PI3K/mTOR is a therapeutically targetable genetic dependency in diffuse intrinsic pontine glioma
Ryan J. Duchatel, et al.
Ryan J. Duchatel, et al.
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Research Article Oncology

PI3K/mTOR is a therapeutically targetable genetic dependency in diffuse intrinsic pontine glioma

Abstract

Diffuse midline glioma (DMG), including tumors diagnosed in the brainstem (diffuse intrinsic pontine glioma; DIPG), are uniformly fatal brain tumors that lack effective treatment. Analysis of CRISPR/Cas9 loss-of-function gene deletion screens identified PIK3CA and MTOR as targetable molecular dependencies across patient derived models of DIPG, highlighting the therapeutic potential of the blood-brain barrier–penetrant PI3K/Akt/mTOR inhibitor, paxalisib. At the human-equivalent maximum tolerated dose, mice treated with paxalisib experienced systemic glucose feedback and increased insulin levels commensurate with patients using PI3K inhibitors. To exploit genetic dependence and overcome resistance while maintaining compliance and therapeutic benefit, we combined paxalisib with the antihyperglycemic drug metformin. Metformin restored glucose homeostasis and decreased phosphorylation of the insulin receptor in vivo, a common mechanism of PI3K-inhibitor resistance, extending survival of orthotopic models. DIPG models treated with paxalisib increased calcium-activated PKC signaling. The brain penetrant PKC inhibitor enzastaurin, in combination with paxalisib, synergistically extended the survival of multiple orthotopic patient-derived and immunocompetent syngeneic allograft models; benefits potentiated in combination with metformin and standard-of-care radiotherapy. Therapeutic adaptation was assessed using spatial transcriptomics and ATAC-Seq, identifying changes in myelination and tumor immune microenvironment crosstalk. Collectively, this study has identified what we believe to be a clinically relevant DIPG therapeutic combinational strategy.

Authors

Ryan J. Duchatel, Evangeline R. Jackson, Sarah G. Parackal, Dylan Kiltschewskij, Izac J. Findlay, Abdul Mannan, Dilana E. Staudt, Bryce C. Thomas, Zacary P. Germon, Sandra Laternser, Padraic S. Kearney, M. Fairuz B. Jamaluddin, Alicia M. Douglas, Tyrone Beitaki, Holly P. McEwen, Mika L. Persson, Emily A. Hocke, Vaibhav Jain, Michael Aksu, Elizabeth E. Manning, Heather C. Murray, Nicole M. Verrills, Claire Xin Sun, Paul Daniel, Ricardo E. Vilain, David A. Skerrett-Byrne, Brett Nixon, Susan Hua, Charles E. de Bock, Yolanda Colino-Sanguino, Fatima Valdes-Mora, Maria Tsoli, David S. Ziegler, Murray J. Cairns, Eric H. Raabe, Nicholas A. Vitanza, Esther Hulleman, Timothy N. Phoenix, Carl Koschmann, Frank Alvaro, Christopher V. Dayas, Christopher L. Tinkle, Helen Wheeler, James R. Whittle, David D. Eisenstat, Ron Firestein, Sabine Mueller, Santosh Valvi, Jordan R. Hansford, David M. Ashley, Simon G. Gregory, Lindsay B. Kilburn, Javad Nazarian, Jason E. Cain, Matthew D. Dun

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Figure 5

High-throughput drug screen identifies synergistic paxalisib drug combinations.

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High-throughput drug screen identifies synergistic paxalisib drug combin...
(A) Bliss synergy analysis using paxalisib in combination with clinically relevant inhibitors in a panel of DIPG cells lines (n = 9), measured by resazurin cell growth and proliferation assays after 72 hours exposure (biological triplicate). (B) CNS-MPO analysis of compounds targeting pathways modulated by paxalisib treatment and plotted against paxalisib combination synergy scores. Cell proliferation and bliss synergy analysis for the combination of (C) paxalisib and enzastaurin, (D) paxalisib and ribociclib, and (E) paxalisib and vandetanib. (F–H) Kaplan-Meier survival analysis of SU-DIPG-XIII-P* xenografts treated with paxalisib (5 mg/kg/b.i.d.) and (F) enzastaurin (100 mg/kg/day), (G) ribociclib (75 mg/kg/day) and (H) vandetanib (25 mg/kg/b.i.w.) (log-rank test, *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001, synergistic comparisons; $P < 0.01, $$P < 0.01, $$$P < 0.001, shaded area indicates treatment time).