Inferring transcriptional and microRNA‐mediated regulatory programs in glioblastoma

• Published in: Molecular systems biology 2012, Vol.8 (1), p.605-n/a
• Main Authors: Setty, Manu, Helmy, Karim, Khan, Aly A, Silber, Joachim, Arvey, Aaron, Neezen, Frank, Agius, Phaedra, Huse, Jason T, Holland, Eric C, Leslie, Christina S
• Format: Article
• Language: English
• Subjects: Animal models; Animals; Brain cancer; Cancer; Cell Line, Tumor; Computational neuroscience; Copy number; DNA methylation; EMBO09; EMBO10; EMBO24; Experiments; Gene expression; Gene Expression Profiling; Gene Expression Regulation, Neoplastic; gene regulatory programs; Genome, Human; Genomes; Genomics; Glioblastoma; Glioblastoma - genetics; Humans; integrative cancer genomics; Mice; Mice, Transgenic; microRNA regulation; MicroRNAs; MicroRNAs - genetics; MicroRNAs - metabolism; microRNAs in glioblastoma; miRNA; Model testing; Models, Biological; Mutation; Neural Stem Cells - metabolism; Neurospheres; Ontology; Platelet-derived growth factor; Regression Analysis; Regulators; Regulatory sequences; Ribonucleic acid; RNA; RNA, Messenger - genetics; RNA, Messenger - metabolism; Stem cells; Transcription factors; Transcription Factors - genetics; Tumors
• ISSN: 1744-4292; 1744-4292
• DOI: 10.1038/msb.2012.37

Large‐scale cancer genomics projects are profiling hundreds of tumors at multiple molecular layers, including copy number, mRNA and miRNA expression, but the mechanistic relationships between these layers are often excluded from computational models. We developed a supervised learning framework for integrating molecular profiles with regulatory sequence information to reveal regulatory programs in cancer, including miRNA‐mediated regulation. We applied our approach to 320 glioblastoma profiles and identified key miRNAs and transcription factors as common or subtype‐specific drivers of expression changes. We confirmed that predicted gene expression signatures for proneural subtype regulators were consistent with in vivo expression changes in a PDGF‐driven mouse model. We tested two predicted proneural drivers, miR‐124 and miR‐132, both underexpressed in proneural tumors, by overexpression in neurospheres and observed a partial reversal of corresponding tumor expression changes. Computationally dissecting the role of miRNAs in cancer may ultimately lead to small RNA therapeutics tailored to subtype or individual. Integration of expression, copy number, methylation, and regulatory sequence information identifies miRNAs and transcription factors that drive the global expression changes associated with different glioblastoma subtypes. Synopsis Integration of expression, copy number, methylation, and regulatory sequence information identifies miRNAs and transcription factors that drive the global expression changes associated with different glioblastoma subtypes. The proneural and mesenchymal transcriptomic subtypes of glioblastoma are associated with distinct regulatory programs. REST, miR‐124 and miR‐132 are potential drivers of expression changes in proneural glioblastoma, and the inferred extent of dysregulation of miR‐132 correlates with survival in the proneural subtype. The expression changes in proneural glioblastoma associated with key regulators in the regression model are consistent with in vivo expression changes in mouse PDGF‐driven tumors. Transfection of miR‐124 and miR‐132 in proneural neurospheres induces expression changes that are concordant with proneural tumor‐versus‐normal expression changes.