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 4

Phosphoproteomic analysis identified potent PKC activation following PI3K inhibition.

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Phosphoproteomic analysis identified potent PKC activation following PI3...
(A) Significantly regulated phosphoproteins following paxalisib treatment (Student’s t test; *P < 0.05, **P < 0.01, ***P < 0.001). (B) Canonical pathways and (C) upstream regulators significantly altered by paxalisib treatment determined by Ingenuity Pathway Analysis (IPA, activated pathways positive z-score (red), inactivated pathways negative z-score (blue)). (D) PhoxTrack predicted activated (red), inactivated (blue). (E) PKC activated using Phorbol-12-myristate-13-acetate (PMA) using SU-DIPG-XIII cells (scale bar: 200 μM, 2-way ANOVA, PMA versus untreated **P < 0.01, ****P < 0.0001). (F) BAPTA-AM inhibition of paxalisib-induced PKC substrates and MARCKS phosphorylation, measured by immunoblotting (n = 3, representative immunoblot presented). (G) Bliss-synergy analysis of the combination of paxalisib with BAPTA-AM and Gabapentin. (H–J) Quantification of signaling protein phosphorylation following combinations of paxalisib and PKC inhibitors after 24 hours (n = 3 biological replicates, 1-way ANOVA, treated versus untreated; #P < 0.05, ##P < 0.01, ###P < 0.001, ####P < 0.0001, intra-treatment comparison; *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001, treated versus paxalisib; ^P < 0.05, ^^P < 0.01, ^^^^P < 0.0001). (K) Proliferation of SU-DIPGXXXVI following CRISPR/Cas9 knockdown of PIK3CA in cell lines compared with nontargeting control (NTC) and treated with enzastaurin for 72 hours (biological triplicate, 1-way ANOVA; ***P < 0.001, ****P < 0.0001).