Biomarker Development Process Research Articles

Radiolabeled EGFR TKI as predictive imaging biomarkers in NSCLC patients - an overview.

Non-small cell lung cancer (NSCLC) has one of the highest cancer-related mortality rates worldwide. In a subgroup of NSCLC, tumor growth is driven by epidermal growth factor receptors (EGFR) that harbor an activating mutation. These patients are best treated with EGFR tyrosine kinase inhibitors (EGFR TKI). Identifying the EGFR mutational status on a tumor biopsy or a liquid biopsy using tumor DNA sequencing techniques is the current approach to predict tumor response on EGFR TKI therapy. However, due to difficulty in reaching tumor sites, and varying inter- and intralesional tumor heterogeneity, biopsies are not always possible or representative of all tumor lesions, highlighting the need for alternative biomarkers that predict tumor response. Positron emission tomography (PET) studies using EGFR TKI-based tracers have shown that EGFR mutational status could be identified, and that tracer uptake could potentially be used as a biomarker for tumor response. However, despite their likely predictive and monitoring value, the EGFR TKI-PET biomarkers are not yet qualified to be used in the routine clinical practice. In this review, we will discuss the currently investigated EGFR-directed PET biomarkers, elaborate on the typical biomarker development process, and describe how the advances, challenges, and opportunities of EGFR PET biomarkers relate to this process on their way to qualification for routine clinical practice.

Biomarkers for Alzheimer's Disease: Context of Use, Qualification, and Roadmap for Clinical Implementation.

Background and Objectives: The US Food and Drug Administration (FDA) defines a biomarker as a characteristic that is measured as an indicator of normal biological processes, pathogenic processes, or responses to an exposure or intervention. Biomarkers may be used in clinical care or as drug development tools (DDTs) in clinical trials. The goal of this review and perspective is to provide insight into the regulatory guidance for the use of biomarkers in clinical trials and clinical care. Materials and Methods: We reviewed FDA guidances relevant to biomarker use in clinical trials and their transition to use in clinical care. We identified instructive examples of these biomarkers in Alzheimer’s disease (AD) drug development and their application in clinical practice. Results: For use in clinical trials, biomarkers must have a defined context of use (COU) as a risk/susceptibility, diagnostic, monitoring, predictive, prognostic, pharmacodynamic, or safety biomarker. A four-stage process defines the pathway to establish the regulatory acceptance of the COU for a biomarker including submission of a letter of intent, description of the qualification plan, submission of a full qualification package, and acceptance through a qualification recommendation. Biomarkers used in clinical care may be companion biomarkers, in vitro diagnostic devices (IVDs), or laboratory developed tests (LDTs). A five-phase biomarker development process has been proposed to structure the biomarker development process. Conclusions: Biomarkers are increasingly important in drug development and clinical care. Adherence to regulatory guidance for biomarkers used in clinical trials and patient care is required to advance these important drug development and clinical tools.

Multi-Omics Interdisciplinary Research Integration to Accelerate Dementia Biomarker Development (MIRIADE).

Proteomics studies have shown differential expression of numerous proteins in dementias but have rarely led to novel biomarker tests for clinical use. The Marie Curie MIRIADE project is designed to experimentally evaluate development strategies to accelerate the validation and ultimate implementation of novel biomarkers in clinical practice, using proteomics-based biomarker development for main dementias as experimental case studies. We address several knowledge gaps that have been identified in the field. First, there is the technology-translation gap of different technologies for the discovery (e.g., mass spectrometry) and the large-scale validation (e.g., immunoassays) of biomarkers. In addition, there is a limited understanding of conformational states of biomarker proteins in different matrices, which affect the selection of reagents for assay development. In this review, we aim to understand the decisions taken in the initial steps of biomarker development, which is done via an interim narrative update of the work of each ESR subproject. The results describe the decision process to shortlist biomarkers from a proteomics to develop immunoassays or mass spectrometry assays for Alzheimer's disease, Lewy body dementia, and frontotemporal dementia. In addition, we explain the approach to prepare the market implementation of novel biomarkers and assays. Moreover, we describe the development of computational protein state and interaction prediction models to support biomarker development, such as the prediction of epitopes. Lastly, we reflect upon activities involved in the biomarker development process to deduce a best-practice roadmap for biomarker development.

Current Status of Biomarkers in Anti-N-Methyl-D-Aspartate Receptor Encephalitis.

The discovery of biomarkers in rare diseases is of paramount importance to allow a better diagnosis, improve predictions of outcomes, and prompt the development of new treatments. Anti-N-methyl-D-aspartate receptor (NMDAR) encephalitis is a rare autoimmune disorder associated with the presence of antibodies targeting the GluN1 subunit of the NMDAR. Since it was discovered in 2007, large efforts have been made towards the identification of clinical, paraclinical, and molecular biomarkers to better understand the immune mechanisms that govern the course of the disease as well as to define predictors of treatment response and long-term outcomes. However, most of these biomarkers are still in an exploratory phase, with only a few candidates reaching the final phases of the always-complex process of biomarker development, mainly due to the low incidence of the disease and its recent description. Clinical and paraclinical markers are probably the most widely explored in anti-NMDAR encephalitis, five of them combined in a clinical score to predict 1 year outcome. On the contrary, soluble molecules, such as persistent antibody positivity, antibody titers, cytokines, and other inflammatory mediators, have been proposed as biomarkers of clinical activity, inflammation, prognosis, and treatment response, but further studies are required for their clinical validation including larger and more homogenous cohorts of patients. Similarly, genetic susceptibility biomarkers are still in the exploratory phase and, therefore, weak conclusions can for now only be achieved. Thus, further studies are warranted to define biomarkers and unravel the underlying mechanisms driving rare diseases such as anti-NMDAR encephalitis. Future international collaborative studies with prospective designs that enable the enrollment of large cohorts will allow for the identification and validation of novel biomarkers for clinical decision-making.

CLRM-07. INCREASING EFFICIENCY IN EARLY PHASE MULTICENTER IMAGING BIOMARKER TRIALS: EMERGING STRATEGIES FROM THE GABLE (GLIOBLASTOMA ACCELERATED BIOMARKER LEARNING ENVIRONMENT) TRIAL

Validated biomarkers that more accurately predict prognosis and/or measure disease burden in patients with high-grade gliomas would help triage which treatment strategies are most promising for evaluation in Phase III multicenter trials. Multicenter trials to evaluate imaging biomarkers in this group face particular challenges; these trials have historically been slow to accrue and have not recently succeeded in validating new imaging biomarkers useful in treatment development. Due to variability in image acquisition protocols, scanner hardware, image analysis, and interpretive schemes, promising results obtained in single centers are poor predictors of success in the multicenter setting. Multicenter preliminary data to support further evaluation of imaging biomarkers is rarely available. The need for more efficient trial designs that bring multicenter data earlier into the process of biomarker development has become increasingly clear. In this presentation, the planning process within ECOG-ACRIN’s Brain Tumor Working Group for a platform multicenter trial called GABLE (Glioblastoma Accelerated Biomarker Learning Environment trial) designed to evaluate biomarkers for distinguishing pseudoprogression from true progression in patients with newly diagnosed GBM is described. In our planning process, it was determined that efficiencies can be gained from evaluating multiple biomarker types in parallel rather than serially; in the context of the proposed trial, not only conventional imaging biomarkers but plasma biomarkers and radiomic biomarkers can be evaluated simultaneously. Patient tolerance limits the feasibility of evaluating multiple non-standard-of-care imaging biomarkers in parallel. For this group of biomarkers, a “fast-switching” serial evaluation strategy using multiple interim analyses was developed to triage out biomarkers unlikely to succeed in identifying patient groups with clinically significant differences in median survival. For biomarker triage, an endpoint of event-free survival (events of either death or NANO progression) was proposed. Simulations were used to evaluate alpha and beta error using this evaluation strategy.

Hybrid design evaluating new biomarkers when there is an existing screening test.

Development of cancer screening biomarkers usually follows the Early Detection Research Network 5-Phase guideline in Pepe et al. A key feature of this guide is that the phased development follows a sequential order, moving to the next phase only when the current phase study is complete and has met its target performance. Motivated by a newly funded Newly onset Diabetes cohort study, we propose a design evaluating new biomarkers to discriminate between cases and controls in the presence of an existing screening test. The proposed design achieves two goals: (1) avoiding bias in estimating sensitivity or specificity in predicting cancer at a given time period prior to clinical diagnosis, using data from both screening detected cancers in Phase IV study and clinically diagnosed cancers in Phase III study; and (2) building a panel with biomarkers for Phase III and IV studies based on all data. A simulation study shows that the proposed design outperforms both a conventional method using data in Phase III arm only and a naive method using data in Phase III and IV arms ignoring the difference between the time of screening the detected cancer and the time of clinical diagnosis. The proposed design yields a smaller standard error of the estimation and increases the statistical power to confirm biomarker performance. This proposed method has the potential to shorten the cancer screening biomarker development process, use resources more effectively, and bring benefits to patients quickly.

Biomarkers in atopic dermatitis—a review on behalf of the International Eczema Council

Atopic dermatitis (AD) is a common yet complex skin disease, posing a therapeutic challenge with increasingly recognized different phenotypes among variable patient populations. Because therapeutic response may vary on the basis of heterogeneous clinical and molecular phenotypes, a shift toward precision medicine approaches may improve AD management. Herein, we will consider biomarkers as potential instruments in the toolbox of precision medicine in AD and will review the process of biomarker development and validation, the opinion of AD experts on the use of biomarkers, types of biomarkers, encompassing biomarkers that may improve AD diagnosis, biomarkers reflecting disease severity, and those potentially predicting AD development, concomitant atopic diseases, or therapeutic response, and current practice of biomarkers in AD. We found that chemokine C-C motif ligand 17/thymus and activation-regulated chemokine, a chemoattractant of TH2 cells, has currently the greatest evidence for robust correlation with AD clinical severity, at both baseline and during therapy, by using the recommendations, assessment, development, and evaluation approach. Although the potential of biomarkers in AD is yet to be fully elucidated, due to the complexity of the disease, a comprehensive approach taking into account both clinical and reliable, AD-specific biomarker evaluations would further facilitate AD research and improve patient management.

Comparing NGS and NanoString platforms in peripheral blood mononuclear cell transcriptome profiling for advanced heart failure biomarker development.

In preparation to create a clinical assay that predicts 1-year survival status of advanced heart failure (AdHF) patients before surgical/interventional therapies and to select the appropriate clinical assay platform for the future assay, we compared the properties of next generation sequencing (NGS) used in the gene discovery phase to the NanoString platform used in the clinical assay development phase. In 25 AdHF patients in a tertiary academic medical center from 2015 to 2016, PBMC samples were collected and aliquoted for NGS RNA whole transcriptome sequencing and compared to 770 genes represented on NanoString’s PanCancer IO 360 Gene Expression research panel. Prior to statistical analysis, NanoString and NGS expression values were log transformed. We computed Pearson correlation coefficients for each sample, comparing gene expression values between NanoString and NGS across the set of matched genes and for each of the matched genes across the set of samples. Genes were grouped by average NGS expression, and the NanoString-NGS correlation for each group was computed. Out of 770 genes from the NanoString panel, 734 overlapped between both platforms and showed high intrasample correlation. Within an individual sample, there was an expression-level dependent correlation between both platforms. The low- vs. intermediate/high-expression groups showed NGS average correlation 0.21 vs. 0.58–0.68, respectively, and NanoString average correlation 0.07–0.34 vs. 0.59–0.70, respectively. NanoString demonstrated high reproducibility (R2 > 0.99 for 100 ng input), sensitivity (probe counts between 100 and 500 detected and quantified), and robustness (similar gene signature scores across different RNA input concentrations, cartridges, and outcomes). Data from NGS and NanoString were highly correlated. These platforms play a meaningful, complementary role in the biomarker development process.

Mass spectrometry: A platform for biomarker discovery and validation for Alzheimer's and Parkinson's diseases.

Accurate, reliable, and objective biomarkers for Alzheimer's disease (AD), Parkinson's disease (PD), and related age-associated neurodegenerative disorders are urgently needed to assist in both diagnosis, particularly at early stages, and monitoring of disease progression. Technological advancements in protein detection platforms over the last few decades have resulted in a plethora of reported molecular biomarker candidates for both AD and PD; however, very few of these candidates are developed beyond the discovery phase of the biomarker development pipeline, a reflection of the current bottleneck within the field. In this review, the expanded use of selected reaction monitoring (SRM) targeted mass spectrometry will be discussed in detail as a platform for systematic verification of large panels of protein biomarker candidates prior to costly validation testing. We also advocate for the coupling of discovery-based proteomics with modern targeted MS-based approaches (e.g., SRM) within a single study in future workflows to expedite biomarker development and validation for AD and PD. It is our hope that improving the efficiency within the biomarker development process by use of an SRM pipeline may ultimately hasten the development of biomarkers that both decrease misdiagnosis of AD and PD and ultimately lead to detection at early stages of disease and objective assessment of disease progression. This article is part of the special issue "Proteomics".

Biomarkers as drug development tools: discovery, validation, qualification and use

The 21st Century Cures Act, approved in the USA in December 2016, has encouraged the establishment of the national Precision Medicine Initiative and the augmentation of efforts to address disease prevention, diagnosis and treatment on the basis of a molecular understanding of disease. The Act adopts into law the formal process, developed by the FDA, of qualification of drug development tools, including biomarkers and clinical outcome assessments, to increase the efficiency of clinical trials and encourage an era of molecular medicine. The FDA and European Medicines Agency (EMA) have developed similar processes for the qualification of biomarkers intended for use as companion diagnostics or for development and regulatory approval of a drug or therapeutic. Biomarkers that are used exclusively for the diagnosis, monitoring or stratification of patients in clinical trials are not subject to regulatory approval, although their qualification can facilitate the conduct of a trial. In this Review, the salient features of biomarker discovery, analytical validation, clinical qualification and utilization are described in order to provide an understanding of the process of biomarker development and, through this understanding, convey an appreciation of their potential advantages and limitations.

Current and emerging biomarkers in tumors of the central nervous system: Possible diagnostic, prognostic and therapeutic applications.

Recent investments in research associated with the discovery of specific tumor biomarkers important for efficient diagnosis and prognosis are beginning to bear fruit. Key biomarkers could potentially outweigh traditional radiological or pathological methods by enabling specificity of early detection, when coupled with tumor molecular profiling and clinical associations. Only few biomarkers are approved by regulatory authorities for Central Nervous System Tumors (CNSTs), despite the evaluation of a large number of CNST related markers during clinical trials. Traditional CNSTs biomarkers include 1p/19q co-deletion, O6-Methylguanine-DNA Methyltransferase Methylation, and mutations in IDH1/IDH2. Recently tested CNSTs biomarkers include VEGFR-2, EGFRvIII, IL2, PDGFR, MMPs, BRAF, STAT3, PTEN, TERT, AKT, NF2, and BCL2. Additional studies have highlighted new and novel MicroRNAs, circular RNAs and long non-coding RNAs as promising biomarkers. Studies on microvesicles pinpoint exosomes as promising, less invasive biomarkers that could be isolated from the serum of cancer patients. Furthermore, Cancer Stem Cells (CSCs) related molecules, such as CD133, SOX2 and Nestin, utilized as CNST biomarkers, might enable efficient monitoring of cancer progression, and/or surveillance of emerging drug resistant cells. Approved protocols that implement novel molecular markers in diagnostics, prognostics and drug development will herald a new era of precision and personalized neuro-oncology. This review summarizes and discusses putative CNST biomarkers that are under clinical development, and are ready to move into diagnostic, prognostic and therapeutic applications. Data presented here is predicted to aid in streamlining the process of biomarker's research and development.

Clinical Features of Psoriatic Arthritis: a Comprehensive Review of Unmet Clinical Needs

Psoriatic arthritis (PsA) is a form of inflammatory arthritis (IA) affecting approximately 0.25% of the population. It is a heterogeneous disorder associated with joint damage, disability, disfiguring skin disease and in severe cases, premature mortality. Inherently irreversible and frequently progressive, the process of joint damage begins at, or before, the clinical onset of disease. Early recognition and intervention is thus crucial to patient outcome. At disease onset, however, PsA often resembles other forms of arthritis-especially rheumatoid arthritis (RA). Despite the similarities between PsA and RA, their distinctive pathologies require different treatments. For example, drugs that are effective in RA may not be effective in PsA and can even cause adverse effects. Since there is no currently validated test for PsA, the diagnosis is often missed or delayed and this has functional consequences for the patient. In the context of PsA and RA, making an accurate diagnosis is not the only challenge faced by rheumatologists. Choosing an effective and safe medication to manage the disease is another significant challenge and currently approximately 40% achieve meaningful responses such as minimal disease activity status. For the patient, several months may be lost as a result of trial and error testing-meanwhile, irreversible joint damage may occur. Clearly, more effective clinical tests are urgently needed to improve personalised patient care in PsA. Specifically, there is need to develop minimally invasive tests predictive of diagnosis, response to treatment and radiographic progression. In this review, we examined the biomarker development process, highlighted the importance of qualifying unmet clinical needs and emphasised the challenges that impede biomarker studies. We have compiled a comprehensive list of potentially clinically relevant biomarkers in PsA and provided a summary of proteomic technologies that might usefully support additional biomarker research in PsA.

Validation of biomarkers to predict response to immunotherapy in cancer: Volume II — clinical validation and regulatory considerations

There is growing recognition that immunotherapy is likely to significantly improve health outcomes for cancer patients in the coming years. Currently, while a subset of patients experience substantial clinical benefit...

The challenge of small lung nodules identified in CT screening: can biomarkers assist diagnosis?

Various biomarkers have been developed as noninvasive tests to indicate the presence of lung cancer in asymptomatic persons, and in particular to provide evidence as to whether indeterminate lung nodules detected by screening are malignant. We performed an overview of the range of biomarkers reported in the literature and described those that can complement low-dose computed tomography screening. Several have promising sensitivity and specificity. However to our knowledge, only three techniques have reached the prospective screening phase (phase 4) of the five-phase biomarker development process. Two miRNA signatures (the miR-Test for serum and the miRNA signature classifier test for plasma) are being assessed in prospective screening trials, as is the EarlyCDT-Lung test based on autoantibodies. All will need to undergo prospective studies to determine their ability to improve outcomes before they can become an established adjunct to lung cancer control strategies.

Introduction to Clinical Proteomics.

Within the context of this section, biomarkers are defined as a panel of proteins and peptides that are predictive of the risk for developing a pathological condition. It is important to note here that the use of the descriptor 'panel' is purposeful in that single "biomarkers" are rarely sufficient to permit accurate prediction of a pathological condition. More specifically, the primary application of a biomarker panel is that it serves as a molecular indicator of the severity of a disease or its early response to treatment. In this way, biomarkers enable the application of precision medicine, an approach that tailors specific interventions to those individuals that would most benefit. For a recent comprehensive review of the proteomic-based biomarker development process with a focus on bladder cancer, the reader is directed to Frantzi et al. [Clin Transl Med 3:7, 2014], or a special issue with multiple reviews [Stuhler and Poschmann, Biochim Biophys Acta Proteins Proteomics 1844:859-1058, Elsevier, B V, 2014].

Neuroimaging-based biomarker discovery and validation.

Though developing biological markers for chronic pain has been a major goal of the field for decades, such biomarkers have not yet made their way into clinical practice. However, given the potential uses of biomarkers in multiple aspects of prevention and treatment—such as pain and risk factor assessment, diagnosis, prognosis, treatment selection, drug discovery, and more—efforts to discover new pain biomarkers have been expanding [5; 6; 8; 30]. Recent advances in human neuroimaging, including functional and structural Magnetic Resonance Imaging (fMRI/sMRI) combined with machine learning techniques, are bringing us closer to the goal of developing objective, brain-based markers of the neural functions and neuropathology that underlie chronic pain [2; 7; 25; 33]. These brain measures are particularly promising as biomarkers for chronic pain. Though pain is reliably induced by peripheral nociceptive input, many forms of chronic pain may arise from neuropathology in the supra-spinal circuits that govern the construction of pain experience and long-term motivation [1; 14; 26; 32]. Particularly, structural neuroimaging measures could provide more stable markers of neuropathology of chronic pain, including stable features underlying pain risk and resilience [2; 3; 11; 19; 28; 29]. Gray-matter changes have also been associated with a number of conditions that are often co-morbid with chronic pain, including depression [4; 22; 24], stress [10; 12; 20], post-traumatic stress disorder [17; 21; 27], and early-life adversity [13; 18; 23; 31]. Therefore, structural measures may provide important clues about supra-spinal contributions to both pain and related risk factors (Fig. 1). Figure 1 Key common brain regions that show structural changes across different conditions related to chronic pain, including depression, stress, post-traumatic stress disorder (PTSD), and early-life adversity. In this issue, Labus et al. [16] developed a new neuroimaging biomarker for irritable bowel syndrome (IBS) using structural MRI data, based on a relatively large sample of 80 IBS patients and 80 healthy controls. They used sparse Partial Least Squares-Discriminant Analysis (sPLS-DA), a method that allowed them to both develop a classification model based on brain structure and identify the regions that make the most important contributions to the classification. They subsequently tested the predictive model on a “holdout” sample of 26 IBS patients and 26 healthy controls. The model discriminated patients from controls with 70% accuracy (compared to a chance accuracy of 50%), providing a moderate but reliable morphological brain signature for IBS. Rather than being the end of the story, this study serves as a starting point for biomarker discovery and validation. Like other brain ‘signatures’ [30], the signature they identified can become a ‘research product’ that can be tested on multiple samples from different laboratories, and validated or challenged in various ways. The more the marker for IBS status or IBS risk holds up to the scrutiny of being characterized across samples and populations, the more useful it will become. Importantly, there is a set of desirable characteristics that a useful neuroimaging biomarker should demonstrate throughout the biomarker development process. We briefly describe several such characteristics (summarized in Table 1), and then relate them to the findings of Labus et al. [16]. Table 1 Desirable characteristics of neuroimaging biomarkers

Language workbench user interfaces for data analysis.

Biological data analysis is frequently performed with command line software. While this practice provides considerable flexibility for computationally savy individuals, such as investigators trained in bioinformatics, this also creates a barrier to the widespread use of data analysis software by investigators trained as biologists and/or clinicians. Workflow systems such as Galaxy and Taverna have been developed to try and provide generic user interfaces that can wrap command line analysis software. These solutions are useful for problems that can be solved with workflows, and that do not require specialized user interfaces. However, some types of analyses can benefit from custom user interfaces. For instance, developing biomarker models from high-throughput data is a type of analysis that can be expressed more succinctly with specialized user interfaces. Here, we show how Language Workbench (LW) technology can be used to model the biomarker development and validation process. We developed a language that models the concepts of Dataset, Endpoint, Feature Selection Method and Classifier. These high-level language concepts map directly to abstractions that analysts who develop biomarker models are familiar with. We found that user interfaces developed in the Meta-Programming System (MPS) LW provide convenient means to configure a biomarker development project, to train models and view the validation statistics. We discuss several advantages of developing user interfaces for data analysis with a LW, including increased interface consistency, portability and extension by language composition. The language developed during this experiment is distributed as an MPS plugin (available at http://campagnelab.org/software/bdval-for-mps/).

Abstract 3834: The National Biomarkers Development Alliance (NBDA): A comprehensive solution

Abstract The development of effective and robust biomarkers is essential to the realization of the vision of precision medicine. Despite huge investments in biomarker discovery and development over decades, less than 100 biomarkers are used routinely in clinical medicine; and approximately 1.3 new protein biomarkers are approved per year by FDA - extremely few new biomarkers are approved by the FDA each year. At present, the high costs and failure rates in biomarker development represent one of the most significant roadblocks to medical progress. The biomarker development process itself requires re-engineering to reduce the massive inefficiencies, fragmentation, and failure rates that now characterize the system. The NBDA is a nonprofit organization of academic, industry and government partners which aims to create a standards-based, comprehensive end-to-end approach to the development of biomarkers of different categories within a given context of use, guided throughout the development pipeline by regulatory and clinical implementation requirements. The NBDA will work with multi-sector stakeholders across the biomarker development continuum to identify where in the process standards matter, determine the status of existing standards and identify gaps, aggregate information referable to specific biomarkers and biomarker classes, and reproduce the work. NBDA will publish the recommendations it develops by consensus and science, building on the best of current practices. It will act in consultation with the FDA and professional standards organizations in order to meet the levels of evidence for biomarker qualification by regulators and validation in clinical use. Through standards and quality management approaches, it will link and integrate discovery, pre-clinical development, assay development and performance, clinical testing, regulatory approval, and post-market applications. Pilot projects in development are focused on newer classes of biomarkers: e.g., genomic biomarkers such as allelic variance as determined by next generation sequencing technologies; proteomics biomarkers generated by mass spectroscopy; volumetric and functional imaging; and complex biomarkers defined as combinations of different classes of biomarkers, the useful value of which is determined computationally. The ever more rapid development of assay technologies dictates that standards that allow them to be applied successfully to biomarker development keep pace. This can only be accomplished through broad coordination of effort and consensus, which is the goal of the NBDA. Citation Format: Carolyn C. Compton, The NBDA Team. The National Biomarkers Development Alliance (NBDA): A comprehensive solution. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 3834. doi:10.1158/1538-7445.AM2014-3834

The national biomarker development alliance: An end-to-end systems approach for biomarker development for precision medicine.

e22129 Background: The development of new robust biomarkers is essential to the realization of the vision of precision medicine. At present, the high costs and failure rates in biomarker development represent a significant roadblock to medical progress. The biomarker development process itself requires re-engineering to reduce the massive inefficiencies, fragmentation, and failure rates that now characterize the system. Methods: The National Biomarker Development Alliance (NBDA) is a new nonprofit organization of academic, industry and government partners that aims to create a standards-based, end-to-end approach to the development of biomarkers of different categories within a given context of use, guided throughout the development pipeline by regulatory and clinical implementation requirements. Results: The NBDA will work with multi-sector stakeholders across the biomarker development continuum to identify where in the process standards matter, determine the status of existing standards, identify gaps, ...

Integration of Proteomics, Bioinformatics, and Systems Biology in Traumatic Brain Injury Biomarker Discovery

Traumatic brain injury (TBI) is a major medical crisis without any FDA-approved pharmacological therapies that have been demonstrated to improve functional outcomes. It has been argued that discovery of disease-relevant biomarkers might help to guide successful clinical trials for TBI. Major advances in mass spectrometry (MS) have revolutionized the field of proteomic biomarker discovery and facilitated the identification of several candidate markers that are being further evaluated for their efficacy as TBI biomarkers. However, several hurdles have to be overcome even during the discovery phase which is only the first step in the long process of biomarker development. The high-throughput nature of MS-based proteomic experiments generates a massive amount of mass spectral data presenting great challenges in downstream interpretation. Currently, different bioinformatics platforms are available for functional analysis and data mining of MS-generated proteomic data. These tools provide a way to convert data sets to biologically interpretable results and functional outcomes. A strategy that has promise in advancing biomarker development involves the triad of proteomics, bioinformatics, and systems biology. In this review, a brief overview of how bioinformatics and systems biology tools analyze, transform, and interpret complex MS datasets into biologically relevant results is discussed. In addition, challenges and limitations of proteomics, bioinformatics, and systems biology in TBI biomarker discovery are presented. A brief survey of researches that utilized these three overlapping disciplines in TBI biomarker discovery is also presented. Finally, examples of TBI biomarkers and their applications are discussed.