Identifying the personalized driver gene sets maximally contributing to abnormality of transcriptome phenotype in glioblastoma multiforme individuals

High heterogeneity in genome and phenotype of cancer populations made it difficult to apply population-based common driver genes to the diagnosis and treatment of cancer individuals. Characterizing and identifying the personalized driver mechanism for glioblastoma multiforme (GBM) individuals were pivotal for the realization of precision medicine. We proposed an integrative method to identify the personalized driver gene sets by integrating the profiles of gene expression and genetic alterations in cancer individuals. This method coupled genetic algorithm and random walk to identify the optimal gene sets that could explain abnormality of transcriptome phenotype to the maximum extent. The personalized driver gene sets were identified for 99 GBM individuals using our method. We found that genomic alterations in between one and seven driver genes could maximally and cumulatively explain the dysfunction of cancer hallmarks across GBM individuals. The driver gene sets were distinct even in GBM individuals with significantly similar transcriptomic phenotypes. Our method identified MCM4 with rare genetic alterations as previously unknown oncogenic genes, the high expression of which were significantly associated with poor GBM prognosis. The functional experiments confirmed that knockdown of MCM4 could significantly inhibit proliferation, invasion, migration, and clone formation of the GBM cell lines U251 and U118MG, and overexpression of MCM4 significantly promoted the proliferation, invasion, migration, and clone formation of the GBM cell line U87MG. Our method could dissect the personalized driver genetic alteration sets that are pivotal for developing targeted therapy strategies and precision medicine. Our method could be extended to identify key drivers from other levels and could be applied to more cancer types.

Automated summary

High heterogeneity in genome and phenotype of cancer populations makes it difficult to apply population-based common driver genes to diagnose and treat individual cancer patients. We proposed an integrative method to identify personalized driver gene sets for glioblastoma multiforme (GBM) patients by integrating gene expression and genetic alteration profiles. Our method identified driver gene sets for 99 GBM patients, and found that genomic alterations in one to seven driver genes could explain dysfunction of cancer hallmarks across GBM patients. Our method also identified MCM4 as a previously unknown oncogenic gene with rare genetic alterations, which was associated with poor prognosis in GBM. Functional experiments confirmed that MCM4 plays a role in GBM cell proliferation, invasion, migration, and clone formation. Our method could be valuable for developing targeted therapy and precision medicine.