Columbia University: Direct Reversal of Glucocorticoid Resistance by AKT inhibition in Acute Lymphoblastic Leukemia (T-ALL)

The goal of this project is to identify key druggable regulators of glucocorticoid resistance in T-ALL. To this end, a reverse-engineered T-ALL context-specific regulatory interaction network was created from a phenotypically diverse T-ALL gene expression dataset, and then this network was interrogated using master regulator analysis to find drivers of glucocorticoid resistance. The T-ALL gene expression dataset represented many different biological conditions, genotypes, signaling and transcriptional states, thus providing significant variation in which to detect gene expression correlations.

The expression level of transcription factors is often a poor predictor of their activity and biological relevance. However, their activity at the protein level can be inferred by measuring changes in the gene expression of their targets between two phenotypes, for example between tumor and normal tissue. This approach, called master regulator analysis, has been used successfully to identify functional drivers of cancer in a number of studies. In this study, master regulator analysis was used to identify regulatory genes whose network targets were enriched in the signal transduction cascade (as reflected in a differential gene expression signature) associated with glucocorticoid resistance. 

Microarray gene expression data used in network generation and master regulator analysis is available in Gene Expression Omnibus under accession number GSE32215.

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Experimental Approaches

Reverse-Engineering of T-ALL Transcriptional Network (ARACNe)

For each gene in a list of regulatory genes (hubs), the ARACNe algorithm1,2 is used to measure the mutual information between that gene and all remaining genes in the dataset. First, a preprocessing run is performed in which a curve relating mutual information to significance is generated. Next, ARACNe is run using the adaptive partitioning algorithm, repeated 100 times with bootstrapping3. A key step after each run of ARACNe is the application of the Data Processing Inequality to remove indirect interactions, typically with a zero threshold. A final consensus network is reconstructed from the bootstrapped networks based on the support of each edge, using a null distribution obtained via permutations.

Gene expression data from 223 T-ALLs (Human U133 Plus2.0 Affymetrix microarray platform) was subjected to GC Robust Multi-Aarray normalization and non-specific filtering (removing probes with no Entrez id, Affymetrix control probes, and non-informative probes by IQR variance filtering with a cutoff of 0.5). A set of hub genes was defined including genes with annotated functions in signaling transduction (GO:0007165) such as kinases, phosphatases, ubiquitin ligases, etc. to establish a signaling factor-centered interactome at the transcriptional level. ARACNe was used to identify targets of these hub genes (that is, genes with significant mutual information with the hub genes). It was run using the adaptive partitioning algorithm with a p-value threshold of 1e-7, DPI tolerance of 0, and 100 rounds of bootstrapping.

Master Regulator Analysis (MARINa)

For master regulator analysis, a group of 22 glucocorticoid resistant and 10 glucocorticoid sensitive T-ALLs was selected from the larger dataset used in network generation. Genes were ranked by their differential expression between these two conditions. The MARINa algorithm uses Gene Set Enrichment Analysis (GSEA)4 to test the differential enrichment of the regulons of hub genes (network first-degree neighbors) in the rank of genes differentially expressed between glucocorticoid sensitive and glucocorticoid resistant samples5. For GSEA method the ‘maxmean’ statistic6 was applied to score the enrichment of the gene set in the glucocorticoid resistant vs. glucocorticoid sensitive leukemias and sample permutation was used to build the null distribution for statistical significance.

Read the detailed Experimental Approaches

If you cannot access the manuscript, or if you have additional questions, please email Kenneth Smith.

Data

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References

  1. Basso K, et al. (2005). Reverse engineering of regulatory networks in human B cells. Nature Genet. 37(4):382-390 (PMID: 15778709)
  2. Margolin AA, et al. (2006). ARACNE: An Algorithm for the Reconstruction of Gene Regulatory Networks in a Mammalian Cellular Context. BMC Bioinformatics. 7(Suppl.1):S7 (PMID: 16723010)
  3. Margolin A, et al. (2006). Reverse Engineering Cellular Networks. Nature Protocols 1(2):663-72 (PMID: 17406294)
  4. Subramanian A, et al. (2005). Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci USA. 102(43):15545-50 (PMID: 16199517)
  5. Carro MS, et al. (2010). The transcriptional network for mesenchymal transformation of brain tumors. Nature. 463(7279):318-25 (PMID: 20032975)
  6. Efron B and Tibshirani R. (2007). On testing the significance of sets of genes. The Annals of Applied Statistics. 1, 107-129.
Last updated: July 01, 2017