Pilot Study Using SELDI-TOF−MS Based Proteomic Profile for the Identification of Diagnostic Biomarkers of Thyroid Proliferative Diseases

Precise engineering of cetuximab encapsulated gadollium nanoassemblies: in vitro ultrasound diagnosis and in vivo thyroid cancer therapy

We report the formulation of nanoassemblies (NAs) comprising C225 conjugates Gd-PFH-NAs (C-Gd-PFH-NAs) for low-intensity focused ultrasound diagnosis ablation of thyroid cancer. C-Gd-PFH-NAs showed excellent stability in water, phosphate-buffered saline (PBS), and 20% rat serum. Transmission electron microscopy (TEM) images also revealed the effective construction of C-Gd-PFH-NAs as common spherical assemblies. The incubation of C625 thyroid carcinoma with C-Gd-PFH-NAs triggers apoptosis, as confirmed by flow cytometry analysis. The C-Gd-PFH-NAs exhibited antitumor efficacy in human thyroid carcinoma xenografts, where histopathological results further confirmed these outcomes. Furthermore, we were able to use low-intensity focused ultrasound diagnosis imaging (LIFUS) to examine the efficiency of C-Gd-PFH-NAs in thyroid carcinoma in vivo. These findings clearly show that the use of LIFUS agents with high performance imaging in different therapeutic settings will have extensive potential for future biomedical applications.

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.

The evolution of biomarkers in thyroid cancer-from mass screening to a personalized biosignature

Thyroid cancer is the most common malignancy of the endocrine system. The diagnosis of thyroid nodules, made by neck examination and ultrasonography, is a common event occurring in over 50% of the patient population over the age of 50. Yet, only 5% of these patients will be diagnosed with cancer. Fine needle aspiration biopsy is the gold standard for diagnosing thyroid nodules. However, 10–15% of these biopsies are inconclusive, ultimately requiring a diagnostic thyroid lobectomy. Consequently, research in thyroid biomarkers has become an area of active interest. In the 40 years since calcitonin was first described as the biomarker for medullary thyroid cancer, new biomarkers in thyroid cancer have been discovered. Advances in genomic and proteomic technologies have defined many of these novel thyroid biomarkers. The purpose of this article is to provide a comprehensive literature review of how these biomarkers have evolved from simple screening tests into a complex array of multiple markers to help predict the malignant potential and genetic signature of thyroid neoplasms.

Protein profiling in pathology: analysis and evaluation of 239 frozen tissue biopsies for diagnosis of B-cell lymphomas

PURPOSE

We determined the potential value of protein profiling of tissue samples by assessing how precise this approach enables discrimination of B-cell lymphoma from reactive lymph nodes, and how well the profiles can be used for lymphoma classification.

EXPERIMENTAL DESIGN

Protein lysates from lymph nodes (n=239) from patients with the diagnosis of reactive hyperplasia (n=44), follicular lymphoma (n=63), diffuse large B-cell lymphoma (n=43), mantle cell lymphoma (n=47), and chronic lymphocytic leukemia/small lymphocytic B-cell lymphoma (n=42) were analysed by SELDI-TOF MS. Data analysis was performed by (i) classification and regression tree-based analysis and (ii) binary and polytomous logistic regression analysis.

RESULTS

After internal validation by the leave-one-out principle, both the classification and regression tree and logistic regression classification correctly identified the majority of the malignant (87 and 96%, respectively) and benign cases (73 and 75%, respectively). Classification was less successful since approximately one-third of the cases of each group were misclassified according to the histological classification. However, an additional mantle cell lymphoma case that was misclassified as chronic lymphocytic leukemia/small lymphocytic B-cell lymphoma initially was identified based on the protein profile.

CONCLUSIONS AND CLINICAL RELEVANCE

SELDI-TOF MS protein profiling allows for reliable identification of the majority of malignant lymphoma cases; however, further validation and testing robustness in a diagnostic setting is needed.

Discovery and identification of potential biomarkers of papillary thyroid carcinoma

Background

Thyroid carcinoma is the most common endocrine malignancy and a common cancer among the malignancies of head and neck. Noninvasive and convenient biomarkers for diagnosis of papillary thyroid carcinoma (PTC) as early as possible remain an urgent need. The aim of this study was to discover and identify potential protein biomarkers for PTC specifically.

Methods

Two hundred and twenty four (224) serum samples with 108 PTC and 116 controls were randomly divided into a training set and a blind testing set. Serum proteomic profiles were analyzed using SELDI-TOF-MS. Candidate biomarkers were purified by HPLC, identified by LC-MS/MS and validated using ProteinChip immunoassays.

Results

A total of 3 peaks (m/z with 9190, 6631 and 8697 Da) were screened out by support vector machine (SVM) to construct the classification model with high discriminatory power in the training set. The sensitivity and specificity of the model were 95.15% and 93.97% respectively in the blind testing set. The candidate biomarker with m/z of 9190 Da was found to be up-regulated in PTC patients, and was identified as haptoglobin alpha-1 chain. Another two candidate biomarkers (6631, 8697 Da) were found down-regulated in PTC and identified as apolipoprotein C-I and apolipoprotein C-III, respectively. In addition, the level of haptoglobin alpha-1 chain (9190 Da) progressively increased with the clinical stage I, II, III and IV, and the expression of apolipoprotein C-I and apolipoprotein C-III (6631, 8697 Da) gradually decreased in higher stages.

Conclusion

We have identified a set of biomarkers that could discriminate PTC from non-cancer controls. An efficient strategy, including SELDI-TOF-MS analysis, HPLC purification, MALDI-TOF-MS trace and LC-MS/MS identification, has been proved successful.

Proteomics in thyroid tumor research

BACKGROUND

In recent years, "OMICS" technologies have paved novel ways for the broad-scale identification of molecular signatures and signaling pathways specific to tumorigenesis. Related to this are high hopes for the discovery of biomarkers facilitating diagnosis and prognosis of cancer as well as the option for pathway-targeted tumor treatment. Among the different OMICS methods, the potential of proteomics is just beginning to emerge, and according to the current literature, the proteome is to date the most feasible tool to reflect tumor biology.

OBJECTIVE

In this review we discuss the application of proteomics to the field of thyroid tumor research.

CONTEXT

First, we provide an overview of different methods for protein expression profiling and then discuss specific requirements and challenges of thyroid proteomics. Furthermore, we summarize results of published proteomics studies on human thyroid tumors and finally explore perspectives of thyroid proteomics, which, combined with mRNA expression profiling and traditional biochemical methods, is increasingly contributing to an improved understanding of thyroid tumorigenesis and may in the future open novel avenues in thyroid cancer therapy.

Perspectives for improved and more accurate classification of thyroid epithelial tumors

CONTEXT

Histologic examination of thyroid nodules is the current standard to distinguish benign from malignant thyroid epithelial tumors and to classify histologic subtypes. This review analyzes the problems in histological differential diagnosis as well as contradictions between histology and molecular data and describes possibilities to combine histology with molecular data in an effort to more accurately classify thyroid epithelial tumors.

EVIDENCE ACQUISITION

Published literature, addressing the current recommendations for thyroid tumor classification, as well as literature on the application of histology and molecular studies on the etiology of thyroid tumors is analyzed.

EVIDENCE SYNTHESIS

The current histologic criteria to classify thyroid tumors, especially follicular-patterned tumors, are hampered by considerable interobserver variability. The detection of somatic mutations via genotyping and the definition of potentially informative gene expression signatures by microarray analyses, which can distinguish cancer subtypes as well as low- and high-risk cohorts, have recently demonstrated significant diagnostic potential. Moreover, in a routine diagnostic setting, micro-RNA profiling appears most promising due to their relative stability and the high accuracy of their expression profiles.

CONCLUSIONS

It is very likely that molecular definitions of thyroid tumors mentioned in the current World Health Organization classification will be further developed, leading to future progress in defining thyroid tumor types by an integrated histologic and molecular approach. These integrated classifications need to be evaluated for their specific impact on thyroid tumor diagnosis and prognosis.

Serum biomarker profile associated with high bone turnover and BMD in postmenopausal women

Early diagnosis of the onset of osteoporosis is key to the delivery of effective therapy. Biochemical markers of bone turnover provide a means of evaluating skeletal dynamics that complements static measurements of BMD by DXA. Conventional clinical measurements of bone turnover, primarily the estimation of collagen and its breakdown products in the blood or urine, lack both sensitivity and specificity as a reliable diagnostic tool. As a result, improved tests are needed to augment the use of BMD measurements as the principle diagnostic modality. In this study, the serum proteome of 58 postmenopausal women with high or low/normal bone turnover (training set) was analyzed by surface enhanced laser-desorption/ionization time-of-flight mass spectrometry, and a diagnostic fingerprint was identified using a variety of statistical and machine learning tools. The diagnostic fingerprint was validated in a separate distinct test set, consisting of serum samples from an additional 59 postmenopausal women obtained from the same Mayo cohort, with a gap of 2 yr. Specific protein peaks that discriminate between postmenopausal patients with high or low/normal bone turnover were identified and validated. Multiple supervised learning approaches were able to classify the level of bone turnover in the training set with 80% sensitivity and 100% specificity. In addition, the individual protein peaks were also significantly correlated with BMD measurements in these patients. Four of the major discriminatory peaks in the diagnostic profile were identified as fragments of interalpha-trypsin-inhibitor heavy chain H4 precursor (ITIH4), a plasma kallikrein-sensitive glycoprotein that is a component of the host response system. These data suggest that these serum protein fragments are the serum-borne reflection of the increased osteoclast activity, leading to the increased bone turnover that is associated with decreasing BMD and presumably an increased risk of fracture. In conjunction with the identification of the individual proteins, this protein fingerprint may provide a novel approach to evaluate high bone turnover states.

Update on the molecular diagnosis of endocrine tumors: toward -omics-based personalized healthcare?

Genetic advances in endocrine neoplasia provided the paradigm for the practice of clinical cancer genetics: germline RET mutations in multiple endocrine neoplasia type 2. In the last 14 yr, both genetics and -omics advances have occurred, almost exponentially in the last 5 yr. The time has come to reevaluate recent advances in genomic medicine's promise to revolutionize personalized healthcare in the context of endocrine neoplasias. This update focuses on two examples of endocrine neoplasias, those of the thyroid and of the adrenal, and discusses recent advances in germline and somatic genetics and genomics, as they relate to clinical application.

Deep proteome profiling of sera from never-smoked lung cancer patients

Previous studies on the serum proteome are hampered by the huge dynamic range of concentration of different protein species. The use of Equalizer Beads coupled with a combinatorial library of ligands has been shown to allow access to many low-abundance proteins or polypeptides undetectable by classical analytical methods. This study focused on never-smoked lung cancer, which is considered to be more homogeneous and distinct from smoking-related cases both clinically and biologically. Serum samples obtained from 42 never-smoked lung cancer patients (28 patients with active untreated disease and 14 patients with tumor resected) were compared with those from 30 normal control subjects using the pioneering Equalizer Beads technology followed by subsequent analysis by surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS). Eighty-five biomarkers were significantly different between lung cancer and normal control. The application of classification algorithms based on significant biomarkers achieved good accuracy of 91.7%, 80% and 87.5% in class-prediction with respect to presence or absence of disease, subsequent development of metastasis and length of survival (longer or shorter than median) respectively. Support vector machine (SVM) performed best overall. We have proved the feasibility and convenience of using the Equalizer Beads technology to study the deep proteome of the sera of lung cancer patients in a rapid and high-throughput fashion, and which enables detection of low abundance polypeptides/proteins biomarkers. Coupling with classification algorithms, the technologies will be clinically useful for diagnosis and prediction of prognosis in lung cancer.

Current literature in mass spectrometry

In order to keep subscribers up‐to‐date with the latest developments in their field, John Wiley & Sons are providing a current awareness service in each issue of the journal. The bibliography contains newly published material in the field of mass spectrometry. Each bibliography is divided into 11 sections: 1 Books, Reviews & Symposia; 2 Instrumental Techniques & Methods; 3 Gas Phase Ion Chemistry; 4 Biology/Biochemistry: Amino Acids, Peptides & Proteins; Carbohydrates; Lipids; Nucleic Acids; 5 Pharmacology/Toxicology; 6 Natural Products; 7 Analysis of Organic Compounds; 8 Analysis of Inorganics/Organometallics; 9 Surface Analysis; 10 Environmental Analysis; 11 Elemental Analysis. Within each section, articles are listed in alphabetical order with respect to author (6 Weeks journals ‐ Search completed at 4th. Oct. 2006)

[Research into the human genome driven by improved methods]

The enormous progress made by research of the human genome is mainly driven by newly established or improved methods for the analysis of nucleic acids and proteins. Among the methods that have gained a wide-spread use within a comparably short time are fluorescence in situ hybridization (FISH), polymerase chain reaction (PCR) including methods for quantitative PCR, and the use of short interfering RNA (siRNA) molecules aimed at gene silencing. The increasing significance of the analysis of secondary modifications of nucleic acids and proteins (genomic imprinting by DNA methylation, posttranslational protein modification) is reflected by an increasing use of mass spectrometry for the analysis and characterization of these biomolecules. Overall, in the future the research into the human genome and the interpretation of data will further benefit from these and other refined tools.