Breaking Down Glioblastoma: The Promise of Integrative Multi-Omics for Precision Medicine

What Is Integrative Multi-omics?

Integrative multi-omics is an approach to studying biological systems that involves integrating multiple types of omics data, such as genomics, transcriptomics, proteomics, metabolomics, and lipidomics, to gain a comprehensive understanding of biological processes.

Each type of omics data provides a different perspective on the biological system of interest, and by integrating them, researchers can create a more complete picture of the underlying mechanisms that drive that biological process. Thus, the integrative multi-omics approach can help overcome the limitations of studying biological systems using a single omics data type.

Multi-omics Profiling for Classification of Glioblastoma

Integrative multi-omics has the potential to bring significant value to precision cancer medicine, allowing researchers to gain a more complete understanding of the underlying mechanisms that drive cancer and identify novel targets for therapy.

Glioblastoma, also known as glioblastoma multiforme (GBM), is a highly malignant brain tumor that arises from glial cells, a type of supportive cells in the brain. It is the most aggressive and deadliest type of primary brain tumor in adults.

In a recent article published in Nature Cancer, Migliozzi et. al. used an integrative multi-omics approach to identify master kinase proteins that underlie differences in four functional glioblastoma subtypes. Master kinases play a critical role in regulating signaling pathways involved in cell growth, differentiation, and survival and are often dysregulated in cancer.

Master Kinases Determine Glioblastoma Subtypes

The researchers leveraged proteomics, phospho-proteomics, acetylomics, metabolomics and lipidomics data from the GBM dataset of the National Cancer Institute’s Clinical Proteomic Tumor Analysis Consortium and developed a machine-learning network that determined the master kinases driving each of the glioblastoma subtypes. They were able to experimentally validate subtype-specific master kinases that sustain cell growth and tumor cell identity in glioblastoma subtypes and found that these master kinases recapitulate metabolic and proliferative tumor cell states across cohorts of multiple cancer types. According to the researchers, these master kinases represent potent and actionable glioblastoma subtype-specific therapeutic targets. 

Lastly, the authors developed a probabilistic classification tool that can be used to assess the association between therapeutic response and glioblastoma subtypes and to aid in patient selection for prospective clinical trials. These kinds of patient stratification tools are extremely useful for clinical trial selection and later, for more effective diagnosis and treatment of disease.

Outsourcing Bioinformatics Analysis: How Bridge Informatics Can Help

Integrative multi-omics analysis requires comprehensive data storage and pipeline development expertise. As experts across data types from cutting-edge sequencing platforms, we can help you tackle the challenging computational tasks of storing, analyzing and interpreting genomic and transcriptomic data. Bridge Informatics’ bioinformaticians are trained bench biologists, so they understand the biological questions driving your computational analysis. From pipeline development and software engineering to deploying existing bioinformatics tools, Bridge Informatics can help you on every step of your research journey. Click here to schedule a free introductory call with a member of our team.

Lauren Dembeck, PhD, Geneticist & Science Writer, Bridge Informatics

Lauren Dembeck is an experienced scientific and medical writer with a PhD in Genetics and postdoctoral research experience in developmental neurobiology. She is part of our team of expert content writers at Bridge Informatics, bringing our readers and customers everything they need to know at the cutting edge of bioinformatics research. If you’re interested in reaching out, please email or

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