Lessons from controversy: ovarian cancer screening and serum proteomics

Genetics of Wool and Cashmere Fibre: Progress, Challenges, and Future Research

Wool (sheep) and cashmere (goat) fibres have unique biological, physical, and chemical properties and these fibres are becoming more important as the demand for natural products increases. However, these complex protein fibres are at times compromised by natural variability in their properties, and this can impact their use and value. Genetic improvement via selection and breeding can partly overcome this problem, enabling the farming of sheep and goats that produce more desirable fibre. This review explores the challenges in improving wool and cashmere fibre characteristics using genetics, with a focus on improving our understanding of the key protein components of fibres, wool keratins and keratin-associated proteins (KAPs). Despite progress in our knowledge of these proteins, gaining a better understanding of them and how they affect these fibres remains an ongoing challenge. This is not straight-forward, given the large number of similar yet unique genes that produce the proteins and the gaps that remain in their identification and characterisation. More research is required to clarify gene and protein sequence variability and the location and patterns of gene expression, which in turn limits our understanding of fibre growth and variation. Several aspects that currently hinder our progress in this quest include the incomplete identification of all the genes and weaknesses in the approaches used to characterise them, including newer omics technologies. We describe future research directions and challenges, including the need for ongoing gene identification, variation characterisation, and gene expression analysis and association studies to enable further improvement to these valuable natural fibres.

Omics technology draws a comprehensive heavy metal resistance strategy in bacteria

The rapid industrial revolution significantly increased heavy metal pollution, becoming a major global environmental concern. This pollution is considered as one of the most harmful and toxic threats to all environmental components (air, soil, water, animals, and plants until reaching to human). Therefore, scientists try to find a promising and eco-friendly technique to solve this problem i.e., bacterial bioremediation. Various heavy metal resistance mechanisms were reported. Omics technologies can significantly improve our understanding of heavy metal resistant bacteria and their communities. They are a potent tool for investigating the adaptation processes of microbes in severe conditions. These omics methods provide unique benefits for investigating metabolic alterations, microbial diversity, and mechanisms of resistance of individual strains or communities to harsh conditions. Starting with genome sequencing which provides us with complete and comprehensive insight into the resistance mechanism of heavy metal resistant bacteria. Moreover, genome sequencing facilitates the opportunities to identify specific metal resistance genes, operons, and regulatory elements in the genomes of individual bacteria, understand the genetic mechanisms and variations responsible for heavy metal resistance within and between bacterial species in addition to the transcriptome, proteome that obtain the real expressed genes. Moreover, at the community level, metagenome, meta transcriptome and meta proteome participate in understanding the microbial interactive network potentially novel metabolic pathways, enzymes and gene species can all be found using these methods. This review presents the state of the art and anticipated developments in the use of omics technologies in the investigation of microbes used for heavy metal bioremediation.

Mass Spectrometry-based Proteomics of Epithelial Ovarian Cancers: a Clinical Perspective

Increasing proteomic studies focused on epithelial ovarian cancer (EOC) have attempted to identify early disease biomarkers, establish molecular stratification, and discover novel druggable targets. Here we review these recent studies from a clinical perspective. Multiple blood proteins have been used clinically as diagnostic markers. The ROMA test integrates CA125 and HE4, while the OVA1 and OVA2 tests analyze multiple proteins identified by proteomics. Targeted proteomics has been widely used to identify and validate potential diagnostic biomarkers in EOCs, but none has yet been approved for clinical adoption. Discovery proteomic characterization of bulk EOC tissue specimens has uncovered a large number of dysregulated proteins, proposed new stratification schemes, and revealed novel targets of therapeutic potential. A major hurdle facing clinical translation of these stratification schemes based on bulk proteomic profiling is intra-tumor heterogeneity, namely that single tumor specimens may harbor molecular features of multiple subtypes. We reviewed over 2500 interventional clinical trials of ovarian cancers since 1990, and cataloged 22 types of interventions adopted in these trials. Among 1418 clinical trials which have been completed or are not recruiting new patients, about 50% investigated chemotherapies. Thirty-seven clinical trials are at phase 3 or 4, of which 12 focus on PARP, 10 on VEGFR, 9 on conventional anti-cancer agents, and the remaining on sex hormones, MEK1/2, PD-L1, ERBB, and FRα. Although none of the foregoing therapeutic targets were discovered by proteomics, newer targets discovered by proteomics, including HSP90 and cancer/testis antigens, are being tested also in clinical trials. To accelerate the translation of proteomic findings to clinical practice, future studies need to be designed and executed to the stringent standards of practice-changing clinical trials. We anticipate that the rapidly evolving technology of spatial and single-cell proteomics will deconvolute the intra-tumor heterogeneity of EOCs, further facilitating their precise stratification and superior treatment outcomes.

Evaluating and minimizing batch effects in metabolomics

Determining metabolomic differences among samples of different phenotypes is a critical component of metabolomics research. With the rapid advances in analytical tools such as ultrahigh-resolution chromatography and mass spectrometry, an increasing number of metabolites can now be profiled with high quantification accuracy. The increased detectability and accuracy raise the level of stringiness required to reduce or control any experimental artifacts that can interfere with the measurement of phenotype-related metabolome changes. One of the artifacts is the batch effect that can be caused by multiple sources. In this review, we discuss the origins of batch effects, approaches to detect interbatch variations, and methods to correct unwanted data variability due to batch effects. We recognize that minimizing batch effects is currently an active research area, yet a very challenging task from both experimental and data processing perspectives. Thus, we try to be critical in describing the performance of a reported method with the hope of stimulating further studies for improving existing methods or developing new methods.

Identification of CDCA8, DSN1 and BIRC5 in Regulating Cell Cycle and Apoptosis in Osteosarcoma Using Bioinformatics and Cell Biology

Introduction:

Osteosarcoma is the most common primary tumor of bone, although some molecular markers have been identified, the detailed molecular mechanisms underlying osteosarcoma are currently not fully understood. In the present study, we attempted to identify the potential key genes and pathways in osteosarcoma using bioinformatics analysis.

Methods:

GSE14359 was downloaded from the GEO database, and analyzed using Limma package. Gene Ontology and pathway enrichment analyses of the DEGs were performed in the DAVID database, followed by the construction of a protein–protein interaction (PPI) network with software Cytoscape, subnetwork modules were subsequently identified and analyzed, and further validation in human osteosarcoma tissues and osteosarcoma cells line was performed.

Results:

964 Differentially expressed genes (DEGs) identified, of which 222 were up-regulated and 742 were down-regulated. Among them, 10 genes (including BIRC5, MAD2L1, Bub1, DSN1, SPC24, CDCA8, STAG2, CENPA, MLF1IP and Mis12) were identified as hub genes and they were mainly enriched in pathways, including mRNA surveillance, RNA transport and PI3K-Akt signaling pathways. Further validation indicated 6 gene (DSN1, BIRC5, CDCA8, MLF1IP, MAD2L1 and SPC24) is highly expressed in osteosarcoma tissues. Among them, CDCA8, DSN1 and BIRC5 significantly promoted the proliferation of osteosarcoma cells in vitro. In terms of mechanism, DSN1 and CDCA8 were mainly involved in cell cycle regulation, while BIRC5 was mainly involved in the regulation of apoptosis pathway.

Conclusions:

We identified some key genes and pathways in osteosarcoma, which might be used as molecular targets or diagnostic biomarker for the diagnosis and therapy of osteosarcoma.

Challenges and Opportunities in Clinical Applications of Blood-Based Proteomics in Cancer

Simple Summary

The traditional approach in identifying cancer related protein biomarkers has focused on evaluation of a single peptide/protein in tissue or circulation. At best, this approach has had limited success for clinical applications, since multiple pathological tumor pathways may be involved during initiation or progression of cancer which diminishes the significance of a single candidate protein/peptide. Emerging sensitive proteomic based technologies like liquid chromatography mass spectrometry (LC-MS)-based quantitative proteomics can provide a platform for evaluating serial serum or plasma samples to interrogate secreted products of tumor–host interactions, thereby revealing a more “complete” repertoire of biological variables encompassing heterogeneous tumor biology. However, several challenges need to be met for successful application of serum/plasma based proteomics. These include uniform pre-analyte processing of specimens, sensitive and specific proteomic analytical platforms and adequate attention to study design during discovery phase followed by validation of discovery-level signatures for prognostic, predictive, and diagnostic cancer biomarker applications.

Abstract

Blood is a readily accessible biofluid containing a plethora of important proteins, nucleic acids, and metabolites that can be used as clinical diagnostic tools in diseases, including cancer. Like the on-going efforts for cancer biomarker discovery using the liquid biopsy detection of circulating cell-free and cell-based tumor nucleic acids, the circulatory proteome has been underexplored for clinical cancer biomarker applications. A comprehensive proteome analysis of human serum/plasma with high-quality data and compelling interpretation can potentially provide opportunities for understanding disease mechanisms, although several challenges will have to be met. Serum/plasma proteome biomarkers are present in very low abundance, and there is high complexity involved due to the heterogeneity of cancers, for which there is a compelling need to develop sensitive and specific proteomic technologies and analytical platforms. To date, liquid chromatography mass spectrometry (LC-MS)-based quantitative proteomics has been a dominant analytical workflow to discover new potential cancer biomarkers in serum/plasma. This review will summarize the opportunities of serum proteomics for clinical applications; the challenges in the discovery of novel biomarkers in serum/plasma; and current proteomic strategies in cancer research for the application of serum/plasma proteomics for clinical prognostic, predictive, and diagnostic applications, as well as for monitoring minimal residual disease after treatments. We will highlight some of the recent advances in MS-based proteomics technologies with appropriate sample collection, processing uniformity, study design, and data analysis, focusing on how these integrated workflows can identify novel potential cancer biomarkers for clinical applications.

The Heterogeneity Problem: Approaches to Identify Psychiatric Subtypes

The imprecise nature of psychiatric nosology restricts progress towards characterizing and treating mental health disorders. One issue is the 'heterogeneity problem': different causal mechanisms may relate to the same disorder, and multiple outcomes of interest can occur within one individual. Our review tackles this heterogeneity problem, providing considerations, concepts, and approaches for investigators examining human cognition and mental health. We highlight the difficulty of pure dimensional approaches due to 'the curse of dimensionality'. Computationally, we consider supervised and unsupervised statistical approaches to identify putative subtypes within a population. However, we emphasize that subtype identification should be linked to a particular outcome or question. We conclude with novel hybrid approaches that can identify subtypes tied to outcomes, and may help advance precision diagnostic and treatment tools.

Identification of potential genes and pathways for response prediction of neoadjuvant chemoradiotherapy in patients with rectal cancer by systemic biological analysis

Currently, neoadjuvant chemoradiotherapy (CRT) followed by radical surgery is the standard of care for locally advanced rectal cancer. However, to the best of our knowledge, there are no effective biomarkers for predicting patients who may benefit from neoadjuvant treatment. The aim of the current study was to screen potential crucial genes and pathways associated with the response to CRT in rectal cancer, and provide valid biological information to assist further investigation of CRT optimization. In the current study, differentially expressed (DE) genes were identified from the tumor samples of responders and non-responders to neoadjuvant CRT in the GSE35452 gene expression profile. Seven hub genes and one significant module were identified from the protein-protein interaction (PPI) network. Functional enrichment analysis of all the DE genes and the hub genes, retrieved from PPI network analysis, revealed their associations with CRT response. Genes were identified that may be used to discriminate patients who would or would not clinically benefit from neoadjuvant CRT. Several important pathways enriched by the DE genes, hub genes and selected module were identified, and revealed to be closely associated with radiation response, including excision repair, homologous recombination, Ras signaling pathway, the forkhead box O signaling pathway, focal adhesion and the Wnt signaling pathway. In conclusion, the current study demonstrated that the identified gene signatures and pathways may be used as molecular biomarkers for predicting CRT response. Furthermore, combinations of these biomarkers may be helpful for optimizing CRT treatment and promoting understanding of the molecular basis of response differences; this needs to be confirmed by further experiments.

Pre- and Post-analytical Factors in Biomarker Discovery

The translation of promising biomarkers, which were identified in biomarker discovery experiments, to clinical assays is one of the key challenges in present-day proteomics research. Many so-called "biomarker candidates" fail to progress beyond the discovery phase, and much emphasis is placed on pre- and post-analytical variability in an attempt to provide explanations for this bottleneck in the biomarker development pipeline. With respect to such variability, there is a large number of pre- and post-analytical factors which may impact the outcomes of proteomics experiments and thus necessitate tight control. This chapter highlights some of these factors and provides guidance for addressing them on the basis of examples from previously published proteomics studies.

Diagnostic Application of Serum Proteomic Patterns in Gastric Cancer Patients by ProteinChip Surface-Enhanced Laser Desorption/Ionization Time-of-Flight Mass Spectrometry

Surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS) is one of the currently used techniques to identify biomarkers for cancers. This study was planned to establish a system to accurately distinguish gastric cancer patients by using SELDI-TOF-MS. A total of 100 serum samples obtained from 60 individuals with gastric cancer and 40 healthy individuals were screened. Protein expression profiles were expressed on CM10 ProteinChip arrays and analyzed. Peak intensities were analyzed with the Biomarker Wizard software to identify peaks showing significantly different intensities between normal and cancer groups. Classification analysis and construction of decision trees were done with the Biomarker Pattern software 5.0. Seventeen protein peaks showed significant differences between the two groups. The decision tree which gave the highest discrimination included four peaks at mass 5,919, 8,583, 10,286, and 13,758 as splitters. The sensitivity and specificity for classification of the decision tree were 96.7% (58/60) and 97.5% (39/40), respectively. When the protein biomarker pattern was tested on a blinded test set, it yielded a sensitivity of 93.3% (28/30) and a specificity of 90% (18/20). These results suggest that serum protein profiling by the SELDI system may distinguish gastric cancer patients from healthy controls with relatively high sensitivity and specificity.

A Guide to Illumina BeadChip Data Analysis

The Illumina Infinium BeadChips are a powerful array-based platform for genome-wide DNA methylation profiling at approximately 485,000 (450K) and 850,000 (EPIC) CpG sites across the genome. The platform is used in many large-scale population-based epigenetic studies of complex diseases, environmental exposures, or other experimental conditions. This chapter provides an overview of the key steps in analyzing Illumina BeadChip data. We describe key preprocessing steps including data extraction and quality control as well as normalization strategies. We further present principles and guidelines for conducting association analysis at the individual CpG level as well as more sophisticated pathway-based association tests.

Comparison of Computational Algorithms for the Classification of Liver Cancer using SELDI Mass Spectrometry: A Case Study

Introduction

As an alternative to DNA microarrays, mass spectrometry based analysis of proteomic patterns has shown great potential in cancer diagnosis. The ultimate application of this technique in clinical settings relies on the advancement of the technology itself and the maturity of the computational tools used to analyze the data. A number of computational algorithms constructed on different principles are available for the classification of disease status based on proteomic patterns. Nevertheless, few studies have addressed the difference in the performance of these approaches. In this report, we describe a comparative case study on the classification accuracy of hepatocellular carcinoma based on the serum proteomic pattern generated from a Surface Enhanced Laser Desorption/Ionization (SELDI) mass spectrometer.

Methods

Nine supervised classification algorithms are implemented in R software and compared for the classification accuracy.

Results

We found that the support vector machine with radial function is preferable as a tool for classification of hepatocellular carcinoma using features in SELDI mass spectra. Among the rest of the methods, random forest and prediction analysis of microarrays have better performance. A permutation-based technique reveals that the support vector machine with a radial function seems intrinsically superior in learning from the training data since it has a lower prediction error than others when there is essentially no differential signal. On the other hand, the performance of the random forest and prediction analysis of microarrays rely on their capability of capturing the signals with substantial differentiation between groups.

Conclusions

Our finding is similar to a previous study, where classification methods based on the Matrix Assisted Laser Desorption/Ionization (MALDI) mass spectrometry are compared for the prediction accuracy of ovarian cancer. The support vector machine, random forest and prediction analysis of microarrays provide better prediction accuracy for hepatocellular carcinoma using SELDI proteomic data than six other approaches.

Waste, Leaks, and Failures in the Biomarker Pipeline

BACKGROUND

The large, expanding literature on biomarkers is characterized by almost ubiquitous significant results, with claims about the potential importance, but few of these discovered biomarkers are used in routine clinical care.

CONTENT

The pipeline of biomarker development includes several specific stages: discovery, validation, clinical translation, evaluation, implementation (and, in the case of nonutility, deimplementation). Each of these stages can be plagued by problems that cause failures of the overall pipeline. Some problems are nonspecific challenges for all biomedical investigation, while others are specific to the peculiarities of biomarker research. Discovery suffers from poor methods and incomplete and selective reporting. External independent validation is limited. Selection for clinical translation is often shaped by nonrational choices. Evaluation is sparse and the clinical utility of many biomarkers remains unknown. The regulatory environment for biomarkers remains weak and guidelines can reach biased or divergent recommendations. Removing inefficient or even harmful biomarkers that have been entrenched in clinical care can meet with major resistance.

SUMMARY

The current biomarker pipeline is too prone to failures. Consideration of clinical needs should become a starting point for the development of biomarkers. Improvements can include the use of more stringent methodology, better reporting, larger collaborative studies, careful external independent validation, preregistration, rigorous systematic reviews and umbrella reviews, pivotal randomized trials, and implementation and deimplementation studies. Incentives should be aligned toward delivering useful biomarkers.

Proteomics as a Tool for Clinically Relevant Biomarker Discovery and Validation

The excitement associated with clinical applications of proteomics was initially focused on its potential to serve as a vehicle for both biomarker discovery and drug discovery and routine clinical sample analysis. Some approaches were thought to be able to “identify” mass spectral characteristics that distinguished between control and disease samples, and thereafter it was believed that the same tool could be employed to screen samples in a high-throughput clinical setting. However, this has been difficult to achieve, and the early promise is yet to be fully realized. While we see an important place for mass spectrometry in drug and biomarker discovery, we believe that alternative strategies will prove more fruitful for routine analysis. Here we discuss the power and versatility of 2D gels and mass spectrometry in the discovery phase of biomarker work but argue that it is better to rely on immunochemical methods for high-throughput validation and routine assay applications.

Evolving Role of Serum Biomarkers in the Management of Ovarian Cancer

The availability of an ideal serum tumor marker would be of great clinical benefit for both the diagnosis and management of patients with epithelial ovarian cancer. Serum cancer antigen 125 assay significantly increases the diagnostic reliability of ultrasound in discriminating a malignant from a benign ovarian mass, especially in postmenopausal women, and it is the only well validated tumor marker for monitoring disease course. Several other tumor-associated antigens have been assessed, including glycoprotein antigens other than cancer antigen 125, soluble cytokeratin fragments, kallikreins, cytokines and cytokine receptors, vascular endothelial growth factor, D-dimer, and lisophosphatidic acid. This article assesses the potential diagnostic and prognostic role of these novel biomarkers, both alone and in combination with cancer antigen 125. The future for serum tumor marker research is represented by the emerging technology of proteomics, which may allow scientific advances comparable to those achieved with the introduction of monoclonal antibody technology.

Lessons learned in the analysis of high-dimensional data in vaccinomics

The field of vaccinology is increasingly moving toward the generation, analysis, and modeling of extremely large and complex high-dimensional datasets. We have used data such as these in the development and advancement of the field of vaccinomics to enable prediction of vaccine responses and to develop new vaccine candidates. However, the application of systems biology to what has been termed "big data," or "high-dimensional data," is not without significant challenges-chief among them a paucity of gold standard analysis and modeling paradigms with which to interpret the data. In this article, we relate some of the lessons we have learned over the last decade of working with high-dimensional, high-throughput data as applied to the field of vaccinomics. The value of such efforts, however, is ultimately to better understand the immune mechanisms by which protective and non-protective responses to vaccines are generated, and to use this information to support a personalized vaccinology approach in creating better, and safer, vaccines for the public health.

Epidemiologic design and analysis for proteomic studies: a primer on -omic technologies

Proteome analysis is increasingly being used in investigations elucidating the molecular basis of disease, identifying diagnostic and prognostic markers, and ultimately improving patient care. We appraised the current status of proteomic investigations using human samples, including the state of the art in proteomic technologies, from sample preparation to data evaluation approaches, as well as key epidemiologic, statistical, and translational issues. We systematically reviewed the most highly cited clinical proteomic studies published between January 2009 and March 2014 that included a minimum of 100 samples, as well as strategies that have been successfully implemented to enhance the translational relevance of proteomic investigations. Limited comparability between studies and lack of specification of biomarker context of use are frequently observed. Nevertheless, there are initial examples of successful biomarker discovery in cross-sectional studies followed by validation in high-risk longitudinal cohorts. Translational potential is currently hindered, as limitations in proteomic investigations are not accounted for. Interdisciplinary communication between proteomics experts, basic researchers, epidemiologists, and clinicians, an orchestrated assimilation of required resources, and a more systematic translational outlook for accumulation of evidence may augment the public health impact of proteomic investigations.

The national biomarker development alliance: confronting the poor productivity of biomarker research and development

Making precision (personalized) medicine a routine clinical reality will require a comprehensive inventory of validated biomarkers and molecular diagnostic tests to stratify disease subtypes and improve accuracy in diagnosis and treatment selection. Realization of this promise has been hindered by the poor productivity of biomarker identification and validation. This situation reflects deficiencies that are pervasive across the entire spectrum of biomarker R&D, from discovery to clinical validation and in the failure of regulatory and reimbursement policies to accommodate new classes of biomarkers. The launch of the National Biomarker Development Alliance is the culmination of a 2-year review and consultation process involving diverse stakeholders to advance standards, best practices and guidelines to enhance biomarker discovery and validation by adoption of systems-based approaches and trans-sector collaboration between academia, clinical medicine and relevant private and public sector stakeholders.

Mass spectrometry-based serum and plasma peptidome profiling for prediction of treatment outcome in patients with solid malignancies

INTRODUCTION

Treatment selection tools are needed to enhance the efficacy of targeted treatment in patients with solid malignancies. Providing a readout of aberrant signaling pathways and proteolytic events, mass spectrometry-based (MS-based) peptidomics enables identification of predictive biomarkers, whereas the serum or plasma peptidome may provide easily accessible signatures associated with response to treatment. In this systematic review, we evaluate MS-based peptide profiling in blood for prompt clinical implementation.

METHODS

PubMed and Embase were searched for studies using a syntax based on the following hierarchy: (a) blood-based matrix-assisted or surface-enhanced laser desorption/ionization time-of-flight MS peptide profiling (b) in patients with solid malignancies (c) prior to initiation of any treatment modality, (d) with availability of outcome data.

RESULTS

Thirty-eight studies were eligible for review; the majority were performed in patients with non-small cell lung cancer (NSCLC). Median classification prediction accuracy was 80% (range: 66%-93%) in 11 models from 14 studies reporting an MS-based classification model. A pooled analysis of 9 NSCLC studies revealed clinically significant median progression-free survival in patients classified as "poor outcome" and "good outcome" of 2.0 ± 1.06 months and 4.6 ± 1.60 months, respectively; median overall survival was also clinically significant at 4.01 ± 1.60 months and 10.52 ± 3.49 months, respectively.

CONCLUSION

Pretreatment MS-based serum and plasma peptidomics have shown promising results for prediction of treatment outcome in patients with solid tumors. Limited sample sizes and absence of signature validation in many studies have prohibited clinical implementation thus far. Our pooled analysis and recent results from the PROSE study indicate that this profiling approach enables treatment selection, but additional prospective studies are warranted.

Identification of gene signatures used to recognize biological characteristics of gastric cancer upon gene expression data

High-throughput gene expression microarrays can be examined by machine-learning algorithms to identify gene signatures that recognize the biological characteristics of specific human diseases, including cancer, with high sensitivity and specificity. A previous study compared 20 gastric cancer (GC) samples against 20 normal tissue (NT) samples and identified 1,519 differentially expressed genes (DEGs). In this study, Classification Information Index (CII), Information Gain Index (IGI), and RELIEF algorithms are used to mine the previously reported gene expression profiling data. In all, 29 of these genes are identified by all three algorithms and are treated as GC candidate biomarkers. Three biomarkers, COL1A2, ATP4B, and HADHSC, are selected and further examined using quantitative real-time polymerase chain reaction (qRT-PCR) and immunohistochemistry (IHC) staining in two independent sets of GC and normal adjacent tissue (NAT) samples. Our study shows that COL1A2 and HADHSC are the two best biomarkers from the microarray data, distinguishing all GC from the NT, whereas ATP4B is diagnostically significant in lab tests because of its wider range of fold-changes in expression. Herein, a data-mining model applicable for small sample sizes is presented and discussed. Our result suggested that this mining model may be useful in small sample-size studies to identify putative biomarkers and potential biological features of GC.

Imaging mass spectrometry: a new tool for pathology in a molecular age

Mass spectrometry (MS) provides unique advantages for the analysis of clinical specimens, and these capabilities have been critical to the advancement of diagnostic medicine. To date, LC-MS is the MS platform most commonly used for diagnostics; however, LC-MS based proteomics is very labor intensive and costly to implement for high volume assays. Furthermore, when analyzing tissue samples, additional laborious sample preparation steps must be employed (e.g. extraction methods or laser microdissection). The direct analysis of cells and tissues by MALDI imaging MS has developed significant momentum for applications that have diagnostic potential. MALDI imaging MS provides molecular information from specific cell types within tissue sections; however, this laser-based approach significantly reduces the analysis time for each location sampled. This Viewpoint discusses the technologies for direct analysis of tissues, the potential for diagnostic applications using MALDI imaging MS, and the challenges faced in the transfer of the technology to the clinical laboratory.

The need for external validation in machine olfaction: emphasis on health-related applications

Over the last two decades, electronic nose research has produced thousands of research works. Many of them were describing the ability of the e-nose technology to solve diverse applications in domains ranging from food technology to safety, security, or health. It is, in fact, in the biomedical field where e-nose technology is finding a research niche in the last years. Although few success stories exist, most described applications never found the road to industrial or clinical exploitation. Most described methodologies were not reliable and were plagued by numerous problems that prevented practical application beyond the lab. This work emphasizes the need of external validation in machine olfaction. I describe some statistical and methodological pitfalls of the e-nose practice and I give some best practice recommendations for researchers in the field.

Serum proteomics for gastric cancer

According to the World Health Organization, 800,000 cancer-related deaths are caused by gastric cancer each year globally, hence making it the second leading cause of cancer-related deaths in the world. Gastric cancer is often either asymptomatic or causing only nonspecific symptoms in its early stages. By the time the symptoms occur, the cancer has usually reached an advanced stage, which is one of the main reasons for its relatively poor prognosis. Therefore, early diagnosis and early treatment are very crucial. The differential analysis of serum protein between cancer patients and healthy controls can be performed using proteomics techniques and can hence be adopted as tumor biomarkers for the early diagnosis of cancer. So far, several serum tumor biomarkers have been identified for gastric cancer. However due to their poor specificity and sensitivity, they have proven to be insufficient for the reliable diagnosis of gastric cancer. Thus, using modern advanced proteomics techniques to find some new and reliable serum tumor biomarkers for earlier and reliable diagnosis of gastric cancer is a must. Nowadays, proteomic-based techniques, such as SELDI and HCLP, are available to discover biomarkers in gastric cancer. Numerous novel serum tumor biomarkers such as SAA, plasminogen and C9c, have been discovered through serological proteomics strategies. This review mainly focuses on the serum proteomics techniques and their application in the research of gastric cancer.

Identification of potential serum peptide biomarkers of biliary tract cancer using MALDI MS profiling

BACKGROUND

The aim of this discovery study was the identification of peptide serum biomarkers for detecting biliary tract cancer (BTC) using samples from healthy volunteers and benign cases of biliary disease as control groups. This work was based on the hypothesis that cancer-specific exopeptidases exist and that their activities in serum can generate cancer-predictive peptide fragments from circulating proteins during coagulation.

METHODS

This case control study used a semi-automated platform incorporating polypeptide extraction linked to matrix-assisted laser desorption/ionisation time-of-flight mass spectrometry (MALDI-TOF MS) to profile 92 patient serum samples. Predictive models were generated to test a validation serum set from BTC cases and healthy volunteers.

RESULTS

Several peptide peaks were found that could significantly differentiate BTC patients from healthy controls and benign biliary disease. A predictive model resulted in a sensitivity of 100% and a specificity of 93.8% in detecting BTC in the validation set, whilst another model gave a sensitivity of 79.5% and a specificity of 83.9% in discriminating BTC from benign biliary disease samples in the training set. Discriminatory peaks were identified by tandem MS as fragments of abundant clotting proteins.

CONCLUSIONS

Serum MALDI MS peptide signatures can accurately discriminate patients with BTC from healthy volunteers.

Population proteomics: addressing protein diversity in humans

In the past several years, proteomics and its subdiscipline clinical proteomics have been engaged in the discovery of the next generation protein of biomarkers. As the effort and the intensive debate it has sparked continue, it is becoming apparent that a paradigm shift is needed in proteomics in order to truly comprehend the complexity of the human proteome and assess its subtle variations among individuals. This review introduces the concept of population proteomics as a future direction in proteomics research. Population proteomics is the study of protein diversity in human populations. High-throughput, top-down mass spectrometric approaches are employed to investigate, define and understand protein diversity and modulations across and within populations. Population proteomics is a discovery-oriented endeavor with a goal of establishing the incidence of protein structural variations and quantitative regulation of these modifications. Assessing human protein variations among and within populations is viewed as a paramount undertaking that can facilitate clinical proteomics' effort in discovery and validation of protein features that can be used as markers for early diagnosis of disease, monitoring of disease progression and assessment of therapy. This review outlines the growing need for analyzing individuals' proteomes and describes the approaches that are likely to be applied in such a population proteomics endeavor.

Association between protein signals and type 2 diabetes incidence

Understanding early determinants of type 2 diabetes is essential for refining disease prevention strategies. Proteomic technology may provide a useful approach to identify novel protein patterns potentially related to pathophysiological changes that lead up to diabetes. In this study, we sought to identify protein signals that are associated with diabetes incidence in a middle-aged population. Serum samples from 519 participants in a nested case-control selection (167 cases and 352 age-, sex- and BMI-matched normoglycemic control subjects, median follow-up 14.0 years) within the Whitehall-II cohort were analyzed by linear matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS). Nine protein peaks were found to be associated with incident diabetes. Rate ratios for high peak intensity ranged between 0.4 (95% CI, 0.2-0.8) and 4.0 (95% CI, 1.7-9.2) and were robust to adjustment for main potential confounders, including obesity, lipids and C-reactive protein. The proteins associated with these peaks may reflect diabetes pathogenesis. Our study exemplifies the utility of an approach that combines proteomic and epidemiological data.

Cultivating cohort studies for observational translational research

BACKGROUND

"Discovery" research about molecular markers for diagnosis, prognosis, or prediction of response to therapy has frequently produced results that were not reproducible in subsequent studies. What are the reasons, and can observational cohorts be cultivated to provide strong and reliable answers to those questions? Experimental

METHODS

Selected examples are used to illustrate: (i) what features of research design provide strength and reliability in observational studies about markers of diagnosis, prognosis, and response to therapy? (ii) How can those design features be cultivated in existing observational cohorts, for example, within randomized controlled clinical trial (RCT), other existing observational research studies, or practice settings like health maintenance organization (HMOs)?

RESULTS

Examples include a study of RNA expression profiles of tumor tissue to predict prognosis of breast cancer, a study of serum proteomics profiles to diagnose ovarian cancer, and a study of stool-based DNA assays to screen for colon cancer. Strengths and weaknesses of observational study design features are discussed, along with lessons about how features that help assure strength might be "cultivated" in the future.

CONCLUSIONS AND IMPACT

By considering these examples and others, it may be possible to develop a process of "cultivating cohorts" in ongoing RCTs, observational cohort studies, and practice settings like HMOs that have strong features of study design. Such an effort could produce sources of data and specimens to reliably answer questions about the use of molecular markers in diagnosis, prognosis, and response to therapy.

[Variability of healthy human proteome]

The purpose of this review is to analyze investigations devoted to characteristic of protein variability and diversity of their posttranslational modifications in healthy humans. The numerous researches have demonstrated that proteomic profile has a considerable both intra- and inter-individual variability, and quite often normal variability of some proteins can be comparable to changes observed in pathological processes. Results obtained by our research group have confirmed high intra-individual variability of serum low-molecular subproteome of healthy volunteers, certified by a special medial committee. Proteins characterized by high variability in normal conditions (e.g. haptoglobin--0-40 mg/ml; lysozyme--0,01-0,1 mg/ml; C-reactive protein--0,01-0,3 mg/ml) should be excluded from the list of potential biomarkers. On the contrary, proteins and peptides characterized by insignificant dispersion in healthy population (such as albumin--coefficient of variation (CV) 9%; transferrin--CV14%; C3c complement--CV 17%, alpha-1 acid glycoprotein--CV 21%, alpha2-macroglobulin--CV 20%; transthyretin fragment--CV 28,3% and beta-chain alpha2-HS-glycoprotein--CV 29,7%) can provide us with important information about state of health. Thus investigations of plasticity in proteomic profiles of healthy humans will help to correct reference intervals used in clinical proteomics.

Diagnostic biomarkers differentiating metastatic melanoma patients from healthy controls identified by an integrated MALDI-TOF mass spectrometry/bioinformatic approach

The prognosis of advanced metastatic melanoma (American Joint Committee on Cancer (AJCC) stage IV) remains dismal with a 5-year survival rate of 6-18%. In the present study, an integrated MALDI mass spectrometric approach combined with artificial neural networks (ANNs) analysis and modeling has been used for the identification of biomarker ions in serum from stage IV melanoma patients allowing the discrimination of metastatic disease from healthy status with high specificities of 92% for protein ions and 100% for peptide biomarkers. Our ANNs model also correctly classified 98% of a blind validation set of AJCC stage I melanoma samples as nonstage IV samples, emphasizing the power of the newly defined biomarkers to identify patients with late-stage metastatic melanoma. Sequence analysis identified peptides derived from metastasis-associated proteins; alpha 1-acid glycoprotein precursor-1/2 (AAG-1/2) and complement C3 component precursor-1 (CCCP-1). Furthermore, quantitation of serum AAG by an immunoassay showed a significant (p<0.001) increase in AAG serum concentration in stage IV patients in comparison with healthy volunteers; moreover; the quantity of AAG plotted against MALDI-MS peak intensity classified the groups into two distinct clusters. Ongoing studies of other disease stages will provide evidence whether our strategy is sufficiently robust to give rise to stage-specific protein/peptide signatures in melanoma.

Use of pharmacokinetic/pharmacodynamic biomarkers to support rational cancer drug development

The process of drug development in oncology has struggled to alter at a pace in keeping with the rapid discovery and testing of agents that act on a wide variety of molecular targets. The rational development of such agents requires an understanding of drug effect(s) on their purported target. It is likely that testing these drugs in a framework designed to examine cytotoxic agents will fail to establish their full potential. We discuss how data gained from biomarker investigation might impact on drug development and provide examples that highlight the development, validation and use of pharmacokinetic, and especially pharmacodynamic biomarkers as drug development moves from the laboratory into clinical testing. The challenges of performing assays to satisfy regulatory requirements have been the subject of much debate. We recommend the implementation of appropriate, fit-for-purpose biomarkers in clinical trials of all new cancer drugs.

From bench to bedside: discovery of ovarian cancer biomarkers using high-throughput technologies in the past decade

Ovarian cancer is the most lethal gynecological malignancy and survival of this disease has remained relatively unchanged over the past 30 years. A contributing factor to this has been the lack of reliable biomarkers for the clinical management of ovarian cancer. Rapid advances in high-throughput technologies over the past decade has allowed for new and exciting opportunities for biomarker discovery in the field of ovarian cancer, especially with respect to serum biomarkers that can be used for various clinical applications. This review highlights the major genomic and proteomic studies dedicated to ovarian cancer biomarker discovery over the past decade. An emphasis will be placed on the HE4, Risk of Malignancy Algorithm (ROMA) and OVA1™ serum-based tests/algorithms that have recently been approved by the US FDA as ovarian cancer biomarkers.

Plasma and Cerebrospinal Fluid-Based Protein Biomarkers for Motor Neuron Disease

Motor neuron diseases (MNDs) and, in particular, amyotrophic lateral sclerosis (ALS), are a heterogeneous group of neurologic disorders characterized by the progressive loss of motor function. In ALS, a selective and relentless degeneration of both upper and lower motor neurons occurs, culminating in mortality typically within 5 years of symptom onset. However, survival rates vary among individual patients and can be from a few months to >10 years from diagnosis. Inadequacies in disease detection and treatment, along with a lack of diagnostic and prognostic tools, have prompted many to turn to proteomics-based biomarker discovery efforts. Proteomics refers to the study of the proteins expressed by a genome at a particular time, and the proteome can respond to and reflect the status of an organism, including health and disease states. Although an emerging field, proteomic applications promise to uncover biomarkers critical for differentiating patients with ALS and other MNDs from healthy individuals and from patients affected by other diseases. Ideally, these studies will also provide mechanistic information to facilitate identification of new drug targets for subsequent therapeutic development. In addition to proper experimental design, standard operating procedures for sample acquisition, preprocessing, and storage must be developed. Biological samples typically analyzed in proteomic studies of neurologic diseases include both plasma and cerebrospinal fluid (CSF). Recent studies have identified individual proteins and/or protein panels from blood plasma and CSF that represent putative biomarkers for ALS, although many of these proteins are not unique to this disease. Continued investigations are required to validate these initial findings and to further pursue the role of these proteins as diagnostic biomarkers or surrogate markers of disease progression. Protein biomarkers specific to ALS will additionally function to evaluate drug efficacy in clinical trials and to identify novel targets for drug design. It is hoped that proteomic technologies will soon integrate the basic biology of ALS with mechanistic disease information to achieve success in the clinical setting.

Candidate biomarker verification: Critical examination of a serum protein pattern for human colorectal cancer

PURPOSE

We critically examine a candidate serum protein pattern for human colorectal cancer (CRC) with respect to reproducibility, sample handling, and disease specificity.

EXPERIMENTAL DESIGN

Serum samples from CRC patients, patients with benign colon tumors and healthy individuals, were obtained at two collection sites and analyzed by SELDI-TOF MS on 8 days, over a period of 5 weeks. The spectra were subjected to multivariate analysis. Tissues from normal colon and CRC were analyzed by SELDI-TOF MS. Selected mass peaks were identified.

RESULTS

Using an elaborate experimental design we developed a multivariate classifier that correctly classified CRC and control serum measured on an independent day. The classifier did not discriminate between samples from CRC patients and patients with benign colon tumors, and, secondly, did not correctly classify serum from an independent collection site. All discriminatory mass peaks were identified as high abundant plasma proteins. Tissue profiling provided support of increased proteolytic activity in CRC tissue.

CONCLUSION AND CLINICAL RELEVANCE

Critical verification did not justify advancing the identified CRC serum protein pattern into clinical validation without improvement. We believe that proteomics biomarker research could benefit if the presented, or a similar, verification scheme was more commonly employed in explorative biomarker studies.

Screening for cancer with molecular markers: progress comes with potential problems

Recent research has raised hopes for impressively accurate screening for cancer with molecular biomarkers. These molecular markers will probably be more sensitive and specific than older screening modalities, as well as easier to use. In this Essay, I argue that these sensitive screening tests might be clinically valuable - but that they will present unique issues in implementation and interpretation. These issues are likely to affect the way clinicians conduct screening and the way that they make diagnoses in individuals who screen positive for cancer.

A pilot study on the effect of short-term consumption of a polyphenol rich drink on biomarkers of coronary artery disease defined by urinary proteomics

Polyphenol rich diets have been associated with a reduced risk of cardiovascular disease. We examined the effect of a polyphenol rich (P-R) drink on biomarkers assessed by urinary proteomics. Thirty nine middle aged and overweight subjects were randomized to P-R drink (n = 20) or placebo (n = 19) in addition to their normal diet. After two weeks urine samples were obtained for assessment of the urinary proteome using capillary electrophoresis coupled to a mass spectrometer. A total of 93 polypeptides were found to be candidates for differential distribution with a nominal p-value <0.05, though these differences did not reach significance when multiple testing was accounted for. Sequences were determined in 19 of these demonstrating that they originate from alpha-1 antitrypsin, collagens, fibrinogen alpha and IgG kappa. Levels of 27 polypeptides were greater than 4-fold different between the two groups. Of these, 7 were previously found to be part of a coronary artery disease (CAD) specific urinary biomarker pattern. Their direction of expression was closer to the healthy state in the P-R drink group and closer to CAD state in the placebo group. Our data suggest that the P-R drink may have beneficial effects on urinary biomarkers of CAD. The data encourage the planning of future prospective studies, aimed at investigating significant effects of polyphenol rich dietary products.

Reproducible cancer biomarker discovery in SELDI-TOF MS using different pre-processing algorithms

Background

There has been much interest in differentiating diseased and normal samples using biomarkers derived from mass spectrometry (MS) studies. However, biomarker identification for specific diseases has been hindered by irreproducibility. Specifically, a peak profile extracted from a dataset for biomarker identification depends on a data pre-processing algorithm. Until now, no widely accepted agreement has been reached.

Results

In this paper, we investigated the consistency of biomarker identification using differentially expressed (DE) peaks from peak profiles produced by three widely used average spectrum-dependent pre-processing algorithms based on SELDI-TOF MS data for prostate and breast cancers. Our results revealed two important factors that affect the consistency of DE peak identification using different algorithms. One factor is that some DE peaks selected from one peak profile were not detected as peaks in other profiles, and the second factor is that the statistical power of identifying DE peaks in large peak profiles with many peaks may be low due to the large scale of the tests and small number of samples. Furthermore, we demonstrated that the DE peak detection power in large profiles could be improved by the stratified false discovery rate (FDR) control approach and that the reproducibility of DE peak detection could thereby be increased.

Conclusions

Comparing and evaluating pre-processing algorithms in terms of reproducibility can elucidate the relationship among different algorithms and also help in selecting a pre-processing algorithm. The DE peaks selected from small peak profiles with few peaks for a dataset tend to be reproducibly detected in large peak profiles, which suggests that a suitable pre-processing algorithm should be able to produce peaks sufficient for identifying useful and reproducible biomarkers.