Candidate diagnostic biomarkers for neurodevelopmental disorders in children and adolescents: a systematic review

Neurodevelopmental disorders – including attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorder, communication disorders, intellectual disability, motor disorders, specific learning disorders, and tic disorders – manifest themselves early in development. Valid, reliable and broadly usable biomarkers supporting a timely diagnosis of these disorders would be highly relevant from a clinical and public health standpoint. We conducted the first systematic review of studies on candidate diagnostic biomarkers for these disorders in children and adolescents. We searched Medline and Embase + Embase Classic with terms relating to biomarkers until April 6, 2022, and conducted additional targeted searches for genome-wide association studies (GWAS) and neuroimaging or neurophysiological studies carried out by international consortia. We considered a candidate biomarker as promising if it was reported in at least two independent studies providing evidence of sensitivity and specificity of at least 80%. After screening 10,625 references, we retained 780 studies (374 biochemical, 203 neuroimaging, 133 neurophysiological and 65 neuropsychological studies, and five GWAS), including a total of approximately 120,000 cases and 176,000 controls. While the majority of the studies focused simply on associations, we could not find any biomarker for which there was evidence – from two or more studies from independent research groups, with results going into the same direction – of specificity and sensitivity of at least 80%. Other important metrics to assess the validity of a candidate biomarker, such as positive predictive value and negative predictive value, were infrequently reported. Limitations of the currently available studies include mostly small sample size, heterogeneous approaches and candidate biomarker targets, undue focus on single instead of joint biomarker signatures, and incomplete accounting for potential confounding factors. Future multivariable and multi-level approaches may be best suited to find valid candidate biomarkers, which will then need to be validated in external, independent samples and then, importantly, tested in terms of feasibility and cost-effectiveness, before they can be implemented in daily clinical practice.     

Oxytocin and vasopressin in the human brain: social neuropeptides for translational medicine

The neuropeptides oxytocin (OXT) and arginine vasopressin (AVP) are evolutionarily highly conserved mediators in the regulation of complex social cognition and behaviour. Recent studies have investigated the effects of OXT and AVP on human social interaction, the genetic mechanisms of inter-individual variation in social neuropeptide signalling and the actions of OXT and AVP in the human brain as revealed by neuroimaging. These data have advanced our understanding of the mechanisms by which these neuropeptides contribute to human social behaviour. OXT and AVP are emerging as targets for novel treatment approaches--particularly in synergistic combination with psychotherapy--for mental disorders characterized by social dysfunction, such as autism, social anxiety disorder, borderline personality disorder and schizophrenia.     

Major depressive disorder: insight into candidate cerebrospinal fluid protein biomarkers from proteomics studies

Introduction: Major Depressive Disorder (MDD) is the leading cause of global disability, and an increasing body of literature suggests different cerebrospinal fluid (CSF) proteins as biomarkers of MDD. The aim of this review is to summarize the suggested CSF biomarkers and to analyze the MDD proteomics studies of CSF and brain tissues for promising biomarker candidates.

Areas covered: The review includes the human studies found by a PubMed search using the following terms: ‘depression cerebrospinal fluid biomarker’, ‘major depression biomarker CSF’, ‘depression CSF biomarker’, ‘proteomics depression’, ‘proteomics biomarkers in depression’, ‘proteomics CSF biomarker in depression’, and ‘major depressive disorder CSF’. The literature analysis highlights promising biomarker candidates and demonstrates conflicting results on others. It reveals 42 differentially regulated proteins in MDD that were identified in more than one proteomics study. It discusses the diagnostic potential of the biomarker candidates and their association with the suggested pathologies.

Expert commentary: One ultimate goal of finding biomarkers for MDD is to improve the diagnostic accuracy to achieve better treatment outcomes; due to the heterogeneous nature of MDD, using bio-signatures could be a good strategy to differentiate MDD from other neuropsychiatric disorders. Notably, further validation studies of the suggested biomarkers are still needed.     

Blood biomarker discovery in drug-free schizophrenia: the contribution of proteomics and multiplex immunoassays

Introduction: Recent evidence supports an association between systemic abnormalities and the pathology of psychotic disorders which has led to the search for peripheral blood-based biomarkers.

Areas covered: Here, we summarize blood biomarker findings in schizophrenia from the literature identified by two methods currently driving biomarker discovery in the human proteome; mass spectrometry and multiplex immunoassay. From a total of 14 studies in the serum or plasma of drug-free schizophrenia patients; 47 proteins were found to be significantly altered twice or more, in the same direction. Pathway analysis was performed on these proteins, and the resulting pathways discussed in relation to schizophrenia pathology. Future directions are also discussed, with particular emphasis on the potential for high-throughput validation techniques such as data-independent analysis for confirmation of biomarker candidates.

Expert commentary: We present promising findings that point to a convergence of pathophysiological mechanisms in schizophrenia that involve the acute-phase response, glucocorticoid receptor signalling, coagulation, and lipid and glucose metabolism.     

The search for novel diagnostic and prognostic biomarkers in cholangiocarcinoma

The poor prognosis of cholangiocarcinoma (CCA) is in part due to late diagnosis, which is currently achieved by a combination of clinical, radiological and histological approaches. Available biomarkers determined in serum and biopsy samples to assist in CCA diagnosis are not sufficiently sensitive and specific. Therefore, the identification of new biomarkers, preferably those obtained by minimally invasive methods, such as liquid biopsy, is important. The development of innovative technologies has permitted to identify a significant number of genetic, epigenetic, proteomic and metabolomic CCA features with potential clinical usefulness in early diagnosis, prognosis or prediction of treatment response. Potential new candidates must be rigorously evaluated prior to entering routine clinical application. Unfortunately, to date, no such biomarker has achieved validation for these purposes. This review is an up-to-date of currently used biomarkers and the candidates with promising characteristics that could be included in the clinical practice in the next future. This article is part of a Special Issue entitled: Cholangiocytes in Health and Disease edited by Jesus Banales, Marco Marzioni, Nicholas LaRusso and Peter Jansen.     

The future of psychopharmacology: a critical appraisal of ongoing phase 2/3 trials,and of some current trends aiming to de-risk trial programmes of novel agents

Despite considerable progress in pharmacotherapy over the past seven decades, many mental disorders remain insufficiently treated. This situation is in part due to the limited knowledge of the pathophysiology of these disorders and the lack of biological markers to stratify and individualize patient selection, but also to a still restricted number of mechanisms of action being targeted in monotherapy or combination/augmentation treatment, as well as to a variety of challenges threatening the successful development and testing of new drugs. In this paper, we first provide an overview of the most promising drugs with innovative mechanisms of action that are undergoing phase 2 or 3 testing for schizophrenia, bipolar disorder, major depressive disorder, anxiety and trauma-related disorders, substance use disorders, and dementia. Promising repurposing of established medications for new psychiatric indications, as well as variations in the modulation of dopamine, noradrenaline and serotonin receptor functioning, are also considered. We then critically discuss the clinical trial parameters that need to be considered in depth when developing and testing new pharmacological agents for the treatment of mental disorders. Hurdles and perils threatening success of new drug development and testing include inadequacy and imprecision of inclusion/exclusion criteria and ratings, sub-optimally suited clinical trial participants, multiple factors contributing to a large/increasing placebo effect, and problems with statistical analyses. This information should be considered in order to de-risk trial programmes of novel agents or known agents for novel psychiatric indications, increasing their chances of success.     

Leveraging nonlinear dynamic models to predict progression of neuroimaging biomarkers

Using biomarkers to model disease course effectively and make early prediction is a challenging but critical path to improving diagnostic accuracy and designing preventive trials for neurological disorders. Leveraging the domain knowledge that certain neuroimaging biomarkers may reflect the disease pathology, we propose a model inspired by the neural mass model from cognitive neuroscience to jointly model nonlinear dynamic trajectories of the biomarkers. Under a nonlinear mixed‐effects model framework, we introduce subject‐ and biomarker‐specific random inflection points to characterize the critical time of underlying disease progression as reflected in the biomarkers. A latent liability score is shared across biomarkers to pool information. Our model allows assessing how the underlying disease progression will affect the trajectories of the biomarkers, and, thus, is potentially useful for individual disease management or preventive therapeutics. We propose an EM algorithm for maximum likelihood estimation, where in the E step, a normal approximation is used to facilitate numerical integration. We perform extensive simulation studies and apply the method to analyze data from a large multisite natural history study of Huntington's Disease (HD). The results show that some neuroimaging biomarker inflection points are early signs of the HD onset. Finally, we develop an online tool to provide the individual prediction of the biomarker trajectories given the medical history and baseline measurements.     

Could a blood test for PTSD and depression be on the horizon?

ABSTRACT

Introduction: Depression and posttraumatic stress disorder (PTSD) are two complex and debilitating psychiatric disorders that result in poor life and destructive behaviors against self and others. Currently, diagnosis is based on subjective rather than objective determinations leading to misdiagnose and ineffective treatments. Advances in novel neurobiological methods have allowed assessment of promising biomarkers to diagnose depression and PTSD, which offers a new means of appropriately treating patients.

Areas covered: Biomarkers discovery in blood represents a fundamental tool to predict, diagnose, and monitor treatment efficacy in depression and PTSD. The potential role of altered HPA axis, epigenetics, NPY, BDNF, neurosteroid biosynthesis, the endocannabinoid system, and their function as biomarkers for mood disorders is discussed. Insofar, we propose the identification of a biomarker axis to univocally identify and discriminate disorders with large comorbidity and symptoms overlap, so as to provide a base of support for development of targeted treatments. We also weigh in on the feasibility of a future blood test for early diagnosis.

Expert commentary: Potential biomarkers have already been assessed in patients’ blood and need to be further validated through multisite large clinical trial stratification. Another challenge is to assess the relation among several interdependent biomarkers to form an axis that identifies a specific disorder and secures the best-individualized treatment. The future of blood-based tests for PTSD and depression is not only on the horizon but, possibly, already around the corner.     

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 Parkinson’s disease (recent update)

Parkinson’s disease (PD) is the second most common neurodegenerative disorder mostly affecting the aging population over sixty. Cardinal symptoms including, tremors, muscle rigidity, drooping posture, drooling, walking difficulty, and autonomic symptoms appear when a significant number of nigrostriatal dopaminergic neurons are already destroyed. Hence we need early, sensitive, specific, and economical peripheral and/or central biomarker(s) for the differential diagnosis, prognosis, and treatment of PD. These can be classified as clinical, biochemical, genetic, proteomic, and neuroimaging biomarkers. Novel discoveries of genetic as well as nongenetic biomarkers may be utilized for the personalized treatment of PD during preclinical (premotor) and clinical (motor) stages. Premotor biomarkers including hyper-echogenicity of substantia nigra, olfactory and autonomic dysfunction, depression, hyposmia, deafness, REM sleep disorder, and impulsive behavior may be noticed during preclinical stage. Neuroimaging biomarkers (PET, SPECT, MRI), and neuropsychological deficits can facilitate differential diagnosis. Single-cell profiling of dopaminergic neurons has identified pyridoxal kinase and lysosomal ATPase as biomarker genes for PD prognosis. Promising biomarkers include: fluid biomarkers, neuromelanin antibodies, pathological forms of α-Syn, DJ-1, amyloid β and tau in the CSF, patterns of gene expression, metabolomics, urate, as well as protein profiling in the blood and CSF samples. Reduced brain regional N-acetyl-aspartate is a biomarker for the in vivo assessment of neuronal loss using magnetic resonance spectroscopy and T₂ relaxation time with MRI. To confirm PD diagnosis, the PET biomarkers include [¹⁸F]-DOPA for estimating dopaminergic neurotransmission, [¹⁸F]dG for mitochondrial bioenergetics, [¹⁸F]BMS for mitochondrial complex-1, [¹¹C](R)-PK11195 for microglial activation, SPECT imaging with ¹²³Iflupane and βCIT for dopamine transporter, and urinary salsolinol and 8-hydroxy, 2-deoxyguanosine for neuronal loss. This brief review describes the merits and limitations of recently discovered biomarkers and proposes coenzyme Q₁₀, mitochondrial ubiquinone-NADH oxidoreductase, melatonin, α-synculein index, Charnoly body, and metallothioneins as novel biomarkers to confirm PD diagnosis for early and effective treatment of PD.     

Schizophrenia proteomics: biomarkers on the path to laboratory medicine?

Over two million Americans are afflicted with schizophrenia, a debilitating mental health disorder with a unique symptomatic and epidemiological profile. Genomics studies have hinted towards candidate schizophrenia susceptibility chromosomal loci and genes. Modern proteomic tools, particularly mass spectrometry and expression scanning, aim to identify both pathogenic-revealing and diagnostically significant biomarkers. Only a few studies on basic proteomics have been conducted for psychiatric disorders relative to the plethora of cancer specific experiments. One such proteomic utility enables the discovery of proteins and biological marker fingerprinting profiling techniques (SELDI-TOF-MS), and then subjects them to tandem mass spectrometric fragmentation and de novo protein sequencing (MALDI-TOF/TOF-MS) for the accurate identification and characterization of the proteins. Such utilities can explain the pathogenesis of neuro-psychiatric disease, provide more objective testing methods, and further demonstrate a biological basis to mental illness. Although clinical proteomics in schizophrenia have yet to reveal a biomarker with diagnostic specificity, methods that better characterize the disorder using endophenotypes can advance findings. Schizophrenia biomarkers could potentially revolutionize its psychopharmacology, changing it into a more hypothesis and genomic/proteomic-driven science.     

Current progress in protein profiling of complex samples

Accurate cancer biomarkers are needed for early detection, disease classification, prediction of therapeutic response and monitoring treatment. While there appears to be no shortage of candidate biomarker proteins, a major bottleneck in the biomarker pipeline continues to be their verification by enzyme linked immunosorbent assays. Multiple reaction monitoring (MRM), also known as selected reaction monitoring, is a targeted mass spectrometry approach to protein quantitation and is emerging to bridge the gap between biomarker discovery and clinical validation. Highly multiplexed MRM assays are readily configured and enable simultaneous verification of large numbers of candidates facilitating the development of biomarker panels which can increase specificity. This review focuses on recent applications of MRM to the analysis of plasma and serum from cancer patients for biomarker verification. The current status of this approach is discussed along with future directions for targeted mass spectrometry in clinical biomarker validation.     

Biomarkers in Spinal Cord Injury: from Prognosis to Treatment

Spinal cord injury (SCI) is considered an incurable condition, having a heterogenous recovery and uncertain prognosis. Therefore, a reliable prediction of the improvement in the acute phase could benefit patients. Physicians are unanimous in insisting that at the initial damage of the spinal cord (SC), the patient should be carefully evaluated in order to help selecting an appropriate neuroprotective treatment. However, currently, neurologic impairment after SCI is measured and classified by functional examination. The identification of prognostic biomarkers of SCI would help to designate SC injured patients and correlate to diagnosis and correct treatment. Some proteins have already been identified as good potential biomarkers of central nervous system injury, both in cerebrospinal fluid (CSF) and blood serum. However, the problem for using them as biomarkers is the way they should be collected, as acquiring CSF through a lumbar puncture is significantly invasive. Remarkably, microRNAs (miRNAs) have emerged as interesting biomarker candidates because of their stability in biological fluids and their tissue specificity. Several miRNAs have been identified to have their expressions altered in SCI in many animal models, making them promising candidates as biomarkers after SCI. Moreover, there are yet no effective therapies for SCI. It is already known that altered lysophospholipids (LPs) signaling are involved in the biology of disorders, such as inflammation. Reports have demonstrated that LPs when locally distributed can regulate SCI repair and key secondary injury processes such as apoptosis and inflammation, and so could become in the future new therapeutic approaches for treating SCI.     

Profiling for novel proteomics biomarkers in neurodevelopmental disorders

Protein biomarker discovery from biological fluids, such as serum, has been widely applied to disorders such as cancer and has more recently also been utilized in neuro-psychiatric disorders with relatively clear biological causes, such as Alzheimer's disease and schizophrenia. The application of the associated technologies for the identification of protein biomarker signatures in neurodevelopmental disorders, such as autism spectrum disorder and attention deficit hyperactivity disorder, is comparatively less well established. The aim of this article is to provide an overview of the various protocols available for such analysis, discuss reports in which these techniques have been previously applied in biomarker discovery/validation in neurodevelopmental disorders, and consider the future development of this area of research.     

Pareto Optimization Identifies Diverse Set of Phosphorylation Signatures Predicting Response to Treatment with Dasatinib

Multivariate biomarkers that can predict the effectiveness of targeted therapy in individual patients are highly desired. Previous biomarker discovery studies have largely focused on the identification of single biomarker signatures, aimed at maximizing prediction accuracy. Here, we present a different approach that identifies multiple biomarkers by simultaneously optimizing their predictive power, number of features, and proximity to the drug target in a protein-protein interaction network. To this end, we incorporated NSGA-II, a fast and elitist multi-objective optimization algorithm that is based on the principle of Pareto optimality, into the biomarker discovery workflow. The method was applied to quantitative phosphoproteome data of 19 non-small cell lung cancer (NSCLC) cell lines from a previous biomarker study. The algorithm successfully identified a total of 77 candidate biomarker signatures predicting response to treatment with dasatinib. Through filtering and similarity clustering, this set was trimmed to four final biomarker signatures, which then were validated on an independent set of breast cancer cell lines. All four candidates reached the same good prediction accuracy (83%) as the originally published biomarker. Although the newly discovered signatures were diverse in their composition and in their size, the central protein of the originally published signature — integrin β4 (ITGB4) — was also present in all four Pareto signatures, confirming its pivotal role in predicting dasatinib response in NSCLC cell lines. In summary, the method presented here allows for a robust and simultaneous identification of multiple multivariate biomarkers that are optimized for prediction performance, size, and relevance.     

Partial summary measures of the predictiveness curve

In the evaluation of a biomarker for risk prediction, one can assess the performance of the biomarker in the population of interest by displaying the predictiveness curve. In conjunction with an assessment of the classification accuracy of a biomarker, the predictiveness curve is an important tool for assessing the usefulness of a risk prediction model. Inference for a single biomarker or for multiple biomarkers can be performed using summary measures of the predictiveness curve. We propose two partial summary measures, the partial total gain and the partial proportion of explained variation, that summarize the predictiveness curve over a restricted range of risk. The methods we describe can be used to compare two biomarkers when there are existing thresholds for risk stratification. We describe inferential tools for one and two samples that are shown to have adequate power in a simulation study. The methods are illustrated by assessing the accuracy of a risk score for predicting the onset of Alzheimer's disease.     

Late-life Onset Bipolar Disorder Presenting as a Case of Pseudo-Dementia: A Case Discussion and Review of Literature

Depression and comorbid cognitive impairment in the elderly can be difficult to distinguish from dementia. Adding to the complex differential is that depression may be part of a bipolar illness rather than a unipolar mood disorder. A diligent workup and close monitoring of patients can inform appropriate treatment and can make the difference between recovery and persistence of symptoms. The present case will illustrate how a comprehensive workup utilizing extensive data gathering, laboratory workup, use of neuropsychological testing, neuroimaging, and timely treatment can lead to successful clinical outcomes that can be sustained for many years.     

Measuring the maturity of the fast-spiking interneuron transcriptional program in autism, schizophrenia, and bipolar disorder

Background

Emerging evidence suggests that fast-spiking (FS) interneurons are disrupted in multiple neuropsychiatric disorders including autism, schizophrenia, and bipolar disorder. FS cells, which are the primary source of synaptic inhibition, are critical for temporally organizing brain activity, regulating brain maturation, and modulating critical developmental periods in multiple cortical systems. Reduced expression of parvalbumin, a marker of mature FS cells, has been reported in individuals with schizophrenia and bipolar disorder and in mouse models of schizophrenia and autism. Although these results suggest that FS cells may be immature in neuropsychiatric disease, this possibility had not previously been formally assessed.

Methods

This study used time-course global expression data from developing FS cells to create a maturation index that tracked with the developmental age of purified cortical FS cells. The FS cell maturation index was then applied to global gene expression data from human cortex to estimate the maturity of the FS cell developmental program in the context of various disease states. Specificity of the index for FS cells was supported by a highly significant correlation of maturation index measurements with parvalbumin expression levels that withstood correction for multiple covariates.

Conclusions

Results suggest the FS cell developmental gene expression program is immature in autism, schizophrenia, and bipolar disorder. More broadly, the current study indicates that cell-type specific maturation indices can be used to measure the maturity of developmental programs even in data from mixed cell types such as those found in brain homogenates.     

Integrated pipeline for mass spectrometry-based discovery and confirmation of biomarkers demonstrated in a mouse model of breast cancer

Despite their potential to impact diagnosis and treatment of cancer, few protein biomarkers are in clinical use. Biomarker discovery is plagued with difficulties ranging from technological (inability to globally interrogate proteomes) to biological (genetic and environmental differences among patients and their tumors). We urgently need paradigms for biomarker discovery. To minimize biological variation and facilitate testing of proteomic approaches, we employed a mouse model of breast cancer. Specifically, we performed LC-MS/MS of tumor and normal mammary tissue from a conditional HER2/Neu-driven mouse model of breast cancer, identifying 6758 peptides representing >700 proteins. We developed a novel statistical approach (SASPECT) for prioritizing proteins differentially represented in LC-MS/MS datasets and identified proteins over- or under-represented in tumors. Using a combination of antibody-based approaches and multiple reaction monitoring-mass spectrometry (MRM-MS), we confirmed the overproduction of multiple proteins at the tissue level, identified fibulin-2 as a plasma biomarker, and extensively characterized osteopontin as a plasma biomarker capable of early disease detection in the mouse. Our results show that a staged pipeline employing shotgun-based comparative proteomics for biomarker discovery and multiple reaction monitoring for confirmation of biomarker candidates is capable of finding novel tissue and plasma biomarkers in a mouse model of breast cancer. Furthermore, the approach can be extended to find biomarkers relevant to human disease.