Investigating autophagy and glutamine metabolism as therapeutic targets for pancreatic cancer.
Bibliographic record
Abstract
381 Background: Pancreatic ductal adenocarcinoma (PDAC) is one of the deadliest types of cancer, for which new therapeutic approaches are urgently needed. We are developing novel combination therapy approaches based on the inhibition of glutamine metabolism and autophagy, to improve current treatments for PDAC. Since both processes are key mediators of multiple cancer hallmarks, and have inter-related but non-redundant roles, this combination may result in a more efficient disruption of cancer cell resistance to treatments. Methods: We interrogated the Pancreas Centre BC tissue micro-array, containing the epithelial component of 252 PDAC samples, for expression of three key glutamine-metabolizing proteins and two autophagy-related proteins: glutamine synthetase (GLUL), asparagine synthetase (ASNS), glutaminase C (GLS-GAC), microtubule-associated protein 1 light chain 3 beta (MAP1-LC3B or LC3B) and autophagy-related protein 4B (ATG4B). While previous efforts by other groups have focused on GLS-GAC, the role of GLUL in cancer has remained less well understood. We thus investigated the functional relevance of GLUL using a panel of PDAC cell lines. We are also investigating interactions between glutamine metabolism and autophagy, including the exploration of strategies to target GLUL and the evaluation of a new class of ATG4B inhibitors for the treatment of pancreatic cancers. Results: We found that GLUL, ASNS, GLS-GAC, LC3B and ATG4B were expressed in 31%, 58%, 99%, 51% and 73%, respectively, of PDAC samples. Furthermore, higher LC3B expression correlated with poor outcome. Our functional studies revealed that various PDAC cells express GLUL, which can be upregulated upon glutamine deprivation. In all cell lines tested, GLUL knockdown sensitized them to gemcitabine, as assessed by a long-term recovery assay. We also found that the candidate ATG4B inhibitors, shown to inhibit this target in cell-free assays, effectively inhibit PDAC cell proliferation at micromolar concentrations. Conclusions: Our study reveals that glutamine metabolism and autophagy are clinically relevant in PDAC and may have potential as therapeutic targets. Supported by Pancreas Centre BC, BC Cancer Foundation and VGH Foundation.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".