Search

Supplementary Figures 1-8 from The Mechanisms of Differential Sensitivity to an Insulin-like Growth Factor-1 Receptor Inhibitor (BMS-536924) and Rationale for Combining with EGFR/HER2 Inhibitors

Supplementary Tables 1-4 from The Mechanisms of Differential Sensitivity to an Insulin-like Growth Factor-1 Receptor Inhibitor (BMS-536924) and Rationale for Combining with EGFR/HER2 Inhibitors

Background Observational data may inform novel drug development programs by identifying previously unappreciated, clinical benefits of existing drugs. Several preclinical and clinical studies have suggested emergent therapeutic utility of drugs acting on the N-methyl-d-aspartate (NMDA) receptor, a subtype of glutamate receptors, including the antidementia drug memantine. Methods Using a self-controlled cohort study design, the association of exposure to the NMDA receptor antagonist memantine with the incidence of all observed disease outcomes in four US administrative claims databases, spanning from January 2000 through January 2019, was assessed. The databases used in this study were the IBM MarketScan® Commercial Database (CCAE), the IBM MarketScan® Multi-State Medicaid Database (MDCD), the IBM MarketScan® Medicare Supplemental Database (MDCR), and the Optum© De-Identified Clinformatics® Data Mart Database. Outcomes were defined according to the unique Systematized Nomenclature of Medicine–Clinical Terms (SNOMED CT) classification system codes and required a diagnosis on two or more distinct dates. Of 20,953 outcomes assessed, only those for which memantine was associated with a ≥ 50% reduction in risk in two or more databases were included. A meta-analysis with random effects was used to pool data across the databases. Results Overall, 312,336 patients were exposed to memantine during the study. After removing conditions related to dementia and memory loss, 60 outcomes met the threshold criteria. Results fell into five disease categories: mental disorders, substance use disorders, pain, gastrointestinal and colon disorders, and demyelinating disease. The bulk of findings fell into the first two groups, with 28 outcomes related to mental disorders and 24 related to substance use disorders. Conclusion The present results confirm that NMDA receptor antagonism may have broader therapeutic utility than previously recognized. Further observational and clinical research may be warranted to explore the therapeutic benefit of NMDA antagonists for the outcomes found in this study. Supplementary Information The online version contains supplementary material available at 10.1007/s40263-020-00789-3.

Background: While cross-sectional associations of inflammatory markers interleukin-6 (IL-6) and C-reactive protein with major depressive disorder are well established, evidence for longitudinal associations mostly comes from studies on depression symptoms, not diagnoses. This study examined cross-sectional and bidirectional longitudinal associations between depression diagnosis and symptoms in an adult sample over a 6-year period. Methods: Data were obtained from the baseline (n = 2416) and 2- and 6-year follow-up assessments (n = 1925 and n = 1924, respectively) of the Netherlands Study of Depression and Anxiety. C-reactive protein and IL-6 were assessed at each wave, as were the Composite International Diagnostic Interview and Inventory of Depressive Symptomatology. Linear mixed models and generalized estimating equation models with a binomial distribution were used to study longitudinal associations between depression and inflammation and vice versa. Results: There was a consistent cross-sectional association between current depressive disorder (vs. no current disorder) and symptoms with IL-6 across all follow-up measurements (Cohen's d depression diagnosis = 0.06, p =.017; B standardized Inventory of Depressive Symptomatology = 0.029, SE = 0.011, p =.008). In longitudinal analyses, higher IL-6 levels predicted subsequent chronic course in those with a diagnosis at baseline in women but not in men (odds ratio women = 1.13, 95% confidence interval = 1.04–1.23), and both depressive disorder and high severity predicted higher IL-6 levels at the subsequent follow-up (p values

Activation of the innate immune system is commonly associated with depression. Immunomodulatory drugs may have efficacy for depressive symptoms that are co-morbidly associated with inflammatory disorders. We report a large-scale re-analysis by standardized procedures (mega-analysis) of patient-level data combined from 18 randomized clinical trials conducted by Janssen or GlaxoSmithKline for one of nine disorders (N = 10,743 participants). Core depressive symptoms (low mood, anhedonia) were measured by the Short Form Survey (SF-36) or the Hospital Anxiety and Depression Scale (HADS), and participants were stratified into high (N = 1921) versus low-depressive strata based on baseline ratings. Placebo-controlled change from baseline after 4–16 weeks of treatment was estimated by the standardized mean difference (SMD) over all trials and for each subgroup of trials targeting one of 7 mechanisms (IL-6, TNF-α, IL-12/23, CD20, COX2, BLγS, p38/MAPK14). Patients in the high depressive stratum showed modest but significant effects on core depressive symptoms (SMD = 0.29, 95% CI [0.12–0.45]) and related SF-36 measures of mental health and vitality. Anti-IL-6 antibodies (SMD = 0.8, 95% CI [0.20–1.41]) and an anti-IL-12/23 antibody (SMD = 0.48, 95% CI [0.26–0.70]) had larger effects on depressive symptoms than other drug classes. Adjustments for physical health outcome marginally attenuated the average treatment effect on depressive symptoms (SMD = 0.20, 95% CI: 0.06–0.35), but more strongly attenuated effects on mental health and vitality. Effects of anti-IL-12/23 remained significant and anti-IL-6 antibodies became a trend after controlling for physical response to treatment. Novel immune-therapeutics can produce antidepressant effects in depressed patients with primary inflammatory disorders that are not entirely explained by treatment-related changes in physical health.

Decoding disease mechanisms for addressing unmet clinical need demands the rapid assimilation of the exponentially growing biomedical knowledge. These are either inherently unstructured and non-conducive to current computing paradigms or siloed into structured databases requiring specialized bioinformatics. Despite the recent renaissance in unsupervised neural networks for deciphering unstructured natural languages and the availability of numerous bioinformatics resources, a holistic platform for real-time synthesis of the scientific literature and seamless triangulation with deep omic insights and real-world evidence has not been advanced. Here, we introduce the nferX platform that makes the highly unstructured biomedical knowledge computable and supports the seamless visual triangulation with statistical inference from diverse structured databases. The nferX platform will accelerate and amplify the research potential of subject-matter experts as well as non-experts across the life science ecosystem (https://academia.nferx.com/).

Cytokines, including interleukin-6 (IL-6), modulate neuronal plasticity and stress coping. Depressive symptoms and major depressive disorder (MDD) have been associated with changes in cytokines and their signaling. The current study examined the effect of IL-6 monoclonal antibody administration on depressive symptoms in patients with rheumatoid arthritis (RA) or multicentric Castleman's disease (MCD). The data were obtained from two phase 2, double-blind, placebo-controlled trials designed to test the efficacy of sirukumab in RA (N=176) or of siltuximab in MCD (N=65), and were analyzed post hoc to investigate the effects of these IL-6 antibodies on depressive symptoms. The SF-36 questionnaire items on depressed-mood and anhedonia were combined as the measure for depressive symptoms. The study participants were grouped by the presence/absence of prevalent depressed mood and anhedonia (PDMA, meaning either depressed mood or anhedonia was present at least 'most of the time' and the other at least 'some of the time' for four weeks) at baseline; 26.1% of the RA sample and 15.4% of the MCD sample met criteria for PDMA at baseline. Compared with placebo, sirukumab and siltuximab produced significantly greater improvements on depressive symptoms. To account for an effect on mood due to changes in RA or MCD, the analysis was (1) adjusted for symptom severities using DAS28-CRP for RA and MCDOS for MCD alone or together with bodily pain and physical functioning, and (2) performed within RA and MCD non-responders. Improvement in depressive symptoms remained significant in the treated group for both drugs. The significance over placebo was also observed in the siltuximab study. The improvement in depressive symptoms by sirukumab correlated positively with the baseline soluble IL-6 receptor levels. The data together suggest that the IL-6 antibodies improve depressive symptoms in patients with RA and MCD. Further studies are needed to elucidate to what extents the IL-6 antibodies improve depressive symptoms through improving primary disease dependent and independent mechanisms, especially in RA patients, and the brain mechanisms underlying depressive symptom improvements.

Background Examining transcriptional regulation by antidepressants in key neural circuits implicated in depression and understanding the relation to transcriptional mechanisms of susceptibility and natural resilience may help in the search for new therapeutic agents. Given the heterogeneity of treatment response in human populations, examining both treatment response and nonresponse is critical. Methods We compared the effects of a conventional monoamine-based tricyclic antidepressant, imipramine, and a rapidly acting, non–monoamine-based antidepressant, ketamine, in mice subjected to chronic social defeat stress, a validated depression model, and used RNA sequencing to analyze transcriptional profiles associated with susceptibility, resilience, and antidepressant response and nonresponse in the prefrontal cortex (PFC), nucleus accumbens, hippocampus, and amygdala. Results We identified similar numbers of responders and nonresponders after ketamine or imipramine treatment. Ketamine induced more expression changes in the hippocampus; imipramine induced more expression changes in the nucleus accumbens and amygdala. Transcriptional profiles in treatment responders were most similar in the PFC. Nonresponse reflected both the lack of response-associated gene expression changes and unique gene regulation. In responders, both drugs reversed susceptibility-associated transcriptional changes and induced resilience-associated transcription in the PFC. Conclusions We generated a uniquely large resource of gene expression data in four interconnected limbic brain regions implicated in depression and its treatment with imipramine or ketamine. Our analyses highlight the PFC as a key site of common transcriptional regulation by antidepressant drugs and in both reversing susceptibility– and inducing resilience–associated molecular adaptations. In addition, we found region-specific effects of each drug, suggesting both common and unique effects of imipramine versus ketamine.

Whole-genome transcription has been measured in peripheral blood samples as a candidate biomarker of inflammation associated with major depressive disorder.We searched for all case-control studies on major depressive disorder that reported microarray or RNA sequencing measurements on whole blood or peripheral blood mononuclear cells. Primary datasets were reanalyzed, when openly accessible, to estimate case-control differences and to evaluate the functional roles of differentially expressed gene lists by technically harmonized methods.We found 10 eligible studies (N = 1754 depressed cases and N = 1145 healthy controls). Fifty-two genes were called significant by 2 of the primary studies (published overlap list). After harmonization of analysis across 8 accessible datasets (n = 1706 cases, n = 1098 controls), 272 genes were coincidentally listed in the top 3% most differentially expressed genes in 2 or more studies of whole blood or peripheral blood mononuclear cells with concordant direction of effect (harmonized overlap list). By meta-analysis of standardized mean difference across 4 studies of whole-blood samples (n = 1567 cases, n = 954 controls), 343 genes were found with false discovery rate5% (standardized mean difference meta-analysis list). These 3 lists intersected significantly. Genes abnormally expressed in major depressive disorder were enriched for innate immune-related functions, coded for nonrandom protein-protein interaction networks, and coexpressed in the normative transcriptome module specialized for innate immune and neutrophil functions.Quantitative review of existing case-control data provided robust evidence for abnormal expression of gene networks important for the regulation and implementation of innate immune response. Further development of white blood cell transcriptional biomarkers for inflamed depression seems warranted.

BACKGROUND: Peripheral inflammation is often associated with major depressive disorder (MDD), and immunological biomarkers of depression remain a focus of investigation.METHODS: We used microarray data on whole blood from two independent case-control studies of MDD: the GlaxoSmithKline-High-Throughput Disease-specific target Identification Program [GSK-HiTDiP] study (113 patients and 57 healthy control subjects) and the Janssen-Brain Resource Company study (94 patients and 100 control subjects). Genome-wide differential gene expression analysis (18,863 probes) resulted in a p value for each gene in each study. A Bayesian method identified the largest p-value threshold (q = .025) associated with twice the number of genes differentially expressed in both studies compared with the number of coincidental case-control differences expected by chance.RESULTS: A total of 165 genes were differentially expressed in both studies with concordant direction of fold change. The 90 genes overexpressed (or UP genes) in MDD were significantly enriched for immune response to infection, were concentrated in a module of the gene coexpression network associated with innate immunity, and included clusters of genes with correlated expression in monocytes, monocyte-derived dendritic cells, and neutrophils. In contrast, the 75 genes underexpressed (or DOWN genes) in MDD were associated with the adaptive immune response and included clusters of genes with correlated expression in T cells, natural killer cells, and erythroblasts. Consistently, the MDD patients with overexpression of UP genes also had underexpression of DOWN genes (correlation > .70 in both studies).CONCLUSIONS: MDD was replicably associated with proinflammatory activation of the peripheral innate immune system, coupled with relative inactivation of the adaptive immune system, indicating the potential of transcriptional biomarkers for immunological stratification of patients with depression.

Although depression with anxious distress appears to be a clinically relevant subtype of major depressive disorder (MDD), whether it involves specific pathophysiology remains unclear. Inflammation has been implicated, but not comprehensively studied. We examined within a large MDD sample whether anxious distress and related anxiety features are associated with differential basal inflammation and innate cytokine production capacity. Data are from 1078 MDD patients from the Netherlands Study of Depression and Anxiety. In addition to the DSM-5 anxious distress specifier, we studied various dimensional anxiety scales (e.g. Inventory of Depressive Symptomatology anxiety arousal subscale [IDS-AA], Beck Anxiety Inventory [BAI], Mood and Anxiety Symptoms Questionnaire Anxious Arousal scale [MASQ-AA]). The specifier was constructed using five self-report items from the IDS and BAI. Basal inflammatory markers included C-reactive protein (CRP), interleukin (IL)-6 and tumor necrosis factor (TNF)-α. Innate production capacity was assessed by 13 lipopolysaccharide (LPS)-stimulated inflammatory markers. Basal and LPS-stimulated inflammation index scores were created. Basal inflammation was not associated with anxious distress (prevalence = 54.3%) in MDD patients, except for a modest positive association for BAI score. However, anxious distress was associated with higher LPS-stimulated levels (interferon-γ, IL-6, monocyte chemotactic protein (MCP)-1, macrophage inflammatory protein (MIP)-1α, matrix metalloproteinase (MMP)-2, TNF-α, LPS-stimulated index). Other anxiety indicators (anxious distress specifier score, BAI, MASQ-AA) were also associated with increased innate production capacity. Within a large MDD sample, the anxious distress specifier was associated with increased innate cytokine production capacity but not with basal inflammation. Results from dimensional anxiety indicators largely confirm these results. These findings provide new insight into the pathophysiology of anxious depression.

Antidepressants (ADs) are the most common treatment for major depressive disorder (MDD). However, only ∼30% of patients experience adequate response after a single AD trial, and this variability remains poorly understood. Here, we investigated microRNAs (miRNAs) as biomarkers of AD response using small RNA-sequencing in paired samples from MDD patients enrolled in a large, randomized placebo-controlled trial of duloxetine collected before and 8 weeks after treatment. Our results revealed differential expression of miR-146a-5p, miR-146b-5p, miR-425-3p and miR-24-3p according to treatment response. These results were replicated in two independent clinical trials of MDD, a well-characterized animal model of depression, and post-mortem human brains. Furthermore, using a combination of bioinformatics, mRNA studies and functional in vitro experiments, we showed significant dysregulation of genes involved in MAPK/Wnt signalling pathways. Together, our results indicate that these miRNAs are consistent markers of treatment response and regulators of the MAPK/Wnt systems., Antidepressant drugs are the most common treatment for depressive episodes but only a fraction of patients experience adequate response. Here the authors find dysregulation of miRNAs in peripheral blood samples from depressed patients after antidepressant treatment, and show that the miRNAs are regulators of psychiatrically relevant signalling pathways.

Background Interleukin-6 (IL-6) is one of the known neuroactive-cytokines involved in neuronal plasticity and stress coping and is associated with depression. Depressive symptoms are common in patients with rheumatoid arthritis (RA), a disease characterized by high peripheral IL-6. Previous analysis of a Phase II study1 showed that sirukumab, a human monoclonal antibody that selectively binds to the IL-6 cytokine with high affinity, can help reduce depressive symptoms in RA patients. Objectives To further assess and validate the effects of sirukumab on relieving depressive symptoms in RA patients. Methods We conducted a post-hoc analysis of 2 Phase III, randomized, double-blind, placebo-controlled trials evaluating efficacy and safety of sirukumab in the treatment of RA. SIRROUND-D enrolled patients with active RA despite disease-modifying anti-rheumatic drugs and having serum C-reactive protein (CRP)≥8mg/L at baseline. SIRROUND-T enrolled patients with active RA despite anti-TNF therapy and having serum CRP≥8mg/L or erythrocyte sedimentation rate≥28mm/h at baseline. Patients using antidepressants were excluded from the analysis. Patients were grouped by presence/absence of prevalent depressed mood and anhedonia (PDMA), based on the self-reported frequencies of these 2 core depressive symptoms in the SF-36, requiring one present at least “most of the time” and the other “some of the time” in the past 4 weeks. A depression score was derived based on the 2 core depressive symptoms of SF-36. Relief of depressive symptoms in patients with PDMA was evaluated by comparing changes in the depression score from baseline to week 8 between the placebo and combined sirukumab groups directly and with adjustment for RA activity as measured by the Disease Activity Score-28 with CRP. Changes in depression score were also analyzed in RA patients who did not have an ACR50 response to sirukumab. The combined effect of anti-IL-6 treatment was estimated using meta-analysis of 2 Phase III, 1 Phase II studies of sirukumab and 1 Phase II study of the anti-IL-6 cytokine antibody siltuximab. Results At baseline, 19% and 22% of patients were classified as PDMA in the 2 studies, respectively. Sirukumab treatment, compared to placebo, significantly improved depressive symptoms by week 8 among PDMA patients (p=0.022 and 0.046 for the 2 studies, respectively) before adjusting for changes in RA activity. Within the sirukumab group, the reduction in depressive symptoms remained significant after adjusting for changes in RA activity (p Conclusions Our findings are consistent with previous results from a phase II RA study. Peripheral anti-IL-6 cytokine treatment is associated with improvement in depressive symptoms in RA patients, possibly supporting a role for IL-6 dysfunction in depression. References Ann Rheum Dis2015;74(Suppl2): 720. Disclosure of Interest Y. Sun Shareholder of: Johnson & Johnson, Employee of: Janssen Research & Develoment, LLC, B. Hsu Shareholder of: Johnson & Johnson, Employee of: Janssen Research & Develoment, LLC, D. Wang Shareholder of: Johnson & Johnson, Employee of: Janssen Research & Develoment, LLC, M. Curran Shareholder of: Johnson & Johnson, Employee of: Janssen Research & Develoment, LLC, W. Drevets: None declared, G. Chen: None declared, G. Wittenberg: None declared

To assess the clinical relevance of sleep duration, hours slept were compared by health status, presence of insomnia, and presence of depression, and the association of sleep duration with BMI and cardiovascular risk was quantified.Cross-sectional analysis of subjects in the US National Health and Nutrition Examination Surveys using adjusted linear and logistic regressions.A total of 22,281 adults were included, 37% slept ≤ 6 hours, 36% were obese, and 45% reported cardiovascular conditions. Mean sleep duration was 6.87 hours. Better health was associated with more hours of sleep. Subjects with poor health reported sleeping 46 min, (95% CI -56.85 to -35.67) less than subjects with excellent health. Individuals with depression (vs. not depressed) reported 40 min less sleep, (95% CI -47.14 to -32.85). Individuals with insomnia (vs. without insomnia) reported 39 min less sleep, (95% CI -56.24 to -22.45). Duration of sleep was inversely related to BMI; for every additional hour of sleep, there was a decrease of 0.18 kg/m(2) in BMI, (95% CI -0.30 to -0.06). The odds of reporting cardiovascular problems were 6.0% lower for every hour of sleep (odds ratio = 0.94, 95% CI [0.91 to 0.97]). Compared with subjects who slept ≤ 6 h, subjects who slept more had lower odds of reporting cardiovascular problems, with the exception of subjects ≥ 55 years old who slept ≥ 9 hours.Long sleep duration is associated with better health. The fewer the hours of sleep, the greater the BMI and reported cardiovascular disease. A difference of 30 minutes of sleep is associated with substantive impact on clinical well-being.

Biological information storage events are often rapid transitions between discrete states. In neural systems, the initiation of bidirectional plasticity by all-or-none events may help confer robustness on memory storage. Here, we report that at CA3–CA1 hippocampal synapses, individual potentiation and depression plasticity events are discrete and heterogeneous in nature. Individual synapses began from extreme high and low strength states. Unitary plasticity events were all-or-none and drove synaptic strength between extremes in

The hippocampus plays a central role in memory consolidation, a process for converting short-term memory into cortically stored, long-lasting memory in the mammalian brain. Here, we review recent data and discuss the 'synaptic re-entry reinforcement' (SRR) hypothesis, which can account for the role of the hippocampus in memory consolidation at both the molecular and systems levels. The central idea of the SRR hypothesis is that reactivation of neural ensembles in the hippocampus during the consolidation period results in multiple rounds of NMDA-receptor-dependent synaptic reinforcement of the hippocampal memory traces created during initial learning. In addition, such reactivation and reinforcement processes permit the hippocampus to act as a 'coincidence regenerator', providing coordinated input that drives the coherent reactivation of cortical neurons, resulting in the progressive strengthening of cortical memory traces through reactivation of cortical NMDA receptors.

The conversion of newly formed declarative memories into long-term memories is known to be dependent on the hippocampus. Recent experiments suggest that memory consolidation requires reactivation of the NMDA receptor in CA1 during the initial week(s) after training. This led to the hypothesis that the repeated post-learning reinforcement of synaptic modifications, termed synaptic reentry reinforcement (SRR), is essential for long-term memory consolidation and storage. Based on experimental observations, we have built a computational model to further illustrate and explore the effect of the SRR process on the formation of long-term memory. We show that SRR is capable of strengthening and maintaining memory traces despite inherent variability in the system due to such processes as the turnover of synaptic receptors and their associated signaling and structural proteins. Furthermore, we demonstrate that new rounds of synaptic modification triggered by memory reactivation, either during conscious recall or sleep, could lead to the selective consolidation of a subset of memory traces. Finally, we show why the SRR process in the hippocampus is required during the initial post-training weeks for synaptic reinforcement based memory consolidation in the cortex. Hippocampus 2002;12:637–647. © 2002 Wiley-Liss, Inc.

IntroductionAlthough depression with anxious distress appears to be a clinically relevant subtype of Major Depressive Disorder (MDD), whether it involves specific pathophysiology remains unclear. Inflammation has been implicated, but not comprehensively studied. We examined within a large MDD sample whether anxious distress and related anxiety features are associated with differential basal inflammation and innate cytokine production capacity.MethodsData are from 1078 MDD patients from the Netherlands study of depression and anxiety. Besides the DSM-5 anxious distress specifier, we studied various dimensional anxiety scales (e.g. Inventory of Depressive Symptomatology anxiety arousal subscale [IDS-AA], Beck Anxiety Inventory [BAI], Mood and Anxiety Symptoms Questionnaire Anxious Arousal scale [MASQ-AA]). Basal inflammatory markers included C-reactive protein, interleukin (IL)-6 and tumor-necrosis factor (TNF)-α. Innate production capacity was assessed by 13 lipopolysaccharide (LPS)-stimulated inflammatory markers. Basal and LPS-stimulated inflammation index scores were created.ResultsBasal inflammation was not associated with anxious distress in MDD patients (anxious distress prevalence 54.3%), except for modest positive associations for IDS-AA and BAI scores. However, anxious distress was associated with higher LPS-stimulated levels (interferon-ɣ, IL-2, IL-6, monocyte chemotactic protein (MCP)-1, macrophage inflammatory protein (MIP)-1α, MIP-1β, matrix metalloproteinase-2, TNF-α, TNF-β, LPS-stimulated index). Oher anxiety indicators (number of specifier items and anxiety diagnoses, IDS-AA, BAI, MASQ-AA) were also associated with increased innate production capacity.ConclusionsWithin a large MDD sample, the anxious distress specifier was associated with increased innate cytokine production capacity but not with basal inflammation. Results from dimensional anxiety indicators largely confirm these results. These findings provide new insight into the pathophysiology of anxious depression.

Recent studies have revealed that melancholic depression, one major subtype of depression, is closely associated with the concentration of some metabolites and biological functions of certain genes and pathways. Meanwhile, recent advances in biotechnologies have allowed us to collect a large amount of genomic data, e.g., metabolites and microarray gene expression. With such a huge amount of information available, one approach that can give us new insights into the understanding of the fundamental biology underlying melancholic depression is to build disease status prediction models using classification or regression methods. However, the existence of strong empirical correlations, e.g., those exhibited by genes sharing the same biological pathway in microarray profiles, tremendously limits the performance of these methods. Furthermore, the occurrence of missing values which are ubiquitous in biomedical applications further complicates the problem. In this paper, we hypothesize that the problem of missing values might in some way benefit from the correlation between the variables and propose a method to learn a compressed set of representative features through an adapted version of sparse coding which is capable of identifying correlated variables and addressing the issue of missing values simultaneously. An efficient algorithm is also developed to solve the proposed formulation. We apply the proposed method on metabolic and microarray profiles collected from a group of subjects consisting of both patients with melancholic depression and healthy controls. Results show that the proposed method can not only produce meaningful clusters of variables but also generate a set of representative features that achieve superior classification performance over those generated by traditional clustering and data imputation techniques. In particular, on both datasets, we found that in comparison with the competing algorithms, the representative features learned by the proposed method give rise to significantly improved sensitivity scores, suggesting that the learned features allow prediction with high accuracy of disease status in those who are diagnosed with melancholic depression. To our best knowledge, this is the first work that applies sparse coding to deal with high feature correlations and missing values, which are common challenges in many biomedical applications. The proposed method can be readily adapted to other biomedical applications involving incomplete and high-dimensional data.

Complex diseases such as major depression affect people over time in complicated patterns. Longitudinal data analysis is thus crucial for understanding and prognosis of such diseases and has received considerable attention in the biomedical research community. Traditional classification and regression methods have been commonly applied in a simple (controlled) clinical setting with a small number of time points. However, these methods cannot be easily extended to the more general setting for longitudinal analysis, as they are not inherently built for time-dependent data. Functional regression, in contrast, is capable of identifying the relationship between features and outcomes along with time information by assuming features and/or outcomes as random functions over time rather than independent random variables. In this paper, we propose a novel sparse generalized functional linear model for the prediction of treatment remission status of the depression participants with longitudinal features. Compared to traditional functional regression models, our model enables high-dimensional learning, smoothness of functional coefficients, longitudinal feature selection and interpretable estimation of functional coefficients. Extensive experiments have been conducted on the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) data set and the results show that the proposed sparse functional regression method achieves significantly higher prediction power than existing approaches.

The characterization of proteins via liquid chromatography-mass spectrometry (LC-MS) and tandem MS is a challenge due to the large dynamic range and the high complexity of the molecules of interest. In LC-MS experiments, the inconsistent variation in the travel time of analytes in the LC column results in nonlinear shifts in the LC retention time (RT). This variability must be corrected to accurately match corresponding peptide features across samples in LC-MS experiments. Standard methods for RT alignment applied to the raw data are computationally expensive, making it impractical to process a large number of samples. More successful algorithms perform the alignment on features that matched across experiments based on pre-specified mass and RT windows. Features that match across multiple experiments are more likely to be true positives and, therefore, will be more suitable to drive the alignment correction. However, depending on the feature matching algorithm, ambiguities can arise when more than one candidate feature match falls within the specified windows which might affect the alignment performance. In addition, some of the feature-based alignment algorithms do not correct for nonlinear RT shifts. We propose a novel feature matching algorithm that incorporates wavelet-based shape information about the features. We tested our algorithm on two different applications of MS. First, we combined the feature matching algorithm with a robust nonparametric kernel-type regression to form a nonlinear feature-based alignment framework for LC-MS experiments. We validated our alignment framework on LC-MS data from complex samples with known spiked-in proteins, demonstrating our ability to correctly identify each of them with higher reproducibility and probability score when comparing with the SuperHirn software. In addition, by using our feature-based alignment framework, we were able to increase the number of matched features and improve the correlation between replicates. Second, we tested our feature matching algorithm on MALDI MS with MS/MS acquisitions. We found that using only features that matched across replicates of tandem mass spectra we could improve the identification of peptides compared with the current state-of-the-art software. Supplementary Material is available online at www.libertonline.com/cmb .

Overexpression and enhanced activity of insulin-like growth factor-I receptor (IGF-IR) in diverse tumor types make it an attractive target for cancer therapy. BMS-536924 is a potent small molecule inhibitor of IGF-IR, which shows antitumor activity in multiple tumor models, including sarcoma. To facilitate the development of IGF-IR inhibitors as cancer therapy, identification of biomarkers for selecting patients most likely to derive clinical benefit is needed. To do so, 28 sarcoma and neuroblastoma cell lines were screened for in vitro response to BMS-536924 to identify sensitive and resistant cell lines. Notably, Ewing's sarcoma, rhabdomyosarcoma, and neuroblastoma are more responsive to BMS-536924, suggesting these specific subtypes may represent potential targeted patient subpopulations for the IGF-IR inhibitor. Gene expression and protein profiling were performed on these cell lines, and candidate biomarkers correlating with intrinsic and/or acquired resistance to BMS-536924 were identified. IGF-I, IGF-II, and IGF-IR were highly expressed in sensitive cell lines, whereas IGFBP-3 and IGFBP-6 were highly expressed in resistant lines. Overexpression of epidermal growth factor receptor (EGFR) and its ligands in resistant cell lines may represent one possible resistance mechanism by the adaptation of IGF-IR–independent growth using alternative signaling pathways. Based on cross-talk between IGF-IR and EGFR pathways, combination studies to target both pathways were performed, and enhanced inhibitory activities were observed. These results provide a strategy for testing combinations of IGF-IR inhibitors with other targeted therapies in clinical studies to achieve improved patient outcomes. Further exploration of mechanisms for intrinsic and acquired drug resistance by these preclinical studies may lead to more rationally designed drugs that target multiple pathways for enhanced antitumor efficacy. [Cancer Res 2009;69(1):161–70]

Background Interleukin-6 (IL-6) is among the known neuroactive cytokines involved in stress coping and neuronal plasticity. Clinical and preclinical studies suggest a role for IL-6 in the pathophysiology of depression; it is unclear whether peripheral anti-IL-6 treatment can directly alleviate depressive symptoms. Depressive symptoms and fatigue commonly affect RA patients, particularly those with high disease activity. Objectives To assess the effects of sirukumab, a human IL6 antibody, on measures of depressed mood and anhedonia, and fatigue from a Ph2 trial in patients with active RA. Methods This was a post-hoc analysis of the SF-36 Mental Health and Vitality domain items from a Ph2, multicenter, randomized, double-blind, placebo-controlled study evaluating efficacy and safety of 4 dose regimens of sirukumab (25mg q4wks up to 100mg q2wks) administered SC in patients with active RA despite MTX. Serum CRP ≥10mg/L was required for enrollment, and history of an uncontrolled psychiatric or emotional disorder that was of sufficient severity to interfere with study participation was excluded. Patients using anti-depressants were excluded from analysis. Patients were grouped by presence or absence of prevalent depressed mood and anhedonia (PDMA) at entry, defined as self-reported depressed mood and anhedonia on the SF-36 with >1 item designated “most of the time” and the other at least “some of the time” for 4wks. Sirukumab treatment was defined as receiving any sirukumab regimen. Change from baseline in PDMA and fatigue at wk12 were analyzed unadjusted and adjusted for baseline and wk12 DAS28-CRP score; to investigate the relationship between depressive symptoms improvement and RA clinical response, change in PDMA symptoms was also investigated separately in ACR50 responders and non-responders Results At entry, about 26% of patients were classified as having PDMA. This group also experienced significantly more fatigue and nervousness than those without PDMA. The severity of PDMA symptoms in this group was not significantly correlated with RA chronicity or severity, or with baseline serum levels of inflammatory biomarkers. Clinical efficacy of sirukumab on RA disease symptoms as measured with the DAS28-CRP occurred in patients with and without PDMA at entry. Patients in the PDMA group receiving sirukumab but not PBO achieved significant improvements at wk12 in PDMA symptoms (p=0.0006), and fatigue (p=0.0157). In patients with PDMA, significant improvements on PDMA symptoms upon sirukumab treatment, but not PBO, were observed in both ACR50 responders (p=0.0024) and non-responders (p=0.0014). In patients with PDMA treated with sirukumab, baseline soluble IL-6 receptor (sIL-6R) level significantly correlated with improvement at wk12 in PDMA symptoms (Spearman r=0.44, p=0.015). Conclusions These novel findings link IL-6 signaling pathway dysregulation to depressive symptoms and fatigue, and suggest that peripheral anti-IL-6 treatment can improve depressive symptoms in RA patients independently of clinical response. References Hodes GE et al,PNAS 2014;111(45):16136-16141. Smolen JS et al,ARD 2014;73:1616-1625. Disclosure of Interest B. Hsu Employee of: Janssen, D. Wang Employee of: Janssen, Y. Sun Employee of: Janssen, G. Salvadore Employee of: Janssen, J. Singh Employee of: Janssen, M. Curran Employee of: Janssen, I. Caers Employee of: Janssen, W. Drevets Employee of: Janssen, G. Wittenberg Employee of: Janssen, G. Chen Employee of: Janssen

The magnitude and direction of synaptic plasticity can be determined by the precise timing of presynaptic and postsynaptic action potentials on a millisecond timescale. In vivo, however, neural activity has structure on longer timescales. Here we show that plasticity at the CA3-CA1 synapse depends strongly on parameters other than millisecond spike timing. As a result, the notion that a single spike-timing-dependent plasticity (STDP) rule alone can fully describe the mapping between neural activity and synapse strength is invalid. We have begun to explore the influence of additional behaviorally relevant activity parameters on STDP and found conditions under which underlying spike-timing-dependent rules for potentiation and depression can be separated from one another. Potentiation requires postsynaptic burst firing at 5 Hz or higher, a firing pattern that occurs during the theta rhythm. Potentiation is measurable after only tens of presynaptic-before-postsynaptic pairings. Depression requires hundreds of pairings but has less stringent long timescale requirements and broad timing dependence. By varying these parameters, we obtain STDP curves that are long-term potentiation only, bidirectional, or long-term depression only. This expanded description of the CA3-CA1 learning rule reconciles apparent contradictions between spike-timing-dependent plasticity and previous work at CA3-CA1 synapses.

Background Side effects, or the adverse effects of drugs, contain important clinical phenotypic information that may be useful in predicting novel or unknown targets of a drug. It has been suggested that drugs with similar side-effect profiles may share common targets. The diagnostic class, Major Depressive Disorder, is increasingly viewed as being comprised of multiple depression subtypes with different biological root causes. One ‘type’ of depression generating substantial interest today focuses on patients with high levels of inflammatory burden, indicated by elevated levels of C-reactive proteins (CRP) and pro-inflammatory cytokines such as interleukin 6 (IL-6). It has been suggested that drugs targeting the immune system may have beneficial effect on this subtype of depressed patients, and several studies are underway to test this hypothesis directly. However, patients have been treated with both anti-inflammatory and antidepressant compounds for decades. It may be possible to exploit similarities in clinical readouts to better understand the antidepressant effects of immune-related drugs. Methods Here we explore the space of approved drugs by comparing the drug side effect profiles of known antidepressants and drugs targeting the immune system, and further examine the findings by comparing the human cell line expression profiles induced by them with those induced by antidepressants. Results We found 7 immune-modulators and 14 anti-inflammatory drugs sharing significant side effect profile similarities with antidepressants. Five of the 7 immune modulators share most similar side effect profiles with antidepressants that modulate dopamine release and/or uptake. In addition, the immunosuppressant rapamycin and the glucocorticoid alclometasone induces transcriptional changes similar to multiple antidepressants. Conclusions These findings suggest that some antidepressants and some immune-related drugs may affect common molecular pathways. Our findings support the idea that certain medications aimed at the immune system may be helpful in relieving depressive symptoms, and suggest that it may be of value to test immune-modulators for antidepressant-like activity in future proof-of-concept studies.

Background Major depressive disorder (MDD) is a heterogeneous disease at the level of clinical symptoms, and this heterogeneity is likely reflected at the level of biology. Two clinical subtypes within MDD that have garnered interest are “melancholic depression” and “anxious depression”. Metabolomics enables us to characterize hundreds of small molecules that comprise the metabolome, and recent work suggests the blood metabolome may be able to inform treatment decisions for MDD, however work is at an early stage. Here we examine a metabolomics data set to (1) test whether clinically homogenous MDD subtypes are also more biologically homogeneous, and hence more predictiable, (2) devise a robust machine learning framework that preserves biological meaning, and (3) describe the metabolomic biosignature for melancholic depression. Results With the proposed computational system we achieves around 80 % classification accuracy, sensitivity and specificity for melancholic depression, but only ~72 % for anxious depression or MDD, suggesting the blood metabolome contains more information about melancholic depression.. We develop an ensemble feature selection framework (EFSF) in which features are first clustered, and learning then takes place on the cluster centroids, retaining information about correlated features during the feature selection process rather than discarding them as most machine learning methods will do. Analysis of the most discriminative feature clusters revealed differences in metabolic classes such as amino acids and lipids as well as pathways studied extensively in MDD such as the activation of cortisol in chronic stress. Conclusions We find the greater clinical homogeneity does indeed lead to better prediction based on biological measurements in the case of melancholic depression. Melancholic depression is shown to be associated with changes in amino acids, catecholamines, lipids, stress hormones, and immune-related metabolites. The proposed computational framework can be adapted to analyze data from many other biomedical applications where the data has similar characteristics. Electronic supplementary material The online version of this article (doi:10.1186/s12864-016-2953-2) contains supplementary material, which is available to authorized users.