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 6

In vivo spatial transcriptomics identifies pathways underpinning therapeutic adaptation.

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In vivo spatial transcriptomics identifies pathways underpinning therape...
(A) Kaplan-Meier survival analysis of RA-055 xenografts treated with the optimized combination of paxalisib (5 mg/kg/b.i.d.) + metformin (175 mg/kg/day) and enzastaurin (100 mg/kg/day) (shaded area indicates treatment time, log-rank test, treated versus untreated; *P < 0.05, **P < 0.01, ***P < 0.001,****P < 0.0001, synergistic comparisons; $$P < 0.01, $$$P < 0.001). (B) Tumor tissue was resected from RA-055 xenografts following 4 weeks of treatment and analyzed by IHC (n = 3 per treatment, representative images shown, scale bar: 50 μm) and (C) images quantified using ImageJ (technical triplicate, across biological replicates, n = 3, 1-way ANOVA, treated versus untreated; *P < 0.05, **P < 0.01, ***P < 0.001, intra-treatment comparison; #P < 0.05, ##P < 0.01, ###P < 0.001). (D) Representative images of 10 × Xenium analysis using a panel of 358 genes, with tumors identified by high PDGFRA expression (scale bar: 1,000 μm). (E) Differential gene expression analysis (Wilcoxon test) on normalized count data, presented as log2FC. (F) IPA pathway analysis of significantly altered genes following treatment. (G) Significantly altered gene transcripts, STAT1, HLA-DRA, TGFB1, and MBP, MAG, MOG visualized using Xenium Explorer, with corresponding violin plots of SCTransform normalized count data (scale bar: 25 μm, 1-way ANOVA, treated versus untreated; ***P < 0.001, ****P < 0.0001).