Ketamine treatment for depression: opportunities for clinical innovation and ethical foresight

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this record.We present a review and analysis of the ethical considerations in off-label ketamine use for severe, treatment-resistant depression. The analysis of ethical considerations is contextualised in an overview of the evidence for ketamine use in depression, and a review of the drug's safety profile. We find that, based on current evidence, ketamine use for severe, treatment-resistant depression does not violate ethical principles; however, clinicians and professional bodies must take steps to ensure that guidelines for good practice are enacted, that all experimental and trial data are made available through national registries, and that the risk potential of ketamine treatment continues to be monitored and modelled. We conclude with a set of key recommendations for oversight bodies that would support safe, effective, and ethical use of ketamine in depression

GliomaPredict: a clinically useful tool for assigning glioma patients to specific molecular subtypes

<p>Abstract</p> <p>Background</p> <p>Advances in generating genome-wide gene expression data have accelerated the development of molecular-based tumor classification systems. Tools that allow the translation of such molecular classification schemas from research into clinical applications are still missing in the emerging era of personalized medicine.</p> <p>Results</p> <p>We developed GliomaPredict as a computational tool that allows the fast and reliable classification of glioma patients into one of six previously published stratified subtypes based on sets of extensively validated classifiers derived from hundreds of glioma transcriptomic profiles. Our tool utilizes a principle component analysis (PCA)-based approach to generate a visual representation of the analyses, quantifies the confidence of the underlying subtype assessment and presents results as a printable PDF file. GliomaPredict tool is implemented as a plugin application for the widely-used GenePattern framework.</p> <p>Conclusions</p> <p>GliomaPredict provides a user-friendly, clinically applicable novel platform for instantly assigning gene expression-based subtype in patients with gliomas thereby aiding in clinical trial design and therapeutic decision-making. Implemented as a user-friendly diagnostic tool, we expect that in time GliomaPredict, and tools like it, will become routinely used in translational/clinical research and in the clinical care of patients with gliomas.</p

Gene expression analysis of glioblastomas identifies the major molecular basis for the prognostic benefit of younger age

<p>Abstract</p> <p>Background</p> <p>Glioblastomas are the most common primary brain tumour in adults. While the prognosis for patients is poor, gene expression profiling has detected signatures that can sub-classify GBMs relative to histopathology and clinical variables. One category of GBM defined by a gene expression signature is termed ProNeural (PN), and has substantially longer patient survival relative to other gene expression-based subtypes of GBMs. Age of onset is a major predictor of the length of patient survival where younger patients survive longer than older patients. The reason for this survival advantage has not been clear.</p> <p>Methods</p> <p>We collected 267 GBM CEL files and normalized them relative to other microarrays of the same Affymetrix platform. 377 probesets on U133A and U133 Plus 2.0 arrays were used in a gene voting strategy with 177 probesets of matching genes on older U95Av2 arrays. Kaplan-Meier curves and Cox proportional hazard analyses were applied in distinguishing survival differences between expression subtypes and age.</p> <p>Results</p> <p>This meta-analysis of published data in addition to new data confirms the existence of four distinct GBM expression-signatures. Further, patients with PN subtype GBMs had longer survival, as expected. However, the age of the patient at diagnosis is not predictive of survival time when controlled for the PN subtype.</p> <p>Conclusion</p> <p>The survival benefit of younger age is nullified when patients are stratified by gene expression group. Thus, the main cause of the age effect in GBMs is the more frequent occurrence of PN GBMs in younger patients relative to older patients.</p

Genomic aberrations associated with outcome in anaplastic oligodendroglial tumors treated within the EORTC phase III trial 26951

Despite similar morphological aspects, anaplastic oligodendroglial tumors (AOTs) form a heterogeneous clinical subgroup of gliomas. The chromosome arms 1p/19q codeletion has been shown to be a relevant biomarker in AOTs and to be perfectly exclusive from EGFR amplification in gliomas. To identify new genomic regions associated with prognosis, 60 AOTs from the EORTC trial 26951 were analyzed retrospectively using BAC-array-based comparative genomic hybridization. The data were processed using a binary tree method. Thirty-three BACs with prognostic value were identified distinguishing four genomic subgroups of AOTs with different prognosis (p < 0.0001). Type I tumors (25%) were characterized by: (1) an EGFR amplification, (2) a poor prognosis, (3) a higher rate of necrosis, and (4) an older age of patients. Type II tumors (21.7%) had: (1) loss of prognostic BACs located on 1p tightly associated with 19q deletion, (2) a longer survival, (3) an oligodendroglioma phenotype, and (4) a frontal location in brain. Type III AOTs (11.7%) exhibited: (1) a deletion of prognostic BACs located on 21q, and (2) a short survival. Finally, type IV tumors (41.7%) had different genomic patterns and prognosis than type I, II and III AOTs. Multivariate analysis showed that genomic type provides additional prognostic data to clinical, imaging and pathological features. Similar results were obtained in the cohort of 45 centrally reviewed–validated cases of AOTs. Whole genome analysis appears useful to screen the numerous genomic abnormalities observed in AOTs and to propose new biomarkers particularly in the non-1p/19q codeleted AOTs

Glioblastoma Subclasses Can Be Defined by Activity among Signal Transduction Pathways and Associated Genomic Alterations

Glioblastoma multiforme (GBM) is an umbrella designation that includes a heterogeneous group of primary brain tumors. Several classification strategies of GBM have been reported, some by clinical course and others by resemblance to cell types either in the adult or during development. From a practical and therapeutic standpoint, classifying GBMs by signal transduction pathway activation and by mutation in pathway member genes may be particularly valuable for the development of targeted therapies.We performed targeted proteomic analysis of 27 surgical glioma samples to identify patterns of coordinate activation among glioma-relevant signal transduction pathways, then compared these results with integrated analysis of genomic and expression data of 243 GBM samples from The Cancer Genome Atlas (TCGA). In the pattern of signaling, three subclasses of GBM emerge which appear to be associated with predominance of EGFR activation, PDGFR activation, or loss of the RAS regulator NF1. The EGFR signaling class has prominent Notch pathway activation measured by elevated expression of Notch ligands, cleaved Notch receptor, and downstream target Hes1. The PDGF class showed high levels of PDGFB ligand and phosphorylation of PDGFRbeta and NFKB. NF1-loss was associated with lower overall MAPK and PI3K activation and relative overexpression of the mesenchymal marker YKL40. These three signaling classes appear to correspond with distinct transcriptomal subclasses of primary GBM samples from TCGA for which copy number aberration and mutation of EGFR, PDGFRA, and NF1 are signature events.Proteomic analysis of GBM samples revealed three patterns of expression and activation of proteins in glioma-relevant signaling pathways. These three classes are comprised of roughly equal numbers showing either EGFR activation associated with amplification and mutation of the receptor, PDGF-pathway activation that is primarily ligand-driven, or loss of NF1 expression. The associated signaling activities correlating with these sentinel alterations provide insight into glioma biology and therapeutic strategies

Gene expression profiling of gliomas: merging genomic and histopathological classification for personalised therapy

The development of DNA microarray technologies over the past decade has revolutionised translational cancer research. These technologies were originally hailed as more objective, comprehensive replacements for traditional histopathological cancer classification systems, based on microscopic morphology. Although DNA microarray-based gene expression profiling (GEP) remains unlikely in the near term to completely replace morphological classification of primary brain tumours, specifically the diffuse gliomas, GEP has confirmed that significant molecular heterogeneity exists within the various morphologically defined gliomas, particularly glioblastoma (GBM). Herein, we provide a 10-year progress report on human glioma GEP, with focus on development of clinical diagnostic tests to identify molecular subtypes, uniquely responsive to adjuvant therapies. Such progress may lead to a more precise classification system that accurately reflects the cellular, genetic, and molecular basis of gliomagenesis, a prerequisite for identifying subsets uniquely responsive to specific adjuvant therapies, and ultimately in achieving individualised clinical care of glioma patients

Pharmacometabolomics of Response to Sertraline and to Placebo in Major Depressive Disorder - Possible Role for Methoxyindole Pathway

10.1371/journal.pone.0068283PLoS ONE87-POLN

The Prolyl Isomerase Pin1 Modulates Development of CD8+ cDC in Mice

We have identified a novel role for Pin1 as a modulator of CD8+ cDC development. Consistent with reduced numbers of CD8+ cDC in Pin1-null mice, we find that the absence of Pin1 impairs CD8+ T cell proliferation in response to infection with Listeria monocytogenes. These data suggest that, via regulation of CD8+ cDC production, Pin1 may serve as an important modulator of adaptive immunity

Cell cycle and aging, morphogenesis, and response to stimuli genes are individualized biomarkers of glioblastoma progression and survival

<p>Abstract</p> <p>Background</p> <p>Glioblastoma is a complex multifactorial disorder that has swift and devastating consequences. Few genes have been consistently identified as prognostic biomarkers of glioblastoma survival. The goal of this study was to identify general and clinical-dependent biomarker genes and biological processes of three complementary events: lifetime, overall and progression-free glioblastoma survival.</p> <p>Methods</p> <p>A novel analytical strategy was developed to identify general associations between the biomarkers and glioblastoma, and associations that depend on cohort groups, such as race, gender, and therapy. Gene network inference, cross-validation and functional analyses further supported the identified biomarkers.</p> <p>Results</p> <p>A total of 61, 47 and 60 gene expression profiles were significantly associated with lifetime, overall, and progression-free survival, respectively. The vast majority of these genes have been previously reported to be associated with glioblastoma (35, 24, and 35 genes, respectively) or with other cancers (10, 19, and 15 genes, respectively) and the rest (16, 4, and 10 genes, respectively) are novel associations. <it>Pik3r1</it>, <it>E2f3, Akr1c3</it>, <it>Csf1</it>, <it>Jag2</it>, <it>Plcg1</it>, <it>Rpl37a</it>, <it>Sod2</it>, <it>Topors</it>, <it>Hras</it>, <it>Mdm2, Camk2g</it>, <it>Fstl1</it>, <it>Il13ra1</it>, <it>Mtap </it>and <it>Tp53 </it>were associated with multiple survival events.</p> <p>Most genes (from 90 to 96%) were associated with survival in a general or cohort-independent manner and thus the same trend is observed across all clinical levels studied. The most extreme associations between profiles and survival were observed for <it>Syne1</it>, <it>Pdcd4</it>, <it>Ighg1</it>, <it>Tgfa</it>, <it>Pla2g7</it>, and <it>Paics</it>. Several genes were found to have a cohort-dependent association with survival and these associations are the basis for individualized prognostic and gene-based therapies. <it>C2</it>, <it>Egfr</it>, <it>Prkcb</it>, <it>Igf2bp3</it>, and <it>Gdf10 </it>had gender-dependent associations; <it>Sox10</it>, <it>Rps20</it>, <it>Rab31</it>, and <it>Vav3 </it>had race-dependent associations; <it>Chi3l1</it>, <it>Prkcb</it>, <it>Polr2d</it>, and <it>Apool </it>had therapy-dependent associations. Biological processes associated glioblastoma survival included morphogenesis, cell cycle, aging, response to stimuli, and programmed cell death.</p> <p>Conclusions</p> <p>Known biomarkers of glioblastoma survival were confirmed, and new general and clinical-dependent gene profiles were uncovered. The comparison of biomarkers across glioblastoma phases and functional analyses offered insights into the role of genes. These findings support the development of more accurate and personalized prognostic tools and gene-based therapies that improve the survival and quality of life of individuals afflicted by glioblastoma multiforme.</p