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Evaluation of Biomarkers in Glioma by Immunohistochemistry on Paraffin-Embedded 3D Glioma Neurosphere Cultures
Published on: January 9, 2019
Integrated molecular and functional characterization of the intrinsic apoptotic machinery identifies therapeutic
Elizabeth G Fernandez1, Wilson X Mai1, Kai Song2
1Department of Molecular and Medical Pharmacology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, 90095, USA.
Abstract:
Genomic profiling often fails to predict therapeutic outcomes in cancer. This failure is, in part, due to a myriad of genetic alterations and the plasticity of cancer signaling networks. Functional profiling, which ascertains signaling dynamics, is an alternative method to anticipate drug responses. It is unclear whether integrating genomic and functional features of solid tumours can provide unique insight into therapeutic vulnerabilities. We perform combined molecular and functional characterization, via BH3 profiling of the intrinsic apoptotic machinery, in glioma patient samples and derivative models. We identify that standard-of-care therapy rapidly rewires apoptotic signaling in a genotype-specific manner, revealing targetable apoptotic vulnerabilities in gliomas containing specific molecular features (e.g., TP53 WT). However, integration of BH3 profiling reveals high mitochondrial priming is also required to induce glioma apoptosis. Accordingly, a machine-learning approach identifies a composite molecular and functional signature that best predicts responses of diverse intracranial glioma models to standard-of-care therapies combined with ABBV-155, a clinical drug targeting intrinsic apoptosis. This work demonstrates how complementary functional and molecular data can robustly predict therapy-induced cell death.
Insights
Integrating genomic and functional profiling, including BH3 profiling, predicts glioma drug responses. This approach reveals targetable apoptotic vulnerabilities and improves therapy prediction for gliomas.
Area of Science:
- Oncology
- Molecular Biology
- Biochemistry
Background:
- Genomic profiling alone often fails to predict cancer treatment outcomes due to genetic complexity and signaling plasticity.
- Functional profiling, assessing signaling dynamics, offers an alternative for predicting drug responses.
- The combined utility of genomic and functional data for identifying solid tumor vulnerabilities remains largely unexplored.
Purpose of the Study:
- To investigate if integrating genomic and functional profiling, specifically BH3 profiling of the apoptotic machinery, can reveal unique therapeutic vulnerabilities in gliomas.
- To determine how standard-of-care therapies affect apoptotic signaling in gliomas with different molecular features.
- To develop a predictive model for glioma response to therapy by combining molecular and functional data.
Main Methods:
- Combined molecular and functional characterization using BH3 profiling on glioma patient samples and models.
- Analysis of genotype-specific alterations in apoptotic signaling following standard-of-care therapy.
- Application of a machine-learning approach to identify predictive signatures integrating molecular and functional data.
Main Results:
- Standard-of-care therapy rapidly alters apoptotic signaling in a genotype-dependent manner, exposing vulnerabilities in specific glioma subtypes (e.g., TP53 WT).
- High mitochondrial priming, identified by BH3 profiling, is crucial for inducing apoptosis in gliomas.
- A composite molecular and functional signature, derived from machine learning, accurately predicted responses of various glioma models to combined standard-of-care therapy and ABBV-155 (a BCL-2 family inhibitor).
Conclusions:
- Complementary functional (BH3 profiling) and molecular data provide robust prediction of therapy-induced cell death in gliomas.
- This integrated approach enhances the understanding of therapeutic vulnerabilities and improves prediction of treatment response in glioma.
- The findings support the development of precision medicine strategies for glioma based on combined molecular and functional profiling.
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