Dataset Information

Mechanism-anchored profiling derived from epigenetic networks predicts outcome in acute lymphoblastic leukemia.

ABSTRACT:

Background

Current 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.

Results

This 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.

Conclusion

To 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.

SUBMITTER: Yang X

PROVIDER: S-EPMC2745693 | biostudies-literature | 2009 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Mechanism-anchored profiling derived from epigenetic networks predicts outcome in acute lymphoblastic leukemia.

Yang Xinan X Huang Yong Y Chen James L JL Xie Jianming J Sun Xiao X Lussier Yves A YA

BMC bioinformatics 20090917

<h4>Background</h4>Current 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 ep ...[more]

PMID: 19761576

Similar Datasets

Project description:UnlabelledGene expression studies have consistently identified a HOXA-overexpressing cluster of T-cell acute lymphoblastic leukemias, but it is unclear whether these constitute a homogeneous clinical entity, and the biological consequences of HOXA overexpression have not been systematically examined. We characterized the biology and outcome of 55 HOXA-positive cases among 209 patients with adult T-cell acute lymphoblastic leukemia uniformly treated during the Group for Research on Adult Acute Lymphoblastic Leukemia (GRAALL)-2003 and -2005 studies. HOXA-positive patients had markedly higher rates of an early thymic precursor-like immunophenotype (40.8% versus 14.5%, P=0.0004), chemoresistance (59.3% versus 40.8%, P=0.026) and positivity for minimal residual disease (48.5% versus 23.5%, P=0.01) than the HOXA-negative group. These differences were due to particularly high frequencies of chemoresistant early thymic precursor-like acute lymphoblastic leukemia in HOXA-positive cases harboring fusion oncoproteins that transactivate HOXA Strikingly, the presence of an early thymic precursor-like immunophenotype was associated with marked outcome differences within the HOXA-positive group (5-year overall survival 31.2% in HOXA-positive early thymic precursor versus 66.7% in HOXA-positive non-early thymic precursor, P=0.03), but not in HOXA-negative cases (5-year overall survival 74.2% in HOXA-negative early thymic precursor versus 57.2% in HOXA-negative non-early thymic precursor, P=0.44). Multivariate analysis further revealed that HOXA positivity independently affected event-free survival (P=0.053) and relapse risk (P=0.039) of chemoresistant T-cell acute lymphoblastic leukemia. These results show that the underlying mechanism of HOXA deregulation dictates the clinico-biological phenotype, and that the negative prognosis of early thymic precursor acute lymphoblastic leukemia is exclusive to HOXA-positive patients, suggesting that early treatment intensification is currently suboptimal for therapeutic rescue of HOXA-positive chemoresistant adult early thymic precursor acute lymphoblastic leukemia.Trial registrationThe GRAALL-2003 and -2005 studies were registered at http://www.clinicaltrials.gov as #NCT00222027 and #NCT00327678, respectively.

Dataset Information

Mechanism-anchored profiling derived from epigenetic networks predicts outcome in acute lymphoblastic leukemia.

Background

Results

Conclusion

Publications

Mechanism-anchored profiling derived from epigenetic networks predicts outcome in acute lymphoblastic leukemia.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets