. For these reasons, protein profiling was considered a promising technique in the early days of proteomics. Ideally a protein profile can be observed in one single measurement. In various clinical studies profiling methods have been successful in the detection of proteome variations as a consequence of an altered homeostasis. Proteins that are differentially expressed as a consequence of a disease are very useful in medical science as they can be used as new biomarkers for the diagnosis, prognosis and as possible therapeutic targets. In order to find such proteins or biomarkers two different kinds of biological material have been used: tissue samples and body fluids. Tissues are obtained from biopsies, from stable cell lines or cell cultures, or from subcellular fractions. Despite their large usage tissues suffer from several disadvantages. Tissue samples are difficult to obtain and are comprised of several different type of cells. Standardization of the methods to obtain subcellular fraction that affects its preparation and purity is a challenge not yet solved. The difference between a cell culture and its corresponding wild type present in the body limits the translation of information derived from the first to the latter. On the contrary body fluids do not suffer from these limitations inherent to tissue samples. Fluids are very easily accessible with non- or very low-invasive methods at relatively low cost. They perfuse all the organs in the body carrying secreted protein from tissues. Therefore the protein profile of the biological fluids can reflect the status of the body. Among biological fluids serum, plasma and urine are the most analyzed samples but also cerebrospinal fluid (CSF), saliva, amniotic fluids have been used. Moreover classical methods to investigate the tissue proteome, aiming at biomarker discovery, are generally based on two-dimensional electrophoresis (2DE) and are not suitable for clinical chemistry lab requirements in which large sample cohorts have to be analyzed in a short time. This addresses another great potential of body fluids profiling: the analysis can be carried out high-throughput without sacrificing robustness and quality of the method. In fact 2DE is a laborious process that is difficult to automate. It still suffers from several technical limitations in terms of repeatability and reproducibility even though progress has been made using three different fluorescent labels that enables simultaneous migration of three samples on the same gel (e.g proteins extracted from control and disease, and the internal standard).Since the beginning of the 1990ties, when this new term (proteomics) was coined, a lot of progress has been made. Among them, several strategies to search these biomarkers in biological fluids have been developed in order to try to tackle some of the limitations of the current methods. Nowadays, mass spectrometry (MS) is the method of choice for the analysis of proteins, and as a consequence the field is now often referred to as MS-based proteomics. Direct analysis of the biological fluids with mass spectrometry is a challenging approach due to the sample complexity. To carry out a repeatable and robust mass spectrometric analysis of proteins in body fluids a suitable clean-up procedure is required in which salts and detergents are removed. The presence of salts can suppress the ionization in the mass spectrometer and chromatographic profiles may be influenced by from tailing due to co-elution of contaminants1. Therefore a pre-fractionation of the fluids is essential in order to increase the number of proteins that can be detected within a single MS-experiment, thus facilitating the discovery of new markers. Moreover, the fractionation of the biological fluids will also enrich low abundant proteins in fractions. These approaches lead to build the protein profile of the different biological fluids. Variations observed in patient profiles of body fluids compared to those of controls can be used to find the best pattern of signals that allows to discriminate two populations or to stratify the patients according to tumour stage or to the response to the therapy. One the major advantages of this strategy is that no pre-knowledge of the identity of signals selected for the cluster is needed to allow their use as biomarkers2. A specific agent to capture proteins enriches the sample and thus contributes to sensitivity enhancement. In general, protein separation techniques are based on different protein physical properties, such as size, isoelectric point, solubility and affinity. Materials known from different chromatographic platforms are coupled to the surface of a carrier in order to obtain peptides and proteins. One of the first approaches to pre-fractionate the body fluid proteome using an activated surface was the Surface-Enhanced Laser Desorption/Ionization (SELDI) technique. The SELDI technique for protein profiling is probably the most known and widely used approach in which biological fluids are applied directly to a target plate that is later introduced into a mass spectrometer. After removing unbound material to the modified surface of the SELDI chip, the molecular weight of the captured proteins on the target plate is determined using a time-of-flight (TOF) mass analyzer3. In this way the body fluid protein profile for the studied population is obtained. This technology is not free of criticism. In particular not very good reproducibility of the results due to drift, noise or the use of different lots of chips are reported. Moreover the direct identification of these markers cannot be carried out using the SELDI-TOF system. Their identity has to be determined with different analytical approaches. Promising alternatives to this technology are based on magnetic beads with a functionalized or activated surface or on miniaturized chromatographic systems that allow off-line fractionation of the proteome present in the fluids before MS analysis. The combination of magnetic bead purification and matrix-assisted laser desorption ionization (MALDI) TOF-MS has been shown a powerful alternative to the SELDI-platform: the active surface of magnetic beads is much larger, resulting in a higher binding capacity, and identification of captured peptides and protein is possible through the use of a more advanced TOF mass analyzer. Moreover, only a small part of the eluted peptide/proteins fractions are used for the protein profile and the remaining sample can be to use to identify markers with other MS-approaches (e.g. MALDI-TOF/TOF or LC-ESI-MS/MS) without the need of additional purification. This review is mainly focussed on the pre-fractionation based on magnetic beads and their applications.

Different expression of fibrinopeptide A and related fragments in serum of type 1 diabetic patients with nephropathy.

Type 1 diabetes (insulin-dependent diabetes mellitus, IDDM) is an autoimmune disease affecting about 0.12% of the worlds population. Diabetic nephropathy (DN) is a major long-term complication of both types of diabetes and retains a high human, social and economic cost. Thus, the identification of markers for the early detection of DN represents a relevant target of diabetic research. The present work is a pilot study focused on proteomic analysis of serum of controls (n=9), IDDM patients (n=10) and DN patients (n=4) by the ClinProt profiling technology based on mass spectrometry. This approach allowed to identify a pattern of peptides able to differentiate the studied populations with sensitivity and specificity close to 100%. Variance of the results allowed to estimate the sample size needed to keep the expected False Discovery Rate low. Moreover, three peptides differentially expressed in the serum of patients as compared to controls were identified by LC-ESI MS/MS as the whole fibrinopeptide A peptide and two of its fragments, respectively. The two fragments were under-expressed in diabetic patients, while Fibrinopeptide A was over-expressed, suggesting that anomalous turnover of Fibrinopeptide A could be involved in the pathogenesis of DN.

Urinary signatures of renal cell carcinoma investigated by peptidomic approaches

Renal Cell Carcinoma (RCC) is typically asymptomatic and surgery usually increases patients lifespan only for early stage tumours. Moreover, solid renal masses cannot be confidently differentiated from RCC. Therefore, markers to distinguish malignant kidney tumours and for their detection are needed. Two different peptide signatures were obtained by a MALDI-TOF profiling approach based on urine pre-purification by C8 magnetic beads. One cluster of 12 signals could differentiate malignant tumours (n = 137) from benign renal masses and controls (n = 153) with sensitivity of 76% and specificity of 87% in the validation set. A second cluster of 12 signals distinguished clear cell RCC (n = 118) from controls (n = 137) with sensitivity and specificity values of 84% and 91%, respectively. Most of the peptide signals used in the two models were observed at higher abundance in patient urines and could be identified as fragments of proteins involved in tumour pathogenesis and progression. Among them: the Meprin 1α with a pro-angiogenic activity, the Probable G-protein coupled receptor 162, belonging to the GPCRs family and known to be associated with several key functions in cancer, the Osteopontin that strongly correlates to tumour stages and invasiveness, the Phosphorylase b kinase regulatory subunit alpha and the SeCreted and TransMembrane protein 1.

A Mutual Information Approach to Data Integration for Alzheimer’s Disease Patients

Clinical data alignment plays a critical role in identifying important features for significant experiments. A central problem is data fusion i.e., how to correctly integrate data provided by different labs. This integration is done in order to increase ability of inferring target classes of controls and patients. Our paper proposes an approach based both on a information theoretic perspective, generally used in a feature construction problem [3] and on the approximated solution for a mathematical programming task (i.e. the weighted bipartite matching problem [6]). Numerical evaluations with two competitive approaches show the improved performance of the proposed method. For this evaluation we used data sets from plasma / ethylenediaminetetraacetic acid (EDTA) of controls and Alzheimer patients collected in three different hospitals.

Robust Conclusions in Mass Spectrometry Analysis

Abstract A central issue in biological data analysis is that uncertainty, resulting from different factors of variability, may change the effect of the events being investigated. Therefore, robustness is a fundamental step to be considered. Robustness refers to the ability of a process to cope well with uncertainties, but the different ways to model both the processes and the uncertainties lead to many alternative conclusions in the robustness analysis. In this paper we apply a framework allowing to deal with such questions for mass spectrometry data. Specifically, we provide robust decisions when testing hypothesis over a case/control population of subject measurements (i.e. proteomic profiles). To this concern, we formulate (i) a reference model for the observed data (i.e., graphs), (ii) a reference method to provide decisions (i.e., test of hypotheses over graph properties) and (iii) a reference model of variability to employ sources of uncertainties (i.e., random graphs). We apply these models to a realcase study, analyzing the mass spectrometry profiles of the most common type of Renal Cell Carcinoma; the Clear Cell variant.

Poster: Characterization of distinguishing regions for Renal Cell Carcinoma discrimination

Summary form only given: Mass Spectrometry (MS)-based technologies represent a promising area of research in clinical analysis. They are primarily concerned with measuring the relative intensity (i.e., signals) of many protein/peptide molecules associated with their mass-to-charge ratios. These measurements provide a huge amount of information which requires adequate tools to be interpreted. Following the methodology for testing hypotheses, we investigate the proteomic signals of the most common type of Renal Cell Carcinoma, the Clear Cell variant (ccRCC). By using mutual information, we detect changes in dependence values between signals from control to case groups (ccRCC or non-ccRCC). To this end, we sample and represent each population group through graphs, thus providing the observed dependence structures (many real domains are best described by relational models). This way, graphs establish abstract frames of reference in our analysis giving the opportunity to test hypotheses over their properties. In other words, changes are detected by testing graph property modifications from group to group. We report the mass-to-charge values which identify bounded regions where changes have been detected. The main interest in handling such regions is to perceive which signal ranges are associated with some specific factors of interest (e.g., studying differentially expressed peaks between cases and controls) and thus, to suggest potential biomarkers for future analysis. This study has been applied to samples collected at the “Ospedale Maggiore Policlinico” Foundation (Milano, Italy) using a standardized protocol. All samples were analyzed using an UltraFlex II MALDI-TOF/TOF MS instrument and mass spectra were acquired in the m=z range of 1000-12000. The samples cohort consists of 85 control subjects and 102 Renal Cell Carcinoma patients. It was possible to classify pathological group in patients affected by clear cell (ccRCC) and other different histological subtypes (respectively 79 ccRCC and 23 non-ccRCC). Table I reports the selected rejection regions (i.e., tests reject the null) at the 5% significance level. Testing hypotheses suggested by the data may induce statistical bias. For this reason, we evaluate the results to independent samples. We investigate whether test decisions are statistically independent from the regions property (i.e., distinguishing (DR) or non-distinguishing (ND) regions) when new samples are given. In other words, we want to know whether the property of a region can be statistically associated to test decisions when new samples are available. After that a new sample is provided, we verify test decisions over both the detected distinguishing regions and these regions out of the m=z bounding values previously detected. Table II summarizes the (Fishers exact test) results confirming a significant association (α = 0.05 level) between decisions and regions property for both the class of tests. This work was supported by grants from the Italian Ministry of University and Research (PRIN n. 69373, FIRB n. RBRN07BMCT 011, FAR 2006-2011), EuroKUP COST Action (BM0702) and the NEDD project (“Regione Lombardia”).