How to improve reliability and efficiency of research about molecular markers: roles of phases, guidelines, and study design

Integrating targeted genetic markers to genotyping-by-sequencing for an ultimate genotyping tool

New selection methods, using trait-specific markers (marker-assisted selection (MAS)) and/or genome-wide markers (genomic selection (GS)), are becoming increasingly widespread in breeding programs. This new era requires innovative and cost-efficient solutions for genotyping. Reduction in sequencing cost has enhanced the use of high-throughput low-cost genotyping methods such as genotyping-by-sequencing (GBS) for genome-wide single-nucleotide polymorphism (SNP) profiling in large breeding populations. However, the major weakness of GBS methodologies is their inability to genotype targeted markers. Conversely, targeted methods, such as amplicon sequencing (AmpSeq), often face cost constraints, hindering genome-wide genotyping across a large cohort. Although GBS and AmpSeq data can be generated from the same sample, an efficient method to achieve this is lacking. In this study, we present the Genome-wide & Targeted Amplicon (GTA) genotyping platform, an innovative way to integrate multiplex targeted amplicons into the GBS library preparation to provide an all-in-one cost-effective genotyping solution to breeders and research communities. Custom primers were designed to target 23 and 36 high-value markers associated with key agronomical traits in soybean and barley, respectively. The resulting multiplex amplicons were compatible with the GBS library preparation enabling both GBS and targeted genotyping data to be produced efficiently and cost-effectively. To facilitate data analysis, we have introduced Fast-GBS.v3, a user-friendly bioinformatic pipeline that generates comprehensive outputs from data obtained following sequencing of GTA libraries. This high-throughput low-cost approach will greatly facilitate the application of DNA markers as it provides required markers for both MAS and GS in a single assay.

Tumor Cell Extrinsic Synaptogyrin 3 Expression as a Diagnostic and Prognostic Biomarker in Head and Neck Cancer

Over 70% of oropharyngeal head and neck squamous cell carcinoma (HNSC) cases in the United States are positive for human papillomavirus (HPV) yet biomarkers for stratifying oropharyngeal head and neck squamous cell carcinoma (HNSC) patient risk are limited. We used immunogenomics to identify differentially expressed genes in immune cells of HPV(+) and HPV(-) squamous carcinomas. Candidate genes were tested in clinical specimens using both quantitative RT-PCR and IHC and validated by IHC using the Carolina Head and Neck Cancer Study (CHANCE) tissue microarray of HNSC cases. We performed multiplex immunofluorescent staining to confirm expression within the immune cells of HPV(+) tumors, receiver operating characteristic (ROC) curve analyses, and assessed survival outcomes. The neuronal gene Synaptogyrin-3 (SYNGR3) is robustly expressed in immune cells of HPV(+) squamous cancers. Multiplex immunostaining and single cell RNA-seq analyses confirmed SYNGR3 expression in T cells, but also unexpectedly in B cells of HPV(+) tumors. ROC curve analyses revealed that combining SYNGR3 and p16 provides more sensitivity and specificity for HPV detection compared to p16 IHC alone. SYNGR3-high HNSC patients have significantly better prognosis with five-year OS and DSS rates of 60% and 71%, respectively. Moreover, combining p16 localization and SYNGR3 expression can further risk stratify HPV(+) patients such that high cytoplasmic, low nuclear p16 do significantly worse (Hazard Ratio, 8.6; P = 0.032) compared to patients with high cytoplasmic, high nuclear p16. SYNGR3 expression in T and B cells is associated with HPV status and enhanced survival outcomes of HNSC patients.

A Novel Method for Constructing Classification Models by Combining Different Biomarker Patterns

Different biomarker patterns, such as those of molecular biomarkers and ratio biomarkers, have their own merits in clinical applications. In this study, a novel machine learning method used in biomedical data analysis for constructing classification models by combining different biomarker patterns (CDBP)is proposed. CDBP uses relative expression reversals to measure the discriminative ability of different biomarker patterns, and selects the pattern with the higher score for classifier construction. The decision boundary of CDBP can be characterized in simple and biologically meaningful manners. The CDBP method was compared with eight state-of-the-art methods on eight gene expression datasets to test its performance. CDBP, with fewer features or ratio features, had the highest classification performance. Subsequently, CDBP was employed to extract crucial diagnostic information from a rat hepatocarcinogenesis metabolomics dataset. The potential biomarkers selected by CDBP provided better classification of hepatocellular carcinoma (HCC)and non-HCC stages than previous works in the animal model. The statistical analyses of these potential biomarkers in an independent human dataset confirmed their discriminative abilities of different liver diseases. These experimental results highlight the potential of CDBP for biomarker identification from high-dimensional biomedical datasets and demonstrate that it can be a useful tool for disease classification.

The issues and challenges with cancer biomarkers

A biomarker is a measurable indicator used to distinguish precisely/objectively either normal biological state/pathological condition/response to a specific therapeutic intervention. The use of novel molecular biomarkers within evidence-based medicine may improve the diagnosis/treatment of disease, improve health outcomes, and reduce the disease's socio-economic impact. Presently cancer biomarkers are the backbone of therapy, with greater efficacy and better survival rates. Cancer biomarkers are extensively used to treat cancer and monitor the disease's progress, drug response, relapses, and drug resistance. The highest percent of all biomarkers explored are in the domain of cancer. Extensive research using various methods/tissues is carried out for identifying biomarkers for early detection, which has been mostly unsuccessful. The quantitative/qualitative detection of various biomarkers in different tissues should ideally be done in accordance with qualification rules laid down by the Early Detection Research Network (EDRN), Program for the Assessment of Clinical Cancer Tests (PACCT), and National Academy of Clinical Biochemistry. Many biomarkers are presently under investigation, but lacunae lie in the biomarker's sensitivity and specificity. An ideal biomarker should be quantifiable, reliable, of considerable high/low expression, correlate with the outcome progression, cost-effective, and consistent across gender and ethnic groups. Further, we also highlight that these biomarkers' application remains questionable in childhood malignancies due to the lack of reference values in the pediatric population. The development of a cancer biomarker stands very challenging due to its complexity and sensitivity/resistance to the therapy. In past decades, the cross-talks between molecular pathways have been targeted to study the nature of cancer. To generate sensitive and specific biomarkers representing the pathogenesis of specific cancer, predicting the treatment responses and outcomes would necessitate inclusion of multiple biomarkers.

Biomarkers and Strategy to Detect Preinvasive and Early Pancreatic Cancer: State of the Field and the Impact of the EDRN

Patients afflicted with pancreatic ductal adenocarcinoma (PDAC) face a dismal prognosis, but headway could be made if physicians could identify the disease earlier. A compelling strategy to broaden the use of surveillance for PDAC is to incorporate molecular biomarkers in combination with clinical analysis and imaging tools. This article summarizes the components involved in accomplishing biomarker validation and an analysis of the requirements of molecular biomarkers for disease surveillance. We highlight the significance of consortia for this research and highlight resources and infrastructure of the Early Detection Research Network (EDRN). The EDRN brings together the multifaceted expertise and resources needed for biomarker validation, such as study design, clinical care, biospecimen collection and handling, molecular technologies, and biostatistical analysis, and studies coming out of the EDRN have yielded biomarkers that are moving forward in validation. We close the article with an overview of the current investigational biomarkers, an analysis of their performance relative to the established benchmarks, and an outlook on the current needs in the field. The outlook for improving the early detection of PDAC looks promising, and the pace of further research should be quickened through the resources and expertise of the EDRN and other consortia.See all articles in this CEBP Focus section, "NCI Early Detection Research Network: Making Cancer Detection Possible."

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 Search for Clinically Useful Biomarkers of Complex Disease: A Data Analysis Perspective

Unmet clinical diagnostic needs exist for many complex diseases, which (it is hoped) will be solved by the discovery of metabolomics biomarkers. However, at present, no diagnostic tests based on metabolomics have yet been introduced to the clinic. This review is presented as a research perspective on how data analysis methods in metabolomics biomarker discovery may contribute to the failure of biomarker studies and suggests how such failures might be mitigated. The study design and data pretreatment steps are reviewed briefly in this context, and the actual data analysis step is examined more closely.

Shortcomings in the evaluation of biomarkers in ovarian cancer: a systematic review

Background

Shortcomings in study design have been hinted at as one of the possible causes of failures in the translation of discovered biomarkers into the care of ovarian cancer patients, but systematic assessments of biomarker studies are scarce. We aimed to document study design features of recently reported evaluations of biomarkers in ovarian cancer.

Methods

We performed a systematic search in PubMed (MEDLINE) for reports of studies evaluating the clinical performance of putative biomarkers in ovarian cancer. We extracted data on study designs and characteristics.

Results

Our search resulted in 1026 studies; 329 (32%) were found eligible after screening, of which we evaluated the first 200. Of these, 93 (47%) were single center studies. Few studies reported eligibility criteria (17%), sampling methods (10%) or a sample size justification or power calculation (3%). Studies often used disjoint groups of patients, sometimes with extreme phenotypic contrasts; 46 studies included healthy controls (23%), but only five (3%) had exclusively included advanced stage cases.

Conclusions

Our findings confirm the presence of suboptimal features in clinical evaluations of ovarian cancer biomarkers. This may lead to premature claims about the clinical value of these markers or, alternatively, the risk of discarding potential biomarkers that are urgently needed.

Adding the Team into T1 Translational Research: A Case Study of Multidisciplinary Team Science in the Evaluation of Biomarkers of Prostate Cancer Risk and Prognosis

BACKGROUND

Given translational research challenges, multidisciplinary team science is promoted to increase the likelihood of moving from discovery to health effect. We present a case study documenting the utility of multidisciplinary team science in prostate cancer tissue biomarker validation.

METHODS

We used primary data generated by a team consisting of a pathologist, cancer biologists, a biostatistician, and epidemiologists. We examined their contributions by phase of biomarker evaluation to identify when, through the practice of team science, threats to internal validity were recognized and solved. Next, we quantified the extent of bias avoided in evaluating the association of Ki67 (immunohistochemistry), stromal cell telomere length (fluorescence in situ hybridization), and microRNA (miRNA) (miR-21, miR-141, miR-221; quantitative RT-PCR) with prostate cancer risk or recurrence in nested case-control studies.

RESULTS

Threats to validity were tissue storage time (Ki67, miRNA) and laboratory equipment maintenance (telomeres). Solutions were all in the data analysis phase and involved using tissue storage-time specific cutpoints and/or batch-specific cutpoints. Bias in the regression coefficient for quantiles of each biomarker ranged from 24% to 423%, and the coefficient for the test for trend ranged from 15% to 910%. The interpretation of the associations changed as follows: Ki67, null to positive; stromal cell telomere length, null to positive; miR-21 and miR-141 remained null; miR-221, weak to moderate inverse.

CONCLUSIONS

In this case study, we documented the inferential benefits of multidisciplinary team science when the team's collaboration and coordination led to the identification of threats to validity and the implementation of appropriate solutions.

A Framework for Development of Useful Metabolomic Biomarkers and Their Effective Knowledge Translation

Despite the significant advantages of metabolomic biomarkers, no diagnostic tests based on metabolomics have been introduced to clinical use. There are many reasons for this, centered around substantial obstacles in developing clinically useful metabolomic biomarkers. Most significant is the need for interdisciplinary teams with expertise in metabolomics, analysis of complex clinical and metabolomic data, and clinical care. Importantly, the clinical need must precede biomarker discovery, and the experimental design for discovery and validation must reflect the purpose of the biomarker. Standard operating procedures for procuring and handling samples must be developed from the beginning, to ensure experimental integrity. Assay design is another challenge, as there is not much precedent informing this. Another obstacle is that it is not yet clear how to protect any intellectual property related to metabolomic biomarkers. Viewing a metabolomic biomarker as a natural phenomenon would inhibit patent protection and potentially stifle commercial interest. However, demonstrating that a metabolomic biomarker is actually a derivative of a natural phenomenon that requires innovation would enhance investment in this field. Finally, effective knowledge translation strategies must be implemented, which will require engagement with end users (clinicians and lab physicians), patient advocate groups, policy makers, and payer organizations. Addressing each of these issues comprises the framework for introducing a metabolomic biomarker to practice.

Methods for the evaluation of biomarkers in patients with kidney and liver diseases: multicentre research programme including ELUCIDATE RCT

Background

Protein biomarkers with associations with the activity and outcomes of diseases are being identified by modern proteomic technologies. They may be simple, accessible, cheap and safe tests that can inform diagnosis, prognosis, treatment selection, monitoring of disease activity and therapy and may substitute for complex, invasive and expensive tests. However, their potential is not yet being realised.

Design and methods

The study consisted of three workstreams to create a framework for research: workstream 1, methodology – to define current practice and explore methodology innovations for biomarkers for monitoring disease; workstream 2, clinical translation – to create a framework of research practice, high-quality samples and related clinical data to evaluate the validity and clinical utility of protein biomarkers; and workstream 3, the ELF to Uncover Cirrhosis as an Indication for Diagnosis and Action for Treatable Event (ELUCIDATE) randomised controlled trial (RCT) – an exemplar RCT of an established test, the ADVIA Centaur® Enhanced Liver Fibrosis (ELF) test (Siemens Healthcare Diagnostics Ltd, Camberley, UK) [consisting of a panel of three markers – (1) serum hyaluronic acid, (2) amino-terminal propeptide of type III procollagen and (3) tissue inhibitor of metalloproteinase 1], for liver cirrhosis to determine its impact on diagnostic timing and the management of cirrhosis and the process of care and improving outcomes.

Results

The methodology workstream evaluated the quality of recommendations for using prostate-specific antigen to monitor patients, systematically reviewed RCTs of monitoring strategies and reviewed the monitoring biomarker literature and how monitoring can have an impact on outcomes. Simulation studies were conducted to evaluate monitoring and improve the merits of health care. The monitoring biomarker literature is modest and robust conclusions are infrequent. We recommend improvements in research practice. Patients strongly endorsed the need for robust and conclusive research in this area. The clinical translation workstream focused on analytical and clinical validity. Cohorts were established for renal cell carcinoma (RCC) and renal transplantation (RT), with samples and patient data from multiple centres, as a rapid-access resource to evaluate the validity of biomarkers. Candidate biomarkers for RCC and RT were identified from the literature and their quality was evaluated and selected biomarkers were prioritised. The duration of follow-up was a limitation but biomarkers were identified that may be taken forward for clinical utility. In the third workstream, the ELUCIDATE trial registered 1303 patients and randomised 878 patients out of a target of 1000. The trial started late and recruited slowly initially but ultimately recruited with good statistical power to answer the key questions. ELF monitoring altered the patient process of care and may show benefits from the early introduction of interventions with further follow-up. The ELUCIDATE trial was an ‘exemplar’ trial that has demonstrated the challenges of evaluating biomarker strategies in ‘end-to-end’ RCTs and will inform future study designs.

Conclusions

The limitations in the programme were principally that, during the collection and curation of the cohorts of patients with RCC and RT, the pace of discovery of new biomarkers in commercial and non-commercial research was slower than anticipated and so conclusive evaluations using the cohorts are few; however, access to the cohorts will be sustained for future new biomarkers. The ELUCIDATE trial was slow to start and recruit to, with a late surge of recruitment, and so final conclusions about the impact of the ELF test on long-term outcomes await further follow-up. The findings from the three workstreams were used to synthesise a strategy and framework for future biomarker evaluations incorporating innovations in study design, health economics and health informatics.

Trial registration

Current Controlled Trials ISRCTN74815110, UKCRN ID 9954 and UKCRN ID 11930.

Funding

This project was funded by the NIHR Programme Grants for Applied Research programme and will be published in full inProgramme Grants for Applied Research; Vol. 6, No. 3. See the NIHR Journals Library website for further project information.

Evolution of proteomic biomarker for chronic liver disease: Promise into reality

Liver is the vital organ for synthesis of proteins whose concentration in blood reflects liver dysfunction. Variations in protein domain can generate clinically significant biomarkers. Biomarker pipeline includes discovery of candidates, qualification, verification, assay optimization, and validation. Advances in proteomic approach can discover protein biomarker candidates based on “up-or-down” regulation or fold change in expression which is correlated with disease state. Despite numerous biomarker candidates been discovered, only few are useful in clinical practice which indicates the need for well-established validation regimen. Hence, the main purpose of this review is to understand the protein biomarker development and pitfalls. Companion diagnostics provide insights into potential cost-effective diagnosis for chronic liver disease.

How Can Omic Science be Improved?

One of the promises of multiomic analysis was to transform the clinical diagnostics to deliver much more exact phenotyping of disease states. However, despite enormous investments, the transformation of clinical routine has not taken place. There are many reasons for this lack of success but one is the failure to deliver quantitative and reproducible data. This failure is not only impeding progress in clinical phenotyping but also in the application of omic science in systems biology. The focus in this Viewpoint will be on lipidomics but the lessons learned are generally applicable.

Prospective evaluation of 64 serum autoantibodies as biomarkers for early detection of colorectal cancer in a true screening setting

Novel blood-based screening tests are strongly desirable for early detection of colorectal cancer (CRC). We aimed to identify and evaluate autoantibodies against tumor-associated antigens as biomarkers for early detection of CRC. 380 clinically identified CRC patients and samples of participants with selected findings from a cohort of screening colonoscopy participants in 2005–2013 (N=6826) were included in this analysis. Sixty-four serum autoantibody markers were measured by multiplex bead-based serological assays. A two-step approach with selection of biomarkers in a training set, and validation of findings in a validation set, the latter exclusively including participants from the screening setting, was applied. Anti-MAGEA4 exhibited the highest sensitivity for detecting early stage CRC and advanced adenoma. Multi-marker combinations substantially increased sensitivity at the price of a moderate loss of specificity. Anti-TP53, anti-IMPDH2, anti-MDM2 and anti-MAGEA4 were consistently included in the best-performing 4-, 5-, and 6-marker combinations. This four-marker panel yielded a sensitivity of 26% (95% CI, 13–45%) for early stage CRC at a specificity of 90% (95% CI, 83–94%) in the validation set. Notably, it also detected 20% (95% CI, 13–29%) of advanced adenomas. Taken together, the identified biomarkers could contribute to the development of a useful multi-marker blood-based test for CRC early detection.

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.

Clinical proteomics in cancer: Where we are

Proteomics has emerged as a promising field in the post-genomic era. Notwithstanding the great advances provided by gene expression analysis in cancer, the lack of a correlation between gene expression and protein levels has highlighted the need for a proteomic focus on cancer. Although the increasing knowledge regarding cancer biology, a reliable marker to improve diagnosis, prognosis and treatment for cancer patients is not a reality at present. In this review, we address the main considerations regarding proteomics-based studies and their clinical applications on cancer research, highlighting some considerations related to strengths and limitations of proteomics-based studies and its application to clinical practice.

Proteomics and peptidomics: moving toward precision medicine in urological malignancies

Urological malignancies are a major cause of morbidity and mortality worldwide. Advances in early detection, diagnosis, prognosis and prediction of treatment response can significantly improve patient care. Proteomic and peptidomic profiling studies are at the center of kidney, prostate and bladder cancer biomarker discovery and have shown great promise for improved clinical assessment. Mass spectrometry (MS) is the most widely employed method for proteomic and peptidomic analyses. A number of MS platforms have been developed to facilitate accurate identification of clinically relevant markers in various complex biological samples including tissue, urine and blood. Furthermore, protein profiling studies have been instrumental in the successful introduction of several diagnostic multimarker tests into the clinic. In this review, we will provide a brief overview of high-throughput technologies for protein and peptide based biomarker discovery. We will also examine the current state of kidney, prostate and bladder cancer biomarker research as well as review the journey toward successful clinical implementation.

Identifying Prognostic SNPs in Clinical Cohorts: Complementing Univariate Analyses by Resampling and Multivariable Modeling

Clinical cohorts with time-to-event endpoints are increasingly characterized by measurements of a number of single nucleotide polymorphisms that is by a magnitude larger than the number of measurements typically considered at the gene level. At the same time, the size of clinical cohorts often is still limited, calling for novel analysis strategies for identifying potentially prognostic SNPs that can help to better characterize disease processes. We propose such a strategy, drawing on univariate testing ideas from epidemiological case-controls studies on the one hand, and multivariable regression techniques as developed for gene expression data on the other hand. In particular, we focus on stable selection of a small set of SNPs and corresponding genes for subsequent validation. For univariate analysis, a permutation-based approach is proposed to test at the gene level. We use regularized multivariable regression models for considering all SNPs simultaneously and selecting a small set of potentially important prognostic SNPs. Stability is judged according to resampling inclusion frequencies for both the univariate and the multivariable approach. The overall strategy is illustrated with data from a cohort of acute myeloid leukemia patients and explored in a simulation study. The multivariable approach is seen to automatically focus on a smaller set of SNPs compared to the univariate approach, roughly in line with blocks of correlated SNPs. This more targeted extraction of SNPs results in more stable selection at the SNP as well as at the gene level. Thus, the multivariable regression approach with resampling provides a perspective in the proposed analysis strategy for SNP data in clinical cohorts highlighting what can be added by regularized regression techniques compared to univariate analyses.

The Human Physiome: how standards, software and innovative service infrastructures are providing the building blocks to make it achievable

Reconstructing and understanding the Human Physiome virtually is a complex mathematical problem, and a highly demanding computational challenge. Mathematical models spanning from the molecular level through to whole populations of individuals must be integrated, then personalized. This requires interoperability with multiple disparate and geographically separated data sources, and myriad computational software tools. Extracting and producing knowledge from such sources, even when the databases and software are readily available, is a challenging task. Despite the difficulties, researchers must frequently perform these tasks so that available knowledge can be continually integrated into the common framework required to realize the Human Physiome. Software and infrastructures that support the communities that generate these, together with their underlying standards to format, describe and interlink the corresponding data and computer models, are pivotal to the Human Physiome being realized. They provide the foundations for integrating, exchanging and re-using data and models efficiently, and correctly, while also supporting the dissemination of growing knowledge in these forms. In this paper, we explore the standards, software tooling, repositories and infrastructures that support this work, and detail what makes them vital to realizing the Human Physiome.

Empirical extensions of the lasso penalty to reduce the false discovery rate in high-dimensional Cox regression models

Correct selection of prognostic biomarkers among multiple candidates is becoming increasingly challenging as the dimensionality of biological data becomes higher. Therefore, minimizing the false discovery rate (FDR) is of primary importance, while a low false negative rate (FNR) is a complementary measure. The lasso is a popular selection method in Cox regression, but its results depend heavily on the penalty parameter λ. Usually, λ is chosen using maximum cross-validated log-likelihood (max-cvl). However, this method has often a very high FDR. We review methods for a more conservative choice of λ. We propose an empirical extension of the cvl by adding a penalization term, which trades off between the goodness-of-fit and the parsimony of the model, leading to the selection of fewer biomarkers and, as we show, to the reduction of the FDR without large increase in FNR. We conducted a simulation study considering null and moderately sparse alternative scenarios and compared our approach with the standard lasso and 10 other competitors: Akaike information criterion (AIC), corrected AIC, Bayesian information criterion (BIC), extended BIC, Hannan and Quinn information criterion (HQIC), risk information criterion (RIC), one-standard-error rule, adaptive lasso, stability selection, and percentile lasso. Our extension achieved the best compromise across all the scenarios between a reduction of the FDR and a limited raise of the FNR, followed by the AIC, the RIC, and the adaptive lasso, which performed well in some settings. We illustrate the methods using gene expression data of 523 breast cancer patients. In conclusion, we propose to apply our extension to the lasso whenever a stringent FDR with a limited FNR is targeted. Copyright © 2016 John Wiley & Sons, Ltd.

Toward precision medicine of breast cancer

In this review, we report on breast cancer's molecular features and on how high throughput technologies are helping in understanding the dynamics of tumorigenesis and cancer progression with the aim of developing precision medicine methods. We first address the current state of the art in breast cancer therapies and challenges in order to progress towards its cure. Then, we show how the interaction of high-throughput technologies with in silico modeling has led to set up useful inferences for promising strategies of target-specific therapies with low secondary effect incidence for patients. Finally, we discuss the challenge of pharmacogenetics in the clinical practice of cancer therapy. All these issues are explored within the context of precision medicine.

Quality management of biorepositories

Biomedical investigators require high quality human tissue to support their research; thus, an important aspect of the provision of tissues by biorepositories is the assurance of high quality and consistency of processing specimens. This is best accomplished by a quality management system (QMS). This article describes the basis of a QMS program designed to aid biorepositories that want to improve their operations. In 1983, the UAB Tissue Collection and Biobanking Facility (TCBF) introduced a QMS program focused on providing solid tissues to support a wide range of research; this QMS included a quality control examination of the specific specimens provided for research. Similarly, the Division of Laboratory Sciences at the Centers for Disease Control and Prevention (CDC) introduced a QMS program for their laboratory analyses, focused primarily on bodily fluids. The authors of this article bring together the experience of the QMS programs at these two sites to facilitate the development or improvement of quality management systems of a wide range of biorepositories.

Empirical evaluation demonstrated importance of validating biomarkers for early detection of cancer in screening settings to limit the number of false-positive findings

OBJECTIVES

Search for biomarkers for early detection of cancer is a very active area of research, but most studies are done in clinical rather than screening settings. We aimed to empirically evaluate the role of study setting for early detection marker identification and validation.

STUDY DESIGN AND SETTING

A panel of 92 candidate cancer protein markers was measured in 35 clinically identified colorectal cancer patients and 35 colorectal cancer patients identified at screening colonoscopy. For each case group, we selected 38 controls without colorectal neoplasms at screening colonoscopy. Single-, two- and three-marker combinations discriminating cases and controls were identified in each setting and subsequently validated in the alternative setting.

RESULTS

In all scenarios, a higher number of predictive biomarkers were initially detected in the clinical setting, but a substantially lower proportion of identified biomarkers could subsequently be confirmed in the screening setting. Confirmation rates were 50.0%, 84.5%, and 74.2% for one-, two-, and three-marker algorithms identified in the screening setting and were 42.9%, 18.6%, and 25.7% for algorithms identified in the clinical setting.

CONCLUSION

Validation of early detection markers of cancer in a true screening setting is important to limit the number of false-positive findings.

Factors Affecting the Use of Human Tissues in Biomedical Research: Implications in the Design and Operation of a Biorepository

The availability of high-quality human tissues is necessary to advance medical research. Although there are inherent and induced limitations on the use of human tissues in research, biorepositories play critical roles in minimizing the effects of such limitations. Specifically, the optimal utilization of tissues in research requires tissues to be diagnosed accurately, and the actual specimens provided to investigators must be carefully described (i.e., there must be quality control of each aliquot of the tissue provided for research, including a description of any damage to tissues). Tissues also should be collected, processed, stored, and distributed (i.e., handled) uniformly under a rigorous quality management system (QMS). Frequently, tissues are distributed to investigators by tissue banks which have collected, processed, and stored them by standard operating procedures (SOPs). Alternatively, tissues for research may be handled via SOPs that are modified to the specific requirements of investigators (i.e., using a prospective biorepository model). The primary goal of any type of biorepository should be to ensure its specimens are of high quality and are utilized appropriately in research; however, approaches may vary based on the tissues available and requested. For example, extraction of specific molecules (e.g., microRNA) to study molecular characteristics of a tissue may require less clinical annotation than tissues that are utilized to identify how the molecular expression might be used to clarify a clinical outcome of a disease or the response to a specific therapy. This review focuses on the limitations of the use of tissues in research and how the design and operations of a tissue biorepository can minimize some of these limitations.

Metabolomic Evaluation of Neutrophilic Airway Inflammation in Cystic Fibrosis

BACKGROUND

Metabolomic evaluation of cystic fibrosis (CF) airway secretions could identify metabolites and metabolic pathways involved in neutrophilic airway inflammation that could serve as biomarkers and therapeutic targets.

METHODS

Mass spectrometry (MS)-based metabolomics was performed on a discovery set of BAL fluid samples from 25 children with CF, and targeted MS methods were used to identify and quantify metabolites related to neutrophilic inflammation. A biomarker panel of these metabolites was then compared with neutrophil counts and clinical markers in independent validation sets of lavage from children with CF and adults with COPD compared with control subjects.

RESULTS

Of the 7,791 individual peaks detected by positive-mode MS metabolomics discovery profiling, 338 were associated with neutrophilic inflammation. Targeted MS determined that many of these peaks were generated by metabolites from pathways related to the metabolism of purines, polyamines, proteins, and nicotinamide. Analysis of the independent validation sets verified that, in subjects with CF or COPD, several metabolites, particularly those from purine metabolism and protein catabolism pathways, were strongly correlated with neutrophil counts and were related to clinical markers, including airway infection and lung function.

CONCLUSIONS

MS metabolomics identified multiple metabolic pathways associated with neutrophilic airway inflammation. These findings provide insight into disease pathophysiology and can serve as the basis for developing disease biomarkers and therapeutic interventions for airways diseases.

Definitive Characterization of CA 19-9 in Resectable Pancreatic Cancer Using a Reference Set of Serum and Plasma Specimens

The validation of candidate biomarkers often is hampered by the lack of a reliable means of assessing and comparing performance. We present here a reference set of serum and plasma samples to facilitate the validation of biomarkers for resectable pancreatic cancer. The reference set includes a large cohort of stage I-II pancreatic cancer patients, recruited from 5 different institutions, and relevant control groups. We characterized the performance of the current best serological biomarker for pancreatic cancer, CA 19–9, using plasma samples from the reference set to provide a benchmark for future biomarker studies and to further our knowledge of CA 19–9 in early-stage pancreatic cancer and the control groups. CA 19–9 distinguished pancreatic cancers from the healthy and chronic pancreatitis groups with an average sensitivity and specificity of 70–74%, similar to previous studies using all stages of pancreatic cancer. Chronic pancreatitis patients did not show CA 19–9 elevations, but patients with benign biliary obstruction had elevations nearly as high as the cancer patients. We gained additional information about the biomarker by comparing two distinct assays. The two CA 9–9 assays agreed well in overall performance but diverged in measurements of individual samples, potentially due to subtle differences in antibody specificity as revealed by glycan array analysis. Thus, the reference set promises be a valuable resource for biomarker validation and comparison, and the CA 19–9 data presented here will be useful for benchmarking and for exploring relationships to CA 19–9.

Evaluating the effects of preanalytical variables on the stability of the human plasma proteome

High quality clinical biospecimens are vital for biomarker discovery, verification, and validation. Variations in blood processing and handling can affect protein abundances and assay reliability. Using an untargeted LC-MS approach, we systematically measured the impact of preanalytical variables on the plasma proteome. Time prior to processing was the only variable that affected the plasma protein levels. LC-MS quantification showed that preprocessing times <6h had minimal effects on the immunodepleted plasma proteome, but by 4 days significant changes were apparent. Elevated levels of many proteins were observed, suggesting that in addition to proteolytic degradation during the preanalytical phase, changes in protein structure are also important considerations for protocols using antibody depletion. As to processing variables, a comparison of single- vs double-spun plasma showed minimal differences. After processing, the impact ⩽3 freeze-thaw cycles was negligible regardless of whether freshly collected samples were processed in short succession or the cycles occurred during 14-17 years of frozen storage (-80 °C). Thus, clinical workflows that necessitate modest delays in blood processing times or employ different centrifugation steps can yield valuable samples for biomarker discovery and verification studies.

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.

Utility of the CKD273 peptide classifier in predicting chronic kidney disease progression

BACKGROUND

Chronic kidney disease (CKD) is a growing public health concern, afflicting approximately one-tenth of adults in developed countries. However, the clinical need for an accurate test, such as a biomarker and/or peptide classifier, for predicting CKD progression and related adverse outcomes remains unaddressed. Recently, a proteomics approach based on capillary electrophoresis-mass spectrometry was employed to develop a urinary peptide-based high-dimensional classifier, namely CKD273, for predicting CKD progression.

OBJECTIVES

The study aims to critically appraise the evidence level of the CKD273 classifier's utility in predicting CKD progression, according to the Oxford Evidence-Based Medicine (EBM) and Strength of Recommendation Taxonomy (SORT) guidelines.

METHODS

Eligible studies were identified by a literature search of MEDLINE and Web of Science Expanded Core Collection databases. Limitations were set to prospective cohort studies evaluating the classifier's accuracy in predicting CKD progression. Data extraction was undertaken according to a predefined protocol by two independent reviewers. The EBM and SORT guidelines were applied to appraise the CKD273 classifier's utility for predicting CKD progression.

RESULTS

The query search results rendered four prospective cohort studies. The classifier performed independently of age, gender and the type of urine storage containers used. The classifier predicted the development of micro- or macroalbuminuria and rapid decline (i.e. >-5% annual decrease) in the estimated glomerular filtration rate. One study assessed the association of the classifier with end-stage renal disease and death but did not take confounding factors into account. The CKD273 classifier attained high evidence levels according to the EBM (score range 1b), supporting its utility for predicting CKD progression. However, lower scores were attained when the studies were scored according the SORT guidelines (score ranges 1-4).

CONCLUSIONS

Initial promising evidence supports the CKD273 classifier's utility in predicting CKD progression. The classifier's applicability should be corroborated with additional evidence arising from inception cohort studies assessing patient-oriented outcomes, which demonstrate its added value beyond currently available clinical risk predictors, as well as its cost-effectiveness in clinical practice.

Biomarkers for early diagnosis of pancreatic cancer

Pancreatic ductal adenocarcinoma is an aggressive malignancy with a 5-year survival rate of approximately 5%. The lack of established strategies for early detection contributes to this poor prognosis. Although several novel candidate biomarkers have been proposed for earlier diagnosis, none have been adopted into routine clinical use. In this review, the authors examine the challenges associated with finding new pancreatic cancer diagnostic biomarkers and explore why translation of biomarker research for patient benefit has thus far failed. The authors also review recent progress and highlight advances in the understanding of the biology of pancreatic cancer that may lead to improvements in biomarker detection and implementation.

Semi-quantitative measurement of a specific glycoform using a DNA-tagged antibody and lectin affinity chromatography for glyco-biomarker development

Aberrant glycosylation-targeted disease biomarker development is based on cumulative evidence that certain glycoforms are mass-produced in a disease-specific manner. However, the development process has been hampered by the absence of an efficient validation method based on a sensitive and multiplexed platform. In particular, ELISA-based analytical tools are not adequate for this purpose, mainly because of the presence of a pair of N-glycans of IgG-type antibodies. To overcome the associated hurdles in this study, antibodies were tagged with oligonucleotides with T7 promoter and then allowed to form a complex with corresponding antigens. An antibody-bound specific glycoform was isolated by lectin chromatography and quantitatively measured on a DNA microarray chip following production of fluorescent RNA by T7-trascription. This tool ensured measurement of targeted glycoforms of multiple biomarkers with high sensitivity and multiplexity. This analytical method was applied to an in vitro diagnostic multivariate index assay where a panel of hepatocellular carcinoma (HCC) biomarkers comprising alpha-fetoprotein, hemopexin, and alpha-2-macroglobulin (A2M) was examined in terms of the serum level and their fuco-fractions. The results indicated that the tests using the multiplexed fuco-biomarkers provided improved discriminatory power between non- hepatocellular carcinoma and hepatocellular carcinoma subjects compared with the alpha-fetoprotein level or fuco-alpha-fetoprotein test alone. The developed method is expected to facilitate the validation of disease-specific glycan biomarker candidates.

The reproducibility of biomedical research: Sleepers awake!

There is increasing concern about the reliability of biomedical research, with recent articles suggesting that up to 85% of research funding is wasted. This article argues that an important reason for this is the inappropriate use of molecular techniques, particularly in the field of RNA biomarkers, coupled with a tendency to exaggerate the importance of research findings.

Toward an integrated pipeline for protein biomarker development

Protein biomarker development is a multidisciplinary task involving basic, translational and clinical research. Integration of multidisciplinary efforts in a single pipeline is challenging, but crucial to facilitate rational discovery of protein biomarkers and alleviate existing disappointments in the field. In this review, we discuss in detail individual phases of biomarker development pipeline, such as biomarker candidate identification, verification and validation. We focus on mass spectrometry as a principal technique for protein identification and quantification, and discuss complementary -omics approaches for selection of biomarker candidates. Proteomic samples, protein-based clinical laboratory tests and limitations of biomarker development are reviewed in detail, and critical assessment of all phases of biomarker development pipeline is provided. This article is part of a Special Issue entitled: Medical Proteomics.

Inhibition of circulating dipeptidyl peptidase 4 activity in patients with metastatic prostate cancer

Cancer is responsible for many deaths and is a major source of healthcare expenditures. The identification of new, non-invasive biomarkers might allow improvement of the direct diagnostic or prognostic ability of already available tools. Here, we took the innovative approach of interrogating the activity of exopeptidases in the serum of cancer patients with the aim of establishing a distinction based on enzymatic function, instead of simple protein levels, as a means to biomarker discovery. We first analyzed two well-characterized mouse models of prostate cancer, each with a distinct genetic lesion, and established that broad exopeptidase and targeted aminopeptidase activity tests reveal proteolytic changes associated with tumor development. We also describe new peptide-based freeze-frame reagents uniquely suited to probe the altered balance of selected aminopeptidases, as opposed to the full array of exopeptidases, and/or their modulators in patient serum or plasma. One particular proteolytic activity was impaired in animals with aggressive disease relative to cancer-free littermates. We identified the protease in question as dipeptidyl peptidase 4 (DPP4) by analyzing selected knockout mice and evaluating the effect of specific inhibitors. DPP4 activity was also reduced in the sera of patients with metastatic prostate cancer relative to patients with localized disease or healthy controls. However, no significant differences in DPP4 serum levels were observed, which established the loss of activity as the result of impaired enzymatic function. Biochemical analysis indicated that reduced activity was the result not of post-translational modifications or allosteric changes, but instead of a low-molecular-weight inhibitor. After we adjusted for age and total prostate-specific antigen, reduced DPP4 activity remained a significant predictor of cancer status. The results of this proof-of-principle study suggest that DPP4 activity might be a potential blood-based indicator of the presence of metastatic cancer of prostatic origin, either by itself or, more likely, as a means to improve the sensitivity and specificity of existing markers.

Plasma protein analysis of patients with different B-cell lymphomas using high-content antibody microarrays

PURPOSE

In this study, plasma samples from a multicentric case-control study on lymphoma were analyzed for the identification of proteins useful for diagnosis.

EXPERIMENTAL DESIGN

The protein content in the plasma of 100 patients suffering from the three most common B-cell lymphomas and 100 control samples was studied with antibody microarrays composed of 810 antibodies that target cancer-associated proteins. Sample pools were screened for an identification of marker proteins. Then, the samples were analyzed individually to validate the usability of these markers.

RESULTS

More than 200 proteins with disease-associated abundance changes were found. The evaluation on individual patients confirmed some molecules as robust informative markers while others were inadequate for this purpose. In addition, the analysis revealed distinct subgroups for each of the three investigated B-cell lymphoma subtypes. With this information, we delineated a classifier that discriminates the different lymphoma entities.

CONCLUSIONS AND CLINICAL RELEVANCE

Variations in plasma protein abundance permit discrimination between different patient groups. After validation on a larger study cohort, the findings could have diagnostic as well as differential diagnostic potential. Beside this, methodological aspects were critically evaluated, such as the value of sample pooling for the identification of biomarkers that are useful for a diagnosis on individual patients.

Blocking and randomization to improve molecular biomarker discovery

Randomization and blocking have the potential to prevent the negative impacts of nonbiologic effects on molecular biomarker discovery. Their use in practice, however, has been scarce. To demonstrate the logistic feasibility and scientific benefits of randomization and blocking, we conducted a microRNA study of endometrial tumors (n = 96) and ovarian tumors (n = 96) using a blocked randomization design to control for nonbiologic effects; we profiled the same set of tumors for a second time using no blocking or randomization. We assessed empirical evidence of differential expression in the two studies. We performed simulations through virtual rehybridizations to further evaluate the effects of blocking and randomization. There was moderate and asymmetric differential expression (351/3,523, 10%) between endometrial and ovarian tumors in the randomized dataset. Nonbiologic effects were observed in the nonrandomized dataset, and 1,934 markers (55%) were called differentially expressed. Among them, 185 were deemed differentially expressed (185/351, 53%) and 1,749 not differentially expressed (1,749/3,172, 55%) in the randomized dataset. In simulations, when randomization was applied to all samples at once or within batches of samples balanced in tumor groups, blocking improved the true-positive rate from 0.95 to 0.97 and the false-positive rate from 0.02 to 0.002; when sample batches were unbalanced, randomization was associated with the true-positive rate (0.92) and the false-positive rate (0.10) regardless of blocking. Normalization improved the detection of true-positive markers but still retained sizeable false-positive markers. Randomization and blocking should be used in practice to more fully reap the benefits of genomics technologies.

Aminopeptidase activities as prospective urinary biomarkers for bladder cancer

PURPOSE

Proteases have been implicated in cancer progression and invasiveness. We have investigated the activities, as opposed to simple protein levels, of selected aminopeptidases in urine specimens to serve as potential novel biomarkers for urothelial cancer.

EXPERIMENTAL DESIGN

The unique urinary proteomes of males and females were profiled to establish the presence of a gender-independent set of aminopeptidases. Samples were also collected from patients with urothelial cancer and matched controls. A SOP for urine processing was developed taking into account hydration variation. Five specific aminopeptidase activity assays, using fluorophoric substrates, were optimized for evaluation of marker potential.

RESULTS

Nineteen exopeptidases and 21 other proteases were identified in urine and the top-five most abundant aminopeptidases, identical in both genders, selected for functional studies. Depending on the enzyme, activities were consistently lower (p ≤ 0.05), higher or unchanged in the cancer samples as compared to controls. Two selected aminopeptidase activities used as a binary classifier resulted in a ROC curve with an AUC = 0.898.

CONCLUSION AND CLINICAL RELEVANCE

We have developed functional assays that characterize aminopeptidase activities in urine specimens with adequate technical and intraindividual reproducibility. With further testing, it could yield a reliable biomarker test for bladder cancer detection or prognostication.

Biomarker research with prospective study designs for the early detection of cancer

This article describes the principles of marker research with prospective studies along with examples for diagnostic tumor markers. A plethora of biomarkers have been claimed as useful for the early detection of cancer. However, disappointingly few biomarkers were approved for the detection of unrecognized disease, and even approved markers may lack a sound validation phase. Prospective studies aimed at the early detection of cancer are costly and long-lasting and therefore the bottleneck in marker research. They enroll a large number of clinically asymptomatic subjects and follow-up on incident cases. As invasive procedures cannot be applied to collect tissue samples from the target organ, biomarkers can only be determined in easily accessible body fluids. Marker levels increase during cancer development, with samples collected closer to the occurrence of symptoms or a clinical diagnosis being more informative than earlier samples. Only prospective designs allow the serial collection of pre-diagnostic samples. Their storage in a biobank upgrades cohort studies to serve for both, marker discovery and validation. Population-based cohort studies, which may collect a wealth of data, are commonly conducted with just one baseline investigation lacking serial samples. However, they can provide valuable information about factors that influence the marker level. Screening programs can be employed to archive serial samples but require significant efforts to collect samples and auxiliary data for marker research. Randomized controlled trials have the highest level of evidence in assessing a biomarker's benefit against usual care and present the most stringent design for the validation of promising markers as well as for the discovery of new markers. In summary, all kinds of prospective studies can benefit from a biobank as they can serve as a platform for biomarker research. This article is part of a Special Issue entitled: Biomarkers: A Proteomic Challenge.

Generating a comprehensive set of standard operating procedures for a biorepository network-The CTRNet experience

Despite the integral role of biorepositories in fueling translational research and the advancement of medicine, there are significant gaps in harmonization of biobanking practices, resulting in variable biospecimen collection, storage, and processing. This significantly impacts accurate downstream analysis and, in particular, creates a problem for biorepository networks or consortia. The Canadian Tumour Repository Network (CTRNet; www.ctrnet.ca ) is a consortium of Canadian tumor biorepositories that aims to enhance biobanking capacity and quality through standardization. To minimize the issue of variable biobanking practices throughout its network, CTRNet has developed and maintained a comprehensive set of 45 standard operating procedures (SOPs). There were four key elements to the CTRNet SOP development process: 1) an SOP development team was formed from members across CTRNet to co-produce each SOP; 2) a principal author was appointed with responsibility for overall coordination of the SOP development process; 3) the CTRNet Management Committee (composed of principal investigators for each member biorepository) reviewed/revised each SOP completed by the development team; and 4) external expert reviewers provided feedback and recommendations on each SOP. Once final Management Committee approval was obtained, the ratified SOP was published on the CTRNet website for public access. Since the SOPs were first published on the CTRNet website (June 2008), there have been approximately 15,000 downloads of one or more CTRNet SOPs/Policies by users from over 60 countries. In accordance with biobanking best practices, CTRNet performs an exhaustive review of its SOPs at set intervals, to coincide with each granting cycle. The last revision was completed in May 2012.

Statistical design for biospecimen cohort size in proteomics-based biomarker discovery and verification studies

Protein biomarkers are needed to deepen our understanding of cancer biology and to improve our ability to diagnose, monitor, and treat cancers. Important analytical and clinical hurdles must be overcome to allow the most promising protein biomarker candidates to advance into clinical validation studies. Although contemporary proteomics technologies support the measurement of large numbers of proteins in individual clinical specimens, sample throughput remains comparatively low. This problem is amplified in typical clinical proteomics research studies, which routinely suffer from a lack of proper experimental design, resulting in analysis of too few biospecimens to achieve adequate statistical power at each stage of a biomarker pipeline. To address this critical shortcoming, a joint workshop was held by the National Cancer Institute (NCI), National Heart, Lung, and Blood Institute (NHLBI), and American Association for Clinical Chemistry (AACC) with participation from the U.S. Food and Drug Administration (FDA). An important output from the workshop was a statistical framework for the design of biomarker discovery and verification studies. Herein, we describe the use of quantitative clinical judgments to set statistical criteria for clinical relevance and the development of an approach to calculate biospecimen sample size for proteomic studies in discovery and verification stages prior to clinical validation stage. This represents a first step toward building a consensus on quantitative criteria for statistical design of proteomics biomarker discovery and verification research.

Markers for nutrition studies: review of criteria for the evaluation of markers

INTRODUCTION

Markers are important tools to assess the nutrition status and effects of nutrition interventions. There is currently insufficient consensus in nutrition sciences on how to evaluate markers, despite the need for properly evaluating them.

OBJECTIVES

To identify the criteria for the evaluation of markers related to nutrition, health and disease and to propose generic criteria for evaluation.

METHOD

The report on "Evaluation of Biomarker and Surrogate Endpoints in Chronic Disease" from the Institute of Medicine was the starting point for the literature search. Additionally, specific search strategies were developed for Pubmed.

RESULTS

In nutrition, no set of criteria or systematic approach to evaluate markers is currently available. There is a reliance on the medical area where statistical methods have been developed to quantify the evaluation of markers. Even here, a systematic approach is lacking-markers are still evaluated on a case-by-case basis. The review of publications from the literature search resulted in a database with definitions, criteria for validity and the rationale behind the criteria. It was recognized that, in nutrition, a number of methodological aspects differ from medical research.

CONCLUSIONS

The following criteria were identified as essential elements in the evaluation of markers: (1) the marker has a causal biological link with the endpoint, (2) there is a significant association between marker and endpoint in the target population, (3) marker changes consistently with the endpoint, e.g., in response to an intervention, and (4) change in the marker explains a substantial proportion of the change in the endpoint in response to the intervention.

Early diagnosis of complex diseases by molecular biomarkers, network biomarkers, and dynamical network biomarkers

Many studies have been carried out for early diagnosis of complex diseases by finding accurate and robust biomarkers specific to respective diseases. In particular, recent rapid advance of high-throughput technologies provides unprecedented rich information to characterize various disease genotypes and phenotypes in a global and also dynamical manner, which significantly accelerates the study of biomarkers from both theoretical and clinical perspectives. Traditionally, molecular biomarkers that distinguish disease samples from normal samples are widely adopted in clinical practices due to their ease of data measurement. However, many of them suffer from low coverage and high false-positive rates or high false-negative rates, which seriously limit their further clinical applications. To overcome those difficulties, network biomarkers (or module biomarkers) attract much attention and also achieve better performance because a network (or subnetwork) is considered to be a more robust form to characterize diseases than individual molecules. But, both molecular biomarkers and network biomarkers mainly distinguish disease samples from normal samples, and they generally cannot ensure to identify predisease samples due to their static nature, thereby lacking ability to early diagnosis. Based on nonlinear dynamical theory and complex network theory, a new concept of dynamical network biomarkers (DNBs, or a dynamical network of biomarkers) has been developed, which is different from traditional static approaches, and the DNB is able to distinguish a predisease state from normal and disease states by even a small number of samples, and therefore has great potential to achieve "real" early diagnosis of complex diseases. In this paper, we comprehensively review the recent advances and developments on molecular biomarkers, network biomarkers, and DNBs in particular, focusing on the biomarkers for early diagnosis of complex diseases considering a small number of samples and high-throughput data (or big data). Detailed comparisons of various types of biomarkers as well as their applications are also discussed.