Morphine Reduces Tumor Growth in Glioma Mouse Xenograft through Modulation of IDH1 Activity and Metabolic Reprogramming
Bibliographic record
Abstract
Background Isocitrate dehydrogenase 1 (IDH1) is a key extra‐mitochondrial enzyme, responsible for converting isocitrate to α‐ketoglutarate. In low grade gliomas, IDH1 is often mutated (Arg132His), altering the enzymatic product to the oncometabolite 2‐hydroxyglutarate (2HG) and inducing drastic metabolic reprogramming 1 . 2HG also inhibits DNA demethylases thus causing DNA hypermethylation. Contrary to expectation, IDH1 mutation slows tumor growth and increases susceptibility to antineoplastic agents, improving patient outcome 2 . We previously found that morphine, a commonly used opiate for pain management in cancer patients, is a mixed partial inhibitor of wild type IDH1. We sought to determine if morphine's effect on IDH1 activity replicates the beneficial changes caused by IDH1 mutation. Methods U87 cells (IDH1 wild type) were treated with clinically relevant concentrations of morphine (0.1‐10µM) or equipotent hydromorphone for 7 days. Hydromorphone has no effect on IDH1 activity and served as a negative opiate control. DNA methylation was quantified by ELISA assay. Targeted metabolomics was performed using LC/MS. Mitochondrial function was determined using a Seahorse assay. The in vivo effect of morphine was tested using a mouse xenograft model (intraperitoneal U87 injection), with daily subcutaneous injections of morphine (5mg/kg or 10mg/kg) or hydromorphone (2mg/kg). Tumors at 30 days were measured by weight and histopathology. Cooperative capacity of morphine and the antineoplastic drug temozolomide (TMZ) was determined by measuring in vitro cell viability. Results We observed a dose‐dependent increase in both DNA methylation (91%, p<0.05) and 2HG oncometabolite levels (75%, p<0.01) relative to vehicle in morphine samples. Levels of 22 metabolites were uniquely affected by morphine (fig. 1). Pathway enrichment analysis highlighted changes to glutamate metabolism, glycolysis, and the pentose phosphate pathway, all previously implicated in the beneficial IDH1 mutant glioma phenotype. Consistent with metabolic reprogramming, morphine reduced basal and ATP‐coupled respiration (15%, p<0.01). Tumors from morphine treated mice were significantly smaller than vehicle (3.7‐fold, p<0.01). Histological staining of these tumors revealed increased apoptosis (172%, p<0.01) and reduced proliferation (26%, p<0.01) (fig. 2). Morphine potentiated the effect of TMZ, co‐treatment exhibited more cell death after 36 hours (18%, p<0.05) than either drug alone. Conclusion Our research has shown that morphine treatment, through its interaction with IDH1, is able to increase 2HG and DNA methylation. The opiate altered pathways significant in glioma metabolism and energy production. Moreover, it significantly reduced glioma tumor growth and enhanced the chemotherapeutic effect of TMZ, much like the known impact of the IDH1 mutation. We conclude that the interaction between morphine and IDH1 mimics the beneficial IDH1 mutation phenotype. Our research highlights the need for personalized medicine pathways, illustrating that that even the choice of opiate for pain management can potentially alter the course of oncologic disease. Sources 1. Dang, L. et al. Nature (2009) 2. Paldor, I. et al . J. Clin. Neurosci. (2016)
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".