Evaluating the potential of a novel oral lesion exudate collection method coupled with mass spectrometry-based proteomics for oral cancer biomarker discovery

Shaping the future of oral cancer diagnosis: advances in salivary proteomics

INTRODUCTION

Saliva has gained increasing attention in the quest for disease biomarkers. Because it is a biological fluid that can be collected is an easy, painless, and safe way, it has been increasingly studied for the identification of oral cancer biomarkers. This is particularly important because oral cancer is often diagnosed at late stages with a poor prognosis.

AREAS COVERED

The review addresses the evolution of the experimental approaches used in salivary proteomics studies of oral cancer over the years and outlines advantages and pitfalls related to each one. In addition, examines the current landscape of oral cancer biomarker discovery and translation focusing on salivary proteomic studies. This discussion is based on an extensive literature search (PubMed, Scopus and Google Scholar).

EXPERT OPINION

The introduction of mass spectrometry has revolutionized the study of salivary proteomics. In the future, the focus will be on refining existing methods and introducing powerful experimental techniques such as mass spectrometry with selected reaction monitoring, which, despite their effectiveness, are still underutilized due to their high cost. In addition, conducting studies with larger cohorts and establishing standardized protocols for salivary proteomics are key challenges that need to be addressed in the coming years.

Tracking the functional meaning of the human oral-microbiome protein-protein interactions

The interactome - the network of protein-protein interactions (PPIs) within a cell or organism - is technically difficult to assess. Bioinformatic tools can, not only, identify potential PPIs that can be later experimentally validated, but also be used to assign functional meaning to PPIs. Saliva's potential as a non-invasive diagnostic fluid is currently being explored by several research groups. But, in order to fully attain its potential, it is necessary to achieve the full characterization of the mechanisms that take place within this ecosystem. The onset of omics technologies, and specifically of proteomics, delivered a huge set of data that is largely underexplored. Quantitative information relative to proteins within a given context (for example a given disease) can be used by computational algorithms to generate information regarding PPIs. These PPIs can be further analyzed concerning their functional meaning and used to identify potential biomarkers, therapeutic targets, defense and pathogenicity mechanisms. We describe a computational pipeline that can be used to identify and analyze PPIs between human and microbial proteins. The pipeline was tested within the scenario of human PPIs of systemic (Zika Virus infection) and of oral conditions (Periodontal disease) and also in the context of microbial interactions (Candida-Streptococcus) and showed to successfully predict functionally relevant PPIs. The pipeline can be applied to different scientific areas, such as pharmacological research, since a functional meaningful PPI network can provide insights on potential drug targets, and even new uses for existing drugs on the market.

Role of Salivary Biomarkers in Oral Cancer Detection

Oral cancers are the sixth most frequent cancer with a high mortality rate. Oral squamous cell carcinoma accounts for more than 90% of all oral cancers. Standard methods used to detect oral cancers remain comprehensive clinical examination, expensive biochemical investigations, and invasive biopsy. The identification of biomarkers from biological fluids (blood, urine, saliva) has the potential of early diagnosis. The use of saliva for early cancer detection in the search for new clinical markers is a promising approach because of its noninvasive sampling and easy collection methods. Human whole-mouth saliva contains proteins, peptides, electrolytes, organic, and inorganic salts secreted by salivary glands and complimentary contributions from gingival crevicular fluids and mucosal transudates. This diagnostic modality in the field of molecular biology has led to the discovery and potential of salivary biomarkers for the detection of oral cancers. Biomarkers are the molecular signatures and indicators of normal biological, pathological process, and pharmacological response to treatment hence may provide useful information for detection, diagnosis, and prognosis of the disease. Saliva's direct contact with oral cancer lesions makes it more specific and potentially sensitive screening tool, whereas more than 100 salivary biomarkers (DNA, RNA, mRNA, protein markers) have already been identified, including cytokines (IL-8, IL-1b, TNF-α), defensin-1, P53, Cyfra 21-1, tissue polypeptide-specific antigen, dual specificity phosphatase, spermidine/spermineN1-acetyltransferase , profilin, cofilin-1, transferrin, and many more. However, further research is still required for the reliability and validation of salivary biomarkers for clinical applications. This chapter provides the latest up-to-date list of known and emerging potential salivary biomarkers for early diagnosis of oral premalignant and cancerous lesions and monitoring of disease activity.

Comparative evaluation of 5-15-kDa salivary proteins from patients with different oral diseases by MALDI-TOF/TOF mass spectrometry

OBJECTIVES

The present study aimed to determine the potential use of matrix-assisted laser desorption/ionization with time-of-flight/time-of-flight mass spectrometry (MALDI-TOF/TOF MS) for analyzing specific patterns of mass signals of low-molecular-weight proteins in saliva from patients with different oral diseases.

MATERIALS AND METHODS

Unstimulated whole saliva samples were collected from healthy subjects (n = 30) and patients with oral diseases including oral cancer (n = 30), oral lichen planus (n = 30), and chronic periodontitis (n = 30). Proteomic profiles of 5,000-15,000-Da salivary proteins were evaluated by MALDI-TOF/TOF MS. Quantification of mass signals was performed by FlexAnalysis and ClinProTool software.

RESULTS

In oral cancer, the percentages of mass signals at 5,592.26 and 8,301.46 Da were significantly increased as compared with other groups (p = 0.002 and p = 0.030, respectively). In oral lichen planus, the percentages of mass signals at 12,964.55 and 13,279.08 Da were significantly increased as compared with other groups (p < 0.001, and p < 0.001, respectively). In chronic periodontitis, the percentages of mass signals at 5,835.73 and 9,801.83 Da were significantly decreased as compared with other groups (p = 0.003 and p = 0.005, respectively).

CONCLUSIONS

The present study demonstrated a potential use of MALDI-TOF/TOF as a rapid screening method to differentiate one oral disease from others by identifying specific patterns of mass signals in saliva from patients. However, MALDI-TOF/TOF has several limitations regarding the identification of the candidate mass signals.

CLINICAL RELEVANCE

MALDI-TOF/TOF MS can be used as a rapid screening method to differentiate one oral disease from others with a caution concerning peptide identity.

Validation of the diagnostic utility of salivary interleukin 8 in the differentiation of potentially malignant oral lesions and oral squamous cell carcinoma in a region with high endemicity

OBJECTIVE

To evaluate the clinical utility of salivary interleukin 8 (IL-8) in the differential diagnosis of potentially malignant lesions (PMLs) and oral squamous cell carcinoma (OSCC) in a region with high oral cancer prevalence.

STUDY DESIGN

Saliva and blood samples were collected from 100 participants in each group (OSCC, PMLs, and healthy controls). Serum and salivary IL-8 levels were measured by enzyme-linked immunosorbent assay. The data were subjected to appropriate statistical analysis.

RESULTS

A significant increase in levels of serum and salivary IL-8 was found in OSCC compared with PMLs and healthy controls. Receiver operating characteristic curve analysis found salivary IL-8 to have superior sensitivity in detecting OSCC. A significant increase in IL-8 levels based on the histologic grading of OSCC was also observed.

CONCLUSIONS

This study confirms that salivary IL-8 can be a potent marker that can be used as a tool in the differential diagnosis of PMLs and OSCC.

Proteomic approaches in biomarker discovery: new perspectives in cancer diagnostics

Despite remarkable progress in proteomic methods, including improved detection limits and sensitivity, these methods have not yet been established in routine clinical practice. The main limitations, which prevent their integration into clinics, are high cost of equipment, the need for highly trained personnel, and last, but not least, the establishment of reliable and accurate protein biomarkers or panels of protein biomarkers for detection of neoplasms. Furthermore, the complexity and heterogeneity of most solid tumours present obstacles in the discovery of specific protein signatures, which could be used for early detection of cancers, for prediction of disease outcome, and for determining the response to specific therapies. However, cancer proteome, as the end-point of pathological processes that underlie cancer development and progression, could represent an important source for the discovery of new biomarkers and molecular targets for tailored therapies.

A two-step database search method improves sensitivity in peptide sequence matches for metaproteomics and proteogenomics studies

Large databases (>10(6) sequences) used in metaproteomic and proteogenomic studies present challenges in matching peptide sequences to MS/MS data using database-search programs. Most notably, strict filtering to avoid false-positive matches leads to more false negatives, thus constraining the number of peptide matches. To address this challenge, we developed a two-step method wherein matches derived from a primary search against a large database were used to create a smaller subset database. The second search was performed against a target-decoy version of this subset database merged with a host database. High confidence peptide sequence matches were then used to infer protein identities. Applying our two-step method for both metaproteomic and proteogenomic analysis resulted in twice the number of high confidence peptide sequence matches in each case, as compared to the conventional one-step method. The two-step method captured almost all of the same peptides matched by the one-step method, with a majority of the additional matches being false negatives from the one-step method. Furthermore, the two-step method improved results regardless of the database search program used. Our results show that our two-step method maximizes the peptide matching sensitivity for applications requiring large databases, especially valuable for proteogenomics and metaproteomics studies.

Salivary proteomics in biomedical research

Proteins that are important indicators of physiological or pathological states, can provide information for the identification of early and differential markers for disease. Saliva, contains an abundance of proteins, offers an easy, inexpensive, safe, and non-invasive approach for disease detection, and possesses a high potential to revolutionize the diagnostics. Discovery of salivary biomarkers could be used to scrutinize health and disease surveillance. The impact of human saliva proteome analysis in the search for clinically relevant disease biomarkers will be realized through advances made using proteomic technologies. The advancements of emerging proteomic techniques have benefited biomarker research to the point where saliva is now recognized as an excellent diagnostic medium for the detection of disease. This review presents an overview of the value of saliva as a credible diagnostic tool and we aim to summarize the proteomic technologies currently used for global analysis of saliva proteins and to elaborate on the application of saliva proteomics to the discovery of disease biomarkers, and discuss some of the critical challenges and perspectives in this field.

Gene expression in uninvolved oral mucosa of OSCC patients facilitates identification of markers predictive of OSCC outcomes

Oral and oropharyngeal squamous cell carcinomas (OSCC) are among the most common cancers worldwide, with approximately 60% 5-yr survival rate. To identify potential markers for disease progression, we used Affymetrix U133 plus 2.0 arrays to examine the gene expression profiles of 167 primary tumor samples from OSCC patients, 58 uninvolved oral mucosae from OSCC patients and 45 normal oral mucosae from patients without oral cancer, all enrolled at one of the three University of Washington-affiliated medical centers between 2003 to 2008. We found 2,596 probe sets differentially expressed between 167 tumor samples and 45 normal samples. Among 2,596 probe sets, 71 were significantly and consistently up- or down-regulated in the comparison between normal samples and uninvolved oral samples and between uninvolved oral samples and tumor samples. Cox regression analyses showed that 20 of the 71 probe sets were significantly associated with progression-free survival. The risk score for each patient was calculated from coefficients of a Cox model incorporating these 20 probe sets. The hazard ratio (HR) associated with each unit change in the risk score adjusting for age, gender, tumor stage, and high-risk HPV status was 2.7 (95% CI: 2.0–3.8, p = 8.8E-10). The risk scores in an independent dataset of 74 OSCC patients from the MD Anderson Cancer Center was also significantly associated with progression-free survival independent of age, gender, and tumor stage (HR 1.6, 95% CI: 1.1–2.2, p = 0.008). Gene Set Enrichment Analysis showed that the most prominent biological pathway represented by the 71 probe sets was the Integrin cell surface interactions pathway. In conclusion, we identified 71 probe sets in which dysregulation occurred in both uninvolved oral mucosal and cancer samples. Dysregulation of 20 of the 71 probe sets was associated with progression-free survival and was validated in an independent dataset.