Integrated molecular and functional characterization of the intrinsic apoptotic machinery identifies therapeutic vulnerabilities in glioma

Elizabeth G. Fernandez, Wilson X. Mai, Kai Song, Nicholas A. Bayley, Jiyoon Kim, Henan Zhu, Marissa Pioso, Pauline Young, Cassidy L. Andrasz, Dimitri Cadet, Linda M. Liau, Gang Li, William H. Yong, Fausto J. Rodriguez, Scott J. Dixon, Andrew J. Souers, Jingyi Jessica Li, Thomas G. Graeber, Timothy F. Cloughesy and David A. Nathanson ()
Additional contact information
Elizabeth G. Fernandez: University of California Los Angeles
Wilson X. Mai: University of California Los Angeles
Kai Song: University of California, Los Angeles
Nicholas A. Bayley: University of California Los Angeles
Jiyoon Kim: Jonathan and Karin Fielding School of Public Health, Los Angeles
Henan Zhu: University of California Los Angeles
Marissa Pioso: University of California Los Angeles
Pauline Young: University of California Los Angeles
Cassidy L. Andrasz: University of California Los Angeles
Dimitri Cadet: University of California Los Angeles
Linda M. Liau: University of California, Los Angeles
Gang Li: Jonathan and Karin Fielding School of Public Health, Los Angeles
William H. Yong: University of California, Los Angeles
Fausto J. Rodriguez: University of California, Los Angeles
Scott J. Dixon: Stanford University
Andrew J. Souers: Inc.
Jingyi Jessica Li: Jonathan and Karin Fielding School of Public Health, Los Angeles
Thomas G. Graeber: University of California Los Angeles
Timothy F. Cloughesy: University of California Los Angeles
David A. Nathanson: University of California Los Angeles

Nature Communications, 2024, vol. 15, issue 1, 1-18

Abstract: 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.

Date: 2024
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DOI: 10.1038/s41467-024-54138-9

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