A Plasma-Based Protein Marker Panel for Colorectal Cancer Detection Identified by Multiplex Targeted Mass Spectrometry

Absolute Quantitative Targeted Monitoring of Potential Plasma Protein Biomarkers: A Pilot Study on Healthy Individuals

BACKGROUND/OBJECTIVES

The development of blood tests for the early detection of individual predisposition to socially significant diseases remains a pressing issue.

METHODS

In this pilot study, multiple reaction monitoring mass spectrometry (MRM-MS) with a BAK-270 assay was applied for protein concentrations analysis in blood plasma from 21 healthy volunteers of the European cohort.

RESULTS

The levels of 138 plasma proteins were reliably and precisely quantified in no less than 50% of samples. The quantified proteins included 66 FDA-approved markers of cardiovascular diseases (CVD), and other potential biomarkers of pathologies such as cancer, diabetes mellitus, and Alzheimer's disease. The analysis of individual variations of the plasma proteins revealed significant differences between the male (11) and female (10) groups. In total, fifteen proteins had a significantly different concentration in plasma; this included four proteins that exhibited changes greater than ±1.5-fold, three proteins (RBP4, APCS, and TTR) with higher levels in males, and one (SHBG) elevated in females. The obtained results demonstrated considerable agreement with the data collected from 20 samples of a North American cohort, which were analyzed with the similar MRM assay. The most significant differences between the cohorts of the two continents were observed in the level of 42 plasma proteins (including 24 FDA markers), of which 17 proteins showed a ≥1.5-fold change, and included proteins increased in North Americans (APOB, CRTAC1, C1QB, C1QC, C9, CRP, HP, IGHG1, IGKV4-1, SERPING1, RBP4, and AZGP1), as well as those elevated in Europeans (APOF, CD5L, HBG2, SELPLG, and TNA).

CONCLUSIONS

The results suggest a different contribution of specific (patho)physiological pathways (e.g., immune system and blood coagulation) to the development of socially significant diseases in Europeans and North Americans, and they should be taken into account when refining diagnostic panels.

Mass spectrometry-based proteomics as an emerging tool in clinical laboratories

Mass spectrometry (MS)-based proteomics have been increasingly implemented in various disciplines of laboratory medicine to identify and quantify biomolecules in a variety of biological specimens. MS-based proteomics is continuously expanding and widely applied in biomarker discovery for early detection, prognosis and markers for treatment response prediction and monitoring. Furthermore, making these advanced tests more accessible and affordable will have the greatest healthcare benefit.This review article highlights the new paradigms MS-based clinical proteomics has created in microbiology laboratories, cancer research and diagnosis of metabolic disorders. The technique is preferred over conventional methods in disease detection and therapy monitoring for its combined advantages in multiplexing capacity, remarkable analytical specificity and sensitivity and low turnaround time.Despite the achievements in the development and adoption of a number of MS-based clinical proteomics practices, more are expected to undergo transition from bench to bedside in the near future. The review provides insights from early trials and recent progresses (mainly covering literature from the NCBI database) in the application of proteomics in clinical laboratories.

Tidyproteomics: an open-source R package and data object for quantitative proteomics post analysis and visualization

BACKGROUND

The analysis of mass spectrometry-based quantitative proteomics data can be challenging given the variety of established analysis platforms, the differences in reporting formats, and a general lack of approachable standardized post-processing analyses such as sample group statistics, quantitative variation and even data filtering. We developed tidyproteomics to facilitate basic analysis, improve data interoperability and potentially ease the integration of new processing algorithms, mainly through the use of a simplified data-object.

RESULTS

The R package tidyproteomics was developed as both a framework for standardizing quantitative proteomics data and a platform for analysis workflows, containing discrete functions that can be connected end-to-end, thus making it easier to define complex analyses by breaking them into small stepwise units. Additionally, as with any analysis workflow, choices made during analysis can have large impacts on the results and as such, tidyproteomics allows researchers to string each function together in any order, select from a variety of options and in some cases develop and incorporate custom algorithms.

CONCLUSIONS

Tidyproteomics aims to simplify data exploration from multiple platforms, provide control over individual functions and analysis order, and serve as a tool to assemble complex repeatable processing workflows in a logical flow. Datasets in tidyproteomics are easy to work with, have a structure that allows for biological annotations to be added, and come with a framework for developing additional analysis tools. The consistent data structure and accessible analysis and plotting tools also offers a way for researchers to save time on mundane data manipulation tasks.

Identifying Novel Biomarkers Ready for Evaluation in Low-Prevalence Populations for the Early Detection of Lower Gastrointestinal Cancers: A Systematic Review and Meta-Analysis

INTRODUCTION

Lower gastrointestinal (GI) cancers are a major cause of cancer deaths worldwide. Prognosis improves with earlier diagnosis, and non-invasive biomarkers have the potential to aid with early detection. Substantial investment has been made into the development of biomarkers; however, studies are often carried out in specialist settings and few have been evaluated for low-prevalence populations.

METHODS

We aimed to identify novel biomarkers for the detection of lower GI cancers that have the potential to be evaluated for use in primary care. MEDLINE, Embase, Emcare and Web of Science were systematically searched for studies published in English from January 2000 to October 2019. Reference lists of included studies were also assessed. Studies had to report on measures of diagnostic performance for biomarkers (single or in panels) used to detect colorectal or anal cancers. We included all designs and excluded studies with fewer than 50 cases/controls. Data were extracted from published studies on types of biomarkers, populations and outcomes. Narrative synthesis was used, and measures of specificity and sensitivity were meta-analysed where possible.

RESULTS

We identified 142 studies reporting on biomarkers for lower GI cancers, for 24,844 cases and 45,374 controls. A total of 378 unique biomarkers were identified. Heterogeneity of study design, population type and sample source precluded meta-analysis for all markers except methylated septin 9 (mSEPT9) and pyruvate kinase type tumour M2 (TuM2-PK). The estimated sensitivity and specificity of mSEPT9 was 80.6% (95% CI 76.6-84.0%) and 88.0% (95% CI 79.1-93.4%) respectively; TuM2-PK had an estimated sensitivity of 81.6% (95% CI 75.2-86.6%) and specificity of 80.1% (95% CI 76.7-83.0%).

CONCLUSION

Two novel biomarkers (mSEPT9 and TuM2-PK) were identified from the literature with potential for use in lower-prevalence populations. Further research is needed to validate these biomarkers in primary care for screening and assessment of symptomatic patients.

Proteomics study of colorectal cancer and adenomatous polyps identifies TFR1, SAHH, and HV307 as potential biomarkers for screening

Colorectal cancer (CRC) is a malignant tumour with high morbidity and mortality worldwide. Efficient screening strategies for CRC and pre-cancerous lesions can promote early medical intervention and treatment, thereby reducing morbidity and mortality. Proteins are generally considered key biomarkers of cancer. Herein, we performed a quantitative, original-tissue proteomics study in a cohort of ninety patients from pre-cancerous to cancerous conditions via liquid chromatography-tandem mass spectrometry. In total, 134,812 peptides, 8697 proteins, 2355 union differentially expressed proteins (DEPs), and 409 shared DEPs (compared with adjacent tissues) were identified. The number of DEPs indicated a positive correlation with increasing severity of illness. The union and shared DEPs were both enriched in the KEGG pathway of focal adhesion, metabolism of xenobiotics by cytochrome P450, and drug metabolism by cytochrome P450. Among the 2355 union DEPs, 32 were selected for identification and validation by multiple reaction monitoring from twenty plasma specimens. Of these, three proteins, transferrin receptor protein 1 (TFR1), adenosylhomocysteinase (SAHH), and immunoglobulin heavy variable 3-7 (HV307), were significantly differentially expressed and displayed the same expression pattern in plasma as observed in the tissue data. In conclusion, TFR1, SAHH, and HV307 may be considered as potential biomarkers for CRC screening. SIGNIFICANCE: Although CRC is a malignant tumour with high morbidity and mortality worldwide, efficient screening strategies for CRC and pre-cancerous lesions can play an important role in addressing these issues. Screening of molecular biomarkers provide a non-invasive, cost-effective, and efficient approach. Proteins are generally considered key molecular biomarkers of cancer. Our study reports a quantitative proteomics analysis of protein biomarkers for colorectal cancer (CRC) and adenomatous polyps, and identifies TFR1, SAHH, and HV307 as potential biomarkers for screening. This research makes a significant contribution to the literature as although mass spectrometry-based proteomics research has been widely used for clinical research, its application to clinical translation as parallel specimens ranging from pre-cancerous to cancerous tissues-according to the degree of disease progression-has not been readily assessed.

A Meta-Analysis of Proteomic Blood Markers of Colorectal Cancer

BACKGROUND

Early diagnosis will significantly improve the survival rate of colorectal cancer (CRC); however, the existing methods for CRC screening were either invasive or inefficient. There is an emergency need for novel markers in CRC's early diagnosis. Serum proteomics has gained great potential in discovering novel markers, providing markers that reflect the early stage of cancer and prognosis prediction of CRC. In this paper, the results of proteomics of CRC studies were summarized through a meta-analysis in order to obtain the diagnostic efficiency of novel markers.

METHODS

A systematic search on bibliographic databases was performed to collect the studies that explore blood-based markers for CRC applying proteomics. The detection and validation methods, as well as the specificity and sensitivity of the biomarkers in these studies, were evaluated. Newcastle- Ottawa Scale (NOS) case-control studies version was used for quality assessment of included studies.

RESULTS

Thirty-four studies were selected from 751 studies, in which markers detected by proteomics were summarized. In total, fifty-nine proteins were classified according to their biological function. The sensitivity, specificity, or AUC varied among these markers. Among them, Mammalian STE20-like protein kinase 1/ Serine threonine kinase 4 (MST1/STK4), S100 calcium-binding protein A9 (S100A9), and Tissue inhibitor of metalloproteinases 1 (TIMP1) were suitable for effect sizes merging, and their diagnostic efficiencies were recalculated after merging. MST1/STK4 obtained a sensitivity of 68% and a specificity of 78%. S100A9 achieved a sensitivity of 72%, a specificity of 83%, and an AUC of 0.88. TIMP1 obtained a sensitivity of 42%, a specificity of 88%, and an AUC of 0.71.

CONCLUSION

MST1/STK4, S100A9, and TIMP1 showed excellent performance for CRC detection. Several other markers also presented optimized diagnostic efficacy for CRC early detection, but further verification is still needed before they are suitable for clinical use. The discovering of more efficient markers will benefit CRC treatment.

Multiplex quantitation of 270 plasma protein markers to identify a signature for early detection of colorectal cancer

Blood-based protein biomarker signatures might be an alternative or supplement to existing methods for early detection of colorectal cancer (CRC) for population-based screening. The objective of this study was to derive a protein biomarker signature for early detection of CRC and its precursor advanced adenoma (AA). In a two-stage design, 270 protein markers were measured by liquid chromatography/multiple reaction monitoring/mass spectrometry in plasma samples of discovery and validation sets. In the discovery set consisting of 100 newly diagnosed CRC cases and 100 age- and sex-matched controls free of neoplasm at screening colonoscopy, the algorithms predicting the presence of early- or late-stage CRC were derived by Lasso regression and .632 + bootstrap. The prediction algorithms were then externally validated in an independent validation set consisting of participants of screening colonoscopy including 56 participants with CRC, 99 with AA and 99 controls without any colorectal neoplasms. Three different signatures for all-, early- and late-stage CRC consisting of five-, three- and eight-protein markers were obtained in the discovery set with areas under the curves (AUCs) after .632 + bootstrap adjustment of 0.85, 0.83 and 0.96, respectively. External validation in the representative screening population yielded AUCs of 0.79 (95% CI, 0.70-0.86), 0.79 (95% CI, 0.66-0.89) and 0.80 (95% CI, 0.70-0.89) for all-, early- and late-stage CRCs, respectively. The three-marker early-stage algorithm yielded an AUC of 0.65 (95% CI, 0.56-0.73) for detection of AA in the validation set. Although not yet competitive with available stool-based tests for CRC early detection, the identified proteins may contribute to the development of powerful blood-based tests for early detection of CRC and its precursors AAs.

Urine Metabolic Fingerprints Encode Subtypes of Kidney Diseases

Metabolic fingerprints of biofluids encode diverse diseases and particularly urine detection offers complete non-invasiveness for diagnostics of the future. Present urine detection affords unsatisfactory performance and requires advanced materials to extract molecular information, due to the limited biomarkers and high sample complexity. Herein, we report plasmonic polymer@Ag for laser desorption/ionization mass spectrometry (LDI-MS) and sparse-learning-based metabolic diagnosis of kidney diseases. Using only 1 μL of urine without enrichment or purification, polymer@Ag afforded urine metabolic fingerprints (UMFs) by LDI-MS in seconds. Analysis by sparse learning discriminated lupus nephritis from various other non-lupus nephropathies and controls. We combined UMFs with urine protein levels (UPLs) and constructed a new diagnostic model to characterize subtypes of kidney diseases. Our work guides urine-based diagnosis and leads to new personalized analytical tools for other diseases.

An external quality assurance trial to assess mass spectrometry protein testing facilities for identifying multiple human peptides

The application of proteomic liquid chromatography mass spectrometry (LC-MS) for identifying proteins and peptides associated with human disease is rapidly growing in clinical diagnostics. However, the ability to accurately and consistently detect disease-associated peptides remains clinically uncertain. Variability in diagnostic testing occurs in part due to the absence of appropriate reference testing materials and standardised clinical guidelines for proteomic testing. In addition, multiple proteomic testing pipelines have not been fully assessed through external quality assurance (EQA). This trial was therefore devised to evaluate the performance of a small number of mass spectrometry (MS) testing facilities to (i) evaluate the EQA material for potential usage in a proteomic quality assurance program, and to (ii) identify key problem areas associated with human peptide testing. Five laboratories were sent six peptide reference testing samples formulated to contain a total of 35 peptides in differing ratios of light (natural) to heavy (labelled) peptides. Proficiency assessment of laboratory data used a modified approach to similarity and dissimilarity testing that was based on Bray-Curtis and Sorensen indices. Proficiency EQA concordant consensus values could not be derived from the assessed data since none of the laboratories correctly identified all reference testing peptides in all samples. However, the produced data may be reflective of specific inter-laboratory differences for detecting multiple peptides since no two testing pipelines used were the same for any laboratory. In addition, laboratory feedback indicated that peptide filtering of the reference material was a common key problem area prior to analysis. These data highlight the importance of an EQA programme for identifying underlying testing issues so that improvements can be made and confidence for clinical diagnostic analysis can be attained.

Applications of multiple reaction monitoring targeted proteomics assays in human plasma

Introduction: Multiple (or selected) reaction monitoring-mass spectrometry (MRM/SRM) is a targeted proteomic method that can be used for relative and absolute quantification. Multiple reports exist supporting the potential of the approach in proteomic biomarker validation. Areas covered: To get an overview of the applications of MRM in protein quantification in plasma, a search in MedLine/PubMed was performed using the keywords: 'MRM/SRM plasma proteomic/proteomics/proteome'. The retrieved studies were further filtered to focus on disease biomarkers and the main results are summarized. Expert opinion: MRM is increasingly employed for the quantification of both well-established but also newly discovered putative biomarkers and occasionally their post-translationally modified forms in plasma. Fractionation is regularly required for the detection of low abundance proteins. Standardized procedures to facilitate assay establishment and marker quantification have been proposed and, in few cases, implemented. Nevertheless, in most cases, absolute quantification is not performed. To advance, multiple technical issues including the regular use of standard labeled peptides and appropriate quality controls to monitor assay performance should be considered. Additionally, clinical aspects involving careful study design to address biomarker clinical use should also be considered.

Proteomics biomarkers for solid tumors: Current status and future prospects

Cancer is a heterogeneous multifactorial disease, which continues to be one of the main causes of death worldwide. Despite the extensive efforts for establishing accurate diagnostic assays and efficient therapeutic schemes, disease prevalence is on the rise, in part, however, also due to improved early detection. For years, studies were focused on genomics and transcriptomics, aiming at the discovery of new tests with diagnostic or prognostic potential. However, cancer phenotypic characteristics seem most likely to be a direct reflection of changes in protein metabolism and function, which are also the targets of most drugs. Investigations at the protein level are therefore advantageous particularly in the case of in-depth characterization of tumor progression and invasiveness. Innovative high-throughput proteomic technologies are available to accurately evaluate cancer formation and progression and to investigate the functional role of key proteins in cancer. Employing these new highly sensitive proteomic technologies, cancer biomarkers may be detectable that contribute to diagnosis and guide curative treatment when still possible. In this review, the recent advances in proteomic biomarker research in cancer are outlined, with special emphasis placed on the identification of diagnostic and prognostic biomarkers for solid tumors. In view of the increasing number of screening programs and clinical trials investigating new treatment options, we discuss the molecular connections of the biomarkers as well as their potential as clinically useful tools for diagnosis, risk stratification and therapy monitoring of solid tumors.

A large-scale and robust dynamic MRM study of colorectal cancer biomarkers

Over the past 20 years, mass spectrometry (MS) has emerged as a dynamic tool for proteomics biomarker discovery. However, published MS biomarker candidates often do not translate to the clinic, failing during attempts at independent replication. The cause can be shortcomings in study design, sample quality, assay quantitation, and/or quality/process control. To address these shortcomings, we developed an MS workflow in accordance with Tier 2 measurement requirements for targeted peptides, defined by the Clinical Proteomic Tumor Analysis Consortium (CPTAC) "fit-for-purpose" approach, using dynamic multiple reaction monitoring (dMRM), which measures specific peptide transitions during predefined retention time (RT) windows. We describe the development of a robust multipex dMRM assay measuring 641 proteotypic peptides from 392 colorectal cancer (CRC) related proteins, and the procedures to track and handle sample processing and instrument variation over a four-month study, during which the assay measured blood samples from 1045 patients with CRC symptoms. After data collection, transitions were filtered by signal quality metrics before entering receiver operating characteristic (ROC) analysis. The results demonstrated CRC signal carried by 127 proteins in the symptomatic population. The workflow might be further developed to build Tier 1 assays for clinical tests identifying symptomatic individuals at elevated risk of CRC.

SIGNIFICANCE

We developed a dMRM MS method with the rigor of a Tier 2 assay as defined by the CPTAC 'fit for purpose approach' [1]. Using quality and process control procedures, the assay was used to quantify 641 proteotypic peptides representing 392 CRC-related proteins in plasma from 1045 CRC-symptomatic patients. To our knowledge, this is the largest MRM method applied to the largest study to date. The results showed that 127 of the proteins carried univariate CRC signal in the symptomatic population. This large number of single biomarkers bodes well for future development of multivariate classifiers to distinguish CRC in the symptomatic population.

Noninvasive Biomarkers of Colorectal Cancer: Role in Diagnosis and Personalised Treatment Perspectives

Colorectal cancer (CRC) is the third leading cause of cancer-related deaths worldwide. It has been estimated that more than one-third of patients are diagnosed when CRC has already spread to the lymph nodes. One out of five patients is diagnosed with metastatic CRC. The stage of diagnosis influences treatment outcome and survival. Notwithstanding the recent advances in multidisciplinary management and treatment of CRC, patients are still reluctant to undergo screening tests because of the associated invasiveness and discomfort (e.g., colonoscopy with biopsies). Moreover, the serological markers currently used for diagnosis are not reliable and, even if they were useful to detect disease recurrence after treatment, they are not always detected in patients with CRC (e.g., CEA). Recently, translational research in CRC has produced a wide spectrum of potential biomarkers that could be useful for diagnosis, treatment, and follow-up of these patients. The aim of this review is to provide an overview of the newer noninvasive or minimally invasive biomarkers of CRC. Here, we discuss imaging and biomolecular diagnostics ranging from their potential usefulness to obtain early and less-invasive diagnosis to their potential implementation in the development of a bespoke treatment of CRC.

Cell-Free DNA Blood Collection Tubes Are Appropriate for Clinical Proteomics: A Demonstration in Colorectal Cancer

BACKGROUND

Optimized blood collection tubes (BCT) have been developed to expand the utility of plasma cell-free DNA (cfDNA) and are in clinical use. The appropriateness of plasma collected and stored in these tubes for proteomic analysis is unknown.

METHODS

Paired blood samples were collected in BCT and traditional K3EDTA (EDTA) tubes from healthy controls and from colorectal cancer (CRC) patients before and after surgery, and stored for between 45 min and 48 h at room temperature. Plasma proteins were analyzed following high-abundant plasma protein depletion in quantitative discovery and targeted proteomics by liquid chromatography tandem-mass spectrometry (LC-MS/MS).

RESULTS

BCT reduced cellular protein contamination in healthy controls over time, and increased the number of high confident low-abundant protein identifications in CRC blood samples compared to matched samples collected in EDTA tubes. The known CRC plasma protein biomarker, carcinoembryonic antigen (CEA), showed elevated levels across patients pre-operatively when collected and stored in BCT compared to EDTA tubes. Emerging CRC biomarkers, Dickkopf-3 (DKK3) and Gelsolin (GSN), showed elevated levels pre-operatively when collected in BCT.

CONCLUSIONS

Optimized BCT are appropriate for low-abundant plasma protein analysis and can be used with confidence for clinical proteomics.

Analytical validation of a novel multiplex test for detection of advanced adenoma and colorectal cancer in symptomatic patients

Early detection of colorectal cancer (CRC) is key to reducing associated mortality. Despite the importance of early detection, approximately 40% of individuals in the United States between the ages of 50-75 have never been screened for CRC. The low compliance with colonoscopy and fecal-based screening may be addressed with a non-invasive alternative such as a blood-based test. We describe here the analytical validation of a multiplexed blood-based assay that measures the plasma concentrations of 15 proteins to assess advanced adenoma (AA) and CRC risk in symptomatic patients. The test was developed on an electrochemiluminescent immunoassay platform employing four multi-marker panels, to be implemented in the clinic as a laboratory developed test (LDT). Under the Clinical Laboratory Improvement Amendments (CLIA) and College of American Pathologists (CAP) regulations, a United States-based clinical laboratory utilizing an LDT must establish performance characteristics relating to analytical validity prior to releasing patient test results. This report describes a series of studies demonstrating the precision, accuracy, analytical sensitivity, and analytical specificity for each of the 15 assays, as required by CLIA/CAP. In addition, the report describes studies characterizing each of the assays' dynamic range, parallelism, tolerance to common interfering substances, spike recovery, and stability to sample freeze-thaw cycles. Upon completion of the analytical characterization, a clinical accuracy study was performed to evaluate concordance of AA and CRC classifier model calls using the analytical method intended for use in the clinic. Of 434 symptomatic patient samples tested, the percent agreement with original CRC and AA calls was 87% and 92% respectively. All studies followed CLSI guidelines and met the regulatory requirements for implementation of a new LDT. The results provide the analytical evidence to support the implementation of the novel multi-marker test as a clinical test for evaluating CRC and AA risk in symptomatic individuals.

Quantitative mass spectrometry analysis reveals a panel of nine proteins as diagnostic markers for colon adenocarcinomas

Adenocarcinomas are cancers originating from the gland forming cells of the colon and rectal lining, and are known to be the most common type of colorectal cancers. The current diagnosis strategies for colorectal cancers include biopsy, laboratory tests, and colonoscopy which are time consuming. Identification of protein biomarkers could aid in the detection of colon adenocarcinomas (CACs). In this study, tissue proteome of colon adenocarcinomas (n = 11) was compared with the matched control specimens (n = 11) using isobaric tags for relative and absolute quantitation (iTRAQ) based liquid chromatography-mass spectrometry (LC-MS/MS) approach. A list of 285 significantly altered proteins was identified in colon adenocarcinomas as compared to its matched controls, which are associated with growth and malignancy of the tumors. Protein interaction analysis revealed the association of altered proteins in colon adenocarcinomas with various transcription factors and their targets. A panel of nine proteins was validated using multiple reaction monitoring (MRM). Additionally, S100A9 was also validated using immunoblotting. The identified panel of proteins may serve as potential biomarkers and thereby aid in the detection of colon adenocarcinomas.

Blood-Based Protein Signatures for Early Detection of Colorectal Cancer: A Systematic Review

OBJECTIVES

Blood-based proteins might be an attractive option for early detection of colorectal cancer (CRC), but individually they are unlikely to achieve the diagnostic performance required for population based screening. We aimed at summarizing current evidence of diagnostic performance of signatures based on multiple proteins for early detection of CRC.

METHODS

A systematic literature review adhering to the PRISMA (preferred reporting items for systematic reviews and meta-analysis) guidelines was performed. PubMed and Web of Science databases were searched for potentially relevant studies published until 28th August, 2017. Relevant studies were identified by predefined eligibility criteria. Estimates of indicators of diagnostic performance such as sensitivity, specificity, and the area under the curve (AUC), along with information on validation and other key methodological procedures were extracted. Study quality was assessed by a QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies 2) instrument tool.

RESULTS

Thirty six eligible studies with numbers of CRC cases ranging from 23 to 512 and the number of proteins included in signatures ranged from 3 to 13 were identified. Reported Youden's Index and AUC ranged from 0.19 to 0.95 and from 0.62 to 0.996, respectively. However most studies, especially those reporting better diagnostic performance, were conducted in clinical rather than screening setting and many studies lacked any internal or external validation of identified algorithm.

CONCLUSIONS

Blood-based tests using signatures of multiple proteins may be a promising approach for non-invasive CRC screening. However, promising signatures identified in clinical settings still require rigorous evaluation in large studies conducted in true screening setting.

Proteomics for early detection of colorectal cancer: recent updates

INTRODUCTION

Colorectal cancer (CRC) is a common type of cancer with a relatively poor survival rate. The survival rate of patients could be improved if CRC is detected early. Biomarkers associated with early stages of tumor development might provide useful tools for the early diagnosis of colorectal cancer. Areas covered: Online searches using PubMed and Google Scholar were performed using keywords and with a focus on recent proteomic studies. The aim of this review is to highlight the need for biomarkers to improve the detection rate of early CRC and provide an overview of proteomic technologies used for biomarker discovery and validation. This review will also discuss recent proteomic studies which focus on identifying biomarkers associated with the early stages of CRC development. Expert commentary: A large number of CRC biomarkers are increasingly being identified by proteomics using diverse approaches. However, the clinical relevance and introduction of these markers into clinical practice cannot be determined without a robust validation process. The size of validation cohorts remains a major limitation in many biomarker studies.

Discovery and validation of a colorectal cancer classifier in a new blood test with improved performance for high-risk subjects

BACKGROUND

The aim was to improve upon an existing blood-based colorectal cancer (CRC) test directed to high-risk symptomatic patients, by developing a new CRC classifier to be used with a new test embodiment. The new test uses a robust assay format-electrochemiluminescence immunoassays-to quantify protein concentrations. The aim was achieved by building and validating a CRC classifier using concentration measures from a large sample set representing a true intent-to-test (ITT) symptomatic population.

METHODS

4435 patient samples were drawn from the Endoscopy II sample set. Samples were collected at seven hospitals across Denmark between 2010 and 2012 from subjects with symptoms of colorectal neoplasia. Colonoscopies revealed the presence or absence of CRC. 27 blood plasma proteins were selected as candidate biomarkers based on previous studies. Multiplexed electrochemiluminescence assays were used to measure the concentrations of these 27 proteins in all 4435 samples. 3066 patients were randomly assigned to the Discovery set, in which machine learning was used to build candidate classifiers. Some classifiers were refined by allowing up to a 25% indeterminate score range. The classifier with the best Discovery set performance was successfully validated in the separate Validation set, consisting of 1336 samples.

RESULTS

The final classifier was a logistic regression using ten predictors: eight proteins (A1AG, CEA, CO9, DPPIV, MIF, PKM2, SAA, TFRC), age, and gender. In validation, the indeterminate rate of the new panel was 23.2%, sensitivity/specificity was 0.80/0.83, PPV was 36.5%, and NPV was 97.1%.

CONCLUSIONS

The validated classifier serves as the basis of a new blood-based CRC test for symptomatic patients. The improved performance, resulting from robust concentration measures across a large sample set mirroring the ITT population, renders the new test the best available for this population. Results from a test using this classifier can help assess symptomatic patients' CRC risk, increase their colonoscopy compliance, and manage next steps in their care.

International scientific communications in the field of colorectal tumour markers

AIM

To analyze scientometrically the dynamic science internationalization on colorectal tumour markers as reflected in five information portals and to outline the significant journals, scientists and institutions.

METHODS

A retrospective problem-oriented search was performed in Web of Science Core Collection (WoS), MEDLINE, BIOSIS Citation Index (BIOSIS) and Scopus for 1986-2015 as well as in Dervent Innovations Index (Derwent) for 1995-2015. Several specific scientometric parameters of the publication output and citation activity were comparatively analyzed. The following scientometric parameters were analyzed: (1) annual dynamics of publications; (2) scientific institutions; (3) journals; (4) authors; (5) scientific forums; (6) patents - number of patents, names and countries of inventors, and (7) citations (number of citations to publications by single authors received in WoS, BIOSIS Citation Index and Scopus).

RESULTS

There is a trend towards increasing publication output on colorectal tumour markers worldwide along with high citation rates. Authors from 70 countries have published their research results in journals and conference proceedings in 21 languages. There is considerable country stratification similar to that in most systematic investigations. The information provided to end users and scientometricians varies between these data-bases in terms of most parameters due to different journal coverage, indexing systems and editorial policy. The lists of the so-called “core” journals and most productive authors in WoS, BIOSIS, MEDLINE and Scopus along with the list of the most productive authors - inventors in Derwent present a particular interest to the beginners in the field, the institutional and national science managers and the journal editorial board members. The role of the purposeful assessment of scientific forums and patents is emphasized.

CONCLUSION

Our results along with this problem-oriented collection containing the researchers’ names, addresses and publications could contribute to a more effective international collaboration of the coloproctologists from smaller countries and thus improve their visibility on the world information market.

Multiplexed panel of precisely quantified salivary proteins for biomarker assessment

An increasingly popular "absolute" quantitative technique involves the SRM or MRM approach with stable isotope-labeled standards (SIS). Using this approach, many proteins in human plasma/serum have been quantified for biomarker assessment and disease stratification. Due to the complexity of plasma and the invasive nature of its collection, alternative biosamples are currently being explored. Here, we present the broadest panel of multiplexed MRM assays with SIS peptides for saliva proteins developed to date. The validated panel consists of 158 candidate human saliva protein biomarkers, inferred from 244 interference-free peptides. The resulting concentrations were reproducibly quantified over a 6 order-of-magnitude concentration range (from 218 μg/mL to 88 pg/mL; average CVs of 12% over analytical triplicates). All concentrations were determined from reverse standard curves, which were generated using a constant concentration of endogenous material with varying concentrations of spiked-in SIS peptides. The large-scale screening of the soluble and membrane-associated proteins contained within the 158-plex assay could present new opportunities for biomarker assessment and clinical diagnostics.

In Silico Prediction of Gamma-Aminobutyric Acid Type-A Receptors Using Novel Machine-Learning-Based SVM and GBDT Approaches

Gamma-aminobutyric acid type-A receptors (GABA_ARs) belong to multisubunit membrane spanning ligand-gated ion channels (LGICs) which act as the principal mediators of rapid inhibitory synaptic transmission in the human brain. Therefore, the category prediction of GABA_ARs just from the protein amino acid sequence would be very helpful for the recognition and research of novel receptors. Based on the proteins' physicochemical properties, amino acids composition and position, a GABA_AR classifier was first constructed using a 188-dimensional (188D) algorithm at 90% cd-hit identity and compared with pseudo-amino acid composition (PseAAC) and ProtrWeb web-based algorithms for human GABA_AR proteins. Then, four classifiers including gradient boosting decision tree (GBDT), random forest (RF), a library for support vector machine (libSVM), and k-nearest neighbor (k-NN) were compared on the dataset at cd-hit 40% low identity. This work obtained the highest correctly classified rate at 96.8% and the highest specificity at 99.29%. But the values of sensitivity, accuracy, and Matthew's correlation coefficient were a little lower than those of PseAAC and ProtrWeb; GBDT and libSVM can make a little better performance than RF and k-NN at the second dataset. In conclusion, a GABA_AR classifier was successfully constructed using only the protein sequence information.