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Reverse engineering of TLX oncogenic transcriptional networks identifies RUNX1 as tumor suppressor in T-ALL.

ABSTRACT: The TLX1 and TLX3 transcription factor oncogenes have a key role in the pathogenesis of T cell acute lymphoblastic leukemia (T-ALL). Here we used reverse engineering of global transcriptional networks to decipher the oncogenic regulatory circuit controlled by TLX1 and TLX3. This systems biology analysis defined T cell leukemia homeobox 1 (TLX1) and TLX3 as master regulators of an oncogenic transcriptional circuit governing T-ALL. Notably, a network structure analysis of this hierarchical network identified RUNX1 as a key mediator of the T-ALL induced by TLX1 and TLX3 and predicted a tumor-suppressor role for RUNX1 in T cell transformation. Consistent with these results, we identified recurrent somatic loss-of-function mutations in RUNX1 in human T-ALL. Overall, these results place TLX1 and TLX3 at the top of an oncogenic transcriptional network controlling leukemia development, show the power of network analyses to identify key elements in the regulatory circuits governing human cancer and identify RUNX1 as a tumor-suppressor gene in T-ALL.

SUBMITTER: Della Gatta G

PROVIDER: S-EPMC3298036 | biostudies-literature | 2012 Feb

REPOSITORIES: biostudies-literature

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Reverse engineering of TLX oncogenic transcriptional networks identifies RUNX1 as tumor suppressor in T-ALL.

Della Gatta Giusy G Palomero Teresa T Perez-Garcia Arianne A Ambesi-Impiombato Alberto A Bansal Mukesh M Carpenter Zachary W ZW De Keersmaecker Kim K Sole Xavier X Xu Luyao L Paietta Elisabeth E Racevskis Janis J Wiernik Peter H PH Rowe Jacob M JM Meijerink Jules P JP Califano Andrea A Ferrando Adolfo A AA

Nature medicine 20120226 3

The TLX1 and TLX3 transcription factor oncogenes have a key role in the pathogenesis of T cell acute lymphoblastic leukemia (T-ALL). Here we used reverse engineering of global transcriptional networks to decipher the oncogenic regulatory circuit controlled by TLX1 and TLX3. This systems biology analysis defined T cell leukemia homeobox 1 (TLX1) and TLX3 as master regulators of an oncogenic transcriptional circuit governing T-ALL. Notably, a network structure analysis of this hierarchical network ...[more]

PMID: 22366949

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Project description:BACKGROUND: A myriad of methods to reverse-engineer transcriptional regulatory networks have been developed in recent years. Direct methods directly reconstruct a network of pairwise regulatory interactions while module-based methods predict a set of regulators for modules of coexpressed genes treated as a single unit. To date, there has been no systematic comparison of the relative strengths and weaknesses of both types of methods. RESULTS: We have compared a recently developed module-based algorithm, LeMoNe (Learning Module Networks), to a mutual information based direct algorithm, CLR (Context Likelihood of Relatedness), using benchmark expression data and databases of known transcriptional regulatory interactions for Escherichia coli and Saccharomyces cerevisiae. A global comparison using recall versus precision curves hides the topologically distinct nature of the inferred networks and is not informative about the specific subtasks for which each method is most suited. Analysis of the degree distributions and a regulator specific comparison show that CLR is 'regulator-centric', making true predictions for a higher number of regulators, while LeMoNe is 'target-centric', recovering a higher number of known targets for fewer regulators, with limited overlap in the predicted interactions between both methods. Detailed biological examples in E. coli and S. cerevisiae are used to illustrate these differences and to prove that each method is able to infer parts of the network where the other fails. Biological validation of the inferred networks cautions against over-interpreting recall and precision values computed using incomplete reference networks. CONCLUSION: Our results indicate that module-based and direct methods retrieve largely distinct parts of the underlying transcriptional regulatory networks. The choice of algorithm should therefore be based on the particular biological problem of interest and not on global metrics which cannot be transferred between organisms. The development of sound statistical methods for integrating the predictions of different reverse-engineering strategies emerges as an important challenge for future research.

Project description:BackgroundOne of the most challenging tasks in the post-genomic era is to reconstruct the transcriptional regulatory networks. The goal is to reveal, for each gene that responds to a certain biological event, which transcription factors affect its expression, and how a set of transcription factors coordinate to accomplish temporal and spatial specific regulations.ResultsHere we propose a supervised machine learning approach to address these questions. We focus our study on the gene transcriptional regulation of the cell cycle in the budding yeast, thanks to the large amount of data available and relatively well-understood biology, although the main ideas of our method can be applied to other data as well. Our method starts with building an ensemble of decision trees for each microarray data to capture the association between the expression levels of yeast genes and the binding of transcription factors to gene promoter regions, as determined by chromatin immunoprecipitation microarray (ChIP-chip) experiment. Cross-validation experiments show that the method is more accurate and reliable than the naive decision tree algorithm and several other ensemble learning methods. From the decision tree ensembles, we extract logical rules that explain how a set of transcription factors act in concert to regulate the expression of their targets. We further compute a profile for each rule to show its regulation strengths at different time points. We also propose a spline interpolation method to integrate the rule profiles learned from several time series expression data sets that measure the same biological process. We then combine these rule profiles to build a transcriptional regulatory network for the yeast cell cycle. Compared to the results in the literature, our method correctly identifies all major known yeast cell cycle transcription factors, and assigns them into appropriate cell cycle phases. Our method also identifies many interesting synergetic relationships among these transcription factors, most of which are well known, while many of the rest can also be supported by other evidences.ConclusionThe high accuracy of our method indicates that our method is valid and robust. As more gene expression and transcription factor binding data become available, we believe that our method is useful for reconstructing large-scale transcriptional regulatory networks in other species as well.

Dataset Information

Reverse engineering of TLX oncogenic transcriptional networks identifies RUNX1 as tumor suppressor in T-ALL.

Publications

Reverse engineering of TLX oncogenic transcriptional networks identifies RUNX1 as tumor suppressor in T-ALL.

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