Inhibition of Autophagy by Mevalonate Pathway Inhibitors, a New Therapeutic Approach to sensitize Glioblastoma Cells to Temozolomide Induced Apoptosis
Bibliographic record
Abstract
Glioblastoma multiforme (GBM) is the deadliest brain tumor with an approximate 20 month survival rate after diagnosis and treatment. Temozolomide (TMZ), the chemotherapeutic drug of choice for GBM, is an alkylating agent that causes DNA damage. TMZ treatment results in the induction of apoptosis in GBM cells, however, it induces autophagy and consequently chemoresistance. Statins are mevalonate (MEV) cascade inhibitors with beneficial effects on the enhancement of the survival rate of patients with different types of cancer, particulary if consumed long term before diagnosis of cancer. Here, we determined the effect of simvastatin (Simva), a blood brain barrier permeable statin, on the sensitization of GBM cells to TMZ induced apoptosis through inhibition of autophagy flux. We pretreated two GMB cell lines, U251 and U87 cells, with low doses of Simva (1 and 2.5 μM, respectively) with or without different intermediates of the mevalonate cascade: MEV (2.5 mM), Farnesyl pyrophosphate (FPP) (15 μM), Geranylgeranyl pyrophosphate (GGPP) (15 μM) and cholesterol (50 μM) for 4 h and then treated cells with TMZ (100 μM) for 48–96 h. A signficiantly reduced viability and increase in the population of apoptotic dead cells were observed in GBM cells treated with the combination of Simva‐TMZ compared to cells treated with TMZ alone. Addition of MEV, FPP, GGPP and cholesterol did not attenuate these effects significantly. Sima‐TMZ treatment did not alter the total cholesterol pool in U87 and U251 cells compared to controls. Western blot analysis, immunocytochemistry and transmission electron microscopy revealed that the combination of Simva with TMZ inhibited autophagic flux. Overall, the results suggest that sensitization of GBM cells to TMZ‐induced apoptosis by Simva is independent on the cholesterol biosynthetic pathway but may involve inhibition of autophagy flux. Support or Funding Information Supported by funding from a Mitacs Accelerate PDF Award & a Health Science Center Foundation Operating Grant. This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".