Qualification of translational safety biomarkers

Safety biomarkers are important drug development tools, both preclinically and clinically. It is a straightforward process to correlate the performance of nonclinical safety biomarkers with histopathology, and ideally, the biomarker is useful in all species commonly used in safety assessment. In clinical validation studies, where histopathology is not feasible, safety biomarkers are compared to the response of standard biomarkers and/or to clinical adjudication. Worldwide, regulatory agencies have put in place processes to qualify biomarkers to provide confidence in the manner of use and interpretation of biomarker data in drug development studies. This paper describes currently qualified safety biomarkers which can be utilized to monitor for nephrotoxicity and cardiotoxicity and ongoing projects to qualify safety biomarkers for liver, skeletal muscle, and vascular injury. In many cases, the development and use of these critical drug development tools is dependent upon partnerships and the precompetitive sharing of data to support qualification efforts.     

Targeted proteomics: a bridge between discovery and validation

New technologies in mass spectrometry are beginning to mature and show unique advantages for the identification and quantitation of proteins. In recent years, one of the significant goals of clinical proteomics has been to identify biomarkers that can be used for clinical diagnosis. As technology has progressed, the list of potential biomarkers has grown. However, the verification and validation of these potential biomarkers is increasingly challenging and require high-throughput quantitative assays, targeting specific candidates. Targeted proteomics bridges the gap between biomarker discovery and the development of clinically applicable biomarker assays.     

Proteomic methods for biomarker discovery and validation. Are we there yet?

Noninvasive molecular biomarkers are becoming attractive tools to monitor disease progression, aid drug development programs and use as surrogate outcome measures in clinical trials. Cutting edge proteomic methods to assay biomarkers in body fluids have been developed in the past few years, but transitioning them to clinical practice has been slow and depends on the qualification of both the method and the biomarker.     

Why have so few proteomic biomarkers “survived” validation? (Sample size and independent validation considerations)

Proteomic biomarker discovery has led to the identification of numerous potential candidates for disease diagnosis, prognosis, and prediction of response to therapy. However, very few of these identified candidate biomarkers reach clinical validation and go on to be routinely used in clinical practice. One particular issue with biomarker discovery is the identification of significantly changing proteins in the initial discovery experiment that do not validate when subsequently tested on separate patient sample cohorts. Here, we seek to highlight some of the statistical challenges surrounding the analysis of LC‐MS proteomic data for biomarker candidate discovery. We show that common statistical algorithms run on data with low sample sizes can overfit and yield misleading misclassification rates and AUC values. A common solution to this problem is to prefilter variables (via, e.g. ANOVA and or use of correction methods such as Bonferonni or false discovery rate) to give a smaller dataset and reduce the size of the apparent statistical challenge. However, we show that this exacerbates the problem yielding even higher performance metrics while reducing the predictive accuracy of the biomarker panel. To illustrate some of these limitations, we have run simulation analyses with known biomarkers. For our chosen algorithm (random forests), we show that the above problems are substantially reduced if a sufficient number of samples are analyzed and the data are not prefiltered. Our view is that LC‐MS proteomic biomarker discovery data should be analyzed without prefiltering and that increasing the sample size in biomarker discovery experiments should be a very high priority.     

Evaluating candidate principal surrogate endpoints

SUMMARY: Frangakis and Rubin (2002, Biometrics 58, 21-29) proposed a new definition of a surrogate endpoint (a "principal" surrogate) based on causal effects. We introduce an estimand for evaluating a principal surrogate, the causal effect predictiveness (CEP) surface, which quantifies how well causal treatment effects on the biomarker predict causal treatment effects on the clinical endpoint. Although the CEP surface is not identifiable due to missing potential outcomes, it can be identified by incorporating a baseline covariate(s) that predicts the biomarker. Given case-cohort sampling of such a baseline predictor and the biomarker in a large blinded randomized clinical trial, we develop an estimated likelihood method for estimating the CEP surface. This estimation assesses the "surrogate value" of the biomarker for reliably predicting clinical treatment effects for the same or similar setting as the trial. A CEP surface plot provides a way to compare the surrogate value of multiple biomarkers. The approach is illustrated by the problem of assessing an immune response to a vaccine as a surrogate endpoint for infection.     

Protein biomarker discovery and validation: the long and uncertain path to clinical utility

Better biomarkers are urgently needed to improve diagnosis, guide molecularly targeted therapy and monitor activity and therapeutic response across a wide spectrum of disease. Proteomics methods based on mass spectrometry hold special promise for the discovery of novel biomarkers that might form the foundation for new clinical blood tests, but to date their contribution to the diagnostic armamentarium has been disappointing. This is due in part to the lack of a coherent pipeline connecting marker discovery with well-established methods for validation. Advances in methods and technology now enable construction of a comprehensive biomarker pipeline from six essential process components: candidate discovery, qualification, verification, research assay optimization, biomarker validation and commercialization. Better understanding of the overall process of biomarker discovery and validation and of the challenges and strategies inherent in each phase should improve experimental study design, in turn increasing the efficiency of biomarker development and facilitating the delivery and deployment of novel clinical tests.     

Comparing biomarkers as principal surrogate endpoints

Recently a new definition of surrogate endpoint, the "principal surrogate," was proposed based on causal associations between treatment effects on the biomarker and on the clinical endpoint. Despite its appealing interpretation, limited research has been conducted to evaluate principal surrogates, and existing methods focus on risk models that consider a single biomarker. How to compare principal surrogate value of biomarkers or general risk models that consider multiple biomarkers remains an open research question. We propose to characterize a marker or risk model's principal surrogate value based on the distribution of risk difference between interventions. In addition, we propose a novel summary measure (the standardized total gain) that can be used to compare markers and to assess the incremental value of a new marker. We develop a semiparametric estimated-likelihood method to estimate the joint surrogate value of multiple biomarkers. This method accommodates two-phase sampling of biomarkers and is more widely applicable than existing nonparametric methods by incorporating continuous baseline covariates to predict the biomarker(s), and is more robust than existing parametric methods by leaving the error distribution of markers unspecified. The methodology is illustrated using a simulated example set and a real data set in the context of HIV vaccine trials.     

Finding cancer biomarkers from mass spectrometry data by decision lists.

Finding accurate biomarkers is key to early diagnosis and successful treatment of many otherwise incurable diseases. In this work, we study the problem of finding biomarkers through mass spectrometry (SELDI-TOF) spectra from cancerous and normal tissues. In contrast to the common practice of using vague methods such as genetic algorithms, or uninterpretable methods such as Support Vector Machines, we look for a method that is simple, intuitive, interpretable, usable, and more accurate. We introduce decision lists to this domain. Our experiments on clinical cancer datasets demonstrate that decision lists can achieve more accurate results than other methods. More interestingly, the resulting decision lists are more interpretable for possible causal relationship between cancer and differentially expressed proteins, and directly usable in clinical biomarker design. In particular, our approach is capable of finding multiple biomarkers with high sensitivity and specificity. Such a feature will provide clues for medical experts to thoroughly investigate the roles of protein in cancer development and progression.     

A targeted proteomics-based pipeline for verification of biomarkers in plasma

High-throughput technologies can now identify hundreds of candidate protein biomarkers for any disease with relative ease. However, because there are no assays for the majority of proteins and de novo immunoassay development is prohibitively expensive, few candidate biomarkers are tested in clinical studies. We tested whether the analytical performance of a biomarker identification pipeline based on targeted mass spectrometry would be sufficient for data-dependent prioritization of candidate biomarkers, de novo development of assays and multiplexed biomarker verification. We used a data-dependent triage process to prioritize a subset of putative plasma biomarkers from >1,000 candidates previously identified using a mouse model of breast cancer. Eighty-eight novel quantitative assays based on selected reaction monitoring mass spectrometry were developed, multiplexed and evaluated in 80 plasma samples. Thirty-six proteins were verified as being elevated in the plasma of tumor-bearing animals. The analytical performance of this pipeline suggests that it should support the use of an analogous approach with human samples.     

Design considerations for two-stage enrichment clinical trials

When there is a predictive biomarker, enrichment can focus the clinical trial on a benefiting subpopulation. We describe a two-stage enrichment design, in which the first stage is designed to efficiently estimate a threshold and the second stage is a “phase III-like” trial on the enriched population. The goal of this paper is to explore design issues: sample size in Stages 1 and 2, and re-estimation of the Stage 2 sample size following Stage 1. By treating these as separate trials, we can gain insight into how the predictive nature of the biomarker specifically impacts the sample size. We also show that failure to adequately estimate the threshold can have disastrous consequences in the second stage. While any bivariate model could be used, we assume a continuous outcome and continuous biomarker, described by a bivariate normal model. The correlation coefficient between the outcome and biomarker is the key to understanding the behavior of the design, both for predictive and prognostic biomarkers. Through a series of simulations we illustrate the impact of model misspecification, consequences of poor threshold estimation, and requisite sample sizes that depend on the predictive nature of the biomarker. Such insight should be helpful in understanding and designing enrichment trials.     

Early biomarkers of joint damage in rheumatoid and psoriatic arthritis

Joint destruction, as evidenced by radiographic findings, is a significant problem for patients suffering from rheumatoid arthritis and psoriatic arthritis. Inherently irreversible and frequently progressive, the process of joint damage begins at and even before the clinical onset of disease. However, rheumatoid and psoriatic arthropathies are heterogeneous in nature and not all patients progress to joint damage. It is therefore important to identify patients susceptible to joint destruction in order to initiate more aggressive treatment as soon as possible and thereby potentially prevent irreversible joint damage. At the same time, the high cost and potential side effects associated with aggressive treatment mean it is also important not to over treat patients and especially those who, even if left untreated, would not progress to joint destruction. It is therefore clear that a protein biomarker signature that could predict joint damage at an early stage would support more informed clinical decisions on the most appropriate treatment regimens for individual patients. Although many candidate biomarkers for rheumatoid and psoriatic arthritis have been reported in the literature, relatively few have reached clinical use and as a consequence the number of prognostic biomarkers used in rheumatology has remained relatively static for several years. It has become evident that a significant challenge in the transition of biomarker candidates to clinical diagnostic assays lies in the development of suitably robust biomarker assays, especially multiplexed assays, and their clinical validation in appropriate patient sample cohorts. Recent developments in mass spectrometry-based targeted quantitative protein measurements have transformed our ability to rapidly develop multiplexed protein biomarker assays. These advances are likely to have a significant impact on the validation of biomarkers in the future. In this review, we have comprehensively compiled a list of candidate biomarkers in rheumatoid and psoriatic arthritis, evaluated the evidence for their potential as biomarkers of bone (joint) damage, and outlined how mass spectrometry-based targeted and multiplexed measurement of candidate biomarker proteins is likely to accelerate their clinical validation and the development of clinical diagnostic tests.     

Predictive markers in early research and companion diagnostic developments in oncology

Predictive biomarkers are discovered and used in oncology research to formulate hypotheses aimed at the identification of patients benefiting from specific therapeutic intervention(s). They pave the way to the development of companion diagnostic tests which are tools readily implemented in the clinic and serve to qualify a patient for treatment with a particular targeted drug or the continued use of a particular drug, thus maximizing the benefit to risk ratio of the medical intervention to the patient. Predictive biomarkers are defined by biological characteristics of the patient's or tumor status that can be measured objectively and correlated with clinical outcome: these can be molecular, cellular or biochemical features. Predictive markers need extensive analytical validation - specific for the tool utilized for their assessment - as well as rigorous clinical qualification in the context of the drug treatment for which they define clinical utility. The process of companion diagnostic development is a highly interdisciplinary and complex one, driven by key crucial milestones and accompanying the same and typical process of a whole drug discovery and development continuum, from marker discovery and validation, assay development, clinical qualification until test approval and commercialization.     

Introduction to proteomics: tools for the new biology

Towards revolutionary biomarkers, a considerable amount of research funds and time have been dedicated to proteomics. Although the discovery of novel biomarkers at the dawn of proteomics was a promising development, only a few identified biomarkers seemed to be beneficial for cancer patients. We may need to approach this issue differently, instead of only extending the conventional approaches that have been used historically. The study of biomarkers is essentially a study of diseases and the biochemistry relating to peptide, protein and post-translational modifications is only a tool. A problem-oriented approach should be needed in biomarker development. Clinician participation in the study of biomarkers will lead to realistic, practical and interesting biomarker candidates, which justify the time and expense involved in validation studies. Although discussion in this article is focused on cancer biomarkers, it can generally be applied to biomarker studies for other diseases.     

Biomarkers in T-cell therapy clinical trials

T-cell therapy represents an emerging and promising modality for the treatment of disease. Data from recent clinical trials of genetically modified T cells, most notably chimeric antigen receptor (CAR) T cells, have yielded dramatic clinical results and highlighted the potential for this approach to mediate anti-tumor activity. Continued progress in the development of such T-cell therapies will require the identification of the relevant biomarker strategies to support and guide clinical development of the candidate products. In this review, we review and discuss (i) principles for development and use of biomarkers in clinical research, (ii) the rationale and a strategy for the integration of biomarker data at all stages of the product development process, from preclinical studies through product manufacture and during the clinical trial and (iii) the different classes of biomarkers that are relevant to T-cell therapy trials. Throughout this review, we discuss how biomarkers can play a central role in the development of novel T-cell therapeutic agents and highlight how appropriately designed biomarker studies can provide critical insights to this process. Finally, we discuss future directions and challenges for the appropriate development of biomarkers to evaluate product bioactivity and treatment efficacy.     

Peptide biomarkers as evidence of perchlorate biodegradation

Perchlorate is a known health hazard for humans, fish, and other species. Therefore, it is important to assess the response of an ecosystem exposed to perchlorate contamination. The data reported here show that a liquid chromatography-mass spectrometry-based proteomics approach for the detection of perchlorate-reducing enzymes can be used to measure the ability of microorganisms to degrade perchlorate, including determining the current perchlorate degradation status. Signature peptides derived from chlorite dismutase (CD) and perchlorate reductase can be used as biomarkers of perchlorate presence and biodegradation. Four peptides each derived from CD and perchlorate reductase subunit A (PcrA) and seven peptides derived from perchlorate reductase subunit B (PcrB) were identified as signature biomarkers for perchlorate degradation, as these sequences are conserved in the majority of the pure and mixed perchlorate-degrading microbial cultures examined. However, chlorite dismutase signature biomarker peptides from Dechloromonas agitata CKB were found to be different from those in other cultures used and should also be included with selected CD biomarkers. The combination of these peptides derived from the two enzymes represents a promising perchlorate presence/biodegradation biomarker system. The biomarker peptides were detected at perchlorate concentrations as low as 0.1 mM and at different time points both in pure cultures and within perchlorate-reducing environmental enrichment consortia. The peptide biomarkers were also detected in the simultaneous presence of perchlorate and an alternate electron acceptor, nitrate. We believe that this technique can be useful for monitoring bioremediation processes for other anthropogenic environmental contaminants with known metabolic pathways.     

Inconvenient truth: Cancer biomarker development by using proteomics

A biomarker is a crucial tool for measuring the progress of disease and the effects of treatment for better clinical outcomes in cancer patients. Diagnostic, predictive, and prognostic biomarkers are required in various clinical settings. The proteome, a functional translation of the genome, is considered a rich source of biomarkers; therefore, sizable time and funding have been spent in proteomics to develop biomarkers. Although significant progress has been made in technologies toward comprehensive protein expression profiling, and many biomarker candidates published, none of the reported biomarkers have proven to be beneficial for cancer patients. The present deceleration in biomarker research can be attributed to technical limitations. Additional efforts are required to further technical progress; however, there are many examples demonstrating that problems in biomarker research are not so much with the technology but in the study design. In the study of biomarkers for early diagnosis, candidates are screened and validated by comparing cases and controls of similar sample size, and the low prevalence of disease is often ignored. Although it is reasonable to take advantage of multiple rather than single biomarkers when studying diverse disease mechanisms, the annotation of individual components of reported multiple biomarkers does not often explain the variety of molecular events underlying the clinical observations. In tissue biomarker studies, the heterogeneity of disease tissues and pathological observations are often not considered, and tissues are homogenized as a whole for protein extraction. In addition to the challenge of technical limitations, the fundamental aspects of biomarker development in a disease study need to be addressed. This article is part of a Special Issue entitled: Biomarkers: A Proteomic Challenge.     

Real Time Intraoperative Functional Brain Mapping Based on RGB Imaging

Intraoperative optical imaging is a localization technique for the functional areas of the human brain cortex during neurosurgical procedures. However, it still lacks robustness to be used as a clinical standard. In particular new biomarkers of brain functionality with improved sensitivity and specificity are needed. We present a method for the real time identification of the activated cortical areas based on the analysis of the cortical hemodynamic using a RGB camera and a white light source. We measure the quantitative oxy and deoxy-hemoglobin concentration changes in the human brain cortex with the modified Beer-Lambert law and Monte Carlo simulations. A functional model has been implemented to define in real time a binary biomarker of the cortical activation following neuronal activation by physiological stimuli. The results show a good correlation between the computed activation maps and the brain areas localized by electrical brain stimulation. We demonstrate that a RGB camera combined with a quantitative modeling of brain hemodynamics biomarkers can evaluate in real time the functional areas during neurosurgery and serve as a tool of choice to complement electrical brain stimulation.     

Biomarker discovery in biological fluids

Discovery of novel protein biomarkers is essential for successful drug discovery and development. These novel protein biomarkers may aid accelerated drug efficacy, response, or toxicity decision making based on their enhanced sensitivity and/or specificity. These biomarkers, if necessary, could eventually be converted into novel diagnostic marker assays. Proteomic platforms developed over the past few years have given us the ability to rapidly identify novel protein biomarkers in various biological matrices from cell cultures (lysates, supernatants) to human clinical samples (serum, plasma, and urine). In this article, we delineate an approach to biomarker discovery. This approach is divided into three steps, (i) identification of markers, (ii) prioritization of identified markers, and (iii) preliminary validation (qualification) of prioritized markers. Using drug-induced idiosyncratic hepatotoxicity as a case study, the article elaborates methods and techniques utilized during the three steps of biomarker discovery process. The first step involves identification of markers using multi-dimensional protein identification technology. The second step involves prioritization of a subset of marker candidates based on several criteria such as availability of reagent set for assay development and literature association to disease biology. The last step of biomarker discovery involves development of preliminary assays to confirm the bio-analytical measurements from the first step, as well as qualify the marker(s) in pre-clinical models, to initiate future marker validation and development.     

DNA methylation based biomarkers: Practical considerations and applications

A biomarker is a molecular target analyzed in a qualitative or quantitative manner to detect and diagnose the presence of a disease, to predict the outcome and the response to a specific treatment allowing personalized tailoring of patient management. Biomarkers can belong to different types of biochemical molecules such as proteins, DNA, RNA or lipids, whereby protein biomarkers have been the most extensively studied and used, notably in blood-based protein quantification tests or immunohistochemistry. The rise of interest in epigenetic mechanisms has allowed the identification of a new type of biomarker, DNA methylation, which is of great potential for many applications. This stable and heritable covalent modification mostly affects cytosines in the context of a CpG dinucleotide in humans. It can be detected and quantified by a number of technologies including genome-wide screening methods as well as locus- or gene-specific high-resolution analysis in different types of samples such as frozen tissues and FFPE samples, but also in body fluids such as urine, plasma, and serum obtained through non-invasive procedures. In some cases, DNA methylation based biomarkers have proven to be more specific and sensitive than commonly used protein biomarkers, which could clearly justify their use in clinics. However, very few of them are at the moment used in clinics and even less commercial tests are currently available. The objective of this review is to discuss the advantages of DNA methylation as a biomarker, the practical considerations for their development, and their use in disease detection, prediction of outcome or treatment response, through multiple examples mainly focusing on cancer, but also to evoke their potential for complex diseases and prenatal diagnostics.