Challenges In Biomarker Discovery Research Articles

Opportunities and challenges for biomarker discovery using electronic health record data.

Electronic health records (EHRs) have become increasingly relied upon as a source for biomedical research. One important research application of EHRs is the identification of biomarkers associated with specific patient states, especially within complex conditions. However, using EHRs for biomarker identification can be challenging because the EHR was not designed with research as the primary focus. Despite this challenge, the EHR offers huge potential for biomarker discovery research to transform our understanding of disease etiology and treatment and generate biological insights informing precision medicine initiatives. This review paper provides an in-depth analysis of how EHR data is currently used for phenotyping and identifying molecular biomarkers, current challenges and limitations, and strategies we can take to mitigate challenges going forward.

Towards a Systems Immunology Approach to Unravel Responses to Cancer Immunotherapy.

Immunotherapy, particularly immune checkpoint blockade and chimeric antigen receptor (CAR)-T cells, holds a great promise against cancer. These treatments have markedly improved survival in solid as well as in hematologic tumors previously considered incurable. However, durable responses occur in a fraction of patients, and existing biomarkers (e.g. PD-L1) have shown limited prediction power. This scenario highlights the need to dissect the complex interplay between immune and tumor cells to identify reliable biomarkers of response to be used for patients’ selection. In this context, systems immunology represents indeed the new frontier to address important clinical challenges in biomarker discovery. Through the integration of multiple layers of data obtained with several high-throughput approaches, systems immunology may give insights on the vast range of inter-individual differences and on the influences of genes and factors that cooperatively shape the individual immune response to a given treatment. In this Mini Review, we give an overview of the current high-throughput methodologies, including genomics, epigenomics, transcriptomics, metabolomics, proteomics, and multi-parametric phenotyping suitable for systems immunology as well as on the key steps of data integration and biological interpretation. Additionally, we review recent studies in which multi-omics technologies have been used to characterize mechanisms of response and to identify powerful biomarkers of response to checkpoint inhibitors, CAR-T cell therapy, dendritic cell-based and peptide-based cancer vaccines. We also highlight the need of favoring the collaboration of researchers with complementary expertise and of integrating multi-omics data into biological networks with the final goal of developing accurate markers of therapeutic response.

Accurate Blood-Based Diagnostic Biosignatures for Alzheimer's Disease via Automated Machine Learning.

Alzheimer’s disease (AD) is the most common form of neurodegenerative dementia and its timely diagnosis remains a major challenge in biomarker discovery. In the present study, we analyzed publicly available high-throughput low-sample -omics datasets from studies in AD blood, by the AutoML technology Just Add Data Bio (JADBIO), to construct accurate predictive models for use as diagnostic biosignatures. Considering data from AD patients and age–sex matched cognitively healthy individuals, we produced three best performing diagnostic biosignatures specific for the presence of AD: A. A 506-feature transcriptomic dataset from 48 AD and 22 controls led to a miRNA-based biosignature via Support Vector Machines with three miRNA predictors (AUC 0.975 (0.906, 1.000)), B. A 38,327-feature transcriptomic dataset from 134 AD and 100 controls led to six mRNA-based statistically equivalent signatures via Classification Random Forests with 25 mRNA predictors (AUC 0.846 (0.778, 0.905)) and C. A 9483-feature proteomic dataset from 25 AD and 37 controls led to a protein-based biosignature via Ridge Logistic Regression with seven protein predictors (AUC 0.921 (0.849, 0.972)). These performance metrics were also validated through the JADBIO pipeline confirming stability. In conclusion, using the automated machine learning tool JADBIO, we produced accurate predictive biosignatures extrapolating available low sample -omics data. These results offer options for minimally invasive blood-based diagnostic tests for AD, awaiting clinical validation based on respective laboratory assays. They also highlight the value of AutoML in biomarker discovery.

Dissecting the Tumor-Immune Landscape in Chimeric Antigen Receptor T-cell Therapy: Key Challenges and Opportunities for a Systems Immunology Approach.

The adoptive transfer of genetically engineered chimeric antigen receptor (CAR) T cells has opened a new frontier in cancer therapy. Unlike the paradigm of targeted therapies, the efficacy of CAR T-cell therapy depends not only on the choice of target but also on a complex interplay of tumor, immune, and stromal cell communication. This presents both challenges and opportunities from a discovery standpoint. Whereas cancer consortia have traditionally focused on the genomic, transcriptomic, epigenomic, and proteomic landscape of cancer cells, there is an increasing need to expand studies to analyze the interactions between tumor, immune, and stromal cell populations in their relevant anatomical and functional compartments. Here, we focus on the promising application of systems biology to address key challenges in CAR T-cell therapy, from understanding the mechanisms of therapeutic resistance in hematologic and solid tumors to addressing important clinical challenges in biomarker discovery and therapeutic toxicity. We propose a systems biology view of key clinical objectives in CAR T-cell therapy and suggest a path forward for a biomedical discovery process that leverages modern technological approaches in systems biology.

Mass spectrometry-based metabolomics in biomarker-assisted drug discovery and oxidative stress research

Biomarker-assisted drug discovery is a major step in identification of drug efficacy and disease proliferation of both communicable and noncommunicable diseases. Biomarker-assisted drug discovery using metabolomics approach is now a reliable “path of opportunity” in drug discovery, as it offers precise insight into the efficacy of tested drug in the biological setup. With the advent of recent advances in instrumentation, accuracy and specificity of biomarker-based drug discovery program is improved. A key challenge in biomarker discovery is the choice of detection method, as diverse metabolites present in the physiological system pose varying levels of detection response with different instrumentation platforms. In biomarker-assisted early diagnosis, mass spectrometry-based metabolomics has a major advantage due to its capability of wide metabolite range coverage with great specificity. This review also discusses the advantage of biomarker-assisted discovery using metabolome analysis.

Disease systems modeling for discovery of mechanistic biomarkers

The use of biomarkers is becoming increasingly integral to to the contemporary practice of medicine and continues to play a central role in preventive, predictive and personalized medicine. However, the limited number of FDA-approved, in use biomarkers, on one hand, and an increasing number of published potential biomarkers, on the other hand, calls for an accelerated approach to translating biomarker research to clinical application. In this talk, I introduce novel concept of "Biomarker-guided Mechanism Discovery" through an integrative disease modeling approach and present the successful application of this methodology to deciphering the network model of genomic hormone interactions underlying dementia and its translational validation through serendipitous off-target effect. Moreover, the current challenges in biomarker discovery is discussed and results of our proposed Biomarker Ontology in collaboration with pharma industry will be presented.

Nanotechnologies in glycoproteomics.

Protein glycosylation, as an important post-translational modification, is implicated in a number of ailments. Applying proteomic approaches, including mass spectrometry (MS) analyses that have played a significant role in biomarker detection and early diagnosis of diseases, to the study of glycoproteins or glycopeptides will facilitate a deeper understanding of many physiological functions and biological pathways involved in cancer, inflammatory and degenerative diseases. The abundance of glycopeptides and their ionization potential are relatively lower compared to those of non-glycopeptides; therefore, sample enrichment is necessary for glycopeptides prior to MS analysis. The application of nanotechnology in the past decade has been rapidly penetrating into many diverse scientific research disciplines. Particularly in what we now refer to as the “glycoproteomics area”, nanotechnologies have enabled enhanced sensitivity and specificity of glycopeptide detection in complex biological fluids, which are critical for disease diagnosis and monitoring. In this review, we highlight some recent studies that combine the capabilities of specific nanotechnologies with the comprehensive features of glycoproteomics. In particular, we focus on the ways in which nanotechnology has facilitated the detection of glycopeptides in complex biological samples and enhanced their characterization by MS, in terms of intensity and resolution. These studies reveal an increasingly important role for nanotechnology in helping to overcome certain technical challenges in biomarker discovery, in general, and glycoproteomics research, in particular.

EPA-1233 – Meaningful psychiatric phenotypes emerge from a bayesian taxonomy of eye movements

Use of DSM and ICD nosology to stratify adult psychiatric illnesses poses significant practical challenges for biomarker discovery. Illness categories and subtypes have not been validated by objective tests. The epistemology and language of diagnostic psychiatric tools prevails during 2013–2014 while continuing to say nothing about the biological underpinnings of mental disorders. Assays mapped onto the current nomenclature remain contentious because empirical analyses and clinical intuition are often in conflict, producing’misclassifications’ that are difficult to interpret. DSM-5 revisions are obliged to incorporate empirical psycho-bio-physiological measurements supporting graduated dimensional descriptions of mental health disorders that properly reflect both quanta and spectra of variation extant in the population. Eye movements elicited by simple cognitive tasks are an endophenotypic bridge between the genetics, neuropathology, cognition, and ethno-sociocultural factors of the major neurodevelopmental psychiatric disorders. We have shown that multivariate measures of oculomotor dysfunction are capable of differentiating schizophrenia, bipolar disorder and endogenous major depression cases with exceptional specificity (Benson et al (2012) Eur Psychiatry 27(Supp1):1; Benson et al (2013) Eur Arch Psychiatry Clin Neurosci 263(Supp1): S19). Here we used unsupervised Bayesian clustering to stratify cases and controls using only their eye movement data. Performance measures from smooth pursuit, free-viewing and steady fixation tasks were used. Twelve clusters described a continuum of phenotypes, associated with schizophrenia, bipolar, mixed affective cases, and undiagnosed controls. Composite clusters evinced cases with shared and/or comorbid symptoms and subclinical groups. Eye movement abnormalities through their task-related substrates represent an objective bottom-up approach to diagnosis outside current categorical systems.

Coefficient of variation, signal-to-noise ratio, and effects of normalization in validation of biomarkers from NMR-based metabonomics studies

A primary goal of metabonomics research is biomarker discovery for human diseases based on differences in metabolic profiles between healthy and diseased patient populations. One of the most significant challenges in biomarker discovery is validation, which implicitly depends on the coefficient of variation (CV) associated with the measurement technique. This paper investigates how the CV of metabolite resonances measured by nuclear magnetic resonance spectroscopy (NMR) depends on signal-to-noise ratio (SNR) and normalization method. CVs were calculated for NMR resonance peaks in a series of NMR spectra of five synthetic urine samples collected over an eight-month period. An inverse correlation was detected between SNR and CV for all normalization methods. Small peaks with SNR<15 tended to have larger CVs (15–30%) compared to peaks with the highest SNR>150, which typically had smaller CVs (5–10%). The inverse relationship between CV and SNR roughly obeyed a log10 dependence. Quotient normalization (QN) tended to produce smaller CVs for smaller peaks, but larger CVs for the strongest peaks in the data, compared to no normalization, normalization to total intensity (NTI) or normalization to an internal standard (NIS). Consequently, quotient normalization appears optimal for validating low concentration metabolites. NTI or NIS appear superior to QN for samples that have very small variation in total signal intensity. While the inverse relationship between CV and log10(SNR) did not strictly hold for all metabolites, weaker concentration metabolites will likely require more rigorous validation as potential biomarkers since they tend to have poorer reproducibility.

Challenges in biomarker discovery: combining expert insights with statistical analysis of complex omics data

INTRODUCTION: The advent of high throughput technologies capable of comprehensive analysis of genes, transcripts, proteins and other significant biological molecules has provided an unprecedented opportunity for the identification of molecular markers of disease processes. However, it has simultaneously complicated the problem of extracting meaningful molecular signatures of biological processes from these complex datasets. The process of biomarker discovery and characterization provides opportunities for more sophisticated approaches to integrating purely statistical and expert knowledge-based approaches. AREAS COVERED: In this review we will present examples of current practices for biomarker discovery from complex omic datasets and the challenges that have been encountered in deriving valid and useful signatures of disease. We will then present a high-level review of data-driven (statistical) and knowledge-based methods applied to biomarker discovery, highlighting some current efforts to combine the two distinct approaches. EXPERT OPINION: Effective, reproducible and objective tools for combining data-driven and knowledge-based approaches to identify predictive signatures of disease are key to future success in the biomarker field. We will describe our recommendations for possible approaches to this problem including metrics for the evaluation of biomarkers.

Biomarkers: hopes and challenges in the path from discovery to clinical practice

Biomarkers are objectively measured indicators of normal or pathological processes that may be helpful in diagnosis, staging, monitoring treatment, or prognostic evaluation of a disease. Although development of genomic, metabolomic and proteomic technologies has contributed to an explosion in identification of candidate analytes, validation remains expensive and challenging, and successful introduction of new biomarkers to clinical practice occurs at a very slow pace. The goal of this introductory overview is to provide the context for a series of review manuscripts published in the special issue on biomarkers. The promises and challenges of biomarker discovery are highlighted. Discovery and implementation of transformative new biomarkers in clinical practice requires close collaborations between scientists, clinicians and industry. High throughput technologies can identify a myriad of promising candidates but cannot predict their clinical value. In addition to rapid effective and systematic approaches for clinical validation, there is a need to study and establish links between the purported biomarker and the pathophysiologic basis of the disease of interest. Biomarkers are most informative when they provide insights into activation of specific pathways, thus serving as windows into the molecular basis of the disease.

Advances and challenges in biomarker development for type 1 diabetes prediction and prevention using ‘omic’ technologies

IMPORTANCE OF THE FIELD: Biomarkers are essential for the identification of high risk children as well as monitoring of prevention outcomes for type 1 diabetes (T1D). AREAS COVERED IN THIS REVIEW: This review discusses progress, opportunities and challenges in biomarker discovery and validation using high throughput genomic, transcriptomic and proteomic technologies. The authors also suggest potential solutions to deal with the current challenges. WHAT THE READER WILL GAIN: Readers will gain an overview of the current status on T1D biomarkers, an integrated review of three omic technologies, their applications and limitations for biomarker discovery and validation, and a critical discussion of the major issues encountered in biomarker development. TAKE HOME MESSAGE: Better biomarkers are still urgently needed for T1D prediction and prevention. The high throughput omic technologies offer great opportunities but also face significant challenges that have to be solved before their potential for biomarker development is fully realized.

Current Technological Challenges in Biomarker Discovery and Validation

In this review we will give an overview of the issues related to biomarker discovery studies with a focus on liquid chromatography-mass spectrometry (LC-MS) methods. Biomarker discovery is based on a close collaboration between clinicians, analytical scientists and chemometritians/statisticians. It is critical to define the final purpose of a biomarker or biomarker pattern at the onset of the study and to select case and control samples accordingly. This is followed by designing the experiment, starting with the sampling strategy, sample collection, storage and separation protocols, choice and validation of the quantitative profiling platform followed by data processing, statistical analysis and validation workflows. Biomarker candidates that result after statistical validation should be submitted for further validation and, ideally, be connected to the disease mechanism after their identification. Since most discovery studies work with a relatively small number of samples, it is necessary to assess the specificity and sensitivity of a given biomarker-based assay in a larger set of independent samples, preferably analyzed at another clinical center. Targeted analytical methods of higher throughput than the original discovery method are needed at this point and LC-tandem mass spectrometry is gaining acceptance in this field. Throughout this review, we will focus on possible sources of variance and how they can be assessed and reduced in order to avoid false positives and to reduce the number of false negatives in biomarker discovery research.

Molecular Genetic Markers in Female Reproductive Cancers

Cancer is a complex genetic disease as a result of accumulated genomic alterations which serve as the driving force in initiating tumor development and propelling tumor progression. Recent advances in molecular genomic technology and the success of human genome project have empowered investigators with new tools in analyzing cancer genome and transcriptome in great details and have expedited the discovery of new cancer-associated genes. Decoding the genetic history present in tumor DNA, as well as identification and characterization of molecular changes involving cancer-associated genes and the pathways they controlled, has not only shed new light on the molecular etiology of cancer, but also has promises for the development of new diagnostic markers and novel therapeutics. In this special issue, the papers focus on recent findings of markers identified in female reproductive cancers including breast, ovarian, endometrial, and trophoblast tumors. The selected papers are not an exhaustive representation of the area of biomarkers in female reproductive cancers. Nonetheless, they represent the rich and many-faceted knowledge that we have the pleasure of sharing that with readers. We would like to thank the authors for their excellent contributions to this special issue and the reviewers' insightful input to ensure the quality of this special issue. Of particularly interest are the two timely review articles by A. M. Karst et al. and A. L. Gross et al. focusing on the possible precursor lesions in the development of female reproductive cancer. The tubal origin of “ovarian” cancer has recently been in the research spot-light because of the identification of potential precursor lesions of ovarian cancer, termed “serous tubal intraepithelial carcinoma” in the fallopian tubes. Both articles critically review the published studies and provide unique perspectives of the impact and significance of this new hypothesis in the field of cancer research. Review articles focusing on biomarkers including genetic (K. M. Hirshfield et al.), microRNA (H. M. Heneghan et al.), and cytokine (F. Barbieri et al.) summarize recent advances in biomarker discovery and the translational application. Those articles will serve as the source for the investigators who wish to pursue in evaluating new potential markers in the female reproductive cancer research. They also present the new challenges in biomarker discovery, validation, and verification. In addition to the five review articles, there are several exciting original research articles describing discovery and validation of biomarkers in female reproductive cancers. Those papers include the studies of fatty acid synthase (S. M. Ueda et al.), miR200c (D. R. Cochrane et al.), gene expression (S. Konstantinovsky et al.), and cytogenetic markers (F. Micci et al.). The findings from those studies could serve as biological foundation for future exploration of how those markers contribute to disease progression. The biomarkers identified in these studies could also serve as a road map for development of novel diagnosis tools and new targeted therapies. This issue also reports a synergistic effect of Pten loss and oncogenic Kras mutation on the endometrial cancer development (I. H. Kim et al.) and the result provides new insight into the pathogenesis of endometrial carcinoma. Finally, K. L. Yap et al. applied molecular genetic markers to demonstrate a lack of a Y-chromosomal complement in the majority of gestational trophoblastic neoplasms, suggesting that most trophoblastic tumors are derived from previous molar gestations. Because the molecular characters in gestational trophoblastic disease are understudied, this short report should provide new insight into the pathogenesis of this rare but highly interesting gynecological neoplasm. In conclusion, this special issue is by no means an attempt to be comprehensive in covering the field of female reproductive cancer, but it provides excellent reviews and research articles that may be useful for readers in this field. We hope that this special issue will inspire investigators in launching new research endeavor in this field and are grateful for the opportunity to manage this special issue. Tian-Li Wang Ben Davidson Phillip J. Buckhaults Chiun-Sheng Huang Gudi Poul

Biomarker discovery in psychiatric disorders

Schizophrenia is a multifaceted neuropsychiatric disorder. Its onset is the result of complex interactions between genetic, developmental and environmental factors. It almost certainly presents a heterogeneous group of aetiologies which may not be reflected in the symptomatic/clinical presentation of patients. Therefore, a better molecular understanding of the disease onset and progression is urgently needed. The high complexity of the disorder and the heterogeneity of patient populations account for the slow progress of biomarker discovery approaches. Multi-omics profiling approaches can be employed to investigate large numbers of patient and control samples in a single experiment. These large scale experiments are required to identify disease intrinsic molecular signatures as well as patient subgroups with potentially distinct biochemical pathways underpinning their symptoms. In this overview, we describe some of the most important challenges for biomarker discovery for psychiatric disorders and emphasize how these problems contribute to the requirement of large sample numbers. Results of MS-based protein profiling studies in schizophrenia research are reviewed and technical advantages and difficulties of the methodologies described. We outline recent technological advances that generated impressive results in other areas of research and point to their applicability for biomarker discovery in psychiatric disorders.

Proteomics in uveal melanoma research: opportunities and challenges in biomarker discovery

Uveal melanoma (UM) is the most frequent primary intraocular tumor in adult humans. Despite the significant advances in diagnosis and treatment of UM in the last decades, the prognosis of UM sufferers is still poor. Metastatic liver disease is the leading cause of death in UM and can develop after a long disease-free interval, suggesting the presence of occult micrometastasis. Proteomics technology has opened new opportunities for elucidating the molecular mechanism of complex diseases, such as cancer. This article will review the recent developments in biomarker discovery for UM research by proteomics. In the last few years, the first UM proteomics-based analyses have been launched, yielding promising results. An update on recent developments on this field is presented.