Protein biomarkers for amyotrophic lateral sclerosis

Amyotrophic lateral sclerosis (ALS) is a progressive motor neuron disease with largely unknown pathogenesis that typically results in death within a few years from diagnosis. There are currently no effective therapies for ALS. Clinical diagnosis usually takes several months to complete and the long delay between symptom onset and diagnosis limits the possibilities for effective intervention and clinical trials. The establishment of protein biomarkers for ALS may aid an earlier diagnosis, facilitating the search for effective therapeutic interventions and monitoring drug efficacy during clinical trials. Biomarkers could also be used to discriminate between subtypes of ALS, to measure disease progression and to detect susceptibility for developing ALS or monitor adverse effects of drug treatment. The present review will discuss the opportunities and proteomic platforms used for biomarker discovery efforts in ALS, summarizing putative ALS protein biomarkers identified in different biofluids.     

Genomic and proteomic biomarkers for cancer: A multitude of opportunities

Biomarkers are molecular indicators of a biological status, and as biochemical species can be assayed to evaluate the presence of cancer and therapeutic interventions. Through a variety of mechanisms cancer cells provide the biomarker material for their own detection. Biomarkers may be detectable in the blood, other body fluids, or tissues. The expectation is that the level of an informative biomarker is related to the specific type of disease present in the body. Biomarkers have potential both as diagnostic indicators and monitors of the effectiveness of clinical interventions. Biomarkers are also able to stratify cancer patients to the most appropriate treatment. Effective biomarkers for the early detection of cancer should provide a patient with a better outcome which in turn will translate into more efficient delivery of healthcare. Technologies for the early detection of cancer have resulted in reductions in disease-associated mortalities from cancers that are otherwise deadly if allowed to progress. Such screening technologies have proven that early detection will decrease the morbidity and mortality from cancer. An emerging theme in biomarker research is the expectation that panels of biomarker analytes rather than single markers will be needed to have sufficient sensitivity and specificity for the presymptomatic detection of cancer. Biomarkers may provide prognostic information of disease enabling interventions using targeted therapeutic agents as well as course-corrections in cancer treatment. Novel genomic, proteomic and metabolomic technologies are being used to discover and validate tumor biomarkers individually and in panels.     

Biomarkers for Alzheimer disease in cerebrospinal fluid, urine, and blood

Alzheimer disease is the most common cause of dementia, yet its clinical diagnosis remains uncertain until an eventual postmortem histopathology examination. Currently, therapy for patients with Alzheimer disease only treats the symptoms; however, it is anticipated that new disease-modifying drugs will soon become available.Diagnostic tools for detecting Alzheimer disease at an incipient stage that can reliably differentiate the disease from other forms of dementia are of key importance for optimal treatment. Biomarkers have the potential to aid in a correct diagnosis, and great progress has been made in the discovery and development of potentially useful biomarkers in recent years. This includes single protein biomarkers in the cerebrospinal fluid, as well as multi-component biomarkers, and biomarkers based on gene expression. Novel biomarkers that use blood and urine, the more easily available clinical samples, are also being discovered and developed. The plethora of potential biomarkers currently being investigated may soon provide biomarkers that fulfill different functions, not only for diagnostic purposes but also for drug development and to follow disease progression.     

Biomarker technology roundup: from discovery to clinical applications, a broad set of tools is required to translate from the lab to the clinic

Biomarkers are being utilized throughout the drug discovery and development process to understand fundamental biological processes and relationships. Specific biomarkers for disease states, prognosis, and response to therapy have been applied to screening tissues and serum, and serve as new tools in the development of therapeutics, to segment the population for specific treatments. The use of specific biomarkers to screen subjects to determine clinical trial eligibility, and for early toxicology studies, holds the potential to decrease drug failure rates in the later phases of the clinical trial process. Traditional research tools have been employed to study the genes, proteins, and metabolites of interest. In addition, new technologies and permutations of existing technologies have been developed particularly for investigation in the preclinical and clinical phases of drug development. More importantly, the transition of a compound from preclinical to clinical is aided by technologies that span both process segments. Identification of biomarkers that can be studied throughout the development process requires technologies that are both feasible and cost-effective for large patient populations.     

So, you want to look for biomarkers (introduction to the special biomarkers issue)

The burgeoning field of proteomics plays a powerful and relevant role in the discovery of biomarkers, which are biometric measurements that convey information about the biological condition of the subject being tested. Biomarkers have changed the manner in which we diagnose disease, monitor the effect of therapy, classify disease, detect toxicity, and develop new drugs. The central part that proteins command in both disease etiology and treatment make them prime biomarker candidates. Indeed, the majority of clinical tests in use today measure proteins. This perspective introduces the Journal of Proteome Research Special Issue on Proteomics and Biomarkers. It outlines the major applications of biomarkers, discusses the basics of statistically assessing them and considers the crucial choice of sample type. Central considerations of biomarker discovery and validation, particularly with respect to their intended clinical and research applications, are highlighted.     

Validation of Molecular and Genomic Biomarkers of Retinal Drug Efficacy: Use of Ocular Fluid Sampling to Evaluate VEGF

The use of tissue- and cell-based methods in developing drugs for retinal diseases is inefficient. Consequently, many aspects of ocular drug therapy for retinal diseases are poorly understood. Biomarkers as prognostic indicators of change are needed to optimize the use of drugs. VEGF is considered an important target of drug therapy and VEGF levels in tissue are indicative of solid tumor growth. However, since many aspects of VEGF as a biomarker of ocular disease have not been validated, it has been difficult to ascertain without invasive procedures whether VEGF in the eye is a biomarker of response to drug therapy. Using published papers, registered clinical trials, and proteomic databases we assessed the earlier evidence for VEGF as an exploratory biomarker of proliferative and vasculopathic disease of the retina and asked whether the molecule has been rigorously validated in clinical trials. The emerging use of aqueous humor sampling has made it possible to explore biomarkers in oculo, and determine whether they are predictive of drug efficacy. We present data supporting the use of aqueous humor to validate drug-signaling pathways and biomarkers in the eye. In addition, we recommend convening a collaborative congress to help standardize the identification, validation, and use of biomarkers in retinal disease.     

The "Alzheimer's disease signature": potential perspectives for novel biomarkers

Alzheimer's disease is a progressive and neurodegenerative disorder which involves multiple molecular mechanisms. Intense research during the last years has accumulated a large body of data and the search for sensitive and specific biomarkers has undergone a rapid evolution. However, the diagnosis remains problematic and the current tests do not accurately detect the process leading to neurodegeneration. Biomarkers discovery and validation are considered the key aspects to support clinical diagnosis and provide discriminatory power between different stages of the disorder. A considerable challenge is to integrate different types of data from new potent approach to reach a common interpretation and replicate the findings across studies and populations. Furthermore, long-term clinical follow-up and combined analysis of several biomarkers are among the most promising perspectives to diagnose and manage the disease. The present review will focus on the recent published data providing an updated overview of the main achievements in the genetic and biochemical research of the Alzheimer's disease. We also discuss the latest and most significant results that will help to define a specific disease signature whose validity might be clinically relevant for future AD diagnosis.     

Future prospects for human genetics and genomics in drug discovery

Evidence from human genetics supporting the therapeutic hypothesis increases the likelihood that a drug will succeed in clinical trials. Rare and common disease genetics yield a wide array of alleles with a range of effect sizes that can proxy for the effect of a drug in disease. Recent advances in large scale population collections and whole genome sequencing approaches have provided a rich resource of human genetic evidence to support drug target selection. As the range of phenotypes profiled increases and ever more alleles are discovered across world-wide populations, these approaches will increasingly influence multiple stages across the lifespan of a drug discovery programme.     

Application of proteomics for discovery of protein biomarkers.

Biomarkers of drug efficacy and toxicity are becoming a key need in the drug development process. Mass spectral-based proteomic technologies are ideally suited for the discovery of protein biomarkers in the absence of any prior knowledge of quantitative changes in protein levels. The success of any biomarker discovery effort will depend upon the quality of samples analysed, the ability to generate quantitative information on relative protein levels and the ability to readily interpret the data generated. This review will focus on the strengths and weaknesses of technologies currently utilised to address these issues.     

Current and proposed biomarkers of anthracycline cardiotoxicity in cancer: emerging opportunities in oxidative damage and autophagy

A biomarker is defined as "a characteristic that is objectively measured and evaluated as an indicator of normal biologic processes, pathogenic processes, or biological responses to a therapeutic intervention". Biomarkers can be utilized to detect disease, evaluate treatment risks, or determine treatment effectiveness. In the case of cancer, anthracyclines such as doxorubicin are front-line therapy to treat a number of different malignancies including breast cancer. However, a significant fraction of patients experience drug-induced cardiotoxicity. This toxicity is dose-limiting and can cause long-term morbidity or mortality. There is an unmet medical need to identify patients who are at risk for doxorubicin-induced cardiotoxicity, to detect cardiac damage early so that patient risk can be minimized, and to monitor the success of cardioprotective strategies. Therefore, doxorubicin treatment of cancer is an excellent example of the need for biomarkers to indicate drug safety in addition to drug efficacy. In this review we will discuss the mechanism of doxorubicinassociated cardiotoxicity, as well as other cancer therapies that induce cardiac toxicity by causing oxidative damage. We will also evaluate established and proposed biomarkers for cardiotoxicity based on our evolving knowledge of oxidative damage and subsequent autophagy. Finally, we will discuss advantages of combining oxidative damage- and autophagy-based protein biomarkers with current biomarkers such as troponins to facilitate early detection and mitigation of cardiotoxicity induced by cancer therapeutic agents.     

The potential role of thioredoxin 1 and CD30 systems as multiple pathway targets and biomarkers in tumor therapy

Our progress in understanding pathological disease mechanisms has led to the identification of biomarkers that have had a considerable impact on clinical practice. It is hoped that the move from generalized to stratified approaches, with the grouping of patients into clinical/therapeutic subgroups according to specific biomarkers, will lead to increasingly more effective clinical treatments in the near future. This success depends on the identification of biomarkers that reflect disease evolution and can be used to predict disease state and therapy response, or represent themselves a target for treatment. Biomarkers can be identified by studying relationships between serum, tissue, or tumor microenvironment parameters and clinical or therapeutic parameters at onset and during the progression of the disease, using systems biology. Given that multiple pathways, such as those responsible for redox and immune regulation, are deregulated or altered in tumors, the future of tumor therapy could lie in the simultaneous targeting of these pathways using extracellular and intracellular targets and biomarkers. With this aim in mind, we evaluated the role of thioredoxin 1, a key redox regulator, and CD30, a cell membrane receptor, in immune regulation. Our results lead us to suggest that the combined use of these biomarkers provides more detailed information concerning the multiple pathways affected in disease and hence the possibility of more effective treatment.     

Exploiting drug-disease relationships for computational drug repositioning

Finding new uses for existing drugs, or drug repositioning, has been used as a strategy for decades to get drugs to more patients. As the ability to measure molecules in high-throughput ways has improved over the past decade, it is logical that such data might be useful for enabling drug repositioning through computational methods. Many computational predictions for new indications have been borne out in cellular model systems, though extensive animal model and clinical trial-based validation are still pending. In this review, we show that computational methods for drug repositioning can be classified in two axes: drug based, where discovery initiates from the chemical perspective, or disease based, where discovery initiates from the clinical perspective of disease or its pathology. Newer algorithms for computational drug repositioning will likely span these two axes, will take advantage of newer types of molecular measurements, and will certainly play a role in reducing the global burden of disease.     

Molecular markers in malignant cutaneous melanoma: Gift horse or one‐trick pony?

The management of malignant cutaneous melanoma is problematic. Current clinical prognostic factors do not adequately predict disease recurrence and overall survival in a significant subset of patients. Adjuvant therapies for melanoma are notoriously toxic and associated with significant morbidity. Furthermore, it has been difficult to predict which patients will respond best to these treatments, if at all. DNA and RNA biomarkers have been developed to help overcome these problems. Biomarkers have been shown to upstage patients with melanoma, but are the assays sensitive and specific enough for clinical use as predictors of disease outcome or treatment response? We review our experience with DNA and RNA biomarkers in terms of their prognostic and predictive capabilities in malignant melanoma and outline their likely role in the future of melanoma staging, surveillance, and treatment. © 2005 Wiley‐Liss, Inc.     

Biomarkers in rare neuromuscular diseases

Neuromuscular diseases (NMDs) comprise a range of rare disorders that include both hereditary peripheral neuropathies and myopathies. The heterogeneity and rarity of neuromuscular disorders are challenges for researchers seeking to develop effective diagnosis and treatment strategies. In particular, clinical trials of new therapies are made more difficult due to lack of reliable and monitorable clinical outcome measures. Biomarkers could be a way to speed up research in this field, shedding light on the pathophysiological mechanisms behind such diseases and providing invaluable tools for monitoring their progression, prognosis and response to drug treatment. Furthermore, biomarkers could represent a surrogate endpoint for clinical trials, enabling better stratification of patient cohorts through more accurate diagnosis and prognosis prediction.     

Proteomics approaches to urologic diseases

Biomarkers are greatly needed for several urologic diseases, such as interstitial cystitis, the symptomatic and clinical progression of benign prostate hyperplasia, as well as the specific detection of urologic cancers, including prostate and bladder cancer. This review aims to: briefly describe the need for biomarkers in the field and biomarkers that are currently available for clinicians; address the limitations and roadblocks to effective biomarker discovery; and provide examples and strategies for implementing biomarkers in clinical practice and/or drug discovery.     

Development of a Multi-Biomarker Disease Activity Test for Rheumatoid Arthritis

Background

Disease activity measurement is a key component of rheumatoid arthritis (RA) management. Biomarkers that capture the complex and heterogeneous biology of RA have the potential to complement clinical disease activity assessment.

Objectives

To develop a multi-biomarker disease activity (MBDA) test for rheumatoid arthritis.

Methods

Candidate serum protein biomarkers were selected from extensive literature screens, bioinformatics databases, mRNA expression and protein microarray data. Quantitative assays were identified and optimized for measuring candidate biomarkers in RA patient sera. Biomarkers with qualifying assays were prioritized in a series of studies based on their correlations to RA clinical disease activity (e.g. the Disease Activity Score 28-C-Reactive Protein [DAS28-CRP], a validated metric commonly used in clinical trials) and their contributions to multivariate models. Prioritized biomarkers were used to train an algorithm to measure disease activity, assessed by correlation to DAS and area under the receiver operating characteristic curve for classification of low vs. moderate/high disease activity. The effect of comorbidities on the MBDA score was evaluated using linear models with adjustment for multiple hypothesis testing.

Results

130 candidate biomarkers were tested in feasibility studies and 25 were selected for algorithm training. Multi-biomarker statistical models outperformed individual biomarkers at estimating disease activity. Biomarker-based scores were significantly correlated with DAS28-CRP and could discriminate patients with low vs. moderate/high clinical disease activity. Such scores were also able to track changes in DAS28-CRP and were significantly associated with both joint inflammation measured by ultrasound and damage progression measured by radiography. The final MBDA algorithm uses 12 biomarkers to generate an MBDA score between 1 and 100. No significant effects on the MBDA score were found for common comorbidities.

Conclusion

We followed a stepwise approach to develop a quantitative serum-based measure of RA disease activity, based on 12-biomarkers, which was consistently associated with clinical disease activity levels.     

Diagnostic and prognostic biomarker discovery strategies for autoimmune disorders

Current clinical, laboratory or radiological parameters cannot accurately diagnose or predict disease outcomes in a range of autoimmune disorders. Biomarkers which can diagnose at an earlier time point, predict outcome or help guide therapeutic strategies in autoimmune diseases could improve clinical management of this broad group of debilitating disorders. Additionally, there is a growing need for a deeper understanding of multi-factorial autoimmune disorders.Proteomic platforms offering a multiplex approach are more likely to reflect the complexity of autoimmune disease processes. Findings from proteomic based studies of three distinct autoimmune diseases are presented and strategies compared. It is the authors' view that such approaches are likely to be fruitful in the movement of autoimmune disease treatment away from reactive decisions and towards a preventative stand point.     

The use of biomarkers in surveillance, medical screening, and intervention

Building on mechanistic information, much of molecular epidemiologic research has focused on validating biomarkers, that is, assessing their ability to accurately indicate exposure, effect, disease, or susceptibility. To be of use in surveillance, medical screening, or interventions, biomarkers must already be validated so that they can be used as outcomes or indicators that can serve a particular function. In surveillance, biomarkers can be used as indicators of hazard, exposure, disease, and population risk. However, to obtain rates for these measures, the population at risk will need to be assessed. In medical screening, biomarkers can serve as early indicators of disease in asymptomatic people. This allows for the identification of those who should receive diagnostic confirmation and early treatment. In intervention (which includes risk assessment and communication, risk management, and various prevention efforts), biomarkers can be used to assess the effectiveness of a prevention or control strategy as well as help determine whether the appropriate individuals are assigned to the correct intervention category. Biomarkers can be used to provide group and individual risk assessments that can be the basis for marshalling resources. Critical for using biomarkers in surveillance, medical screening, and intervention is the justification that the biomarkers can provide information not otherwise accessible by a less expensive and easier-to-obtain source of information, such as medical records, surveys, or vital statistics. The ability to use validated biomarkers in surveillance, medical screening, and intervention will depend on the extent to which a strategy for evidence-based procedures for biomarker knowledge transfer can be developed and implemented. This will require the interaction of researchers and decision-makers to collaborate on public health and medical issues.