Abstract A011: Enhancing chaperone-mediated autophagy to impede glioblastoma growth
Bibliographic record
Abstract
Abstract Chaperone-mediated autophagy (CMA) is a selective form of autophagy that targets proteins with the KFERQ-like motif for lysosomal degradation. This process involves the binding of substrate proteins to the chaperone HSC70 and their subsequent transport to the receptor LAMP2A on the lysosome membrane for degradation. Previous studies have shown a significant increase in the expression level of LAMP2A and the CMA activity in glioblastoma (GBM). However, the comprehensive role of CMA in GBM pathogenesis remains unclear. In this study, we observed a progressive elevation in LAMP2A expression with increasing GBM cell confluence. Notably, inhibition of CMA through LAMP2A knockdown markedly promoted GBM growth in orthotopic mouse models, accompanied by upregulation of positive cell cycle regulators. Conversely, overexpression of LAMP2A delayed GBM cell growth and increased sub-G1 cell percentages. Treatment with CMA activators induced cell death in GBM cells while preserving normal astrocyte viability at equivalent concentrations. Our results suggest that the upregulated expression of LAMP2A in GBM cells may be influenced by the tumor microenvironment rather than being a primary driver of GBM aggressiveness. Furthermore, activating CMA could serve as a potential therapeutic strategy to inhibit GBM growth while safeguarding normal cell viability. Citation Format: Wanjun Tang, Karrie Mei Yee Kiang, Gilberto Ka Kit Leung. Enhancing chaperone-mediated autophagy to impede glioblastoma growth [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Expanding and Translating Cancer Synthetic Vulnerabilities; 2024 Jun 10-13; Montreal, Quebec, Canada. Philadelphia (PA): AACR; Mol Cancer Ther 2024;23(6 Suppl):Abstract nr A011.
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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) | 0.005 | 0.001 |
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".