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Towards mechanism classifiers: expression-anchored Gene Ontology signature predicts clinical outcome in lung adenocarcinoma patients.

ABSTRACT: We aim to provide clinically applicable, reproducible, mechanistic interpretations of gene expression changes that lack in gene overlap among predictive gene-signatures. Using a method we recently developed, Functional Analysis of Individual Microarray Expression (FAIME), we provide evidence that Gene Ontology-anchored signatures (GO-signatures) show reliable prognosis in lung cancer. In order to demonstrate the biological congruence and reproducibility of FAIME-derived mechanism classifiers, we chose a disease where gene expression classifiers signatures alone had failed to significantly stratify a larger collection of samples and that exhibited poor or no genetic overlap. For each patient in the two lung adenocarcinoma studies, personalized FAIME-profiles of GO biological processes are generated from genome-wide expression profiles. For both training studies, GO-signatures significantly associated to patient mortality were identified (Prediction Analysis for Microarrays; three-fold cross-validation). These two GO-signatures could effectively stratify patients from an independent validation cohort into sub-groups that show significant differences in disease-free survival (log-rank test P=0.019; P=0.001). Importantly, significant mechanism overlaps assessed by information-theory similarity were detected between the two GO-signatures (Fischer Exact Test p=0.001). Hence, together with machine learning technologies, FAIME could be utilized to develop an ontology-driven and expression-anchored prognostic signature that is personalized for an individual patient.

SUBMITTER: Yang X

PROVIDER: S-EPMC3540430 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

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Towards mechanism classifiers: expression-anchored Gene Ontology signature predicts clinical outcome in lung adenocarcinoma patients.

Yang Xinan X Li Haiquan H Regan Kelly K Li Jianrong J Huang Yong Y Lussier Yves A YA

AMIA ... Annual Symposium proceedings. AMIA Symposium 20121103

We aim to provide clinically applicable, reproducible, mechanistic interpretations of gene expression changes that lack in gene overlap among predictive gene-signatures. Using a method we recently developed, Functional Analysis of Individual Microarray Expression (FAIME), we provide evidence that Gene Ontology-anchored signatures (GO-signatures) show reliable prognosis in lung cancer. In order to demonstrate the biological congruence and reproducibility of FAIME-derived mechanism classifiers, we ...[more]

PMID: 23304380

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Project description:BackgroundCurrent outcome predictors based on "molecular profiling" rely on gene lists selected without consideration for their molecular mechanisms. This study was designed to demonstrate that we could learn about genes related to a specific mechanism and further use this knowledge to predict outcome in patients - a paradigm shift towards accurate "mechanism-anchored profiling". We propose a novel algorithm, PGnet, which predicts a tripartite mechanism-anchored network associated to epigenetic regulation consisting of phenotypes, genes and mechanisms. Genes termed as GEMs in this network meet all of the following criteria: (i) they are co-expressed with genes known to be involved in the biological mechanism of interest, (ii) they are also differentially expressed between distinct phenotypes relevant to the study, and (iii) as a biomodule, genes correlate with both the mechanism and the phenotype.ResultsThis proof-of-concept study, which focuses on epigenetic mechanisms, was conducted in a well-studied set of 132 acute lymphoblastic leukemia (ALL) microarrays annotated with nine distinct phenotypes and three measures of response to therapy. We used established parametric and non parametric statistics to derive the PGnet tripartite network that consisted of 10 phenotypes and 33 significant clusters of GEMs comprising 535 distinct genes. The significance of PGnet was estimated from empirical p-values, and a robust subnetwork derived from ALL outcome data was produced by repeated random sampling. The evaluation of derived robust network to predict outcome (relapse of ALL) was significant (p = 3%), using one hundred three-fold cross-validations and the shrunken centroids classifier.ConclusionTo our knowledge, this is the first method predicting co-expression networks of genes associated with epigenetic mechanisms and to demonstrate its inherent capability to predict therapeutic outcome. This PGnet approach can be applied to any regulatory mechanisms including transcriptional or microRNA regulation in order to derive predictive molecular profiles that are mechanistically anchored. The implementation of PGnet in R is freely available at http://Lussierlab.org/publication/PGnet.

Project description:We investigated the local immune status and its prognostic value in lung adenocarcinoma. In total, 513 lung adenocarcinoma samples from TCGA and ImmPort databases were collected and analyzed. The R package coxph was employed to mine immune-related genes that were significant prognostic indicators using both univariate and multivariate analyses. The R software package glmnet was then used for Lasso Cox regression analysis, and a prognosis prediction model was constructed for lung adenocarcinoma; clusterProfiler was selected for functional gene annotations and KEGG enrichment analysis. Finally, correlations between the RiskScore and clinical features or signaling pathways were established. Sixty-four immune-related genes remarkably correlated with patient prognosis and were further applied. Samples were hierarchically clustered into two subgroups. Accordingly, the LASSO regression algorithm was employed to screen the 14 most representative immune-related genes (PSMD11, PPIA, MIF, BMP5, DKK1, PDGFB, ANGPTL4, IL1R2, THRB, LTBR, TNFRSF1, TNFRSF17, IL20RB, and MC1R) with respect to patient prognosis. Then, the prognosis prediction model for lung adenocarcinoma patients (namely, the RiskScore equation) was constructed, and the training set samples were incorporated to evaluate the efficiency of this model to predict and classify patient prognosis. Subsequently, based on functional annotations and KEGG pathway analysis, the 14 immune-related genes were mainly enriched in pathways closely associated with lung adenocarcinoma and its immune microenvironment, such as cytokine-cytokine receptor interaction and human T-cell leukemia virus 1 infection. Furthermore, correlations between the RiskScore and clinical features of the training set samples and signaling pathways (such as p53, cell cycle, and DNA repair) were also demonstrated. Finally, the test set sample data were employed for independent testing and verifying the model. We established a prognostic prediction RiskScore model based on the expression profiles of 14 immune-related genes, which shows high prediction accuracy and stability in identifying immune features. This could provide clinical guidance for the diagnosis and prognosis of different immunophenotypes, and suggest multiple targets for precise advanced lung adenocarcinoma therapy based on subtype-specific immune molecules.

Dataset Information

Towards mechanism classifiers: expression-anchored Gene Ontology signature predicts clinical outcome in lung adenocarcinoma patients.

Publications

Towards mechanism classifiers: expression-anchored Gene Ontology signature predicts clinical outcome in lung adenocarcinoma patients.

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