Pharmacological PP2A reactivation overcomes multikinase inhibitor tolerance across brain tumor cell models
Bibliographic record
Abstract
ABSTRACT Background Glioblastoma is characterized by hyperactivation of kinase signaling pathways. Regardless, most glioblastoma clinical trials targeting kinase signaling have failed. We hypothesized that overcoming the glioblastoma kinase inhibitor tolerance requires efficient shut-down of phosphorylation-dependent signaling rewiring by simultaneous inhibition of multiple critical kinases combined with reactivation of Protein Phosphatase 2A (PP2A). Methods Live-cell imaging and colony growth assays were used to determine long-term impact of therapy effects on ten brain tumor cell models. Immunoblotting, MS-phosphoproteomics, and Seahorse metabolic assay were used for analysis of therapy-induced signaling rewiring. BH3 profiling was used to understand the mitochondrial apoptosis mechanisms. Medulloblastoma models were used to expand the importance to other brain cancer. Intracranial xenografts were used to validate the in vivo therapeutic impact of the triplet therapy. Results Collectively all tested ten glioblastoma and medulloblastoma cell models were effectively eradicated by the newly discovered triplet therapy combining inhibition of AKT and PDK1-4 kinases with pharmacological PP2A reactivation. Mechanistically, the brain tumor cell selective lethality of the triplet therapy could be explained by its combinatorial effects on therapy-induced signaling rewiring, OXPHOS, and apoptosis priming. The brain-penetrant triplet combination had a significant in vivo efficacy in intracranial glioblastoma and medulloblastoma models. Conclusion The results confirm highly heterogenous responses of brain cancer cells to mono - and doublet combination therapies targeting phosphorylation-dependent signaling. However, the brain cancer cells cannot escape the triplet therapy targeting of AKT, PDK1-4, and PP2A. The results encourage evaluation of brain tumor PP2A status for design of future kinase inhibitor combination trials. Key Points Development of triplet kinase-phosphatase targeting therapy strategy for overcoming therapy tolerance across brain tumor models. Identification of interplay between therapy-induced signaling rewiring, OXPHOS, and BH3 protein-mediated apoptosis priming as a cause for kinase inhibitor tolerance in brain cancers. Validation of the results in intracranial in vivo models with orally bioavailable and brain penetrant triplet therapy combination. Importance of the Study Based on current genetic knowledge, glioblastoma should be particularly suitable target for kinase inhibitor therapies, However, in glioblastoma alone over 180 clinical trials with kinase inhibitors have failed. In this manuscript, we recapitulate this clinical observation by demonstrating broad tolerance of brain cancer cell models to kinase inhibitors even when combined with reactivation of PP2A. However, we discover that the therapy-induced signaling rewiring, and therapy tolerance, can be overcome by triplet targeting of AKT, PDK1-4 and PP2A. We provide strong evidence for the translatability of the findings by orally dosed brain penetrant triplet therapy combination in intracranial brain cancer models. The results encourage biomarker profiling of brain tumors for their PP2A status for clinical trials with combination of AKT and PDK1-4 inhibitors. Further, the results indicate that rapidly developing PP2A reactivation therapies will constitute an attractive future therapy option for brain tumors when combined with multi-kinase inhibition.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".