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127. Pathway-Based Molecular Subtyping of Schizophrenia Accounting for Population Stratification and Sex

ABSTRACT: Abstract Background: The development of successful DNA-driven, molecular subtyping methods for SZ is highly desirable because identifying subtypes that are predictive of symptoms, outcome, or treatment response could lead to better understanding of the etiology of SZ. Here, a molecular subtyping method was developed and tested based on data from the largest collection of SZ case–control samples available. The method developed and tested here accounts for sex and ancestry. Methods: Model-based recursive partitioning was applied to (1) 346 human, biological, pathway-based polygenic risk scores based on extant GWAS enrichment data from the PGC, (2) C4 gene expression data, (3) corresponding major histocompatibility complex single-nucleotide polymorphisms (MHC SNPs) rs210133 and rs123360412, (4) 10 empirically derived ancestry principle components, and (5) binary sex data. Models were tested using the Wellcome Trust case–control consortium (WTCCC) SZ data. Results: Resulting molecular subtypes explain significant heterogeneity in schizophrenia symptoms and age at onset. In this application, sex and factors pertaining to general DNA/RNA function and repair were as relevant as factors related to axon development. C4 expression, MHC SNPs, and ancestry components were dropped in the best step-wise fitting model, in addition to all two-way interactions. Molecular subtypes accounted for significant variation in AAO (P = 5.8*10–5) and in both negative and positive symptoms (P = .027 and 5.5*10–4, respectively). Conclusion: Based on a partitioning method using biological pathway data, we were able to derive molecular subtypes that are phenotypically informative. It was unexpected that (1) axonal pathways are overall less important than their cell RMR homologues and (2) C4 expression is not involved in the resulting subtypes. Application of partitioning methods to empirically derived, pathway-based polygenic risk scores, accounting for critical covariates, can lead to important insights into complex, heterogeneous disorders.

SUBMITTER: Docherty A

PROVIDER: S-EPMC5475635 | biostudies-literature | 2017 Mar

REPOSITORIES: biostudies-literature

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Project description:Background:The purpose of this study was to analyze the potential of radiomics for disease stratification beyond key molecular, clinical, and standard imaging features in patients with glioblastoma. Methods:Quantitative imaging features (n = 1043) were extracted from the multiparametric MRI of 181 patients with newly diagnosed glioblastoma prior to standard-of-care treatment (allocated to a discovery and a validation set, 2:1 ratio). A subset of 386/1043 features were identified as reproducible (in an independent MRI test-retest cohort) and selected for analysis. A penalized Cox model with 10-fold cross-validation (Coxnet) was fitted on the discovery set to construct a radiomic signature for predicting progression-free and overall survival (PFS and OS). The incremental value of a radiomic signature beyond molecular (O6-methylguanine-DNA methyltransferase [MGMT] promoter methylation, DNA methylation subgroups), clinical (patient's age, KPS, extent of resection, adjuvant treatment), and standard imaging parameters (tumor volumes) for stratifying PFS and OS was assessed with multivariate Cox models (performance quantified with prediction error curves). Results:The radiomic signature (constructed from 8/386 features identified through Coxnet) increased the prediction accuracy for PFS and OS (in both discovery and validation sets) beyond the assessed molecular, clinical, and standard imaging parameters (P ? 0.01). Prediction errors decreased by 36% for PFS and 37% for OS when adding the radiomic signature (compared with 29% and 27%, respectively, with molecular + clinical features alone). The radiomic signature was-along with MGMT status-the only parameter with independent significance on multivariate analysis (P ? 0.01). Conclusions:Our study stresses the role of integrating radiomics into a multilayer decision framework with key molecular and clinical features to improve disease stratification and to potentially advance personalized treatment of patients with glioblastoma.

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127. Pathway-Based Molecular Subtyping of Schizophrenia Accounting for Population Stratification and Sex

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