ReProMSig: an integrative platform for development and application of reproducible multivariable models for cancer prognosis supporting guideline-based transparent reporting.

Adequate reporting is essential for evaluating the performance and clinical utility of a prognostic prediction model. Previous studies indicated a prevalence of incomplete or suboptimal reporting in translational and clinical studies involving development of multivariable prediction models for prognosis, which limited the potential applications of these models. While reporting templates introduced by the established guidelines provide an invaluable framework for reporting prognostic studies uniformly, there is a widespread lack of qualified adherence, which may be due to miscellaneous challenges in manual reporting of extensive model details, especially in the era of precision medicine. Here, we present ReProMSig (Reproducible Prognosis Molecular Signature), a web-based integrative platform providing the analysis framework for development, validation and application of a multivariable prediction model for cancer prognosis, using clinicopathological features and/or molecular profiles. ReProMSig platform supports transparent reporting by presenting both methodology details and analysis results in a strictly structured reporting file, following the guideline checklist with minimal manual input needed. The generated reporting file can be published together with a developed prediction model, to allow thorough interrogation and external validation, as well as online application for prospective cases. We demonstrated the utilities of ReProMSig by development of prognostic molecular signatures for stage II and III colorectal cancer respectively, in comparison with a published signature reproduced by ReProMSig. Together, ReProMSig provides an integrated framework for development, evaluation and application of prognostic/predictive biomarkers for cancer in a more transparent and reproducible way, which would be a useful resource for health care professionals and biomedical researchers.

A Bayesian mixed effects support vector machine for learning and predicting daily substance use disorder patterns.

Background: Substance use disorder (SUD) is a heterogeneous disorder. Adapting machine learning algorithms to allow for the parsing of intrapersonal and interpersonal heterogeneity in meaningful ways may accelerate the discovery and implementation of clinically actionable interventions in SUD research.Objectives: Inspired by a study of heavy drinkers that collected daily drinking and substance use (ABQ DrinQ), we develop tools to estimate subject-specific risk trajectories of heavy drinking; estimate and perform inference on patient characteristics and time-varying covariates; and present results in easy-to-use Jupyter notebooks. Methods: We recast support vector machines (SVMs) into a Bayesian model extended to handle mixed effects. We then apply these methods to ABQ DrinQ to model alcohol use patterns. ABQ DrinQ consists of 190 heavy drinkers (44% female) with 109,580 daily observations. Results: We identified male gender (point estimate; 95% credible interval: -0.25;-0.29,-0.21), older age (-0.03;-0.03,-0.03), and time varying usage of nicotine (1.68;1.62,1.73), cannabis (0.05;0.03,0.07), and other drugs (1.16;1.01,1.35) as statistically significant factors of heavy drinking behavior. By adopting random effects to capture the subject-specific longitudinal trajectories, the algorithm outperforms traditional SVM (classifies 84% of heavy drinking days correctly versus 73%). Conclusions: We developed a mixed effects variant of SVM and compare it to the traditional formulation, with an eye toward elucidating the importance of incorporating random effects to account for underlying heterogeneity in SUD data. These tools and examples are packaged into a repository for researchers to explore. Understanding patterns and risk of substance use could be used for developing individualized interventions.

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

Background Shortcomings in study design have been hinted at as one of the possible causes of failures in the translation of discovered biomarkers into the care of ovarian cancer patients, but systematic assessments of biomarker studies are scarce. We aimed to document study design features of recently reported evaluations of biomarkers in ovarian cancer. Methods We performed a systematic search in PubMed (MEDLINE) for reports of studies evaluating the clinical performance of putative biomarkers in ovarian cancer. We extracted data on study designs and characteristics. Results Our search resulted in 1026 studies; 329 (32%) were found eligible after screening, of which we evaluated the first 200. Of these, 93 (47%) were single center studies. Few studies reported eligibility criteria (17%), sampling methods (10%) or a sample size justification or power calculation (3%). Studies often used disjoint groups of patients, sometimes with extreme phenotypic contrasts; 46 studies included healthy controls (23%), but only five (3%) had exclusively included advanced stage cases. Conclusions Our findings confirm the presence of suboptimal features in clinical evaluations of ovarian cancer biomarkers. This may lead to premature claims about the clinical value of these markers or, alternatively, the risk of discarding potential biomarkers that are urgently needed.

Alternative empirical Bayes models for adjusting for batch effects in genomic studies.

BACKGROUND: Combining genomic data sets from multiple studies is advantageous to increase statistical power in studies where logistical considerations restrict sample size or require the sequential generation of data. However, significant technical heterogeneity is commonly observed across multiple batches of data that are generated from different processing or reagent batches, experimenters, protocols, or profiling platforms. These so-called batch effects often confound true biological relationships in the data, reducing the power benefits of combining multiple batches, and may even lead to spurious results in some combined studies. Therefore there is significant need for effective methods and software tools that account for batch effects in high-throughput genomic studies. RESULTS: Here we contribute multiple methods and software tools for improved combination and analysis of data from multiple batches. In particular, we provide batch effect solutions for cases where the severity of the batch effects is not extreme, and for cases where one high-quality batch can serve as a reference, such as the training set in a biomarker study. We illustrate our approaches and software in both simulated and real data scenarios. CONCLUSIONS: We demonstrate the value of these new contributions compared to currently established approaches in the specified batch correction situations.

Biomarker Development for Intraductal Papillary Mucinous Neoplasms Using Multiple Reaction Monitoring Mass Spectrometry.

Intraductal papillary mucinous neoplasm (IPMN) is a common precursor of pancreatic cancer (PC). Much clinical attention has been directed toward IPMNs due to the increase in the prevalence of PC. The diagnosis of IPMN depends primarily on a radiological examination, but the diagnostic accuracy of this tool is not satisfactory, necessitating the development of accurate diagnostic biomarkers for IPMN to prevent PC. Recently, high-throughput targeted proteomic quantification methods have accelerated the discovery of biomarkers, rendering them powerful platforms for the evolution of IPMN diagnostic biomarkers. In this study, a robust multiple reaction monitoring (MRM) pipeline was applied to discovery and verify IPMN biomarker candidates in a large cohort of plasma samples. Through highly reproducible MRM assays and a stringent statistical analysis, 11 proteins were selected as IPMN marker candidates with high confidence in 184 plasma samples, comprising a training (n = 84) and test set (n = 100). To improve the discriminatory power, we constructed a six-protein panel by combining marker candidates. The multimarker panel had high discriminatory power in distinguishing between IPMN and controls, including other benign diseases. Consequently, the diagnostic accuracy of IPMN can be improved dramatically with this novel plasma-based panel in combination with a radiological examination.

Applications of Deep Learning in Biomedicine.

Increases in throughput and installed base of biomedical research equipment led to a massive accumulation of -omics data known to be highly variable, high-dimensional, and sourced from multiple often incompatible data platforms. While this data may be useful for biomarker identification and drug discovery, the bulk of it remains underutilized. Deep neural networks (DNNs) are efficient algorithms based on the use of compositional layers of neurons, with advantages well matched to the challenges -omics data presents. While achieving state-of-the-art results and even surpassing human accuracy in many challenging tasks, the adoption of deep learning in biomedicine has been comparatively slow. Here, we discuss key features of deep learning that may give this approach an edge over other machine learning methods. We then consider limitations and review a number of applications of deep learning in biomedical studies demonstrating proof of concept and practical utility.

Genomics of Esophageal Cancer and Biomarkers for Early Detection.

In-depth molecular characterization of esophageal oncogenesis has improved over the recent years. Advancement in molecular biology and bioinformatics has led to better understanding of its genomic landscape. More specifically, analysis of its pathogenesis at the genetic level has uncovered the involvement of a number of tumor suppressor genes, cell cycle regulators, and receptor tyrosine kinases. Due to its poor prognosis, the development of clinically applicable biomarkers for diagnosis, progression, and treatment has been the focus of many research studies concentrating on upper gastrointestinal malignancies. As in other cancers, early detection and subsequent intervention of the preneoplastic condition significantly improves patient outcomes. Currently, clinically approved surveillance practices heavily depend on expensive, invasive, and sampling-error-prone endoscopic procedures. There is, therefore, a great demand to establish clearly reliable biomarkers that could identify those patients at higher risk of neoplastic progression and hence would greatly benefit from further monitoring and/or intervention. This chapter will present the most recent advances in the analysis of the esophageal cancer genome serving as basis for biomarker development.

Developing Potential Candidates of Preclinical Preeclampsia.

The potential for developing molecules of interest in preclinical preeclampsia from candidate genes that were discovered on gene expression microarray analysis has been challenged by limited access to additional first trimester trophoblast and decidual tissues. The question of whether these candidates encode secreted proteins that may be detected in maternal circulation early in pregnancy has been investigated using various proteomic methods. Pilot studies utilizing mass spectrometry based proteomic assays, along with enzyme linked immunosorbent assays (ELISAs), and Western immunoblotting in first trimester samples are reported. The novel targeted mass spectrometry methods led to robust multiple reaction monitoring assays. Despite detection of several candidates in early gestation, challenges persist. Future antibody-based studies may lead to a novel multiplex protein panel for screening or detection to prevent or mitigate preeclampsia.

Exploring the role of gut microbes in spondyloarthritis: Implications for pathogenesis and therapeutic strategies.

The gut microbiota plays a pivotal role in regulating host immunity, and dysregulation of this interaction is implicated in autoimmune and inflammatory diseases, including spondyloarthritis (SpA). This review explores microbial dysbiosis and altered metabolic function observed in various forms of SpA, such as ankylosing spondylitis (AS), psoriatic arthritis (PsA), acute anterior uveitis (AAU), and SpA-associated gut inflammation. Studies on animal models and clinical samples highlight the association between gut microbial dysbiosis, metabolic perturbations and immune dysregulation in SpA pathogenesis. These studies have received impetus through next-generation sequencing methods, which have enabled the characterization of gut microbial composition and function, and host gene expression. Microbial/metabolomic studies have revealed potential biomarkers and therapeutic targets, such as short-chain fatty acids, and tryptophan metabolites, offering insights into disease mechanisms and treatment approaches. Further studies on microbial function and its modulation of the immune response have uncovered molecular mechanisms underlying various SpA. Understanding the complex interplay between microbial community structure and function holds promise for improved diagnosis and management of SpA and other autoimmune disorders.

Evolution of the Umbilical Cord Blood Proteome Across Gestational Development.

Neonatal health is dependent on early risk stratification, diagnosis, and timely management of potentially devastating conditions, particularly in the setting of prematurity. Many of these conditions are poorly predicted in real-time by clinical data and current diagnostics. Umbilical cord blood may represent a novel source of molecular signatures that provides a window into the state of the fetus at birth. In this study, we comprehensively characterized the cord blood proteome of infants born between 24 to 42 weeks using untargeted mass spectrometry and functional enrichment analysis. We determined that the cord blood proteome at birth varies significantly across gestational development. Proteins that function in structural development and growth (e.g., extracellular matrix organization, lipid particle remodeling, and blood vessel development) are more abundant earlier in gestation. In later gestations, proteins with increased abundance are in immune response and inflammatory pathways, including complements and calcium-binding proteins. Furthermore, these data contribute to the knowledge of the physiologic state of neonates across gestational age, which is crucial to understand as we strive to best support postnatal development in preterm infants, determine mechanisms of pathology causing adverse health outcomes, and develop cord blood biomarkers to help tailor our diagnosis and therapeutics for critical neonatal conditions.

Development of metabolomic biomarkers to discriminate the geographical origin of Korean and Russian snow crabs using CE-TOF/MS.

The quantity of snow crabs (Chionoecetes opilio) harvested in Korea is subject to seasonal restrictions; therefore, snow crabs are imported from Russia. Metabolites in snow crabs from two geographic origins were compared. The metabolites were subjected to metabolomic analysis to prevent fraudulent sales of marine products from a particular country. Capillary electrophoresis time-of-flight mass spectrometry was used. Seventy-seven target metabolites were identified using a mass spectral library. Through orthogonal partial least squares discriminant analysis, the top 25 biomarker candidates were evaluated based on p-values and fold changes. A total of 246 peaks (187 and 59 in the cation and anion modes, respectively) were identified. Among the biomarker candidates, 2-oxovaleric acid, asymmetric dimethylarginine, hypotaurine, and allo-threonine were selected as final biomarkers to unequivocally determine the geographic origin. Overall, metabolic analyses allowed us to differentiate snow crabs from different geographic origins. This method could also be extended of other marine products.

Engineering cGAS-agonistic oligonucleotides as therapeutics for cancer immunotherapy.

Activating cyclic GMP-AMP synthase-stimulator of interferon genes (cGAS-STING) holds great potential for cancer immunotherapy by eliciting type-I interferon (IFN-I) responses. Yet, current approaches to cGAS-STING activation rely on STING agonists, which suffer from difficult formulation, poor pharmacokinetics, and marginal clinical therapeutic efficacy. Here, we report nature-inspired oligonucleotide, Svg3, as a cGAS agonist for cGAS-STING activation in tumor combination immunotherapy. The hairpin-shaped Svg3 strongly binds to cGAS and enhances phase separation to form Svg3-cGAS liquid-like droplets. This results in cGAS-specific immunoactivation and robust IFN-I responses. Remarkably, Svg3 outperforms several state-of-the-art STING agonists in murine and human cells/tissues. Nanoparticle-delivered Svg3 reduces tumor immunosuppression and potentiates immune checkpoint blockade therapeutic efficacy of multiple syngeneic tumor models in wild-type mice, but in neither cGas-/- nor Sting-/- mice. Overall, these results demonstrate the great potential of Svg3 as a cGAS agonistic oligonucleotide for cancer combination immunotherapy.

MicroRNA-centered theranostics for pulmoprotection in critical COVID-19.

Elucidating the pathobiological mechanisms underlying post-acute pulmonary sequelae following SARS-CoV-2 infection is essential for early interventions and patient stratification. Here, we investigated the potential of microRNAs (miRNAs) as theranostic agents for pulmoprotection in critical illness survivors. Multicenter study including 172 ICU survivors. Diffusion impairment was defined as a lung-diffusing capacity for carbon monoxide (DLCO) <80% within 12 months postdischarge. A disease-associated 16-miRNA panel was quantified in plasma samples collected at ICU admission. Bioinformatic analyses were conducted using KEGG, Reactome, GTEx, and Drug-Gene Interaction databases. The results were validated using an external RNA-seq dataset. A 3-miRNA signature linked to diffusion impairment (miR-27a-3p, miR-93-5p, and miR-199a-5p) was identified using random forest. Levels of miR-93-5p and miR-199a-5p were independently associated with the outcome, improving patient classification provided by the electronic health record. The experimentally validated targets of these miRNAs exhibited enrichment across diverse pathways, with telomere length quantification in an additional set of samples (n = 83) supporting the role of cell senescence in sequelae. Analysis of an external dataset refined the pathobiological fingerprint of pulmonary sequelae. Gene-drug interaction analysis revealed four FDA-approved drugs. Overall, this study advances our understanding of lung recovery in postacute respiratory infections, highlighting the potential of miRNAs and their targets for pulmoprotection.

Exosomal noncoding RNA: A potential therapy for retinal vascular diseases.

Exosomes are extracellular vesicles that can contain DNA, RNA, proteins, and metabolites. They are secreted by cells and play a regulatory role in various biological responses by mediating cell-to-cell communication. Moreover, exosomes are of interest in developing therapies for retinal vascular disorders because they can deliver various substances to cellular targets. According to recent research, exosomes can be used as a strategy for managing retinal vascular diseases, and they are being investigated for therapeutic purposes in eye conditions, including glaucoma, dry eye syndrome, retinal ischemia, diabetic retinopathy, and age-related macular degeneration. However, the role of exosomal noncoding RNA in retinal vascular diseases is not fully understood. Here, we reviewed the latest research on the biological role of exosomal noncoding RNA in treating retinal vascular diseases. Research has shown that noncoding RNAs, including microRNAs, circular RNAs, and long noncoding RNAs play a significant role in the regulation of retinal vascular diseases. Furthermore, through exosome engineering, the expression of relevant noncoding RNAs in exosomes can be controlled to regulate retinal vascular diseases. Therefore, this review suggests that exosomal noncoding RNA could be considered as a biomarker for diagnosis and as a therapeutic target for treating retinal vascular disease.

Promising immunotherapy targets: TIM3, LAG3, and TIGIT joined the party.

Immune checkpoint inhibitors (ICIs) have shown great promise as immunotherapy for restoring T cell function and reactivating anti-tumor immunity. The US Food and Drug Administration (FDA) approved the first immune checkpoint inhibitor, ipilimumab, in 2011 for advanced melanoma patients, leading to significant improvements in survival rates. Subsequently, other immune checkpoint-targeting antibodies were tested. Currently, seven ICIs, namely ipilimumab (anti-cytotoxic T lymphocyte-associated protein 4 [CTLA4]), pembrolizumab, nivolumab (anti-programmed cell death protein 1 [PD-1]), atezolizumab, avelumab, durvalumab, and cemiplimab (anti-PD-L1), have been approved for various cancer types. However, the efficacy of antibodies targeting CTLA4 or PD-1/programmed death-ligand 1 (PD-L1) remains suboptimal. Consequently, ongoing studies are evaluating the next generation of ICIs, such as lymphocyte activation gene-3 (LAG3), T cell immunoglobulin and mucin-domain containing 3 (TIM3), and T cell immunoglobulin and ITIM domain (TIGIT). Our review provides a summary of clinical trials evaluating these novel immune checkpoints in cancer treatment.

Simulating progressive motor neuron degeneration and collateral reinnervation in motor neuron diseases using a dynamic muscle model based on human single motor unit recordings.

Objective.To simulate progressive motor neuron loss and collateral reinnervation in motor neuron diseases (MNDs) by developing a dynamic muscle model based on human single motor unit (MU) surface-electromyography (EMG) recordings.Approach.Single MU potentials recorded with high-density surface-EMG from thenar muscles formed the basic building blocks of the model. From the baseline MU pool innervating a muscle, progressive MU loss was simulated by removal of MUs, one-by-one. These removed MUs underwent collateral reinnervation with scenarios varying from 0% to 100%. These scenarios were based on a geometric variable, reflecting the overlap in MU territories using the spatiotemporal profiles of single MUs and a variable reflecting the efficacy of the reinnervation process. For validation, we tailored the model to generate compound muscle action potential (CMAP) scans, which is a promising surface-EMG method for monitoring MND patients. Selected scenarios for reinnervation that matched observed MU enlargements were used to validate the model by comparing markers (including the maximum CMAP and a motor unit number estimate (MUNE)) derived from simulated and recorded CMAP scans in a cohort of 49 MND patients and 22 age-matched healthy controls.Main results.The maximum CMAP at baseline was 8.3 mV (5th-95th percentile: 4.6 mV-11.8 mV). Phase cancellation caused an amplitude drop of 38.9% (5th-95th percentile, 33.0%-45.7%). To match observations, the geometric variable had to be set at 40% and the efficacy variable at 60%-70%. The Δ maximum CMAP between recorded and simulated CMAP scans as a function of fitted MUNE was -0.4 mV (5th-95th percentile = -4.0 - +2.4 mV).Significance.The dynamic muscle model could be used as a platform to train personnel in applying surface-EMG methods prior to their use in clinical care and trials. Moreover, the model may pave the way to compare biomarkers more efficiently, without directly posing unnecessary burden on patients.

Biomarker Development in Cardiology: Reviewing the Past to Inform the Future.

Cardiac biomarkers have become pivotal to the clinical practice of cardiology, but there remains much to discover that could benefit cardiology patients. We review the discovery of key protein biomarkers in the fields of acute coronary syndrome, heart failure, and atherosclerosis, giving an overview of the populations they were studied in and the statistics that were used to validate them. We review statistical approaches that are currently in use to assess new biomarkers and overview a framework for biomarker discovery and evaluation that could be incorporated into clinical trials to evaluate cardiovascular outcomes in the future.

Serologically assessed heat shock protein 47 is related to fibrosis stage in early compensated alcohol-related liver disease.

BACKGROUND AND AIMS: Heat shock protein (HSP)47 is a collagen-specific chaperone, essential for the correct formation of fibrillar procollagens. Collagen accumulation in the extracellular matrix (ECM) is a hallmark of fibrogenesis. The expression of HSP47 is proportional to the rate of collagen formation. Thus, HSP47 is a potential drug target for fibrotic diseases. We hypothesized that a C-terminal fragment of HSP47 (HSP47-C) could be quantified serologically and related to liver fibrosis stage. For this, a novel competitive enzyme-linked immunosorbent assay (ELISA) was developed. METHOD: An ELISA employing a monoclonal antibody targeting HSP47-C was developed and technically validated. The assay was evaluated in serum from a cross-sectional biopsy-controlled study of 281 patients with alcohol-related liver disease (ALD) and 50 gender, age and BMI matched healthy controls (HC). All liver biopsies from ALD patients were scored by one pathologist according to fibrosis stage (F0-4). RESULTS: The HSP47-C assay was technically robust and specific for the target sequence. HSP47-C was 39% higher in ALD patients (median 17.7 ng/mL, IQR 12.4-24.0 ng/mL) compared to HC (median 12.7 ng/mL, IQR 9.4-15.7 ng/mL, p < 0.0001). In addition, HSP47-C was elevated in patients with severe fibrosis (F3-4, median 22.8 ng/mL, IQR 17.5-33.3 ng/mL) compared to none-to-moderate fibrosis (F0-2, median 16.5 ng/mL, IQR 11.8-22.5 ng/mL) with an AUROC of 0.72 (p < 0.0001). HSP47-C also correlated with other liver disease parameters, albumin, bilirubin and aspartate transaminase. CONCLUSION: We developed a competitive ELISA for serological detection of HSP47-C. The study supports HSP47 as a potential marker of liver fibrosis in ALD.

Probabilistic Boolean Modeling of Pre-clinical Tumor Models for Biomarker Identification in Cancer Drug Development.

As high-throughput sequencing experiments become more widely used in pre-clinical and clinical settings, pharmacogenetic and pharmacogenomic biomarker development plays an increasingly important role in oncology drug development pipelines and programs. Consequently, computer-based learning approaches have entered into use at multiple stages in pre-clinical and clinical pipelines. However, few approaches are available to identify interpretable and implementable biomarkers of response early in the drug development process when only small pre-clinical data packages are available. To address the need for early-stage biomarker development using pre-clinical tumor models, we have adapted the previously published Probabilistic Target Inhibitor Map (PTIM) platform to the challenge of biomarker hypothesis development, and denoted this approach the Probabilistic Target Map-Biomarker (PTM-Biomarker). In this article, we detail the history and design philosophy of PTM-Biomarker, and present two case studies using the biomarker discovery tool to illustrate its utility in guiding cancer drug development. © 2021 Wiley Periodicals LLC.

Development of acoustically isolated extracellular plasma vesicles for biomarker discovery in allogeneic hematopoietic stem cell transplantation.

BACKGROUND: Infection and graft-versus-host disease (GvHD) are the major causes for mortality and morbidity of allogeneic hematopoietic stem cell transplantation (allo-HSCT). Plasma-derived extracellular vesicles (EVs) contain disease-related proteins, DNAs and RNAs, and have recently been suggested as potential biomarker candidates for transplantation complications. However, EV isolation from small plasma volumes in clinical biomarker studies using conventional methods is challenging. We therefore investigated if EVs isolated by novel automated acoustic trapping could be developed as potential biomarkers for allo-HSCT complications by performing a clinical proof-of-principle study. RESULTS: Plasma samples were collected from twenty consecutive patients with high-risk/relapsed hematologic malignancies undergoing allo-HSCT before transplantation and post-transplant up to 12 weeks. EVs were isolated from small plasma sample volumes (150 µl) by an automated, acoustofluidic-based particle trapping device, which utilizes a local λ/2 ultrasonic standing wave in a borosilicate glass capillary to capture plasma EVs among pre-seeded polystyrene microbeads through sound scatter interactions. We found that EVs could be reliably isolated from all plasma samples (n = 173) and that EV numbers increased more than 2-fold in the majority of patients after transplantation. Also, sufficient quantities of RNA for downstream microRNA (miRNA) analysis were obtained from all samples and EV miRNA profiles were found to differ from whole plasma profiles. As a proof of principle, expression of platelet-specific miR-142-3p in EVs was shown to correlate with platelet count kinetics after transplantation as expected. Importantly, we identified plasma EV miRNAs that were consistently positively correlated with infection and GvHD, respectively, as well as miRNAs that were consistently negatively correlated with these complications. CONCLUSIONS: This study demonstrates that acoustic enrichment of EVs in a clinical biomarker study setting is feasible and that downstream analysis of acoustically-enriched EVs presents a promising tool for biomarker development in allo-HSCT. Certainly, these findings warrant further exploration in larger studies, which will have significant implications not only for biomarker studies in transplantation but also for the broad field of EV-based biomarker discovery.