P02.16.B COMPENSATORY CROSS-TALK BETWEEN AUTOPHAGY AND GLYCOLYSIS REGULATES SENESCENCE AND STEMNESS IN HETEROGENEOUS GLIOBLASTOMA TUMOR SUBPOPULATIONS
Bibliographic record
Abstract
Abstract BACKGROUND Despite tremendous research efforts, the successful targeting of aberrant tumor metabolism in clinical practice has remained elusive. Tumor heterogeneity and metabolic plasticity may play a role in the clinical failure of metabolism-targeting interventions for treating cancer patients. Moreover, compensatory growth-related processes and adaptive responses exhibited by heterogeneous tumor subpopulations to metabolic inhibitors are poorly understood. Hence, a deeper understanding of the cellular adaptations in response to metabolic interventions is urgently required to develop more effective therapeutic options against deadly glioblastoma (GBM) brain tumors. MATERIAL AND METHODS Using clinically-relevant patient-derived GBM models, we discovered a cross-talk between glycolysis, autophagy, and senescence. Protein and mRNA expression of glycolytic enzymes and stemness markers were measured using western blot and bioinformatic analysis, respectively. We were able to measure senescence with the use of β-galactosidase staining and upregulation of cell cycle regulators, such as p21/CDKN1A and p16/CDKN2A. Autophagy flux and EGFP-MAP1LC3B+ puncta formation analysis was used to determine autophagy induction. RESULTS We found that stem cell-like GBM tumor subpopulations possessed higher basal levels of glycolytic activity and increased expression of several glycolysis-related enzymes including, GLUT1/SLC2A1, PFKP, ALDOA, GAPDH, ENO1, PKM2, and LDH, compared to their non-stem-like counterparts. Importantly, the mRNA expression of glycolytic enzymes positively correlates with stemness markers (CD133/PROM1 and SOX2) in patient GBM tumors. While treatment with glycolysis inhibitors induced senescence in stem cell-like GBM tumor subpopulations, these cells maintained their aggressive stemness features and failed to undergo apoptotic cell death. Moreover, we determined that inhibition of glycolysis led to the induction of autophagy in stem cell-like GBM tumor subpopulations, but not in their non-stem-like counterparts. Similarly, blocking autophagy in stem cell-like GBM tumor subpopulations induced senescence-associated growth arrest without hampering stemness capacity or triggering apoptosis while reciprocally upregulating glycolytic activity. Combinatorial treatment of stem cell-like GBM tumor subpopulations with autophagy and glycolysis inhibitors blocked the induction of senescence while drastically impairing their stemness capacity which drove cells towards apoptotic cell death. CONCLUSION These findings identify a novel and complex compensatory interplay between glycolysis, autophagy, and senescence that helps maintain stemness in heterogeneous GBM tumor subpopulations and provides a survival advantage during metabolic stress. This work was supported by the Cancer Research Society (CRS).
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".