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.

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.

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 Hashimoto's 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.

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.

Tumor size, stage and grade alterations of urinary peptidome in RCC.

BACKGROUND: Several promising biomarkers have been found for RCC, but none of them has been used in clinical practice for predicting tumour progression. The most widely used features for predicting tumour aggressiveness still remain the cancer stage, size and grade. Therefore, the aim of our study is to investigate the urinary peptidome to search and identify peptides whose concentrations in urine are linked to tumour growth measure and clinical data. METHODS: A proteomic approach applied to ccRCC urinary peptidome (n = 117) based on prefractionation with activated magnetic beads followed by MALDI-TOF profiling was used. A systematic correlation study was performed on urinary peptide profiles obtained from MS analysis. Peptide identity was obtained by LC-ESI-MS/MS. RESULTS: Fifteen, twenty-six and five peptides showed a statistically significant alteration of their urinary concentration according to tumour size, pT and grade, respectively. Furthermore, 15 and 9 signals were observed to have urinary levels statistically modified in patients at different pT or grade values, even at very early stages. Among them, C1RL, A1AGx, ZAG2G, PGBM, MMP23, GP162, ADA19, G3P, RSPH3, DREB, NOTC2 SAFB2 and CC168 were identified. CONCLUSIONS: We identified several peptides whose urinary abundance varied according to tumour size, stage and grade. Among them, several play a possible role in tumorigenesis, progression and aggressiveness. These results could be a useful starting point for future studies aimed at verifying their possible use in the managements of RCC patients.

Proteome analysis in thyroid pathology.

The incidence of thyroid cancer has continuously increased due to its detection in the preclinical stage. Clinical research in thyroid pathology is focusing on the development of new diagnostic tools to improve the stratification of nodules that have biological, practical and economic consequences on the management of patients. Several clinical questions related to thyroid carcinoma remain open and the use of proteomic research in the hunt for new targets with potential diagnostic applications has an important role in the solutions. Many different proteomic approaches are used to investigate thyroid lesions, including mass spectrometry profiling and imaging technologies. These approaches have been applied to different human tissues (cytological specimens, frozen sections, formalin-fixed paraffin embedded tissue or Tissue Micro Arrays). Moreover, other specimens are used for biomarker discovery, such as cell lines and the secretome. Alternative approaches, such as metabolomics and lipidomics, are also used and integrated within proteomics.

Intraluminal proteome and peptidome of human urinary extracellular vesicles.

PURPOSE: 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. EXPERIMENTAL DESIGN: UEVs were dialyzed and ultrafiltered after reduction and alkylation. The retained fraction was digested with trypsin to reduce the remaining THP and incubated with deoxycholate (DOC). The internal peptidome and internal proteome were analyzed by LC-ESI-MS. RESULTS: A total of 942 different proteins and 3115 unique endogenous peptide fragments deriving from 973 different protein isoforms were identified. Around 82% of the key endosomal sorting complex required for transport components of UEVs generation could be detected from the intraluminal content. CONCLUSIONS AND CLINICAL RELEVANCE: Our UEVs preparation protocol provides a simplified way to investigate the intraluminal proteome and peptidome, in particular the subpopulation of UEVs of the trypsin-resistant class of exosomes (positive for tumor susceptibility gene101) and eliminates the majority of interfering proteins such as THP. This method allows the possibility to study endoproteome and endopeptidome of UEVs, thus greatly facilitating biomarker discovery.

Urinary signatures of Renal Cell Carcinoma investigated by peptidomic approaches.

Renal Cell Carcinoma (RCC) is typically asymptomatic and surgery usually increases patient's 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.