The Canadian Biomarker Integration Network in ...

Current Pharmaceutical Design, 2012, 18, 000-000

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The Canadian Biomarker Integration Network in Depression (CAN-BIND): Advances in Response Prediction Sidney H. Kennedy*, Jonathan Downar, Kenneth R. Evans, Harriet Feilotter, Raymond W. Lam, Glenda M. MacQueen, Roumen Milev, Sagar V. Parikh, Susan Rotzinger and Claudio Soares Psychiatrist-in-Chief, UHN, Professor of Psychiatry, University of Toronto, EN8-222 - 200 Elizabeth Street, Toronto, Ontario M5G 2C4 Abstract: Identifying biological and clinical markers of treatment response in depression is an area of intense research that holds promise for increasing the efficiency and efficacy of resolving a major depressive episode and preventing future episodes. Collateral benefits include decreased healthcare costs and increased workplace productivity. Despite research advances in many areas, efforts to identify biomarkers have not revealed any consistently validated candidates. Studies of clinical characteristics, genetic, neuroimaging, and various biochemical markers have all shown promise in discrete studies, but these findings have not translated into a personalized medicine approach to treating individual patients in the clinic. We propose that an integrated study of a range of biomarker candidates from across different modalities is required. Furthermore, advanced mathematical modeling and pattern recognition methods are required to detect important biological signatures associated with treatment outcome. Through an informatics-based integration of the various clinical, molecular and imaging parameters that are known to be important in the pathophysiology of depression, it becomes possible to encompass the complexity of contributing factors and phenotypic presentations of depression, and identify the key signatures of treatment response.

Keywords: Mood disorders, depression, biomarkers, genomics, proteomics, neuroimaging, clinical characterization, bioinformatics, personalized medicine. INTRODUCTION Major Depressive Disorder (MDD) and Bipolar Disorder (BD) are highly prevalent and disabling disorders with substantial societal costs [1]; [2]. A comprehensive Canadian study placed the total economic burden of mental health problems at $51 billion in 2003 [3]. This included valuation of all health services utilization, longand short-term work loss, and health-related quality of life. A study of the prevalence of a broad range of neurological and mental disorders in the European Union (EU) found that in every year over one third of the EU population suffers from a mental or neurological disorder [4]. Of all disorders of the brain, depression is the most disabling. On a global scale, unipolar depressive disorder was the third leading cause of Disability Adjusted Life Years (DALYs) in high-income countries in 2001 [5, 6], and is projected to be the second leading cause by 2020 [7]. Worldwide, depression affects over 120 million people. Depression is also associated with an increased risk and worse prognosis for many chronic medical illnesses including cardiovascular disease, cancer, diabetes and dementia [8]. People with depression are 20-27 times more likely to commit suicide than the general population [9], and have an increased risk of death by all causes compared with the general population [9-11]. Globally, investment in research into the treatment and prevention of mental illness is disproportionately low, relative to the disease burden. According to the “Grand Challenges in Global Mental Health”, an NIMH funded initiative to address Mental, Neurological and Substance use disorders, involving over 1000 experts, the identification of risk and protective factors, and biomarkers should be a primary research goal in the next ten years [12]. The NIMH has created a strategic plan, in an attempt to close the gap between

*Address correspondence to this author at the Psychiatrist-in-Chief, UHN, Professor of Psychiatry, University of Toronto, EN8-222 - 200 Elizabeth Street, Toronto, Ontario M5G 2C4; Tel: (416) 340-3888; Fax: (416) 340-4198; E-mail: [email protected] *Sidney H. Kennedy is the Principal Investigator for the CAN-BIND study. Co-authors are listed in alphabetical order and contributed equally to the preparation of this manuscript. 1381-6128/12 $58.00+.00

the remarkable advances in neuroscientific understanding and the lack of progress in improving mental health care [13]. The EU study found that since a previous review in 2005, there are no indications of improvements in low treatment rates, delayed treatment provision and “grossly” inadequate treatment for disorders of the brain [4]. One of the biggest clinical challenges in managing a Major Depressive Episode (MDE) is selecting the appropriate treatment for a given individual. If there were a way to select the appropriate pharmacotherapy, psychotherapy or neurostimulation treatment based on a patient’s particular constellation of clinical, molecular and imaging data, it would represent a major step forward in psychiatry. Currently, remission rates in MDD and BD after a first treatment intervention are only 30% [14]; [15]; [16]; [17]; [18]; [19]; [20]. It often requires 6-8 weeks to rule out efficacy at each stage, therefore two thirds of patients will require a second or additional treatment, and wait another 6-8 weeks to gauge its efficacy. This process has been referred to as a period of “serial trial and error” [21]. Many patients discontinue medication during this time of uncertainty [22, 23]. Recurrence of a MDE is also very high, ranging from 35-85% after 15 years [24]. Further complicating the treatment of an MDE is the difficulty in differentiating an MDE associated with MDD and an MDE associated with BD, particularly bipolar type 2 (BDII). While the distinction between MDD and BD depends on the presence of a manic (BDI) or hypomanic (BDII) episode, both disorders share diagnostic criteria for a MDE, and the long term course of BD is characterized by chronic depressive symptoms and episodes. It is virtually certain that no single treatment will be effective for all individuals. Many different pharmacotherapies are available, as well as types of psychotherapy and neurostimulation treatments. Each works well in a subpopulation of depressed patients. However, identifying a priori criteria which indicate which treatment is most appropriate for a given individual would represent a major advance in the treatment of depression. Depression is a heterogeneous illness with a confluence of variables contributing to an array of phenotypic presentations. If there were a better way to select the appropriate medication, neurostimulation, psychotherapy or combination of treatments, this would greatly reduce the amount of time © 2012 Bentham Science Publishers

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taken to achieve remission, and reduce the cost and burden associated with MDD, as well as reducing the individual suffering. It has been suggested that the pathway to personalized medicine starts with the discovery of predictors of treatment response [13]. This is achieved through measuring clinical history, genomics, physiology, imaging, and proteomics, and then proceeding through a clinical trial with multiple outcomes evaluated [13]. Once a predictor has been identified, a prospective clinical trial can be designed in which patients are stratified by predictors, and the value of the predictor can be assessed following treatment. Biomarkers are biological changes associated with depression that could be used to indicate the presence, severity, and prognosis, as well as predicting response to a particular treatment [25]. The search for a biological measure that may help in the diagnosis and treatment of mood disorders is not new [26]. Measuring monoamines and their metabolites in CSF, urine or serum has been a research focus for five decades and is still an active area of research [27], as is the search for neuropeptide biomarkers in body fluids [28]. Numerous candidate gene and single-nucleotide polymorphism (SNP) associations have also been identified in MDD, but these studies have generally shown poor replication which may be related to heterogeneity of the MDD phenotype [29] . Neuroimaging studies also show promise in identifying subgroups of patients, but have not yet produced reliable biomarkers [30]. It is highly unlikely that there will be a single biomarker that can be used to predict course of illness or treatment outcome for a complex disorder such as major depressive disorder (MDD), and the results of multi-modal studies combining information from across clinical, molecular and imaging domains will be required for a biologicallybased classification system [21, 26, 30]. Single biomarkers generally have small effect sizes and therefore are not useful in making predictions about outcome. On the other hand, when a number of these single biomarkers with small effect size are combined into an algorithm incorporating biological, social, and environmental risk factors, there is great potential for the discovery of powerful predictors [31, 32]. Here we describe the rationale for a Canadian study employing an integrative, informatics-based approach to identifying markers to predict treatment response in patients experiencing an MDE and following a standardized treatment reflective of standard practice. This includes detailed clinical assessments with standard and novel instruments, combined with neuroimaging and neurophysiological measures and blood samples for proteomic and genomic analyses. These data will be combined and analyzed using high dimensional modeling techniques. BIOMARKER DISCOVERY IN MOOD DISORDERS Clinical Currently, diagnoses for mental disorders rely on criteria listed in either the Diagnostic and Statistical Manual (DSM) or International Classification of Diseases (ICD), and are based on the presence, duration and functional impact of specific symptoms. There is no identified brain pathology or antibody titre to confirm diagnosis and guide treatment selection, as there are in other medical illnesses. Unfortunately, the diagnostic categories for mental illness contain overlapping symptoms and generally lack specificity. For patients with a Major Depressive Episode (MDE), this is particularly important with respect to anxiety, suicidal intent, and comorbid substance use. Diagnostic criteria for a MDE do not differ in patients with a diagnosis of MDD and those with BD; furthermore, there are no provisions to distinguish episodes of depression in BDI and BDII patients. Heterogeneity of diagnostic criteria is further compounded by limitations in the outcome measures to quantify treatment response. Current “gold standard” clinical outcome scales for depression treatment studies - Hamilton Depression Rating Scale (HAMD) [33] and Montgomery-Asberg Depression Rating Scale (MADRS) [34] - were developed 3 to 5 decades ago. They do

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not capture the full spectrum of symptoms associated with depressive episodes [35] and have been inconsistent as predictors of response or non-response [36]. Finally, traditional depressive symptoms do not reflect the underlying neurochemistry, neurocircuitry and brain systems based on our latest understanding of MDE pathophysiology [37]. Patients in a MDE often experience deficits beyond the criteria listed in DSM-IV, including affect regulation, cognition, executive function, thought processing, perception, sleep-wake regulation, social interactions, personality dimensions and motor function [3840], which exist on a continuum from normal to abnormal. Thus, one approach to a better nosology of mental disorders, with the potential to predict treatment outcomes, is to integrate traditional symptom-based categorical diagnoses with behavioural dimensions that relate to specific brain systems and circuits, as a means of defining subtypes of depression. Hence, a critical component for the discovery of biomarkers is a careful and comprehensive clinical characterization that is not restricted to existing diagnostic frameworks. Consistent with this view, the Genome based Therapeutic Drugs for Depression (GENDEP) study classified patients at baseline as melancholic, atypical, anxious and anxious-somatizing depression subtypes. These subtypes were not robust predictors of outcome with either escitalopram or nortriptyline treatment [41]. In a secondary analysis of the GENDEP data, a dimensional approach was used to predict outcome, with higher baseline scores on interest-activity symptoms predicting lower remission rates in the GENDEP sample, and which was replicated with data from the STAR*D sample [42]. Thus, the dimensional approach may more accurately predict outcome, particularly if combined with other clinical or biological features. Imaging Neuroimaging and electrophysiology offer a window into brain structure and function, thus bridging the gap between the genomic and proteomic foundations of psychiatric illness and its outward clinical manifestations. For a heterogenous disorder such as MDD, neuroimaging and electrophysiology provide a vantage point to identify ‘neurophenotypes’ that may arise from a particular genotype. They may also reveal whether the clinical profile of an MDE represents any one of a number of distinct neural subtypes, each with its own pathophysiology, prognosis, and optimal treatment strategy. At present, the integration of structural and functional neuroimaging with clinical and molecular measures for these purposes is in a nascent stage. Structural Abnormalities Associated with MDD and BD: While whole brain volumes of patients with mood disorders do not differ from those of healthy controls, there are regional deficits in the frontal lobe, mainly the anterior cingulate and the orbitofrontal cortex, compared to controls [43, 44]. Subcortical structures, including the striatum, amygdala, and hippocampus, may be differentially affected in MDD and BD. Reduction in hippocampal volume in relation to depression and treatment response has been extensively studied in recent years. In aggregate, people with MDD have hippocampal volumes that are 5-8% smaller than healthy controls (reviewed in [45, 46]), and small hippocampal volumes are associated with poor short- and long-term clinical outcome [47-51]. In addition to gray matter abnormalities, white matter fibre tracts are an important focus for neuroimaging studies of mood disorders. Diffusion tensor imaging in MDD has consistently shown decreases in fractional anisotropy in the right superior longitudinal fasciculus within the frontal lobe, as well as right middle frontal and left parietal white matter regions [52, 53], with decreases in fractional anisotropy of up to 7.8% in dorsolateral prefrontal cortical regions, as well as in thalamic projection fibres and the corpus callosum [54]. Furthermore, tract-based spatial statistics have detected decreased fractional anisotropy in the left sagittal striatum and right cingulate cortex, right parahippocampal gyrus, the left

The Canadian Biomarker Integration Network in Depression (CAN-BIND)

posterior cingulate cortex and within left anterior regions of the internal capsule in MDD [55]. Decreased fractional anisotropy in this region negatively correlated with symptom severity [56]. Functional Abnormalities Associated with MDD and BD: Evidence from functional neuroimaging studies supports dysregulation of corticolimbic emotional processing circuits in MDD and BD. Several investigators have confirmed preliminary findings from Mayberg et al., [57] that high resting metabolic rates in the subcallosal cingulate area predict response to various pharmacotherapies, psychotherapy and neurostimulation in MDD [58-63]. Amygdala activation in response to emotional facial expressions in MDD patients also predicts symptom resolution [64-66]. Resting state functional connectivity has also been used to provide task-independent, network-based measures of brain function. The dorsal ACC shows reduced resting state connectivity with the medial thalamus, amygdala, and striatum in MDD [67]. Conversely, the thalamus and the subgenual cingulate gyrus (BA25) show increased resting state connectivity in MDD vs healthy controls, and connectivity increases with the duration of the current depressive episode [68]. Recently, new methods have been developed to allow a more formal mathematical analysis of the topology of functional networks in the resting brain, using the principles of graph theory [69]. These tools are now being used to identify changes in network topology associated with depression. For example, MDD patients show increased centrality of functional network nodes in the hippocampus and caudate nucleus, which correlate with disease severity [70]. Combining Structural and Functional Neuroimaging: To date, relatively few studies of mood disorders have combined functional and structural neuroimaging in MDD and BD. Versace et al. [71] combined functional connectivity with fractional anisotropy in BD and controls, finding increased fractional anisotropy in the left uncinate fasciculus in controls, alongside increased functional connectivity between the left amygdala and orbitofrontal cortex in response to sad facial expressions for the healthy individuals. Wang et al. [72] reported an association between pACC-amygdala functional connectivity measures and the structural integrity of ventrofrontal white matter, including the uncinate fasciculus where fractional anisotropy was significantly decreased in BD. Building on these demonstrations of group differences in brain structure and function, techniques are now being developed to classify individual patients based on neuroimaging. Functional neuroimaging, combined with support vector machine (SVM) pattern classification methods, has been able to correctly sort depressed patients and controls into their appropriate categories [64, 73, 74], and to detect depressed patients as outliers against a healthy sample [75]. These SVM classifiers, if trained on an adequately large and multi-modality data set, could be developed into well-validated clinical decision support tools, with immediate practical applications in health care, neuroscience research, and future biomedical discovery. Electrophysiological Techniques: A multi-modal approach combining the complementary techniques of electroencephalography (EEG) and neuroimaging may provide a more comprehensive account of the spatiotemporal dynamics of brain networks in MDD, BD, and controls. EEG permits near-instantaneous detection of brain activity via scalp electrical signals, which directly reflect post-synaptic potentials. This allows EEG to be linked to specific transmitter systems and to neuronal (rather than hemodynamic) activity. A variety of analytic approaches are available, including spectral analyses, time-locked signal averaging, and frequencydomain analysis, to reveal evoked and induced oscillations. Source localization algorithms can compute three-dimensional intracerebral distributions of current density for specified frequency bands or event-related activity. These patterns can identify both local and network parameters that may be unique trait biomarkers of underly-

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ing pathogenic processes, as well as state markers that predict (or mediate) antidepressant treatment response. There are a number of important findings linking EEG parameters to affective processing and to MDD. Frontal alpha asymmetry (left>right) moderates emotional reactivity to valenced stimuli, and changes with induced affective experiences [76]. This asymmetry also appears to be a robust trait marker of MDD, which is not modulated by treatment response [77]. Both frontal theta cordance values (which are moderately correlated with regional cerebral blood flow) and theta current density in the rostral anterior cingulate are elevated in MDD and predict antidepressant response [7881]. The ATR (a non-linear weighted combination of frontal theta and alpha power derived from two frontal sites) extracted at baseline and week 1, shows promise in predicting responsiveness and remission with various antidepressants [82, 83]. Alpha asymmetry in posterior regions is poorly modulated in patients in response to emotional stimuli, and predicts antidepressant response [84]. Although not yet applied widely in MDD, analyses of functional connectivity and signal variability have been used to demonstrate changes in brain network integrity and functional capacity in other CNS disorders such as dementia and epilepsy, and have shown some utility in predicting recovery (e.g., from traumatic brain injury [85-87]). Genomics and Proteomics A molecular contribution to mood disorders is well established. Family studies show an increased risk of depression in individuals with shared genes [88]. Twin studies suggest that genetic influences in depression are moderate, perhaps explaining up to 40% of the variance in adult MDD [88, 89], and approximately 70% in BD [90]. In recent years, Genome Wide Association Studies (GWAS), such as the Wellcome Trust Case Control Consortium study, identified a number of vulnerability genes [91] including the GABA(A) receptor [92] and CACNA1C and ANK3 [93] in BD and MDD [94]. There is also evidence that gene environment interactions contribute to the heritability of mood disorders. For example, Hosang and colleagues [95] have examined the interplay between the BDNF Val(66)/Met polymorphism and stressful life events (SLEs) in BD, concluding that both Met carrier BDNF genotype and SLEs were significantly associated with the most severe bipolar depressive episodes, and that these effects of SLEs were also significantly moderated by BDNF genotype. Altered circadian rhythms have also been suggested as a potential biomarker of mood disorders, and genetic association studies have linked circadian genes such as CLOCK, ARNTL1, NPAS2, PER3, and NR1D1 to BD and MD [96]. In the context of identifying previously unknown biomarkers, genomic discovery approaches aim to identify predictive DNA sequence and/or epigenetic markers, as well as reliable changes of expression in genes following treatment. So far, predictive approaches based on genome wide association studies (GWAS), have met with limited success [97-100], although emerging data support the idea that functional alterations to drug metabolizing enzymes through copy number changes may be associated with drug response [101]. Furthermore, recent advances from the GENDEP project study [102]) demonstrate an association between copy number variants (CNVs) spanning non-traditional candidate genes and efficacy of antidepressants [99]. ABCB1 is a transporter molecule that regulates the ability of some drugs to cross the blood-brain barrier. Polymorphisms in the ABCB1 gene may be used to tailor treatment for a particular individual. The combined knowledge of which medications are substrates for the ABCB1 transporter and a patient’s ABCB1 genotype is a strong predictor of response to antidepressant treatment [103]. In terms of pharmacodynamic targets, most investigators have focused on polymorphisms in genes from the serotonergic, adrenergic, dopaminergic and hypothalamic-pituitary-adrenal (HPA) path-


ways as mediators of antidepressant response [104-107]. While McMahon and associates did not replicate previous reports linking allelic variation in the serotonin transporter to antidepressant response [108], they did identify an association of intronic single nucleotide polymorphisms (SNPs) in serotonin receptor 2A gene (HTR2A) with citalopram response in the STAR*D (Sequenced Treatment Alternatives to Relieve Depression) study, and this was replicated with escitalopram response in the GENDEP study. In the GENDEP study, polymorphisms in the glucocorticoid receptor gene (NR3C1) were associated with response in a combined sample of patients treated with escitalopram or nortriptyline. A polymorphism in FKBP5 (FK506 binding protein 51) was found to be associated with treatment response in a German sample [109-111], but not in other studies [112, 113]. Recently, association of a polymorphism in the interleukin 11 gene with escitalopram response and a polymorphism in the uronyl 2-sulphotransferase gene with nortriptyline response was reported [99]. Similarly, other unreplicated reports implicate SNPs around the ubiquitin protein ligase E3C (UBE3C), bone morphogenetic protein 7 (BMP7) and in the intron of RARrelated orphan receptor A (RORA) with response to citalopram [114]. Dynamic molecule profiling can significantly augment such findings through investigation of pre- and post-treatment samples from the same individual. Such studies have the capacity to identify molecular profiles that are critical to drug response (i.e. novel drug targets), but can potentially also identify differentially expressed molecules that underlie alternate pathophysiologies to apparently similar diseases. Therefore, the power of profiling dynamic molecules such as RNA species and proteins, could generate novel pathway and biomarker targets of both drug response and disease state. Identification of drug targets through whole transcriptome profiling studies have focused on model systems [115-118] and cultured cells to study drug response [119-121]. There is also mounting evidence that epigenetic mechanisms underlie the pathophysiology of stress related disorders, including MDD and BD [122-125]. For example, valproate, clozapine, olanzapine and quetiapine all have demethylating effect on the promoter site of GAD67 and reelin in the neurons of frontal cortex of mice [126, 127]. The demethylating effect of valproate has been further replicated in GLT-1 gene of astrocytes in rat brain [128]. Furthermore, in rodent models of depression, escitalopram reduced promoter methylation of P11, a gene implicated in intracellular trafficking and involved in the pathophysiology of depression [129], with concordant upregulation of the mRNA [130] and decreased DNMT1 and DNMT3 expression. Proteomics was originally defined as “the study of the total set of expressed proteins by a cell, tissue or organism at a given time under a determined condition” [131], but has been expanded to include modifications made to a particular set of proteins, which can vary with environmental circumstances [132]. Advances in the field of proteomics facilitate investigations into the relationship between the expression of proteins and the underlying pathophysiology of psychiatric conditions [132, 133]. It can also be used to identify phenotypes that are responsive to particular treatment interventions as well as in understanding the pharmacodynamic effects [134] of therapies. Thus, information may be subsequently used in the identification of novel drug targets, and in the development of personalized medicines. Proteomics can be used to identify subpopulations of patients with unique biochemical profiles, and thus has the potential to increase the accuracy of diagnosis by revealing state and trait markers that align with different phenotypic expression of the psychiatric condition. Animal models of psychiatric illnesses are inherently inadequate to deal with the range of clinical features associated with psychiatric illnesses. Therefore, proteomics methods using human tissues are essential to understanding the molecular basis of these

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illnesses as well as identifying predictors of treatment response. A few studies have examined differentially expressed proteins in brain regions from human postmortem samples or from CSF [132]. Utilizing biological fluids such as blood and urine for proteomic analyses has obvious advantages over the use of CSF as it avoids the risk associated with an invasive procedure, is less expensive and ultimately a more practical approach in a clinical setting. Moreover, markers identified in blood have shown some early promise in mood disorders, with relevance to diagnosis [135-137], prognosis, and in anticipating treatment response [135]. Summary In summary, the attempt to identify biomarkers of treatment response on the basis of individual characteristics is an active area of investigation. Clinically, some dimensional features of symptom severity and cognitive or personality subtypes have been found to contribute to treatment response. Similarly, neuroimaging studies have identified smaller regional frontal lobe grey matter volumes in depressed patients, that may be associated with poor outcome, and decreases in fractional anisotropy. Neuroimaging and electrophysiological studies have shown dysregulation in emotional processing circuits and functional connectivity in depressed patients which are related to antidepressant response. Finally, genomic and proteomic studies indicate that specific genes and proteins may be related to treatment response. However, what is most apparent from these studies is that the combination of features from across domains is far more powerful at predicting outcome than any one feature alone. For example, a GWAS study examining antidepressant treatment outcome in a European cohort and samples from the STAR*D study found no single SNP signals that reached significance, whereas drug response could be predicted from a cluster of “response alleles” and that this prediction was significantly improved when combined with clinical characteristics [98]. Furthermore, neuroimaging studies that combine cognitive assessments with functional neuroimaging have potential for predicting antidepressant treatment response [138]. Combination of clinical and neuroendocrine features also improves prediction of treatment outcome [139]. The multifactorial nature of depressive illness suggests that a multifactorial approach will provide the most accurate predictions of treatment outcome. INNOVATIVE APPROACH OF THE CAN-BIND STUDY The CAN-BIND project provides an integrated approach to the analysis of clinical, imaging, and molecular data, through novel mathematical modeling and bioinformatics techniques. We will utilize a standardized treatment schedule as a platform from which to study biomarker indices at baseline, and following 2 weeks, 8 weeks and 16 weeks of treatment. It is envisaged that this platform can be expanded after initial studies to include a wider range of pharmacotherapies, device therapies and psychotherapies. In a secondary analysis of data from the Genome-Based Therapeutic Drugs for Depression (GENDEP) study, in which patients with MDD received escitalopram or nortriptyline for 12 weeks, nine different subgroups of responders were modeled [140]. There were three different classes of nonresponders, three classes of early improvers (in the first three weeks) and three classes in which improvement was predominantly in the second three weeks. However, this paper did not examine potential biomarkers or predictors with which to differentiate the subgroups. Clinical Depressive diagnoses and symptoms will be assessed using both established and novel measures. The most widely used clinician-rated depression rating scales are the MADRS [34], and the HAMD [33]. The MADRS will be the primary outcome measure due to reports suggesting greater sensitivity to treatment effects than the HAMD. However, since the HAMD and MADRS were


developed 3 to 5 decades ago, they do not capture the full spectrum of symptoms associated with depressive episodes [35] and have been inconsistent as predictors of response or non-response [36]. Traditional depressive symptoms do not reflect the underlying neurochemistry, neurocircuitry and brain systems based on our latest understanding of MDE pathophysiology [37]. Given these limiting factors, in the proposed research on predictor biomarker discovery, we intend to supplement current “gold standard” scales with additional methods to characterize MDEs including assessment of various behavioural dimensions and symptom clusters. The Depression Inventory Development (DID) research program is a multinational approach to developing a broader range of symptoms in large populations of depressed patients with a goal of developing more sensitive assessment tools [141, 142]. The DID program was designed to identify cardinal symptoms that need to be assessed in order to detect differences in the severity of the disorder, based on input from patients, experts and literature. The subset of symptoms with the greatest sensitivity and specificity characterized were ultimately included in the DID scale [143], which is a semi-structured clinical interview that includes anger and irritability, anhedonia, memory and cognition, painful somatic symptoms, and fatigue and general malaise. All centres will receive training in standardized conventions for scoring, anchor and item definitions and in the use of a structured interview guide. A number of investigators on this project have been part of the DID project for 5-7 years. Alterations in neurocognition are frequently acknowledged as a component of a MDE but are not assessed in routine clinical practice [144]. Some studies have suggested that cognitive impairment associated with depression is limited, difficult to detect, and related to severity [145, 146] while others have shown that neurocognitive changes are apparent in unipolar depression (MDD) [147] and that specific neurocognitive profiles can distinguish between unipolar and bipolar depressions [148, 149]. Moreover, cognitive deficits at baseline may predict poorer response to treatment [150] and specific deficits in cognitive and executive functioning may persist after treatment response [151, 152], suggesting that it is a trait marker. Hence, assessment of neurocognitive status is an important component of any clinical and biomarker approach to bioinformatics. Behavioural and Dimensional Scales In addition to symptoms, several behavioural dimensions will be assessed using self-rated scales. Based on prevailing research and principal components analysis of depressive symptoms [153], these include: behavioural activation, personality, somatisation/anxiety, pain, and chronobiology/sleep disorder. Carver and White [154] developed a brief 24-item self-report scale to measure individual differences in the sensitivity of the Behavioural Inhibition System (BIS) and the Behavioural Activation System (BAS) that are well established and validated. The tripartite dimensional model of depression will be assessed using the Positive and Negative Affect Schedule (PANAS) [155]. Dimensional aspects of personality using the widely-accepted Costa and McRae 5-factor model will be assessed with the self-rated NEO-PI [156]. The Symptom Checklist-90 (SCL-90) [157] is a measure or current psychopathology across 9 constructs: somatisation, obsessivecompulsive symptoms, interpersonal sensitivity, depression, anxiety, hostility, phobic-anxiety, paranoid ideation and psychoticism. To assess levels of pain, the Brief Pain inventory (BPI-SF) [158] will be used. This scale provides information on the intensity of pain as well as the degree to which pain interferes with function, and enquires about pain relief, pain quality, and the patient’s perception of the cause of pain. Circadian and chronobiologic rhythms are regarded as another important dimensional construct in mood disorders. The Biological Rhythms Interview of Assessment in Neuropsychiatry (BRIAN)


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[159] is a recently validated scale designed to assess circadian rhythms in mood disorders, including important information relating to sleep dysregulation. In this study we will validate the BRIAN against the Morningness-Eveningness Questionnaire (MEQ) a widely used measure of chronotyping that shows good correlation with physiological indices of circadian rhythms, including core body temperature [160]. Another chronobiologic dimension, seasonality, will be assessed using the Seasonal Pattern Assessment Questionnaire (SPAQ) [161]. Neurocognitive Test Battery Neurocognition will be assessed using a computerized neuropsychological package that contains 7 common neuropsychological measures, including verbal and visual memory, finger tapping, symbol-digit coding, a Stroop test, a shifting attention test, and a continuous performance test. The battery generates 15 primary scores that are used to calculate 5 domain scores (Memory, Psychomotor Speed, Reaction Time, Cognitive Flexibility, and Complex Attention), all relative to a normative sample. The measures have adequate test-retest reliability, concurrent validity with traditional paper-and-pencil measures and other computerized tests, and the domain scores have been shown to identify neurocognitive deficits in patients with depression [162, 163] and bipolar disorder [164]. Environmental Assessments It is widely accepted that gene-environment interactions play a pivotal role in the etiopathology of depression, yet previous research on biomarkers in depression have not adequately assessed relevant environmental factors [165]. Childhood maltreatment, particularly severe physical, sexual, and/or emotional abuse, is the strongest distal environmental predictor of depression and it mediates the influence of other forms of early adversity (e.g., parental loss; [166, 167]). Severe childhood maltreatment has multiple neurobiological consequences, including dysregulation of the HPA axis response to stress [168, 169], pronounced limbic volume loss and abnormalities in limbic function [170], and, in very recent preclinical work, dysregulation of markers of mature oligodendrocytes and genes involved in protein translation, and methylation changes in the promoter regions of dysregulated genes [171]. As such, childhood maltreatment is a strong moderator of the effects of biomarkers on depression. For example, childhood maltreatment has emerged as the most valid and reliable environmental moderator of serotonin transporter allelic variation [172]. Further, and of specific relevance to the present proposal, childhood maltreatment significantly moderates response to pharmacological treatment, and, in particular, differentially moderates response to pharmacological treatment over other forms of intervention (e.g., cognitivebehavioural therapy) [173]. To address the effects of stress and early life experience on neurobehavioural biomarkers in depression, we will carefully assess a history of childhood maltreatment using the Childhood Experience of Care and Abuse (CECA) [166], a semi-structured clinicianrated interview. The CECA includes the following scales: (a) antipathy – hostility and coldness directed toward the child; (b) neglect – indifference to the child’s physical and emotional needs; (c) physical abuse – violence directed toward the child by parents; and (d) sexual abuse – non-consensual sexual contact by any perpetrator. Ratings will be based on manualized examples to ensure standardization and to prevent rater drift. In addition, adult patterns of attachment and recent stressful life events will be captured using the following self-report scales, respectively: the Experience in Close Relationships (ECR-R) [174] scale and the List of Threatening Experiences (LTE) [175]. Functioning/Quality of Life Scales Patient functioning and quality of life is an increasingly recognized target when evaluating response to antidepressant treatment. Regulatory bodies in the US and Europe (FDA and EMEA) have


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emphasized the importance of these functional measures for new drug submissions. Functionality will be assessed using self-rated scales according to the following dimensions: functional disability and impairment (Sheehan Disability Scale (SDS) [176]), healthrelated quality of life (EuroQol 5 questionnaire (EQ-5D) [177, 178], and occupational functioning (Lam Employment Absence and Productivity Scale (LEAPS) [179]. The Quality of Life, Enjoyment and Satisfaction Questionnaire (QLESQ) [180] will also be used to provide a more depression-specific quality of life assessment. Demographic data as well as information on patients’ health status and body mass index (BMI) will also be included in the analyses. Patients with MDD have been reported to have higher BMI than non-depressed controls, and high BMI is associated with a slower response to antidepressant treatment [181]. Overweight patients may define a specific subgroup of depressed patients with a unique biomarker profile.

Analysis will involve 4 stages: i) data preprocessing; ii) extraction of parameters of interest for each scan in individuals; iii) characterization of the variation among individual subjects into subgroups using clinical outcomes and unsupervised clustering algorithms; iv) construction of classifier algorithms capable of reliably assigning individual subjects to a specific subgroup. The application of graph theoretical analysis to MRI data is an increasingly valuable technique in advancing the understanding of brain networks [187]. Calculus on graphs [188] incorporates machine learning algorithms known as kernel methods which can be generalized to networks and can be extended to fMRI-derived networks [189]. We will apply these established and novel graph theoretical methods to the structural and functional MRI data to be acquired in this project, in order to explore differences in functional connectivity associated with drug response and associated with disease state (i.e. MDD vs. BD).

Imaging Building on previous work in neuroimaging and EEG in depression, we will collect a broad-based, multimodal set of structural, functional, and EEG data for ultimate use in: i) characterizing the pathophysiology of an MDE, in relation to and irrespective of polarity; ii) classifying patients into robust and clinically meaningful subgroups; iii) predicting response and remission iv) predicting the optimal choice of treatment in any given patient. Patients will undergo 3T MRI at 0, 2, and 8 weeks after beginning treatment. These timepoints are chosen to reflect baseline and early predictors of response, and changes in brain structure and function associated with response to monotherapy. Control subjects, group-matched for age and sex, will also undergo neuroimaging under the same protocol. Each session will consist of a 46-min structural and functional MRI protocol comprising: 1) a wholebrain T1-weighted anatomical scan 2) a whole-brain diffusiontensor imaging series 3) a whole-brain, T2*-weighted BOLD EPI series 4) a BOLD series during the Emotional Face Categorization/Conflict (EFCC) task 5) a BOLD series during the Mood Induction (MI) task 6) a BOLD series during a second period of postmood-induction awake resting state. Patients will complete an EEG assessment at 0, 2, and 8 weeks post-treatment, which will consist of EEG-specific modifications of the resting state and Emotional Face Categorization/Conflict (EFCC) tasks. Modifications will include periods of eyes-closed and eyes-open recordings during resting state as some studies have observed EEG activity characterizing depression and may vary on activational state. The EFCC will be adapted so as to include a greater number of stimulus presentations that will be required to enhance signal-to-noise ratio in ERP averaging paradigms. Resting state acquisitions will take place with subjects viewing a fixation cross, instructed to remain awake with the eyes open [182]. In the EFCC task, subjects categorize happy and fearful male or female faces using a standardized, validated fMRI protocol [183, 184]. Either congruent or incongruent descriptor words are superimposed over the faces, as in a Stroop task. 4 conditions result: congruent or incongruent responses required for either sex or emotion. The task reliably distinguishes between MDD and control subjects on neural (lateral prefrontal cortex activation and anterior cingulate-amygdala connectivity) measures [185]. In the MI task, subjects view sad or neutral 45s film clips interspersed with 30s reflection periods ending with sadness ratings. The task reliably predicts the likelihood of relapse in MDD patients, based on activation in medial prefrontal and visual cortex on the task [186]. Resting state acquisitions will take place with subjects viewing a fixation cross, instructed to remain awake with the eyes open; separate scans will be acquired before and after the MI task to permit identification of persistent (ruminative) changes in resting activity subsequent to mood induction [182].

Proteomic/Genomic The integrated analysis of DNA, RNA and protein biomarkers in a series of patient samples may identify pathways that are consistently disrupted in disease state relative to controls, and may also identify biomarkers that predict response to treatment. Our objectives are to: 1) Investigate candidate biomarkers for disease state or drug response in baseline samples and controls 2) Investigate global DNA alterations that may correlate with disease state or drug response using baseline samples and controls 3) Investigate dynamic molecules in pre and post treatment samples to identify pathways of drug response and select additional targets for investigation by targeted methods. Candidate Gene Selection: In order to comprehensively examine genes most relevant to antidepressant response, we will genotype drug metabolism variants as well as pharmacological targets. In terms of metabolism, we will focus on three known functional markers of the CYP2C19 gene, that we have studied previously in escitalopram clearance [190]. As well, we will study a polymorphism in the ABCB1 drug transporter recently shown to influence treatment response to quetiapine [191]. Further genetic variants tested will include SNPs in genes encoding pharmacological targets. To increase the scope of the candidate gene selection, we also propose to investigate the copy number variant patterns in each sample using DNA derived from whole blood samples collected at baseline. Genomic DNA will be extracted from DNA PAX tubes (Qiagen) and assessed for quality using the Agilent Bioanalyzer. Each sample will be compared to a common commercial reference DNA. We will use the CGH/SNP chip because it allows the generation of single nucleotide polymorphism data for no substantial increase in cost, and those data may become relevant for downstream analysis once target pathways or genes are identified. The array we propose to use also supports identification of areas of uniparental disomy, a mechanism for the unmasking of recessive alleles that may be associated with clinical phenotypes of interest. Regions of interest following analysis will be validated using fluorescence in situ hybridization (FISH). Finally, we will make use of the fact that collection of biological samples from patients at base line and again at defined intervals following treatment presents an opportunity to examine dynamic changes that may be associated with response to a particular therapy, or which may point to alternative pathologies underlying the disease states. Due to the relatively high costs of carrying out complete profiling for each sample collected in the earliest phases of the study, a limited number of samples that represent extremes of response/non response will be profiled. These will be used for profiling at the RNA and protein level, and data from these limited profiling studies can then be used to identify pathways for more targeted analyses in the larger sample sets.



7

Assays to investigate the relative levels of transcription for all coding and non-coding RNAs will be applied to gain an overall snapshot of the transcription and DNA methylation state of each sample at baseline, and following treatment. Messenger RNA, regulatory RNA species and whole genome methylation patterns will be assessed at baseline using microarray approaches. Each sample will then be assessed again following treatment. Comparison of the baseline to the post treatment assays will identify transcripts which appear to undergo changes in regulation following treatment, leading to a list of candidate genes that may be influenced by the treatment. Comparison of this list of putative candidate genes between responders and non responders should identify candidate genes which may be critical in treatment response. These genes, together with candidate genes chosen through the methods detailed below, will be used as input to create a list of protein targets to study during treatment response.

Selected reaction monitoring-mass spectrometry (SRM-MS) analysis will be used to target and quantify hundreds of proteins of interest selected on the basis of their direct relevance to depression, drug mechanism of action, or their involvement in upstream and downstream, disease-relevant biological pathways. SRM-MS techniques have been in use for several decades to quantify small molecules (e.g., hormones, drugs and their metabolites) in regulatorycompliant pharmaceutical research and in clinical laboratories [197, 198]. In addition, developments in quantitative MS, through the application of stable isotope dilution (ID) [199] in conjunction with MRM have greatly enhanced MS-based assays to the point where their sensitivity rivals that of immunoassays for some analytes and avoids the need to develop dozens of antibodies in order to conduct the testing. Indeed, quantitative MS has been a major driving force in the field of proteomics, achieving detection limits of tens of attomoles.

Candidate Biomarker Selection The selection of markers or candidate pathways for testing will be based on a number of criteria. First, we will mine the literature to identify molecules, pathways or molecular alterations for which multiple lines of evidence support involvement in mood disorders. We will then expand this list of putative targets through sophisticated computational methods we have previously developed and applied for identifying genes and proteins connected with pathways of interest and with functional relevance to onset and progression of disease [192]. These methods involve the construction of proteinprotein interaction (PPI) networks and the application of graph theoretic measures for the identification of functional units [193] to identify and rank genes and proteins which are critical to global network connectivity and signal transmission. Candidate selection will also be aided through the use of the GeneMANIA [194] function prediction algorithm which incorporates multiple proteomic and genomic data resources and generates functional association networks representing the functional similarities of genes given by experimental or in silico evidence. Taken together, these methods will provide us with a rich source of candidate genes and proteins for systematic, quantitative investigations into the molecular profiles associated with mood disorders.

Sample Size Considerations Both univariate and multivariate analyses will be conducted to identify candidate prognostic markers of response. Standalone predictors will be identified with univariate methods, which may be parametric or non-parametric tests (e.g. t test, Mann-Whitney U test) as appropriate for the underlying distribution of the variable of interest, and standard techniques can be used to derive an adequately powered sample size for such analyses. For example, given an input list of 5000 baseline study variables (which may include clinical, neuroimaging or molecular measures), of which at least 40 are variables for which the mean absolute difference between responders and non-responders within a given study arm is greater than 2 times the within-subgroup standard deviation, and assuming a false discovery rate of 5% as determined by the BenjaminiHochberg procedure [200], a minimum of 18 subjects in either subgroup would be required so that the design has a power of at least 90% to correctly identify a given variable which differs significantly between responders and non-responders in a given study arm. Multivariate analysis will involve the development of prognostic classification models, or classifiers, of response consisting of a combination of study variables which may span modalities. Univariate analysis can be used as a filtering step to reduce dimensionality to the subset of variables displaying statistical significance after adjusting for multiple testing. Despite this reduction, a multimodal dataset used as the input for classifier development may contain thousands of variables and, therefore, methods suitable for high-dimensional classification must be used, such as support vector machines, binary logistic regression, or random forests. Dobbin and Simon [201] have examined the statistical consequences of utilizing training sets of finite size n to identify a classifier for differentiating between responders and non-responders, in terms of the prediction accuracy of the theoretically best classifier that can be identified within this finite training set, relative to the prediction accuracy P() of an optimal classifier derived from an infinitely large training set. For example, given a standardized fold-change of 1.0 (i.e. a difference between class means on the base 2 log scale which is equal to the within-class standard deviation) and an input matrix of up to 25,000 study variables, a total sample size of 110 (i.e., 55 per category) would be sufficient to construct a classifier with an expected prediction accuracy within 5% of P(). In other words, this sample size is sufficient for the purpose of constructing classifiers with expected predictive performance which is very close to those of the optimum classifiers that could be constructed using a training set of infinite size. Both univariate and multivariate analyses can be coupled to Kfold cross-validation and leave-one-out cross-validation procedures, providing internal validation of candidate models and estimation of sensitivity, specificity, positive and negative predictive values, and overall classification accuracy of a given candidate marker.

Targeted Quantitative Proteomics Our proteomics strategy relies on proven liquid chromatography (LC) mass spectrometry (MS)-based methods, in particular, selected reaction monitoring (SRM, sometimes referred to as multiple reaction monitoring or MRM) MS [195]. This technology is used for simultaneously quantifying large numbers of high interest proteins for the purposes of systematically exploring biological pathways thought to have a relationship to depressive illness. SRMMS experiments are conducted using hybrid triple quadrupole/ linear ion trap mass spectrometers (5500 and 4000 QTRAPs). The QTRAP selectively isolates peptides and breaks them apart into fragments. The combination of a peptide and a specific fragment of the peptide is referred to as an SRM “transition”. In an SRM-MS experiment, SRM transitions associated with proteins of interest are selected and the signal of each transition is recorded as a function of chromatographic elution time. The area under the curve of the resulting Guassian-shaped peak for a given transition is a measure of the relative abundance of the associated peptide and protein when comparing clinical samples (e.g., to derive a fold-change between drug responders and non-responders). For added specificity and quality control, multiple transitions per peptide and multiple peptides per protein are targeted in each assay. SRM-MS quantitation of peptides relies on a number of parameters, the most important of which lies with the optimization step in which “proteotypic” [196] peptides (i.e. peptides that are unique to the protein and are easily detected by mass spectrometry) for each protein are selected.


Data Integration While data from each analytical platform will be independently analyzed, important relationships between modalities can be detected through an integration of data collected across assessment platforms for a given study subject. Such relationships may be correlational or causal, and may be critical to uncovering or elaborating the as yet poorly understood biochemical and neurological mechanisms underlying depressive illness and therapeutic response. Furthermore, integrative analysis may yield more robust and accurate predictive or prognostic models of drug response than those derived from any one modality alone. For example, integrative analysis that combines genomic platforms may enable the detection of more robust biomarkers than the analysis of individual platforms. To enable such an integration, we will standardize the collection and cataloguing of diverse data types in this project through centralized databases for each modality. A database-level federation of these different systems will ensure that all variables associated with a given subject are linked across modalities and can be readily extracted to prepare integrated datasets for downstream analysis. These datasets will be generated for various combinations of data types and modalities to explore specific hypotheses and relationships, such as those between: clinical scale items and imaging features; demographic data, clinical history, and molecular features; and various additional relevant combinations. Predictive models will be constructed on integrated datasets using a variety of algorithms appropriate for high-dimensional integrative analysis such as those based on decision trees, random forests, and kernel methods (e.g. support vector machines). In addition to these methods, a battery of novel and established mathematical modeling techniques, including those based on hierarchical clustering and discrete dynamical systems, will be used for the purposes of identifying groups of patients that tend to cluster together in terms of symptom profile, molecular signature, imaging profile, or combinations of variables from these different domains. These “bottom up” techniques provide powerful exploratory approaches to identifying potentially meaningful subpopulations. Knowledge Translation Knowledge Translation (KT) is an important component of any research program and is particularly important when the objective is to alter how a disorder is diagnosed or a treatment is selected. An integrated KT approach involves engagement of patients both during the research process as well through implementation of findings. As well, an integrated KT approach also involves collaboration with mental health treatment providers during the process of research, to garner suggestions about research directions as well as to plan dissemination and implementation strategies. The end of grant KT will involve uptake of research findings by primary audience of mental health treatment providers, principally psychiatrists and family physicians. KT, Researchers and research users communicate during as well as after the research process, to improve both the research relevance and applicability, both of which foster more uptake of findings. DISCUSSION The process of identification and verification of biomarkers will involve many sequential phases. The current proposal involves using a standardized treatment trial as a platform, upon which to measure multiple molecular, imaging and clinical variables, to search for signatures of illness and treatment response. Once biomarker candidates are selected, they can then be tested upon this platform with a wide range of treatments in prospective studies. Many empirical questions can then be addressed, such as the optimal time to measure biomarkers, and the optimal number of biomarkers to measure. The vision of the CAN-BIND is to create such a platform, for the discovery, development and evaluation of biomarkers of treatment response across treatment modalities.

Kennedy et al.

If such biomarker signatures can be identified, what does this mean for the individual patient, and how can this information be used clinically? Neuroimaging, proteomics, genomics, and even extensive clinical characterization are costly, time-consuming and not readily available to the average patient or physician. The goal is to discover a few key markers that can then be developed into inexpensive and readily available diagnostics that would have widespread applicability for clinical trials as well as individual patients. One challenge that has been put to the field of biomarker discovery is how to identify valid biomarkers when so little is known of the pathophysiology of MDD. The CAN-BIND study will produce large data sets from multiple technological platforms and data sources as well as biospecimen banks for future research activities, all of which will provide researchers with a rich resource for identifying depression-relevant biological systems and for generating hypotheses with regard to novel therapies, diagnostics and predictive tools. The advantage of employing a “bottom up” and agnostic informatics approach is that nothing is assumed about the data, and analysis does not rely upon a model, theory, or understanding of the mechanisms of illness. The analysis is entirely data driven, and the data reveals what features are important. It has been argued that the deluge of data now available in biology is making the traditional scientific method obsolete [202]. Mathematics, statistics, and computing methods can be used to search for patterns in the data, free of hypotheses. “Convergence science” is a term used to describe the sharing of methodologies across disciplines such as engineering, physical sciences and life sciences, to solve problems in unique ways, and is seen as critical for major advances in medicine and biotechnology [203]. Nevertheless, analyses of large numbers of data points can create significant issues with regard to overfitting of data and requires appropriate caution and corrections associated with the conduct of multiple comparisons. Stratifying the population into training and test groups can mitigate many of these issues, though at the cost of reducing the N available for these comparisons. While the CAN-BIND study is relatively large, it will be important to establish linkages with other large data sets using different study designs, patient populations, treatment algorithms and therapeutic approaches in order to both expand the nature of the questions that can be addressed, as well as to generate opportunities for confirmation of hypotheses and validation of biomarkers. Indeed, such linkages are already being established through CANBIND's connection with the Ontario Brain Institute as well as through other depression initiatives. There are ethical issues that are already being raised in biomarker discovery. The implications of identifying “risk profiles”, and the associated labels, stigma and psychological burden that may accompany these profiles must be considered [31]. As well, there are issues of privacy, and legal implications with regard to employment and insurance eligibility, and issues associated with commercialization of biomarker profiles [31]. Although the focus of the current project is on biomarkers for treatment response, it is conceivable that risk markers will also be identified, and the ethical implications must be considered. Biomarkers for treatment response in major depression represent an area of enormous opportunity to improve the treatment of individuals with depression, and this has the potential to translate into improvements in many areas. First, we can hope to create a robust, biologically-based nosology for mood disorders, including both clinical and non-clinical features. Second, develop improved prediction of response, remission, and relapse in individual patients. There is also the potential for the development of a decision support tool for evidence-based, individualized selection of optimal antidepressant treatment, and eventual extension of this tool to nonpharmacological treatments (psychotherapy, brain stimulation therapies) for individualized selection of optimal treatment modality. Finally, improvements in treatment selection will decrease the time to treat an individual, resulting in a decreased burden on the


healthcare system, and potential savings in workplace absenteeism and presenteeism. On a longer-term scale, this project may lead to a better understanding of the fundamental network of interactions by which genetic, proteomic, neural, and cognitive mechanisms give rise to the clinical entities known as major depression and bipolar disorder. ABBREVIATIONS ACC = ATR = BA25 = BAS = BD = BD I = BD II = BDNF = BIS = BMP7 = BOLD = BPI-SF = BRIAN = CANTAB

=

CECA CNS CNV CSF DALY DID DNMT1 DSM ECR-R EEG EFCC EMEA EPI EQ-5D ERP EU FDA GABA(A) GAD67 GENDEP

= = = = = = = = = = = = = = = = = = = =

GLT-1 GWAS HAMD HPA ICD ID KT LC LEAPS

= = = = = = = = =

Anterior Cingulate Cortex Antidepressant Treatment Response Broadmann’s Area 25 Behavioural Activation System Bipolar Disorder Bipolar Disorder I Bipolar Disorder II Brain Derived Neurotropic Factor Behavioural Inhibition System Bone Morphogenetic Protein 7 Brain Oxygenation Level Dependent Brief Pain Inventory - Short Form Biological Rhythms Interview Assessment of Neuropsychiatry Cambridge Neuropsychological Test Automated Battery Childhood Experience of Care and Abuse Central Nervous System Copy Number Variable Cerebro-Spinal Fluid Disability Adjusted Life Years Depression Inventory Development DNA methyltransferase-1 Diagnostic Statistical Manual Experience in Close Relationships Electroencephalography Emotional Face Categorization/Conflict European Medicines Agency Echo-Planar Imaging EuroQol-5D Event Related Potential European Union Food and Drug Administration Gamma-Aminobutyric Acid (A) Glutamate decarboxylase-67 Genome based Therapeutic Drugs for Depression Glutamate Transporter-1 Genome Wide Association Studies Hamilton Depression Rating Scale Hypothalamic-Pituitary-Adrenal International Classification of Diseases Isotope Dilution Knowledge Translation Liquid Chromatography Lam Employment Absence and Productivity Scale


LTE MADRS MDD MDE MEQ MI MRM MS NIMH NR3C1

= = = = = = = = = =

PANAS PPI QLESQ

= = =

QTRAP RORA SCL-90 SDS SLE SNP SPAQ SRM STAR*D

= = = = = = = = =

SVM UBE3C

= =

9

List of Threatening Events Montgomery Asberg Rating Scale Major Depressive Disorder Major Depressive Episode Morningness-Eveninginess Questionnaire Motor Induction Multiple Reaction Monitoring Mass Spectometry National Institute of Mental Health Nuclear Receptor subfamily 3, group C, member 1 Positive and Negative Affect Scale Protein-protein Interaction Quality of Life, Employment and Satisfaction Questionnaire Quadrupole/linear ion trap RAR-related orphan receptor A Short Check-List 90 Sheehan Disability Scale Stressful Life Events Single-nucleotide polymorphism Seasonal Patter Assessment Questionnaire Selective Reaction Monitoring Sequenced Treatment Alternatives to Relieve Depression Support Vector Machine Ubiquitin Protein Ligase E3C

CONFLICT OF INTEREST Dr. Sidney Kennedy has received honoraria or grant funding from AstraZeneca, Biovail, Boehringer-Ingelheim, Eli Lilly, GlaxoSmithKline, Janssen-Ortho, Lundbeck, Merck Frost, Pfizer, Servier, Bristol Myers Squibb, and St. Jude Medical. Dr. Lam is on ad hoc Speaker/Advisory Boards for, or has received research funds from: Aquaceutica, AstraZeneca, Biovail, Bristol Myers Squibb, Canadian Institutes of Health Research, Canadian Network for Mood and Anxiety Treatments, Common Drug Review, Eli Lilly, Litebook Company Ltd., Lundbeck, Lundbeck Institute, Pfizer, Servier, St. Jude’s Medical, and UBC Institute of Mental Health/Coast Capital Savings. Dr. Evans has received honoraria or funding from Ascenta Therapeutics, Becton Dickinson, Boehringer-Ingelheim, Cannasat, Eli Lilly, Expression Pathology, Lundbeck, TransTech, QLT and YM Biosciences. Dr. Roumen Milev has received honoraria and grants from AstraZeneca, BMS, CANMAT, CIHR, Ely Lilly, Lundbeck, Servier, Pfizer. Dr. Parikh has received honoraria or grant funding in the past two years from AstraZeneca, Bristol-Myers-Squibb, Eli Lilly, Lundbeck, Pfizer as well as the Canadian Network for Mood and Anxiety Treatments, Canadian Institutes for Health Research, and the Canadian Psychiatric Association. ACKNOWLEDGEMENTS The authors wish to acknowledge the contributions of Roger McIntyre, Gustavo Turecki, Daniel Mueller, Benicio Frey, Alastair Flint, Sakina Rizvi, Peter Giacobbe, Tim Salomons, Franca Placenza, Kate Harkness, Mary Pat McAndrews, Lena Quilty, Verner Knott, Georg Northoff, Pierre Blier, Andrea Levinson, Jeff Daskalakis, Arun Ravindran, Stefanie Hassel, David Bond, Mario


Liotti, Anthony Vaccarino, Terrence Sills, Girish Sardana, Paulo Nuin, Moyez Dharsee, Joseph Geraci, Peter Kupchak, Suzanne Ackloo, for intellectual contributions to the design and development of this research. The authors acknowledge a grant from H. Lundbeck A/S and Servier Canada in the form of unrestricted research funds.

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Received: May 2, 2012

Accepted: May 16, 2012

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