Gabriele De Sio

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.

An alternative approach in endocrine pathology research: MALDI-IMS in papillary thyroid carcinoma

To the Editor, Many different molecular techniques (polymerase chain reaction (PCR), DNA sequencing, fluorescence in situ hybridization (FISH)) have been introduced in thyroid pathology [1]. Fewer studies evaluated the role of proteomic analysis in the research of new useful targets [2, 3]. Matrixassisted laser desorption/ionization (MALDI) imaging mass spectrometry (IMS) is a unique technology that explores the spatial distribution of biomolecules directly in situ, thus integrating molecular and morphological information. Therefore, we are investigating the potential role of MALDIIMS in detecting new diagnostic targets in papillary thyroid carcinoma (PTC). We have addressed the issue of molecular stratification of PTC in a small cohort of samples, evaluating both the genomic profile of the main genes of interest (BRAF, N-H-K RAS point mutations, and PAX8/PPARγ or RET/PTC rearrangements) and the proteomic profile shown byMALDIIMS analysis. We have collected ex vivo cytological specimens (see “Technical Note”), in order to analyse a sample perfectly mimicking the in vivo fine-needle aspiration (FNA) biopsy, while respecting the ethical requirements. Unsupervised proteomic analysis of the samples was followed by comparison with histopathology and genetic classification of the patients (Table 1). Data generated by MALDI-IMS were submitted to hierarchical cluster analysis (HCA), in order to evaluate the different proteomic expressions in the cases under study. HCA allowed to cluster proteomic spectra based on their similarity on a dendrogram: spectra showing comparable features were grouped under the same node of the dendrogram; then, it was possible to select nodes and assign them a specific colour (Fig. 1) [4]. Node A groups together three cytological specimens (two collected from patient 1 and one from patient 2), all histologically diagnosed as hyperplastic benign nodules. Node B groups together specimens collected from patients 3, 4 and 5, who were affected by PTC comparable for stage and histotype (Table 1). Node C, instead, groups a microcarcinoma (follicular variant (fv) of PTC), from patient 6, and a nodule histologically classified as uncertain malignant potential (UMP) tumour (patient 7). The last one, originated in a multinodular goitre environment and showed ambiguous morphological features, defined as borderline between a hyperplastic nodule and an “incipient” fv,PTC. The three distinct nodes (A, B, C) generated by HCA analysis confirmed that MALDI-IMS can potentially discriminate between benign and malignant thyroid lesions. Moreover, the homologies between cases 6 and 7 highlight that MALDI-IMS is able to identify a PTC even when the classic diagnostic morphological aspects are still unclear and ambiguous (mild nuclear clearing, rare grooves, no pseudoinclusions). This finding is in agreement Veronica Mainini and Fabio Pagni equally contributed to this paper.

α‐1‐antitrypsin detected by MALDI‐Imaging in the study of glomerulonephritis: its relevance in chronic kidney disease progression

Idiopathic glomerulonephritis (GN), such as membranous glomerulonephritis, focal segmental glomerulosclerosis (FSGS), and IgA nephropathy (IgAN), represent the most frequent primary glomerular kidney diseases (GKDs) worldwide. Although the renal biopsy currently remains the gold standard for the routine diagnosis of idiopathic GN, the invasiveness and diagnostic difficulty related with this procedure highlight the strong need for new diagnostic and prognostic biomarkers to be translated into less invasive diagnostic tools. MALDI‐MS imaging MALDI‐MSI was applied to fresh‐frozen bioptic renal tissue from patients with a histological diagnosis of FSGS (n = 6), IgAN, (n = 6) and membranous glomerulonephritis (n = 7), and from controls (n = 4) in order to detect specific molecular signatures of primary glomerulonephritis. MALDI‐MSI was able to generate molecular signatures capable to distinguish between normal kidney and pathological GN, with specific signals (m/z 4025, 4048, and 4963) representing potential indicators of chronic kidney disease development. Moreover, specific disease‐related signatures (m/z 4025 and 4048 for FSGS, m/z 4963 and 5072 for IgAN) were detected. Of these signals, m/z 4048 was identified as α‐1‐antitrypsin and was shown to be localized to the podocytes within sclerotic glomeruli by immunohistochemistry. α‐1‐Antitrypsin could be one of the markers of podocyte stress that is correlated with the development of FSGS due to both an excessive loss and a hypertrophy of podocytes.

Proteomics in thyroid cytopathology: Relevance of MALDI-imaging in distinguishing malignant from benign lesions.

Several proteomic strategies are used extensively for the purpose of biomarker discovery and in order to obtain insights into the molecular aspects of cancers, using either body fluids or tissue as samples. Among them, MALDI‐imaging can be applied to cytological thyroid specimens to investigate the molecular signatures of different pathological conditions and highlight differences in the proteome that are of relevance for diagnostic and pathogenetic research. In this study, 26 ex‐vivo fine needle aspirations from benign thyroid nodules (n = 13) and papillary thyroid carcinomas (n = 13) were analyzed by MALDI‐imaging. Based on the specific protein signatures capable of distinguishing the aforementioned patients, MALDI‐imaging was able to correctly assign, in blind, the specimens from ten additional FNABs to a malignant or benign class, as later confirmed by the morphological classification. Moreover, some proteins presented a progressive overexpression in malignant phenotypes when compared with Hashimotos thyroiditis and hyperplastic/follicular adenoma. This data not only suggests that a MALDI‐imaging based approach can be a valuable tool in the diagnosis of thyroid lesions but also in the detection of proteins that have a possible role in the promotion of tumorigenic activity.

Proteomic profiles of thyroid tumors by mass spectrometry-imaging on tissue microarrays.

The current study proposes the successful use of a mass spectrometry-imaging technology that explores the composition of biomolecules and their spatial distribution directly on-tissue to differentially classify benign and malignant cases, as well as different histotypes. To identify new specific markers, we investigated with this technology a wide histological Tissue Microarray (TMA)-based thyroid lesion series. Results showed specific protein signatures for malignant and benign specimens and allowed to build clusters comprising several proteins with discriminant capabilities. Among them, FINC, ACTB1, LMNA, HSP7C and KAD1 were identified by LC-ESI-MS/MS and found up-expressed in malignant lesions. These findings represent the opening of further investigations for their translation into clinical practice, e.g. for setting up new immunohistochemical stainings, and for a better understanding of thyroid lesions. This article is part of a Special Issue entitled: MALDI Imaging, edited by Dr. Corinna Henkel and Prof. Peter Hoffmann.

Intraluminal proteome and peptidome of human urinary extracellular vesicles.

Urinary extracellular vesicles (UEVs) are a novel source for disease biomarker discovery. However, Tamm–Horsfall protein (THP) is still a challenge for proteomic analysis since it can inhibit detection of low‐abundance proteins. Here, we introduce a new approach that does not involve an ultracentrifugation step to enrich vesicles and that reduces the amount of THP to manageable levels.

A Support Vector Machine Classification of Thyroid Bioptic Specimens Using MALDI-MSI Data

Biomarkers able to characterise and predict multifactorial diseases are still one of the most important targets for all the “omics” investigations. In this context, Matrix-Assisted Laser Desorption/Ionisation-Mass Spectrometry Imaging (MALDI-MSI) has gained considerable attention in recent years, but it also led to a huge amount of complex data to be elaborated and interpreted. For this reason, computational and machine learning procedures for biomarker discovery are important tools to consider, both to reduce data dimension and to provide predictive markers for specific diseases. For instance, the availability of protein and genetic markers to support thyroid lesion diagnoses would impact deeply on society due to the high presence of undetermined reports (THY3) that are generally treated as malignant patients. In this paper we show how an accurate classification of thyroid bioptic specimens can be obtained through the application of a state-of-the-art machine learning approach (i.e., Support Vector Machines) on MALDI-MSI data, together with a particular wrapper feature selection algorithm (i.e., recursive feature elimination). The model is able to provide an accurate discriminatory capability using only 20 out of 144 features, resulting in an increase of the model performances, reliability, and computational efficiency. Finally, tissue areas rather than average proteomic profiles are classified, highlighting potential discriminating areas of clinical interest.

MP85-19 URINARY PEPTIDOME AND PROTEOME ALTERATIONS RELATED TO TUMOR PROGRESSION AND INVASION IN RCC

renal cell carcinoma (RCC) patients and to evaluate their validity regarding their diagnostic and outcome potential. METHODS: The study cohort included 40 healthy controls and 229 RCC patients (145 without metastases before nephrectomy, 84 with metastases before/during targeted therapy). cfDNA was isolated (1 ml EDTA plasma;QIAamp Circulating Nucleic Acid Kit) and qPCR analyses of genomic cfDNA of various gene fragments of amyloid beta (A4) precursor protein (67, 180, 306 bp) and ALU sequences (248/79 bp) and of mitochondrial cfDNA fragments (175/65 bp) were performed. RESULTS: The concentrations of the single cfDNA markers partially differed between the study groups while the five integrity indices (long to short fragments) differed between controls and metastatic patients, and two APP indices also to non-metastatic patients. Optimized combinations of different genomic and mitochondrial fragments (binary logistic regression) with two or three assays resulted in areas under the ROC curve of 1⁄40.78. In multivariate Cox regression analyses, optimized models for recurrence-free and overall survival were established using the prognostic potential of the individual cfDNA variables combined with the clinicopathological variables. Harrell’s C-indices showed distinctly improved prognostic capabilities of these models in comparison to models based only on clinicopathological variables (increase:from 0.725 to 0.827; 0.701 to 0.836). CONCLUSIONS: Combinations of genomic and mitochondrial cfDNA are promising tools for urologists in the management of ccRCC patients both in diagnosis and prognosis.