Revolutionizing Glioblastoma Treatment: How Multi-Omics is UnlockingPersonalized Therapies
Written by: Amir Barzegar Behrooz
Glioblastoma (GB) is the most aggressive and lethal primary brain tumor, with a median survival
rate of about 15 months despite advances in surgical techniques, radiotherapy, and
chemotherapy. This poor prognosis highlights the urgent need for innovative treatment strategies
[1] . In recent years, a multi-omics approach, which integrates data from genomics,
transcriptomics, proteomics, metabolomics, and epigenomics, has emerged as a powerful tool in
developing targeted therapies for GB [2, 3] . This article explores how this comprehensive
approach is being utilized to unravel the complexity of GB and guide the design of personalized
treatment strategies.
Understanding Glioblastoma Heterogeneity: GB is characterized by significant inter- and
intratumoral heterogeneity, making it challenging to treat with one-size-fits-all therapies.
Traditional treatment strategies have often failed to address this complexity. Multi-omics
approaches allow for a more detailed understanding of the molecular subtypes and the
underlying mechanisms driving tumor growth and resistance to therapy [2] . By integrating data
from various omics layers, researchers can identify distinct molecular signatures that define GB
subtypes, which can be targeted more effectively with tailored therapies.
Genomics and Transcriptomics: Genomic and transcriptomic analyses are foundational in
multi-omics. Genomics provides insights into mutations, copy number variations, and other
genetic alterations that drive GB, while transcriptomics offers a snapshot of gene expression
profiles. For example, identifying gene mutations such as EGFR, PTEN, and IDH1 has led to
targeted therapies for these alterations [4, 5] . Additionally, RNA sequencing helps uncover
dysregulated signaling pathways and potential therapeutic targets by revealing the active
transcriptional networks within tumor cells.
Proteomics and Metabolomics: Proteomics and metabolomics extend the analysis beyond the
genome and transcriptome by examining the functional products of genes—proteins and
metabolites. Proteomic analyses can identify aberrant protein expression and post-translational
modifications contributing to GB pathogenesis [6] . For instance, the activation of the
PI3K/AKT/mTOR pathway, which is often upregulated in GB, has been targeted using specific
inhibitors. Metabolomics, conversely, helps elucidate the metabolic adaptations that GB cells
undergo to sustain their rapid growth and survival in the tumor microenvironment [7] . Targeting
these metabolic vulnerabilities offers another avenue for therapeutic intervention.
Epigenomics: Epigenetic modifications, such as DNA methylation and histone modifications,
are critical in regulating gene expression in GB. Epigenomic studies have revealed that
2 epigenetic changes can drive tumorigenesis and contribute to resistance to therapy [8] . For
example, the methylation status of the MGMT promoter is a well-known biomarker used to
predict the response to temozolomide, a joint chemotherapeutic agent used in GB treatment.
Researchers can identify novel epigenetic targets and develop therapies to reverse these
modifications by integrating epigenomic data with other omics layers.
From Extracellular to Intracellular: A Journey Through Omics (Created with BioRender.com)
3 Examples of Multi-Omics Applications in GB Therapy
1. Targeting the Tumor Microenvironment: The tumor microenvironment (TME) plays a
crucial role in GB progression and resistance to therapy. A multi-omics approach can be used to
study the interactions between tumor cells and their microenvironment, including immune cells,
stromal cells, and the extracellular matrix [9, 10] . For example, spatially resolved multi-omics
techniques have been employed to map the heterogeneity within the TME and identify regions of
immune suppression or metabolic stress. This information can be used to design combination
therapies that target both the tumor cells and their supportive microenvironment.
2. Overcoming Therapy Resistance: Resistance to conventional therapies is a major challenge
in GB treatment. Multi-omics approaches have been instrumental in identifying the mechanisms
underlying therapy resistance [11] . For instance, resistance to antiangiogenic therapies, which
target the blood vessels that supply nutrients to the tumor, has been linked to the activation of
alternative proangiogenic pathways and increased invasiveness of tumor cells. By integrating
data from genomics, proteomics, and metabolomics, researchers can identify these resistance
mechanisms and develop strategies to overcome them, such as combination therapies that target
multiple pathways simultaneously.
3. Precision Medicine in GB: The ultimate goal of the multi-omics approach is to enable
precision medicine, where treatments are tailored to the unique molecular profile of each
patient’s tumor. For example, The Cancer Genome Atlas (TCGA) and other large-scale projects
have provided a wealth of multi-omics data to classify GB into distinct molecular subtypes, each
with its vulnerabilities. This information is used to design clinical trials that test targeted
therapies in specific patient populations, thereby increasing the likelihood of treatment success.
The multi-omics approach represents a paradigm shift in the treatment of glioblastoma, offering
a comprehensive view of the molecular landscape of this complex and heterogeneous tumor. By
integrating genomics, transcriptomics, proteomics, metabolomics, and epigenomics data,
researchers can better understand the mechanisms driving GB and develop more effective,
personalized therapies. While challenges remain, particularly in translating these findings into
clinical practice, the potential for improving patient outcomes through multi-omics-guided
targeted therapy is significant. As research advances, the hope is that this approach will lead to
more durable responses and, ultimately, better survival rates for patients with this devastating
disease.
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