Development of a Multiplexed Assay for Oral Cancer Candidate Biomarkers Using Peptide Immunoaffinity Enrichment and Targeted Mass Spectrometry

Clinical validation of a saliva-based matrix metalloproteinase-1 rapid strip test for detection of oral cavity cancer

BACKGROUND

We previously identified matrix metalloproteinase-1 (MMP-1) as one of the most promising salivary biomarkers for oral squamous cell carcinoma (OSCC) and developed a sensitive ELISA for MMP-1 with good performance in detection of OSCC using a cohort of 1160 saliva samples.

METHODS

A time-saving rapid strip test (RST) for MMP-1 was developed in this study and its diagnostic performance compared with ELISA using saliva samples from a new cohort of 603 subjects (171 healthy controls, 236 patients with oral potentially malignant disorders, and 196 OSCC patients).

RESULTS

Salivary MMP-1 levels measured using RST and ELISA were highly comparable and both assays could effectively distinguish between OSCC and non-cancerous groups. Similar to ELISA, receiver operating characteristic curve analysis of the MMP-1 RST was effective in identifying patients with OSCC at different oral cavity sites and stages.

CONCLUSIONS

Salivary MMP-1 can be sensitively detected using both RST and ELISA methods. Our newly developed point-of-care MMP-1 RST is a promising in vitro diagnostic device (IVD) that may serve as a novel auxiliary tool in the routine clinical detection and monitoring of OSCC.

Targeted Analysis of Permethylated N-Glycans Using MRM/PRM Approaches

Targeted mass spectrometric analysis is widely employed across various omics fields as a validation strategy due to its high sensitivity and accuracy. The approach has been successfully employed for the structural analysis of proteins, glycans, lipids, and metabolites. Multiple reaction monitoring (MRM) and parallel reaction monitoring (PRM) have been the methods of choice for targeted structural studies of biomolecules. These target analyses simplify the analytical workflow, reduce background interference, and increase selectivity/specificity, allowing for a reliable quantification of permethylated N-glycans in complex biological matrices.

Targeted protein quantitation in human body fluids by mass spectrometry

Human body fluids (biofluids) contain various proteins, some of which reflect individuals' physiological conditions or predict diseases. Therefore, the analysis of biofluids can provide substantial information on novel biomarkers for clinical diagnosis and prognosis. In the past decades, mass spectrometry (MS)-based technologies have been developed as proteomic strategies not only for the identification of protein biomarkers but also for biomarker verification/validation in body fluids for clinical applications. The main advantage of targeted MS-based methodologies is the accurate and specific simultaneous quantitation of multiple biomarkers with high sensitivity. Here, we review MS-based methodologies that are currently used for the targeted quantitation of protein components in human body fluids, especially in plasma, urine, cerebrospinal fluid, and saliva. In addition, the currently used MS-based methodologies are summarized with a specific focus on applicable clinical sample types, MS configurations, and acquisition modes.

Use of Longitudinal Serum Analysis and Machine Learning to Develop a Classifier for Cancer Early Detection

Early detection of solid tumors through a simple screening process, such as the proteomic analysis of biofluids, has the potential to significantly alter the management and outcomes of cancers. The application of advanced targeted proteomics measurements and data analysis strategies to uniformly collected serum or plasma samples would enable longitudinal studies of cancer risk, progression, and response to therapy that have the potential to significantly reduce cancer burden in general. In this article, we describe a generalizable workflow combining robust, multiplexed targeted proteomics measurements applied to longitudinal samples from the Department of Defense Serum Repository with a Random Forest machine learning method for developing and initially evaluating the performance of candidate biomarker panels for early detection of cancers. The effectiveness of this approach was demonstrated in a cohort of 175 head and neck squamous cell carcinoma patients. The outlined protocols include methods for sample preparation, instrument analysis, and data analysis and interpretation using this workflow.

Advances in the Diagnosis, Monitoring, and Progression of Oral Cancer through Saliva: An Update

The early detection of cancer, and in particular oral cancer, has been a priority objective of study in recent years. Saliva has been proposed as an easy-to-obtain means of providing the necessary information to diagnose malignant lesions in the oral cavity, since it can be obtained very easily and completely noninvasively. There are a number of molecules, known as biomarkers, which may be involved in the malignant transformation of oral lesions, and which have different natures. The involvement of proteins (“proteomics”), metabolites (“metabolomics”), and even certain genes in the structural changes of altered tissue has been investigated in order to establish validated parameters for the early diagnosis of oral cancer. In addition, the development of new analytical assay methods that can reduce costs and obtain better results in terms of sensitivity and specificity has been a key point in recent research in this field. Even though there are numerous biomarkers with results showing high sensitivity and specificity, there is still a need for more studies, with a larger sample and with analytical methods that can constitute a real advance in time and cost. Although salivary biomarkers are a promising new diagnostic tool for oral cancer, for the moment they do not replace biopsy as the “gold standard”.

The Parallel Reaction Monitoring-Parallel Accumulation-Serial Fragmentation (prm-PASEF) Approach for Multiplexed Absolute Quantitation of Proteins in Human Plasma

Mass spectrometry (MS)-based quantitative proteomic methods have become some of the major tools for protein biomarker discovery and validation. The recently developed parallel reaction monitoring-parallel accumulation-serial fragmentation (prm-PASEF) approach on a Bruker timsTOF Pro mass spectrometer allows the addition of ion mobility as a new dimension to LC-MS-based proteomics and increases proteome coverage at a reduced analysis time. In this study, a prm-PASEF approach was used for the multiplexed absolute quantitation of proteins in human plasma using isotope-labeled peptide standards for 125 plasma proteins, over a broad (10⁴-10⁶) dynamic range. Optimization of LC and MS parameters, such as accumulation time and collision energy, resulted in improved sensitivity for more than half of the targets (73 out of 125 peptides) by increasing the signal-to-noise ratio by a factor of up to 10. Overall, 41 peptides showed up to a 2-fold increase in sensitivity, 25 peptides showed up to a 5-fold increase in sensitivity, and 7 peptides showed up to a 10-fold increase in sensitivity. Implementation of the prm-PASEF method allowed absolute protein quantitation (down to 1.13 fmol) in human plasma samples. A comparison of the concentration values of plasma proteins determined by MRM on a QTRAP instrument and by prm-PASEF on a timsTOF Pro revealed an excellent correlation (R² = 0.97) with a slope of close to 1 (0.99), demonstrating that prm-PASEF is well suited for "absolute" quantitative proteomics.

A systematic review of proteomic biomarkers in oral squamous cell cancer

BACKGROUND

Head and neck squamous cell cancer (HNSCC) is the most common cancer associated with chewing tobacco, in the world. As this is divided in to sites and subsites, it does not make it to top 10 cancers. The most common subsite is the oral cancer. At the time of diagnosis, more than 50% of patients with oral squamous cell cancers (OSCC) had advanced disease, indicating the lack of availability of early detection and risk assessment biomarkers. The new protein biomarker development and discovery will aid in early diagnosis and treatment which lead to targeted treatment and ultimately a good prognosis.

METHODS

This systematic review was performed as per PRISMA guidelines. All relevant studies assessing characteristics of oral cancer and proteomics were considered for analysis. Only human studies published in English were included, and abstracts, incomplete articles, and cell line or animal studies were excluded.

RESULTS

A total of 308 articles were found, of which 112 were found to be relevant after exclusion. The present review focuses on techniques of cancer proteomics and discovery of biomarkers using these techniques. The signature of protein expression may be used to predict drug response and clinical course of disease and could be used to individualize therapy with such knowledge.

CONCLUSIONS

Prospective use of these markers in the clinical setting will enable early detection, prediction of response to treatment, improvement in treatment selection, and early detection of tumor recurrence for disease monitoring. However, most of these markers for OSCC are yet to be validated.

A SISCAPA-based approach for detection of SARS-CoV-2 viral antigens from clinical samples

SARS-CoV-2, a novel human coronavirus, has created a global disease burden infecting > 100 million humans in just over a year. RT-PCR is currently the predominant method of diagnosing this viral infection although a variety of tests to detect viral antigens have also been developed. In this study, we adopted a SISCAPA-based enrichment approach using anti-peptide antibodies generated against peptides from the nucleocapsid protein of SARS-CoV-2. We developed a targeted workflow in which nasopharyngeal swab samples were digested followed by enrichment of viral peptides using the anti-peptide antibodies and targeted parallel reaction monitoring (PRM) analysis using a high-resolution mass spectrometer. This workflow was applied to 41 RT-PCR-confirmed clinical SARS-CoV-2 positive nasopharyngeal swab samples and 30 negative samples. The workflow employed was highly specific as none of the target peptides were detected in negative samples. Further, the detected peptides showed a positive correlation with the viral loads as measured by RT-PCR Ct values. The SISCAPA-based platform described in the current study can serve as an alternative method for SARS-CoV-2 viral detection and can also be applied for detecting other microbial pathogens directly from clinical samples.

Affinity capture in bottom-up protein analysis - Overview of current status of proteolytic peptide capture using antibodies and molecularly imprinted polymers

Antibody-based affinity capture has become the gold standard in sample preparation for determination of low-abundance protein biomarkers in biological matrices prior to liquid chromatography-mass spectrometry (LC-MS) determination. This comprises both capture of intact proteins prior to the digestion step and capture of proteolytic peptides after digestion of the sample. The latter can be performed both using antibodies specifically developed to capture target proteolytic peptides, as well as by the less explored use of anti-protein antibodies to capture the proteolytic epitope peptide. Molecularly imprinted polymers (MIPs), also called plastic antibodies are another affinity-based approach emerging as sample preparation technique in LC-MS based protein biomarker analysis. The current review gives a critical and comprehensive overview of proteolytic peptide capture using antibodies and MIPs in LC-MS based protein biomarker determination during the last five years. The main emphasis is on capture of non-modified peptides, while a brief overview of affinity capture of peptides containing post-translational modifications (PTMs) is provided.

Serum HSP90-Alpha and Oral Squamous Cell Carcinoma : A Prospective Biomarker

AIM

Current study aims to perform differential protein expression analysis of serum samples from Oral squamous cell carcinoma (OSCC) patients and healthy controls in search of potential diagnostic and/or prognostic biomarker(s).

OBJECTIVE

OSCC is diagnosed late, resulting in poor survival and high mortality. Identification of non-invasive prognostic biomarker is of utmost importance for early diagnosis and proper management of the disease; hence we used proteomic approach to identify potential biomarkers from serum.

METHODS

Serum samples (OSCC n=45 and control n=30) were depleted and proteins were separated using 2-D gel electrophoresis followed by identification by mass spectrometric analysis. Gene expression analysis of identified proteins in malignant and normal tissue was also performed to complement proteomics studies.

RESULTS

Among differentially expressed proteins, a noteworthy observation was up regulation of Heat shock protein alpha (HSP90α) from serum of oral cancer patients. We also observed elevated levels of Haptoglobin (HP) along with down regulation of Type II keratin cytoskeletal 1(KRT1) and serum Albumin (ALB) in oral cancer patients. Gene expression studies of identified proteins in malignant and normal tissue revealed a similar pattern with the exception of KRT1. We believe that elevated levels of serum HSP90 alpha might be used as a potential biomarker.

CONCLUSION

Our findings suggest the contribution of HSP90 alpha and other identified proteins in oral pathology as pro/anti apoptotic modulators, thus they are being considered as predictive biomarkers.

Proteomic approaches to assist in diagnosis and prognosis of oral cancer

Introduction: Oral squamous cell carcinoma (OSCC) ranks among the top 10 leading causes of cancer worldwide, with 5-year survival rate of about 50%, high lymph node metastasis, and relapse rates. The OSCC diagnosis, prognosis, and treatment are mostly based on the clinical TNM classification. There is an urgent need for the discovery of biomarkers and therapeutic targets to assist in the clinical decision-making process.Areas covered: We summarize proteomic studies of the OSCC tumor, immune microenvironment, potential liquid biopsy sites, and post-translational modifications trying to retrieve information in the discovery and verification or (pre)validation phases. The search strategy was based on the combination of MeSH terms and expert refinement.Expert opinion: Untargeted combined with targeted proteomics are strategies that provide reliable and reproducible quantitation of proteins and are the methods of choice of many groups worldwide. Undoubtedly, proteomics has been contributing to the understanding of OSCC progression and uncovers potential candidates as biomarker or therapeutic targets. Nevertheless, none of these targets are available in the clinical practice yet. The scientific community needs to overcome the limitations by investing in robust experimental designs to strengthen the value of the findings, leveraging the translation of knowledge, and further supporting clinical decisions.

Verification of Saliva Matrix Metalloproteinase-1 as a Strong Diagnostic Marker of Oral Cavity Cancer

Oral squamous cell carcinoma (OSCC) accounts for >90% of cases of oral cancer, including cancer at the lip and oral cavity and cancer at the oropharynx. Most OSCCs develop from oral potentially malignant disorders (OPMDs), which consist of heterogeneous lesions with different malignant transformation potentials that make early detection of OSCC a challenge. Using a targeted mass spectrometry-based assay to compare multiple candidate proteins, we previously identified matrix metalloproteinase-1 (MMP-1) as one of the most promising salivary OSCC biomarkers. To explore the clinical utility of MMP-1 in OSCC detection, we developed an in-house, sensitive enzyme-linked immunosorbent assay (ELISA) for measuring MMP-1 content, and tested it on saliva samples from 1160 subjects (313 healthy controls, and 578 OPMD and 269 OSCC patients) collected at two medical centers. Salivary MMP-1 levels measured by our in-house ELISA significantly discriminated OSCC patients from non-cancerous groups. A receiver operating characteristic curve analysis showed that MMP-1 was effective in separating non-cancer groups from patients with OSCCs at the oral cavity. Additionally, salivary MMP-1 levels in oral cavity cancer patients were highly correlated with tumor progression (tumor size, lymph node metastasis, and overall stage). Collectively, our results indicate that salivary MMP-1 is an effective biomarker for OSCC that can be sensitively detected using our newly developed ELISA. The newly developed MMP-1 ELISA may be used as a new adjunctive tool to aid in detecting and monitoring OSCC.

Identification of SPRR3 as a novel diagnostic/prognostic biomarker for oral squamous cell carcinoma via RNA sequencing and bioinformatic analyses

Oral squamous cell carcinoma (OSCC) has always been one of the most aggressive and invasive cancers among oral and maxillofacial malignancies. As the morbidity and mortality of the disease have increased year by year, the search for a promising diagnostic and prognostic biomarker for the disease is becoming increasingly urgent. Tumorous and adjacent tissues were collected from three OSCC sufferers and we obtained 229 differentially expressed genes (DEGs) between tumor and normal tissues via high-throughput RNA sequence. Function and pathway enrichment analyses for DEGs were conducted to find a correlation between tumorigenesis status and DEGs. Protein interaction network and molecular complex detection (MCODE) were constructed to detect core modules. Two modules were enriched in MCODE. The diagnostic and prognostic values of the candidate genes were analyzed, which provided evidence for the candidate genes as new tumor markers. Small Proline Rich Protein 3 (SPRR3), a potential tumor marker that may be useful for the diagnosis of OSCC, was screened out. The survival analysis showed that SPRR3 under expression predicted the poor prognosis of OSCC patients. Further experiments have also shown that the expression of SPRR3 decreased as the malignancy of OSCC increased. Therefore, we believe that SPRR3 could be used as a novel diagnostic and prognostic tumor marker.

Detection of Head and Neck Cancer Based on Longitudinal Changes in Serum Protein Abundance

BACKGROUND

Approximately 85% of the U.S. military active duty population is male and less than 50 years of age, with elevated levels of known risk factors for oropharyngeal squamous cell carcinoma (OPSCC), including smoking, excessive use of alcohol, and greater numbers of sexual partners, and elevated prevalence of human papilloma virus (HPV). Given the recent rise in incidence of OPSCC related to the HPV, the Department of Defense Serum Repository provides an unparalleled resource for longitudinal studies of OPSCC in the military for the identification of early detection biomarkers.

METHODS

We identified 175 patients diagnosed with OPSCC with 175 matched healthy controls and retrieved a total of 978 serum samples drawn at the time of diagnosis, 2 and 4 years prior to diagnosis, and 2 years after diagnosis. Following immunoaffinity depletion, serum samples were analyzed by targeted proteomics assays for multiplexed quantification of a panel of 146 candidate protein biomarkers from the curated literature.

RESULTS

Using a Random Forest machine learning approach, we derived a 13-protein signature that distinguishes cases versus controls based on longitudinal changes in serum protein concentration. The abundances of each of the 13 proteins remain constant over time in control subjects. The AUC for the derived Random Forest classifier was 0.90.

CONCLUSIONS

This 13-protein classifier is highly promising for detection of OPSCC prior to overt symptoms.

IMPACT

Use of longitudinal samples has significant potential to identify biomarkers for detection and risk stratification.

Analytical techniques for multiplex analysis of protein biomarkers

INTRODUCTION

The importance of biomarkers for pharmaceutical drug development and clinical diagnostics is more significant than ever in the current shift toward personalized medicine. Biomarkers have taken a central position either as companion markers to support drug development and patient selection, or as indicators aiming to detect the earliest perturbations indicative of disease, minimizing therapeutic intervention or even enabling disease reversal. Protein biomarkers are of particular interest given their central role in biochemical pathways. Hence, capabilities to analyze multiple protein biomarkers in one assay are highly interesting for biomedical research.

AREAS COVERED

We here review multiple methods that are suitable for robust, high throughput, standardized, and affordable analysis of protein biomarkers in a multiplex format. We describe innovative developments in immunoassays, the vanguard of methods in clinical laboratories, and mass spectrometry, increasingly implemented for protein biomarker analysis. Moreover, emerging techniques are discussed with potentially improved protein capture, separation, and detection that will further boost multiplex analyses.

EXPERT COMMENTARY

The development of clinically applied multiplex protein biomarker assays is essential as multi-protein signatures provide more comprehensive information about biological systems than single biomarkers, leading to improved insights in mechanisms of disease, diagnostics, and the effect of personalized medicine.

Analysis of Endogenous Peptides Released from Osteoarthritic Cartilage Unravels Novel Pathogenic Markers

Osteoarthritis (OA) is a pathology characterized by the loss of articular cartilage. In this study, we performed a peptidomic strategy to identify endogenous peptides (neopeptides) that are released from human osteoarthritic tissue, which may serve as disease markers. With this aim, secretomes of osteoarthritic and healthy articular cartilages obtained from knee and hip were analyzed by shotgun peptidomics. This discovery step led to the identification of 1175 different peptides, corresponding to 101 proteins, as products of the physiological or pathological turnover of cartilage extracellular matrix. Then, a targeted multiple reaction monitoring-mass spectrometry method was developed to quantify the panel of best marker candidates on a larger set of samples (n = 62). Statistical analyses were performed to evaluate the significance of the observed differences and the ability of the neopeptides to classify the tissue. Eight of them were differentially abundant in the media from wounded zones of OA cartilage compared with the healthy tissue (p < 0.05). Three neopeptides belonging to Clusterin and one from Cartilage Oligomeric Matrix Protein showed a disease-dependent decrease specifically in hip OA, whereas two from Prolargin (PRELP) and one from Cartilage Intermediate Layer Protein 1 were significantly increased in samples from knee OA. The release of one peptide from PRELP showed the best metrics for tissue classification (AUC = 0.834). The present study reveals specific neopeptides that are differentially released from knee or hip human osteoarthritic cartilage compared with healthy tissue. This evidences the intervention of characteristic pathogenic pathways in OA and provides a novel panel of peptidic candidates for biomarker development.

Quantification of the Lamin A/C Transcript Variants in Cancer Cell Lines by Targeted Absolute Quantitative Proteomics and Correlation with mRNA Expression

Lamin A/C proteins have key roles in nuclear structural integrity and chromosomal stability. Lamin A/C cumulative protein expression of all variants is reported by semi-quantitative Western blotting. To date, there have not been specific antibodies for the individual Lamin A/C transcript variants. We developed a mass spectrometric approach for the quantification of Lamin A/C transcript variants. A signature peptide for each specific splice variant of Lamin A/C was selected. A LC–MS/MS assay based on the selected signature peptides and their labeled internal standards was established to measure the expression of Lamin A/C transcript variant concentrations. The method validation was carried out according to Food and Drug Administration (FDA) guidelines. The expression levels of the Lamin A/C transcript variants were measured in samples derived from MCF7 and U937 cell lines. RT-qPCR assay was also used to quantitate and compare the mRNA expression of splice variants of Lamin A/C. The established and validated method showed a great linearity, sensitivity, and precision. The different expressed Lamin A/C variants in different cell lines were measured and their levels were in concordance with qRT-PCR results. The developed method is reproducible, reliable, and sensitive for measuring different Lamin A/C transcript variants in different cell lines.