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

AI-Assisted High-Throughput Tissue Microarray Workflow

Immunohistochemical (IHC) studies of formalin-fixed paraffin-embedded (FFPE) samples are a gold standard in oncology for tumor characterization, and the identification of prognostic and predictive markers. However, despite the abundance of archived FFPE samples, their research use is limited due to the labor-intensive nature of IHC on large cohorts. This study aimed to create a high-throughput workflow using modern technologies to facilitate IHC biomarker studies on large patient groups. Semiautomatic constructed tissue microarrays (TMAs) were created for two tumor patient cohorts and IHC stained for seven antibodies (ABs). AB expression in the tumor and surrounding stroma was quantified using the AI-supported image analysis software QuPath. The data were correlated with clinicopathological information using an R-script, all results were automatically compiled into formatted reports. By minimizing labor time to 7.7%-compared to whole-slide studies-the established workflow significantly reduced human and material resource consumption. It successfully correlated AB expression with overall patient survival and additional clinicopathological data, providing publication-ready figures and tables. The AI-assisted high-throughput TMA workflow, validated on two patient cohorts, streamlines modern histopathological research by offering cost and time efficiency compared to traditional whole-slide studies. It maintains research quality and preserves patient tissue while significantly reducing material and human resources, making it ideal for high-throughput research centers and collaborations.

Full-Automatic High-Efficiency Mueller Matrix Microscopy Imaging for Tissue Microarray Inspection

This paper proposes a full-automatic high-efficiency Mueller matrix microscopic imaging (MMMI) system based on the tissue microarray (TMA) for cancer inspection for the first time. By performing a polar decomposition on the sample's Mueller matrix (MM) obtained by a transmissive MMMI system we established, the linear phase retardance equivalent waveplate fast-axis azimuth and the linear phase retardance are obtained for distinguishing the cancerous tissues from the normal ones based on the differences in their polarization characteristics, where three analyses methods including statistical analysis, the gray-level co-occurrence matrix analysis (GLCM) and the Tamura image processing method (TIPM) are used. Previous MMMI medical diagnostics typically utilized discrete slices for inspection under a high-magnification objective (20×-50×) with a small field of view, while we use the TMA under a low-magnification objective (5×) with a large field of view. Experimental results indicate that MMMI based on TMA can effectively analyze the pathological variations in biological tissues, inspect cancerous cervical tissues, and thus contribute to the diagnosis of postoperative cancer biopsies. Such an inspection method, using a large number of samples within a TMA, is beneficial for obtaining consistent findings and good reproducibility.

Differential Protein Expression in Response to Varlitinib Treatment in Oral Cancer Cell Line: an In Vitro Therapeutic Approach

Epidermal growth factor receptor (EGFR) is the most frequently overexpressed receptor histologically exhibited by oral squamous cell carcinoma (OSCC) patients. Aberrated EGFR signaling may lead to recurrence and metastasis, thus laying the foundation of targeted therapy. Deactivating EGFR is likely to prevent downstream signaling thus resulting in apoptosis. Tyrosine kinase inhibitors (TKIs) have come into play to revert aggressiveness of OSCC. We exploited comparative proteomic analyses based on anti-EGFR potential of varlitinib, using cellular proteomes from treated and untreated groups of oral cancer cells to identify protein players functional during oral carcinogenesis. Following separation by two-dimensional electrophoresis, differentially expressed cellular proteins (varlitinib-treated and untreated cells) were analyzed and later identified using QTOF mass spectrometer. In silico analysis for protein-protein interaction was carried out using STRING. Six differentially expressed proteins were identified as binding immunoglobulin protein (BiP), heat shock protein 7 C (HSP7C), protein disulfide isomerase 1 A (PDIA1), vimentin (VIME), keratin type I cytoskeletal 14 (K1C14), and β-Actin (ACTB). Relative expression of five proteins was found to be downregulated upon varlitinib treatment, whereas only K1C14 was upregulated in treated cells compared to control. Protein network analysis depicts the interaction between BiP, PDIA1, VIME, etc. indicating their role in oral carcinogenesis. Oral cancer cells show proteome shift based on varlitinib treatment compared to corresponding controls. Our data suggest candidature of varlitinib as a potent therapeutic agent and BiP, PDIA1, HSP7C, VIME, and β-Actin as complementary/prognostic markers of OSCC.

Paving the path toward multi-omics approaches in the diagnostic challenges faced in thyroid pathology

INTRODUCTION

Despite advancements in diagnostic methods, the classification of indeterminate thyroid nodules still poses diagnostic challenges not only in pre-surgical evaluation but even after histological evaluation of surgical specimens. Proteomics, aided by mass spectrometry and integrated with artificial intelligence and machine learning algorithms, shows great promise in identifying diagnostic markers for thyroid lesions.

AREAS COVERED

This review provides in-depth exploration of how proteomics has contributed to the understanding of thyroid pathology. It discusses the technical advancements related to immunohistochemistry, genetic and proteomic techniques, such as mass spectrometry, which have greatly improved sensitivity and spatial resolution up to single-cell level. These improvements allowed the identification of specific protein signatures associated with different types of thyroid lesions.

EXPERT COMMENTARY

Among all the proteomics approaches, spatial proteomics stands out due to its unique ability to capture the spatial context of proteins in both cytological and tissue thyroid samples. The integration of multi-layers of molecular information combining spatial proteomics, genomics, immunohistochemistry or metabolomics and the implementation of artificial intelligence and machine learning approaches, represent hugely promising steps forward toward the possibility to uncover intricate relationships and interactions among various molecular components, providing a complete picture of the biological landscape whilst fostering thyroid nodule diagnosis.

Applications of spatially resolved omics in the field of endocrine tumors

Endocrine tumors derive from endocrine cells with high heterogeneity in function, structure and embryology, and are characteristic of a marked diversity and tissue heterogeneity. There are still challenges in analyzing the molecular alternations within the heterogeneous microenvironment for endocrine tumors. Recently, several proteomic, lipidomic and metabolomic platforms have been applied to the analysis of endocrine tumors to explore the cellular and molecular mechanisms of tumor genesis, progression and metastasis. In this review, we provide a comprehensive overview of spatially resolved proteomics, lipidomics and metabolomics guided by mass spectrometry imaging and spatially resolved microproteomics directed by microextraction and tandem mass spectrometry. In this regard, we will discuss different mass spectrometry imaging techniques, including secondary ion mass spectrometry, matrix-assisted laser desorption/ionization and desorption electrospray ionization. Additionally, we will highlight microextraction approaches such as laser capture microdissection and liquid microjunction extraction. With these methods, proteins can be extracted precisely from specific regions of the endocrine tumor. Finally, we compare applications of proteomic, lipidomic and metabolomic platforms in the field of endocrine tumors and outline their potentials in elucidating cellular and molecular processes involved in endocrine tumors.

Identification of potential diagnostic and prognostic biomarkers for papillary thyroid microcarcinoma (PTMC) based on TMT-labeled LC-MS/MS and machine learning

OBJECTIVES

To explore the molecular mechanisms underlying aggressive progression of papillary thyroid microcarcinoma and identify potential biomarkers.

METHODS

Samples were collected and sequenced using tandem mass tag-labeled liquid chromatography-tandem mass spectrometry. Differentially expressed proteins (DEPs) were identified and further analyzed using Mfuzz and protein-protein interaction analysis (PPI). Parallel reaction monitoring (PRM) and immunohistochemistry (IHC) were performed to validate the DEPs.

RESULTS

Five thousand, two hundred and three DEPs were identified and quantified from the tumor/normal comparison group or the N1/N0 comparison group. Mfuzz analysis showed that clusters of DEPs were enriched according to progressive status, followed by normal tissue, tumors without lymphatic metastases, and tumors with lymphatic metastases. Analysis of PPI revealed that DEPs interacted with and were enriched in the following metabolic pathways: apoptosis, tricarboxylic acid cycle, PI3K-Akt pathway, cholesterol metabolism, pyruvate metabolism, and thyroid hormone synthesis. In addition, 18 of the 20 target proteins were successfully validated with PRM and IHC in another 20 paired validation samples. Based on machine learning, the five proteins that showed the best performance in discriminating between tumor and normal nodules were PDLIM4, ANXA1, PKM, NPC2, and LMNA. FN1 performed well in discriminating between patients with lymph node metastases (N1) and N0 with an AUC of 0.690. Finally, five validated DEPs showed a potential prognostic role after examining The Cancer Genome Atlas database: FN1, IDH2, VDAC1, FABP4, and TG. Accordingly, a nomogram was constructed whose concordance index was 0.685 (confidence interval: 0.645-0.726).

CONCLUSIONS

PDLIM4, ANXA1, PKM, NPC2, LMNA, and FN1 are potential diagnostic biomarkers. The five-protein nomogram could be a prognostic biomarker.

The evolving landscape of anatomic pathology

Anatomic pathology has changed dramatically in recent years. Although the microscopic assessment of tissues and cells is and will remain the mainstay of cancer diagnosis molecular profiling has become equally relevant. Thus, to stay abreast of the evolving landscape of today's anatomic pathology, modern pathologists must be able to master the intricate world of predictive molecular pathology. To this aim, pathologists have had to acquire additional knowledge to bridge the gap between clinicians and molecular biologists. This new role is particularly important, as cases are now collegially discussed in molecular tumor boards (MTBs). Moreover, as opposed to traditional pathologists, modern pathologists have also adamantly embraced innovation while keeping a constant eye on tradition. In this article, we depict the highlights and shadows of the upcoming "Anatomic Pathology 2.0" by placing particular emphasis on the pathologist's growing role in the management of cancer patients.

Molecular Pathological Diagnosis of Thyroid Tumors Using Spatially Resolved Metabolomics

The pathological diagnosis of benign and malignant follicular thyroid tumors remains a major challenge using the current histopathological technique. To improve diagnosis accuracy, spatially resolved metabolomics analysis based on air flow-assisted desorption electrospray ionization mass spectrometry imaging (AFADESI-MSI) technique was used to establish a molecular diagnostic strategy for discriminating four pathological types of thyroid tumor. Without any specific labels, numerous metabolite features with their spatial distribution information can be acquired by AFADESI-MSI. The underlying metabolic heterogeneity can be visualized in line with the cellular heterogeneity in native tumor tissue. Through micro-regional feature extraction and in situ metabolomics analysis, three sets of metabolic biomarkers for the visual discrimination of benign follicular adenoma and differentiated thyroid carcinomas were discovered. Additionally, the automated prediction of tumor foci was supported by a diagnostic model based on the metabolic profile of 65 thyroid nodules. The model prediction accuracy was 83.3% when a test set of 12 independent samples was used. This diagnostic strategy presents a new way of performing in situ pathological examinations using small molecular biomarkers and provides a model diagnosis for clinically indeterminate thyroid tumor cases.

Age-related long-term response in rat thyroid tissue and plasma after internal low dose exposure to 131I

131I is used clinically for therapy, and may be released during nuclear accidents. After the Chernobyl accident papillary thyroid carcinoma incidence increased in children, but not adults. The aims of this study were to compare 131I irradiation-dependent differences in RNA and protein expression in the thyroid and plasma of young and adult rats, and identify potential age-dependent biomarkers for 131I exposure. Twelve young (5 weeks) and twelve adult Sprague Dawley rats (17 weeks) were i.v. injected with 50 kBq 131I (absorbed dose to thyroid = 0.1 Gy), and sixteen unexposed age-matched rats were used as controls. The rats were killed 3-9 months after administration. Microarray analysis was performed using RNA from thyroid samples, while LC-MS/MS analysis was performed on proteins extracted from thyroid tissue and plasma. Canonical pathways, biological functions and upstream regulators were analysed for the identified transcripts and proteins. Distinct age-dependent differences in gene and protein expression were observed. Novel biomarkers for thyroid 131I exposure were identified: (PTH), age-dependent dose response (CA1, FTL1, PVALB (youngsters) and HSPB6 (adults)), thyroid function (Vegfb (adults)). Further validation using clinical samples are needed to explore the role of the identified biomarkers.

Complement C4-A and Plasminogen as Potential Biomarkers for Prediction of Papillary Thyroid Carcinoma

BACKGROUND

Early diagnosis and therapy of papillary thyroid carcinoma (PTC) is essential for reducing recurrence and improving the long-term survival. In this study, we aimed to investigate the proteome profile of plasma and screen unique proteins which could be used as a biomarker for predicting PTC.

METHODS

Serum samples were collected from 29 PTC patients and 29 nodular goiter (NG) patients. Five PTC serum samples and five NG serum samples were selected for proteome profiles by proteomics. Eight proteins in PTC and NG serum samples were selected for confirmation by enzyme-linked immunosorbent assay analysis. Receiver operating characteristic curves was used to evaluate the diagnostic value of potential biomarkers.

RESULTS

Complement C4-A (C4A) and plasminogen (PLG) were significantly lower in serum samples of PTC patients compared with NG patients. C4A was observed to have excellent diagnostic accuracy for PTC, with a sensitivity of 91.67% and specificity of 83.33%. The diagnostic value of PLG for PTC was demonstrated by a sensitivity at 87.50% and specificity at 75.00%. The AUC for C4A and PLG was 0.97 ± 0.02 and 0.89 ± 0.05.

CONCLUSION

C4A and PLG appeared to be excellent potential biomarkers for the prediction of PTC.

Classification of Thyroid Tumors Based on Mass Spectrometry Imaging of Tissue Microarrays; a Single-Pixel Approach

The primary diagnosis of thyroid tumors based on histopathological patterns can be ambiguous in some cases, so proper classification of thyroid diseases might be improved if molecular biomarkers support cytological and histological assessment. In this work, tissue microarrays representative for major types of thyroid malignancies—papillary thyroid cancer (classical and follicular variant), follicular thyroid cancer, anaplastic thyroid cancer, and medullary thyroid cancer—and benign thyroid follicular adenoma and normal thyroid were analyzed by mass spectrometry imaging (MSI), and then different computation approaches were implemented to test the suitability of the registered profiles of tryptic peptides for tumor classification. Molecular similarity among all seven types of thyroid specimens was estimated, and multicomponent classifiers were built for sample classification using individual MSI spectra that corresponded to small clusters of cells. Moreover, MSI components showing the most significant differences in abundance between the compared types of tissues detected and their putative identity were established by annotation with fragments of proteins identified by liquid chromatography-tandem mass spectrometry in corresponding tissue lysates. In general, high accuracy of sample classification was associated with low inter-tissue similarity index and a high number of components with significant differences in abundance between the tissues. Particularly, high molecular similarity was noted between three types of tumors with follicular morphology (adenoma, follicular cancer, and follicular variant of papillary cancer), whose differentiation represented the major classification problem in our dataset. However, low level of the intra-tissue heterogeneity increased the accuracy of classification despite high inter-tissue similarity (which was exemplified by normal thyroid and benign adenoma). We compared classifiers based on all detected MSI components (n = 1536) and the subset of the most abundant components (n = 147). Despite relatively higher contribution of components with significantly different abundance and lower overall inter-tissue similarity in the latter case, the precision of classification was generally higher using all MSI components. Moreover, the classification model based on individual spectra (a single-pixel approach) outperformed the model based on mean spectra of tissue cores. Our result confirmed the high feasibility of MSI-based approaches to multi-class detection of cancer types and proved the good performance of sample classification based on individual spectra (molecular image pixels) that overcame problems related to small amounts of heterogeneous material, which limit the applicability of classical proteomics.

Matrix-assisted laser desorption/ionization mass spectrometry imaging to uncover protein alterations associated with the progression of IgA nephropathy

IgA nephropathy (IgAN) is one of the most diffuse glomerulonephrites worldwide, and many issues still remain regarding our understanding of its pathogenesis. The disease is diagnosed by renal biopsy examination, but potential pitfalls still persist with regard to discriminating its primary origin and, as a result, determining patient outcome remains challenging. In this pilot study, matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) was performed on renal biopsies obtained from patients with IgAN (n = 11) and other mesangioproliferative glomerulonephrites (MesPGN, n = 6) in order to enlighten proteomic alterations that may be associated with the progression of IgAN. Differences in the proteomic profiles of IgAN and MesPGN tissue could clearly be detected using this approach and, furthermore, 14 signals (AUC ≥ 0.8) were observed to have an altered intensity among the different CKD stages within the IgAN group. In particular, large increases in the intensity of these signals could be observed at CKD stages II and above. These signals primarily corresponded to proteins involved in either inflammatory and healing pathways and their increased intensity was localized within regions of tissue with large amounts of inflammatory cells or sclerosis. Despite much work in recent years, our molecular understanding of IgAN progression remains incomplete. This pilot study represents a promising starting point in the search for novel protein markers that can assist clinicians in better understanding the pathogenesis of IgAN and highlighting those patients who may progress to end-stage renal disease.

Molecular Heterogeneity of Papillary Thyroid Cancer: Comparison of Primary Tumors and Synchronous Metastases in Regional Lymph Nodes by Mass Spectrometry Imaging

Intra-tumor heterogeneity results from both genetic heterogeneity of cancer (sub)clones and phenotypic plasticity of cancer cells that could be induced by different local microenvironments. Here, we used mass spectrometry imaging (MSI) to compare molecular profiles of primary tumors located in the thyroid gland and their synchronous metastases in regional lymph nodes to analyze phenotypic heterogeneity in papillary thyroid cancer. Two types of cancerous (primary tumor and metastasis) and two types of not cancerous (thyroid gland and lymph node) regions of interest (ROIs) were delineated in postoperative material from 11 patients, then the distribution of tryptic peptides (spectral components) was analyzed by MSI in all tissue regions. Moreover, tryptic peptides identified by shotgun proteomics in corresponding tissue lysates were matched to components detected by MSI to enable their hypothetical protein annotation. Unsupervised segmentation of all cancer ROIs revealed that different clusters dominated in tumor ROIs and metastasis ROIs. The intra-patient similarity between thyroid and tumor ROIs was higher than the intra-patient similarity between tumor and metastasis ROIs. Moreover, the similarity between tumor and its metastasis from the same patients was lower than similarities among tumors and among metastases from different patients (inter-patient similarity was higher for metastasis ROIs than for tumor ROIs). Components differentiating between tumor and its metastases were annotated as proteins involved in the organization of the cytoskeleton and chromatin, as well as proteins involved in immunity-related functions. We concluded that phenotypical heterogeneity between primary tumor and lymph node metastases from the same patient was higher than inter-tumor heterogeneity between primary tumors from different patients.

Preoperative metabolic classification of thyroid nodules using mass spectrometry imaging of fine-needle aspiration biopsies

Thyroid neoplasia is common and requires appropriate clinical workup with imaging and fine-needle aspiration (FNA) biopsy to evaluate for cancer. Yet, up to 20% of thyroid nodule FNA biopsies will be indeterminate in diagnosis based on cytological evaluation. Genomic approaches to characterize the malignant potential of nodules showed initial promise but have provided only modest improvement in diagnosis. Here, we describe a method using metabolic analysis by desorption electrospray ionization mass spectrometry (DESI-MS) imaging for direct analysis and diagnosis of follicular cell-derived neoplasia tissues and FNA biopsies. DESI-MS was used to analyze 178 tissue samples to determine the molecular signatures of normal, benign follicular adenoma (FTA), and malignant follicular carcinoma (FTC) and papillary carcinoma (PTC) thyroid tissues. Statistical classifiers, including benign thyroid versus PTC and benign thyroid versus FTC, were built and validated with 114,125 mass spectra, with accuracy assessed in correlation with clinical pathology. Clinical FNA smears were prospectively collected and analyzed using DESI-MS imaging, and the performance of the statistical classifiers was tested with 69 prospectively collected clinical FNA smears. High performance was achieved for both models when predicting on the FNA test set, which included 24 nodules with indeterminate preoperative cytology, with accuracies of 93% and 89%. Our results strongly suggest that DESI-MS imaging is a valuable technology for identification of malignant potential of thyroid nodules.

Peptide Profile Differences of Noninvasive Follicular Thyroid Neoplasm with Papillary-Like Nuclear Features, Encapsulated Follicular Variant, and Classical Papillary Thyroid Carcinoma: An Application of Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry Imaging

Introduction: The lack of papillary structures and faint and/or unclear core features of follicular variant of papillary thyroid carcinoma (FV-PTC) may hamper the definitive fine needle aspiration biopsy -based diagnosis. Recently, the nomenclature of noninvasive encapsulated FV-PTC was revised to "noninvasive follicular thyroid neoplasms with papillary-like nuclear features" (NIFTP). However, it remains inconclusive whether or not the peptide patterns differ between NIFTP and encapsulated FV-PTC. The main objectives of this study were to investigate the viability of matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI) in the pathological assessment of NIFTP and to evaluate the discriminatory power of MALDI MSI for the classification of classical variant of PTC (CV-PTC), NIFTP, and encapsulated FV-PTC. Methods: MALDI MSI was employed to investigate the changes in peptide profiles from 21 formalin-fixed paraffin-embedded (FFPE) tissue samples (n = 7 from each group of CV-PTC, NIFTP, and FV-PTC). Six out of seven FV-PTC FFPE tissue samples were encapsulated FV-PTC; only one was infiltrative FV-PTC. Liquid chromatography-tandem mass spectrometry was used for the identification of the peptide signals detected in MALDI MSI. Results: Using receiver operating characteristics analysis, 10 peptide signals distinguished NIFTP from normal thyroid parenchyma (area under the curve [AUC] >0.80). To evaluate the discriminatory power of MALDI MSI, statistically significant peptide signals (n = 88) within three groups were used for hierarchical clustering. The method had high discriminatory power for distinguishing CV-PTC from NIFTP and FV-PTC (encapsulated and infiltrative). The majority of the NIFTP and encapsulated FV-PTC were clustered together, indicating that NIFTP could not be distinguished from encapsulated FV-PTC. However, infiltrative FV-PTC FFPE tissue samples had the furthest distance from all the NIFTP cases. High signal intensities of S100-A6, vimentin, and cytoplasmic actin 1 were detected in FV-PTC, prelamin A/C in CV-PTC, and 60S ribosomal protein L6 and L8 in NIFTP tissues. Conclusions: MALDI MSI, a powerful tool combining histological and mass spectrometric data, enabled the differentiation of NIFTP from normal thyroid parenchyma. Although NIFTP is a recent definition that replaces noninvasive encapsulated FV-PTC, the peptide profiles of NIFTP and encapsulated FV-PTC were found to be similar.

MALDI Imaging for Proteomic Painting of Heterogeneous Tissue Structures

PURPOSE

To present matrix-assisted laser desorption/ionization (MALDI) imaging as a powerful method to highlight various tissue compartments.

EXPERIMENTAL DESIGN

Formalin-fixed paraffin-embedded (FFPE) tissue of a uterine cervix, a pancreas, a duodenum, a teratoma, and a breast cancer tissue microarray (TMA) are analyzed by MALDI imaging and by immunohistochemistry (IHC). Peptide images are visualized and analyzed using FlexImaging and SCiLS Lab software. Different histological compartments are compared by hierarchical cluster analysis.

RESULTS

MALDI imaging highlights tissue compartments comparable to IHC. In cervical tissue, normal epithelium can be discerned from intraepithelial neoplasia. In pancreatic and duodenal tissues, m/z signals from lymph follicles, vessels, duodenal mucosa, normal pancreas, and smooth muscle structures can be visualized. In teratoma, specific m/z signals to discriminate squamous epithelium, sebaceous glands, and soft tissue are detected. Additionally, tumor tissue can be discerned from the surrounding stroma in small tissue cores of TMAs. Proteomic data acquisition of complex tissue compartments in FFPE tissue requires less than 1 h with recent mass spectrometers.

CONCLUSION AND CLINICAL RELEVANCE

The simultaneous characterization of morphological and proteomic features in the same tissue section adds proteomic information for histopathological diagnostics, which relies at present on conventional hematoxylin and eosin staining, histochemical, IHC and molecular methods.

High sensitivity proteomics of prostate cancer tissue microarrays to discriminate between healthy and cancerous tissue

Biopsies, in the form of tissue microarrays (TMAs) were studied to identify anomalies indicative of prostate cancer at the proteome level. TMAs offer a valuable source of well-characterized biological material. However, because of the small tissue sample size method development was essential to provide the sensitivity and reliability necessary for the analysis. Surface digestion of TMA cores was followed by peptide extraction and shotgun proteomics analysis. About 5 times better sensitivity was achieved by the optimized surface digestion compared to bulk digestion of the same TMA spot and it allowed the identification of over 500 proteins from individual prostate TMA cores. Label-free quantitation showed that biological variability among all samples was about 3 times larger than the technical reproducibility. We have identified 189 proteins which showed statistically significant changes (t-test p-value <.05) in abundance between healthy and cancerous tissue samples. The proteomic profile changed according to cancer grade, but did not show a correlation with cancer stage. Results of this pilot study were further evaluated using bioinformatics tools, identifying various protein pathways affected by prostate cancer progression indicating the usefulness of studying TMA cores to identify quantitative changes in tissue proteomics. SIGNIFICANCE: Detailed proteomics analysis of TMAs presents a good alternative for tissue analysis. Here we present a novel method, based on tissue surface digestion and nano-LC-MS measurements, which is capable of identifying and quantifying over 500 proteins from a 1.5 mm diameter tissue section. We compared healthy and cancerous prostate tissue samples, and tissues with various grades and stages of cancer. Tissue proteomics clearly distinguished healthy and cancerous samples, furthermore the results correlated well with cancer grade, but not with cancer stage. Over 100 proteins showed statistically significant abundance changes (t-test p-value <.05) between various groups. This was sufficient for a meaningful bioinformatics evaluation; showing e.g. increased abundance of proteins in cancer in the KEGG ribosome pathway, GO mRNA splicing via spliceosome, and chromatin assembly biological processes. The results highlight the feasibility of the developed method for future large-scale tissue proteomics studies using commercially available TMAs.

Update on: proteome analysis in thyroid pathology - part II: overview of technical and clinical enhancement of proteomic investigation of the thyroid lesions

INTRODUCTION

An accurate diagnostic classification of thyroid lesions remains an important clinical aspect that needs to be addressed in order to avoid 'diagnostic' thyroidectomies. Among the several 'omics' techniques, proteomics is playing a pivotal role in the search for diagnostic markers. In recent years, different approaches have been used, taking advantage of the technical improvements related to mass spectrometry that have occurred. Areas covered: The review provides an update of the recent findings in diagnostic classification, in genetic definition and in the investigation of thyroid lesions based on different proteomics approaches and on different type of specimens: cytological, surgical and biofluid samples. A brief section will discuss how these findings can be integrated with those obtained by metabolomics investigations. Expert commentary: Among the several proteomics approaches able to deepen our knowledge of the molecular alterations of the different thyroid lesions, MALDI-MSI is strongly emerging above all. In fact, MS-imaging has also been demonstrated to be capable of distinguishing thyroid lesions, based on their different molecular signatures, using cytological specimens. The possibility to use the material obtained by the fine needle aspiration makes MALDI-MSI a highly promising technology that could be implemented into the clinical and pathological units.

Decreased apolipoprotein A4 and increased complement component 3 as potential markers for papillary thyroid carcinoma: A proteomic study

BACKGROUND:

Thyroid carcinomas have comprised the fastest rising incidence of cancer in the past decade. Currently, the diagnosis of thyroid tumors is performed by the fine-needle aspiration biopsy (FNAB) method, which still holds some challenges and limitations, mostly in discriminating malignant and benign lesions. Therefore, the development of molecular markers to distinguish between these lesion types are in progress.

METHODS:

A 2D-PAGE separation of proteins was performed followed by tandem mass spectrometry with the aim of discovering potential serum protein markers for papillary thyroid carcinoma and multinodular goiter. Protein-protein interaction network analysis revealed the most important pathways involved in the progression of papillary thyroid cancer. The enzyme-linked immunosorbent assay method was used to confirm a part of the results.

RESULTS:

The significantly altered proteins included C3, C4A, GC, HP, TTR, APOA4, APOH, ORM2, KRT10, AHSG, IGKV3-20, and IGKC. We also confirmed that increased complement component 3 and decreased apolipoprotein A4 occurred in papillary thyroid cancer. Network investigations demonstrated that complement activation cascades and PPAR signaling might play a role in the pathogenesis of thyroid cancer.

CONCLUSION:

The results demonstrated that serum proteomics could serve as a viable method for proposing novel potential markers for thyroid tumors. Surely, further research must be performed in larger cohorts to validate the results.

Molecular Signature of Indeterminate Thyroid Lesions: Current Methods to Improve Fine Needle Aspiration Cytology (FNAC) Diagnosis

Fine needle aspiration cytology (FNAC) represents the gold standard for determining the nature of thyroid nodules. It is a reliable method with good sensitivity and specificity. However, indeterminate lesions remain a diagnostic challenge and researchers have contributed molecular markers to search for in cytological material to refine FNAC diagnosis and avoid unnecessary surgeries. Nowadays, several "home-made" methods as well as commercial tests are available to investigate the molecular signature of an aspirate. Moreover, other markers (i.e., microRNA, and circulating tumor cells) have been proposed to discriminate benign from malignant thyroid lesions. Here, we review the literature and provide data from our laboratory on mutational analysis of FNAC material and circulating microRNA expression obtained in the last 6 years.