Pan-Cancer Proteomics Analysis to Identify Tumor-Enriched and Highly Expressed Cell Surface Antigens as Potential Targets for Cancer Therapeutics.

The National Cancer Institute's Clinical Proteomic Tumor Analysis Consortium (CPTAC) provides unique opportunities for cancer target discovery using protein expression. Proteomics data from CPTAC tumor types have been primarily generated using a multiplex tandem mass tag (TMT) approach, which is designed to provide protein quantification relative to reference samples. However, relative protein expression data are suboptimal for prioritization of targets within a tissue type, which requires additional reprocessing of the original proteomics data to derive absolute quantitation estimation. We evaluated the feasibility of using differential protein analysis coupled with intensity-based absolute quantification (iBAQ) to identify tumor-enriched and highly expressed cell surface antigens, employing tandem mass tag (TMT) proteomics data from CPTAC. Absolute quantification derived from TMT proteomics data was highly correlated with that of label-free proteomics data from the CPTAC colon adenocarcinoma cohort, which contains proteomics data measured by both approaches. We validated the TMT-iBAQ approach by comparing the iBAQ value to the receptor density value of HER2 and TROP2 measured by flow cytometry in about 30 selected breast and lung cancer cell lines from the Cancer Cell Line Encyclopedia. Collections of these tumor-enriched and highly expressed cell surface antigens could serve as a valuable resource for the development of cancer therapeutics, including antibody-drug conjugates and immunotherapeutic agents.

Resistance to Pyrrolobenzodiazepine Dimers Is Associated with SLFN11 Downregulation and Can Be Reversed through Inhibition of ATR.

Resistance to antibody-drug conjugates (ADCs) has been observed in both preclinical models and clinical studies. However, mechanisms of resistance to pyrrolobenzodiazepine (PBD)-conjugated ADCs have not been well characterized and thus, this study was designed to investigate development of resistance to PBD dimer warheads and PBD-conjugated ADCs. We established a PBD-resistant cell line, 361-PBDr, by treating human breast cancer MDA-MB-361 cells with gradually increasing concentrations of SG3199, the PBD dimer released from the PBD drug-linker tesirine. 361-PBDr cells were over 20-fold less sensitive to SG3199 compared with parental cells and were cross-resistant to other PBD warhead and ADCs conjugated with PBDs. Proteomic profiling revealed that downregulation of Schlafen family member 11 (SLFN11), a putative DNA/RNA helicase, sensitizing cancer cells to DNA-damaging agents, was associated with PBD resistance. Confirmatory studies demonstrated that siRNA knockdown of SLFN11 in multiple tumor cell lines conferred reduced sensitivity to SG3199 and PBD-conjugated ADCs. Treatment with EPZ011989, an EZH2 inhibitor, derepressed SLFN11 expression in 361-PBDr and other SLFN11-deficient tumor cells, and increased sensitivity to PBD and PBD-conjugated ADCs, indicating that the suppression of SLFN11 expression is associated with histone methylation as reported. Moreover, we demonstrated that combining an ataxia telangiectasia and Rad3-related protein (ATR) inhibitor, AZD6738, with SG3199 or PBD-based ADCs led to synergistic cytotoxicity in either resistant 361-PBDr cells or cells that SLFN11 was knocked down via siRNA. Collectively, these data provide insights into potential development of resistance to PBDs and PBD-conjugated ADCs, and more importantly, inform strategy development to overcome such resistance.

Statistical Models for the Analysis of Isobaric Tags Multiplexed Quantitative Proteomics.

Mass spectrometry is being used to identify protein biomarkers that can facilitate development of drug treatment. Mass spectrometry-based labeling proteomic experiments result in complex proteomic data that is hierarchical in nature often with small sample size studies. The generalized linear model (GLM) is the most popular approach in proteomics to compare protein abundances between groups. However, GLM does not address all the complexities of proteomics data such as repeated measures and variance heterogeneity. Linear models for microarray data (LIMMA) and mixed models are two approaches that can address some of these data complexities to provide better statistical estimates. We compared these three statistical models (GLM, LIMMA, and mixed models) under two different normalization approaches (quantile normalization and median sweeping) to demonstrate when each approach is the best for tagged proteins. We evaluated these methods using a spiked-in data set of known protein abundances, a systemic lupus erythematosus (SLE) data set, and simulated data from multiplexed labeling experiments that use tandem mass tags (TMT). Data are available via ProteomeXchange with identifier PXD005486. We found median sweeping to be a preferred approach of data normalization, and with this normalization approach there was overlap with findings across all methods with GLM being a subset of mixed models. The conclusion is that the mixed model had the best type I error with median sweeping, whereas LIMMA had the better overall statistical properties regardless of normalization approaches.

Diagnostic and Prognostic Utility of the Synaptic Marker Neurogranin in Alzheimer Disease.

IMPORTANCE: Synaptic loss is an early pathologic substrate of Alzheimer disease (AD). Neurogranin is a postsynaptic neuronal protein that has demonstrated utility as a cerebrospinal fluid (CSF) marker of synaptic loss in AD. OBJECTIVE: To investigate the diagnostic and prognostic utility of CSF neurogranin levels in a large, well-characterized cohort of individuals with symptomatic AD and cognitively normal controls. DESIGN, SETTING, AND PARTICIPANTS: A cross-sectional and longitudinal observational study of cognitive decline in patients with symptomatic AD and cognitively normal controls was performed. Participants were individuals with a clinical diagnosis of early symptomatic AD and cognitively normal controls who were enrolled in longitudinal studies of aging and dementia at the Charles F. and Joanne Knight Alzheimer Disease Research Center, Washington University School of Medicine, from January 21, 2000, through March 21, 2011. Data analysis was performed from November 1, 2013, to March 31, 2015. MAIN OUTCOMES AND MEASURES: Correlations between baseline CSF biomarker levels and future cognitive decline in patients with symptomatic AD and cognitively normal controls over time. RESULTS: A total of 302 individuals (mean [SE] age, 73.1 [0.4] years) were included in this study (95 patients [52 women and 43 men] with AD and 207 controls [125 women and 82 men]). The CSF neurogranin levels differentiated patients with early symptomatic AD from controls with comparable diagnostic utility (mean [SE] area under the receiver operating characteristic curve, 0.71 [0.03]; 95% CI, 0.64-0.77) to the other CSF biomarkers. The CSF neurogranin levels correlated with brain atrophy (normalized whole-brain volumes: adjusted r = -0.38, P = .02; hippocampal volumes: adjusted r = -0.36, P = .03; entorhinal volumes: adjusted r = -0.46, P = .006; and parahippocampal volumes: adjusted r = -0.47, P = .005, n = 38) in AD and with amyloid load (r = 0.39, P = .02, n = 36) in preclinical AD. The CSF neurogranin levels predicted future cognitive impairment (adjusted hazard ratio, 1.89; 95% CI, 1.29-2.78; P = .001 as a continuous measure, and adjusted hazard ratio, 2.78; 95% CI, 1.13-5.99; P = .02 as a categorical measure using the 85th percentile cutoff value) in controls and rates of cognitive decline (Clinical Dementia Rating sum of boxes score: ß estimate, 0.29; P = .001; global composite scores: ß estimate, -0.11; P = .001; episodic memory scores: ß estimate, -0.18; P < .001; and semantic memory scores: ß estimate, -0.06; P = .04, n = 57) in patients with symptomatic AD over time, similarly to the CSF proteins VILIP-1, tau, and p-tau181. CONCLUSIONS AND RELEVANCE: The CSF levels of the synaptic marker neurogranin offer diagnostic and prognostic utility for early symptomatic AD that is comparable to other CSF markers of AD. Importantly, CSF neurogranin complements the collective ability of these markers to predict future cognitive decline in cognitively normal individuals and, therefore, will be a useful addition to the current panel of AD biomarkers.

Susceptibility-weighted imaging helps to discriminate pediatric multiple sclerosis from acute disseminated encephalomyelitis.

BACKGROUND: Susceptibility-weighted imaging is a relatively new magnetic resonance imaging sequence that can identify lesions of multiple sclerosis in adults. This study was designed to determine if susceptibility-weighted imaging is a useful discriminator between children who develop multiple sclerosis and children with monophasic acute disseminated encephalomyelitis. METHODS: Eighteen children who presented with acute central nervous system demyelination and had a brain magnetic resonance imaging study including susceptibility-weighted imaging within 6 months of the first clinical attack were studied. Final diagnosis was based on international consensus definitions. Brain lesions detected on the fluid-attenuated inversion recovery sequence were assessed for abnormal signal on susceptibility-weighted imaging. The burden of susceptibility abnormalities was then analyzed for differences between the multiple sclerosis and acute disseminated encephalomyelitis groups. RESULTS: Eight patients had a final diagnosis of acute disseminated encephalomyelitis and ten had multiple sclerosis. Twenty-two percent of fluid-attenuated inversion recovery lesions were identified on susceptibility-weighted imaging. The percentage of fluid-attenuated inversion recovery lesions identified on susceptibility-weighted imaging differed between the multiple sclerosis and acute disseminated encephalomyelitis groups (P = 0.04). The median percentage (minimum-maximum) of lesions identified on susceptibility-weighted imaging in the multiple sclerosis group was 0.22 (0-0.68) and in the acute disseminated encephalomyelitis group was 0.0 (0-0.17). CONCLUSION: Susceptibility-weighted imaging may be a useful technique in differentiating acute disseminated encephalomyelitis from multiple sclerosis at initial presentation.

Comparison of brain structural variables, neuropsychological factors, and treatment outcome in early-onset versus late-onset late-life depression.

OBJECTIVE: To compare differences in gray matter volumes, white matter and subcortical gray matter hyperintensities, neuropsychological factors, and treatment outcome between early- and late-onset late-life depressed (LLD) subjects. METHODS: We conducted a prospective, nonrandomized, controlled trial at the outpatient clinics at Washington University and Duke University on 126 subjects, aged 60 years or older, who met Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition criteria for major depression, scored 20 or more on the Montgomery-Asberg Depression Rating Scale (MADRS), and received neuropsychological testing and magnetic resonance imaging. Subjects were excluded for cognitive impairment or severe medical disorders. After 12 weeks of sertraline treatment, subjects' MADRS scores over time and neuropsychological factors were studied. RESULTS: Left anterior cingulate thickness was significantly smaller in the late-onset depressed group than in the early-onset LLD subjects. The late-onset group also had more hyperintensities than the early-onset LLD subjects. No differences were found in neuropsychological factor scores or treatment outcome between early-onset and late-onset LLD subjects. CONCLUSION: Age at onset of depressive symptoms in LLD subjects are associated with differences in cortical thickness and white matter and subcortical gray matter hyperintensities, but age at onset did not affect neuropsychological factors or treatment outcome.

Quantitative analysis of PiB-PET with FreeSurfer ROIs.

In vivo quantification of ß-amyloid deposition using positron emission tomography is emerging as an important procedure for the early diagnosis of the Alzheimer's disease and is likely to play an important role in upcoming clinical trials of disease modifying agents. However, many groups use manually defined regions, which are non-standard across imaging centers. Analyses often are limited to a handful of regions because of the labor-intensive nature of manual region drawing. In this study, we developed an automatic image quantification protocol based on FreeSurfer, an automated whole brain segmentation tool, for quantitative analysis of amyloid images. Standard manual tracing and FreeSurfer-based analyses were performed in 77 participants including 67 cognitively normal individuals and 10 individuals with early Alzheimer's disease. The manual and FreeSurfer approaches yielded nearly identical estimates of amyloid burden (intraclass correlation = 0.98) as assessed by the mean cortical binding potential. An MRI test-retest study demonstrated excellent reliability of FreeSurfer based regional amyloid burden measurements. The FreeSurfer-based analysis also revealed that the majority of cerebral cortical regions accumulate amyloid in parallel, with slope of accumulation being the primary difference between regions.

Application of copulas to improve covariance estimation for partial least squares.

Dimension reduction techniques, such as partial least squares, are useful for computing summary measures and examining relationships in complex settings. Partial least squares requires an estimate of the covariance matrix as a first step in the analysis, making this estimate critical to the results. In addition, the covariance matrix also forms the basis for other techniques in multivariate analysis, such as principal component analysis and independent component analysis. This paper has been motivated by an example from an imaging study in Alzheimer's disease where there is complete separation between Alzheimer's and control subjects for one of the imaging modalities. This separation occurs in one block of variables and does not occur with the second block of variables resulting in inaccurate estimates of the covariance. We propose the use of a copula to obtain estimates of the covariance in this setting, where one set of variables comes from a mixture distribution. Simulation studies show that the proposed estimator is an improvement over the standard estimators of covariance. We illustrate the methods from the motivating example from a study in the area of Alzheimer's disease.

Bootstrapping GEE models for fMRI regional connectivity.

An Alzheimer's fMRI study has motivated us to evaluate inter-regional correlations during rest between groups. We apply generalized estimating equation (GEE) models to test for differences in regional correlations across groups. Both the GEE marginal model and GEE transition model are evaluated and compared to the standard pooling Fisher-z approach using simulation studies. Standard errors of all methods are estimated both theoretically (model-based) and empirically (bootstrap). Of all the methods, we find that the transition models have the best statistical properties. Overall, the model-based standard errors and bootstrap standard errors perform about the same. We also demonstrate the methods with a functional connectivity study in a healthy cognitively normal population of ApoE4+ participants and ApoE4- participants who are recruited from the Adult Children's Study conducted at the Washington University Knight Alzheimer's Disease Research Center.

Missing Data Methods for Partial Correlations.

In the dementia area it is often of interest to study relationships among regional brain measures; however, it is often necessary to adjust for covariates. Partial correlations are frequently used to correlate two variables while adjusting for other variables. Complete case analysis is typically the analysis of choice for partial correlations with missing data. However, complete case analysis will lead to biased and inefficient results when the data are missing at random. We have extended the partial correlation coefficient in the presence of missing data using the expectation-maximization (EM) algorithm, and compared it with a multiple imputation method and complete case analysis using simulation studies. The EM approach performed the best of all methods with multiple imputation performing almost as well. These methods were illustrated with regional imaging data from an Alzheimer's disease study.

Treatment course with antidepressant therapy in late-life depression.

OBJECTIVE: In order to assess the effect of gray matter volumes and cortical thickness on antidepressant treatment response in late-life depression, the authors examined the relationship between brain regions identified a priori and Montgomery-Åsberg Depression Rating Scale (MADRS) scores over the course of an antidepressant treatment trial. METHOD: In a nonrandomized prospective trial, 168 patients who were at least 60 years of age and met DSM-IV criteria for major depression underwent MRI and were enrolled in a 12-week treatment study. Exclusion criteria included cognitive impairment or severe medical disorders. The volumes or cortical thicknesses of regions of interest that differed between the depressed group and a comparison group (N=50) were determined. These regions of interest were used in analyses of the depressed group to predict antidepressant treatment outcome. Mixed-model analyses adjusting for age, education, age at depression onset, race, baseline MADRS score, scanner, and interaction with time examined predictors of MADRS scores over time. RESULTS: Smaller hippocampal volumes predicted a slower response to treatment. With the inclusion of white matter hyper-intensity severity and neuropsychological factor scores, the best model included hippocampal volume and cognitive processing speed to predict rate of response over time. A secondary analysis showed that hippocampal volume and frontal pole thickness differed between patients who achieved remission and those who did not. CONCLUSIONS: These data expand our understanding of the prediction of treatment course in late-life depression. The authors propose that the primary variables of hippocampal volume and cognitive processing speed, subsuming other contributing variables (episodic memory, executive function, language processing) predict antidepressant response.

Elevated hemostasis markers after pneumonia increases one-year risk of all-cause and cardiovascular deaths.

BACKGROUND: Acceleration of chronic diseases, particularly cardiovascular disease, may increase long-term mortality after community-acquired pneumonia (CAP), but underlying mechanisms are unknown. Persistence of the prothrombotic state that occurs during an acute infection may increase risk of subsequent atherothrombosis in patients with pre-existing cardiovascular disease and increase subsequent risk of death. We hypothesized that circulating hemostasis markers activated during CAP persist at hospital discharge, when patients appear to have recovered clinically, and are associated with higher mortality, particularly due to cardiovascular causes. METHODS: In a cohort of survivors of CAP hospitalization from 28 US sites, we measured D-Dimer, thrombin-antithrombin complexes [TAT], Factor IX, antithrombin, and plasminogen activator inhibitor-1 at hospital discharge, and determined 1-year all-cause and cardiovascular mortality. RESULTS: Of 893 subjects, most did not have severe pneumonia (70.6% never developed severe sepsis) and only 13.4% required intensive care unit admission. At discharge, 88.4% of subjects had normal vital signs and appeared to have clinically recovered. D-dimer and TAT levels were elevated at discharge in 78.8% and 30.1% of all subjects, and in 51.3% and 25.3% of those without severe sepsis. Higher D-dimer and TAT levels were associated with higher risk of all-cause mortality (range of hazard ratios were 1.66-1.17, p = 0.0001 and 1.46-1.04, p = 0.001 after adjusting for demographics and comorbid illnesses) and cardiovascular mortality (p = 0.009 and 0.003 in competing risk analyses). CONCLUSIONS: Elevations of TAT and D-dimer levels are common at hospital discharge in patients who appeared to have recovered clinically from pneumonia and are associated with higher risk of subsequent deaths, particularly due to cardiovascular disease.

Serotonin signaling is associated with lower amyloid-ß levels and plaques in transgenic mice and humans.

Aggregation of amyloid-ß (Aß) as toxic oligomers and amyloid plaques within the brain appears to be the pathogenic event that initiates Alzheimer's disease (AD) lesions. One therapeutic strategy has been to reduce Aß levels to limit its accumulation. Activation of certain neurotransmitter receptors can regulate Aß metabolism. We assessed the ability of serotonin signaling to alter brain Aß levels and plaques in a mouse model of AD and in humans. In mice, brain interstitial fluid (ISF) Aß levels were decreased by 25% following administration of several selective serotonin reuptake inhibitor (SSRI) antidepressant drugs. Similarly, direct infusion of serotonin into the hippocampus reduced ISF Aß levels. Serotonin-dependent reductions in Aß were reversed if mice were pretreated with inhibitors of the extracellular regulated kinase (ERK) signaling cascade. Chronic treatment with an SSRI, citalopram, caused a 50% reduction in brain plaque load in mice. To test whether serotonin signaling could impact Aß plaques in humans, we retrospectively compared brain amyloid load in cognitively normal elderly participants who were exposed to antidepressant drugs within the past 5 y to participants who were not. Antidepressant-treated participants had significantly less amyloid load as quantified by positron emission tomography (PET) imaging with Pittsburgh Compound B (PIB). Cumulative time of antidepressant use within the 5-y period preceding the scan correlated with less plaque load. These data suggest that serotonin signaling was associated with less Aß accumulation in cognitively normal individuals.

Visinin-like protein-1: diagnostic and prognostic biomarker in Alzheimer disease.

OBJECTIVE: There is a growing need to identify cerebrospinal fluid (CSF) markers that can detect Alzheimer's disease (AD) pathology in cognitively normal individuals because it is in this population that disease-modifying therapies may have the greatest chance of success. While AD pathology is estimated to begin ~10-15 years prior to the onset of cognitive decline, substantial neuronal loss is present by the time the earliest signs of cognitive impairment appear. Visinin-like protein-1 (VILIP-1) has demonstrated potential utility as a marker of neuronal injury. Here we investigate CSF VILIP-1 and VILIP-1/amyloid-ß42 (Aß42) ratio as diagnostic and prognostic markers in early AD. METHODS: We assessed CSF levels of VILIP-1, tau, phosphorylated-tau181 (p-tau181), and Aß42 in cognitively normal controls (CNC) (n = 211), individuals with early symptomatic AD (n = 98), and individuals with other dementias (n = 19). Structural magnetic resonance imaging (n = 192) and amyloid imaging with Pittsburgh Compound-B (n = 156) were obtained in subsets of this cohort. Among the CNC cohort, 164 individuals had follow-up annual cognitive assessments for 2-3 years. RESULTS: CSF VILIP-1 levels differentiated individuals with AD from CNC and individuals with other dementias. CSF VILIP-1 levels correlated with CSF tau, p-tau181, and brain volumes in AD. VILIP-1 and VILIP-1/Aß42 predicted future cognitive impairment in CNC over the follow-up period. Importantly, CSF VILIP-1/Aß42 predicted future cognitive impairment at least as well as tau/Aß42 and p-tau181/Aß42. INTERPRETATION: These findings suggest that CSF VILIP-1 and VILIP-1/Aß42 offer diagnostic utility for early AD, and can predict future cognitive impairment in cognitively normal individuals similarly to tau and tau/Aß42, respectively.

Preexisting statin use is associated with greater reperfusion in hyperacute ischemic stroke.

BACKGROUND AND PURPOSE: Statin pretreatment has been associated with improved outcomes in patients with ischemic stroke. Although several mechanisms have been examined in animal models, few have been examined in patients. We hypothesized that patients using statins before stroke onset may have greater reperfusion than patients not using statins. METHODS: Acute ischemic stroke patients underwent 2 MR scans: within 4.5 (tp1) and at 6 hours (tp2) after stroke onset. Regions of reperfusion were defined by prolonged mean transit time (MTT) at tp1, which normalized at tp2. Four MTT thresholds were assessed to ensure that results were not spuriously based on an arbitrary threshold. Baseline characteristics, relative reperfusion, and change in NIHSS between tp1 and 1-month follow-up (ΔNIHSS) were compared between patients who were using statins at stroke onset and those who were not. RESULTS: Thirty-one stroke patients were prospectively enrolled; 12 were using statins and 19 were not. Baseline characteristics did not differ between the 2 groups except the statin group had greater coronary artery disease (P=0.03). Patients using statins showed significantly greater reperfusion compared to untreated patients across all MTT thresholds. For MTT of 4 seconds, median relative reperfusion was 50% (interquartile range, 30%-56%) in the preexisting statin group versus 13% (interquartile range, 5%-36%) in the untreated group (P=0.014). The statin group had greater ΔNIHSS (8.8±4.0 points) compared to the untreated group (4.4±5.7 points; P=0.028). CONCLUSIONS: Statin use before ischemic stroke onset was associated with greater early reperfusion and NIHSS improvement. Further studies in larger populations are required to confirm our preliminary findings.

A generalized estimating equations approach for resting-state functional MRI group analysis.

An Alzheimer's fMRI study has motivated us to evaluate inter-regional correlations between groups. The overall objective is to assess inter-regional correlations at a resting-state with no stimulus or task. We propose using a generalized estimating equation (GEE) transition model and a GEE marginal model to model the within-subject correlation for each region. Residuals calculated from the GEE models are used to correlate brain regions and assess between group differences. The standard pooling approach of group averages of the Fisher-z transformation assuming temporal independence is a typical approach used to compare group correlations. The GEE approaches and standard Fisher-z pooling approach are demonstrated with an Alzheimer's disease (AD) connectivity study in a population of AD subjects and healthy control subjects. We also compare these methods using simulation studies and show that the transition model may have better statistical properties.

APOE4 allele disrupts resting state fMRI connectivity in the absence of amyloid plaques or decreased CSF Aß42.

Identifying high-risk populations is an important component of disease prevention strategies. One approach for identifying at-risk populations for Alzheimer's disease (AD) is examining neuroimaging parameters that differ between patients, including functional connections known to be disrupted within the default-mode network. We have previously shown these same disruptions in cognitively normal elderly who have amyloid-ß (Aß) plaques [detected using Pittsburgh Compound B (PIB) PET imaging], suggesting neuronal toxicity of plaques. Here we sought to determine if pathological effects of apolipoprotein E ε4 (APOE4) genotype could be seen independent of Aß plaque toxicity by examining resting state fMRI functional connectivity (fcMRI) in participants without preclinical fibrillar amyloid deposition (PIB-). Cognitively normal participants enrolled in longitudinal studies (n = 100, mean age = 62) who were PIB- were categorized into those with and without an APOE4 allele and studied using fcMRI. APOE4 allele carriers (E4+) differed significantly from E4- in functional connectivity of the precuneus to several regions previously defined as having abnormal connectivity in a group of AD participants. These effects were observed before any manifestations of cognitive changes and in the absence of brain fibrillar Aß plaque deposition, suggesting that early manifestations of a genetic effect can be detected using fcMRI and that these changes may antedate the pathological effects of fibrillar amyloid plaque toxicity.

YKL-40: a novel prognostic fluid biomarker for preclinical Alzheimer's disease.

BACKGROUND: Disease-modifying therapies for Alzheimer's disease (AD) would be most effective during the preclinical stage (pathology present, cognition intact) before significant neuronal loss occurs. Therefore, biomarkers that detect AD pathology in its early stages and predict dementia onset and progression will be invaluable for patient care and efficient clinical trial design. METHODS: AD-associated changes in cerebrospinal fluid (CSF) were measured using two-dimensional difference gel electrophoresis and liquid chromatography tandem mass spectrometry. Subsequently, CSF YKL-40 was measured by enzyme-linked immunosorbent assay in the discovery cohort (n = 47), validation cohort (n = 292) with paired plasma samples (n = 237), frontotemporal lobar degeneration (n=9) [corrected], and progressive supranuclear palsy (PSP; n = 6). Immunohistochemistry was performed to identify source(s) of YKL-40 in human AD brain. RESULTS: Discovery and validation cohorts, showed higher mean CSF YKL-40 in very mild and mild AD-type dementia (Clinical Dementia Rating [CDR] 0.5 and 1) versus control subjects (CDR 0) and PSP subjects. Importantly, CSF YKL-40/Aß42 ratio predicted risk of developing cognitive impairment (CDR 0 to CDR > 0 conversion), as well as the best CSF biomarkers identified to date, tau/Aß42 and p-tau 181/Aß42. Mean plasma YKL-40 was higher in CDR 0.5 and 1 versus CDR 0, and correlated with CSF levels. YKL-40 immunoreactivity labeled astrocytes near a subset of amyloid plaques, implicating YKL-40 in the neuroinflammatory response to Aß deposition. CONCLUSIONS: These data demonstrate that YKL-40, a putative indicator of neuroinflammation, is elevated in AD and, together with Aß42, has potential prognostic utility as a biomarker for preclinical AD.

Support for the vascular depression hypothesis in late-life depression: results of a 2-site, prospective, antidepressant treatment trial.

CONTEXT: Research on vascular depression has used 2 approaches to subtype late-life depression, based on executive dysfunction or white matter hyperintensity severity. OBJECTIVE: To evaluate the relationship of neuropsychological performance and white matter hyperintensity with clinical response in late-life depression. DESIGN: Two-site, prospective, nonrandomized controlled trial. SETTING: Outpatient clinics at Washington University and Duke University. PARTICIPANTS: A total of 217 subjects aged 60 years or older met DSM-IV criteria for major depression, scored 20 or more on the Montgomery-Asberg Depression Rating Scale (MADRS), and received vascular risk factor scores, neuropsychological testing, and magnetic resonance imaging; they were excluded for cognitive impairment or severe medical disorders. Fazekas rating was conducted to grade white matter hyperintensity lesions. Intervention Twelve weeks of sertraline treatment, titrated by clinical response. Main Outcome Measure Participants' MADRS scores over time. RESULTS: Baseline neuropsychological factor scores correlated negatively with baseline Fazekas scores. A mixed model examined effects of predictor variables on MADRS scores over time. Baseline episodic memory (P = .002), language (P = .007), working memory (P = .01), processing speed (P < .001), executive function factor scores (P = .002), and categorical Fazekas ratings (P = .05) predicted MADRS scores, controlling for age, education, age of onset, and race. Controlling for baseline MADRS scores, these factors remained significant predictors of decrease in MADRS scores, except for working memory and Fazekas ratings. Thirty-three percent of subjects achieved remission (MADRS < or =7). Remitters differed from nonremitters in baseline cognitive processing speed, executive function, language, episodic memory, and vascular risk factor scores. CONCLUSIONS: Comprehensive neuropsychological function and white matter hyperintensity severity predicted MADRS scores prospectively over a 12-week treatment course with selective serotonin reuptake inhibitors in late-life depression. Baseline neuropsychological function differentiated remitters from nonremitters and predicted time to remission in a proportional hazards model. Predictor variables correlated highly with vascular risk factor severity. These data support the vascular depression hypothesis and highlight the importance of linking subtypes based on neuropsychological function and white matter integrity. TRIAL REGISTRATION: clinicaltrials.gov Identifier: NCT00045773.