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Integrated regulatory models for inference of subtype-specific susceptibilities in glioblastoma.

ABSTRACT: Glioblastoma multiforme (GBM) is a highly malignant form of cancer that lacks effective treatment options or well-defined strategies for personalized cancer therapy. The disease has been stratified into distinct molecular subtypes; however, the underlying regulatory circuitry that gives rise to such heterogeneity and its implications for therapy remain unclear. We developed a modular computational pipeline, Integrative Modeling of Transcription Regulatory Interactions for Systematic Inference of Susceptibility in Cancer (inTRINSiC), to dissect subtype-specific regulatory programs and predict genetic dependencies in individual patient tumors. Using a multilayer network consisting of 518 transcription factors (TFs), 10,733 target genes, and a signaling layer of 3,132 proteins, we were able to accurately identify differential regulatory activity of TFs that shape subtype-specific expression landscapes. Our models also allowed inference of mechanisms for altered TF behavior in different GBM subtypes. Most importantly, we were able to use the multilayer models to perform an in silico perturbation analysis to infer differential genetic vulnerabilities across GBM subtypes and pinpoint the MYB family member MYBL2 as a drug target specific for the Proneural subtype.

SUBMITTER: Liu Y

PROVIDER: S-EPMC7516378 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

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Integrated regulatory models for inference of subtype-specific susceptibilities in glioblastoma.

Liu Yunpeng Y Shi Ning N Regev Aviv A He Shan S Hemann Michael T MT

Molecular systems biology 20200901 9

Glioblastoma multiforme (GBM) is a highly malignant form of cancer that lacks effective treatment options or well-defined strategies for personalized cancer therapy. The disease has been stratified into distinct molecular subtypes; however, the underlying regulatory circuitry that gives rise to such heterogeneity and its implications for therapy remain unclear. We developed a modular computational pipeline, Integrative Modeling of Transcription Regulatory Interactions for Systematic Inference of ...[more]

PMID: 32974985

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Project description:BackgroundA high heterogeneity and activation of multiple oncogenic pathways have been implicated in failure of targeted therapies in glioblastoma (GBM).MethodsUsing The Cancer Genome Atlas data, we identified subtype-specific prognostic core genes by a combined approach of genome-wide Cox regression and Gene Set Enrichment Analysis. The results were validated with 8 combined public datasets containing 608 GBMs. We further examined prognostic chromosome aberrations and mutations.ResultsIn classical and mesenchymal subtypes, 2 receptor tyrosine kinases (RTKs) (MET and IGF1R), and the genes in RTK downstream pathways such as phosphatidylinositol-3 kinase (PI3K)/Akt/mammalian target of rapamycin (mTOR), and nuclear factor-kappaB (NF-kB), were commonly detected as prognostic core genes. Classical subtype-specific prognostic core genes included those in cell cycle, DNA repair, and the Janus kinase/signal transducers and activators of transcription (JAK-STAT) pathway. Immune-related genes were enriched in the prognostic genes showing negative promoter cytosine-phosphate-guanine (CpG) methylation/expression correlations. Mesenchymal subtype-specific prognostic genes were those related to mesenchymal cell movement, PI3K/Akt, mitogen-activated protein kinase (MAPK)/extracellular signal-regulated kinase (ERK), Wnt/β-catenin, and Wnt/Ca2+ pathways. In copy number alterations and mutations, 6p loss and TP53 mutation were associated with poor and good survival, respectively, in the classical subtype. In the mesenchymal subtype, patients with PIK3R1 or PCLO mutations showed poor prognosis. In the glioma CpG island methylator phenotype (G-CIMP) subtype, patients harboring 10q loss, 12p gain, or 14q loss exhibited poor survival. Furthermore, 10q loss was significantly associated with the recently recognized G-CIMP subclass showing relatively low CpG methylation and poor prognosis.ConclusionThese subtype-specific alterations have promising potentials as new prognostic biomarkers and therapeutic targets combined with surrogate markers of GBM subtypes. However, considering the small number of events, the results of copy number alterations and mutations require further validations.

Dataset Information

Integrated regulatory models for inference of subtype-specific susceptibilities in glioblastoma.

Publications

Integrated regulatory models for inference of subtype-specific susceptibilities in glioblastoma.

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