Metabolic control points in cancer (85.3)
Bibliographic record
Abstract
All cells must manage their energetic resources to survive. This is particularly true for cancer cells, which initiate changes in their cellular metabolism as they transition from a normal to a cancerous state. Tumor cells must engage pathways of cellular metabolism to generate the energy and biosynthetic intermediates required to support increased cell growth and division. In addition, for growing tumours, overcoming metabolic stress induced by nutrient limitation and/or hypoxia is a critical step for solid tumour growth. It is now appreciated that many of the predominant mutations observed in cancer also influence tumor metabolism as part of their mode of action. Here I discuss the role that cellular energy sensors ‐ notably the Liver Kinase‐B1 (LKB1) and the AMP‐activated protein kinase (AMPK) ‐ play in regulating tumor metabolism and adaptation to metabolic stress. We have found that the LKB1‐AMPK pathway negatively regulates aerobic glycolysis (the “Warburg effect”) in cancer cells, and that disruption of this pathway promotes a metabolic shift to aerobic glycolysis and supports increased cell growth. This metabolic shift is mediated by the hypoxia‐inducible factor‐1α (HIF‐1α), and silencing HIF‐1α reverses the biosynthetic and proliferative advantages conferred by reduced LKB1‐AMPK signaling. Finally, I will present evidence that AMPK activity is dynamically regulated in tumors to enhance pro‐growth metabolism, but that this pathway is required to maintain tumor cell viability in response to stress. Finally, I will discuss whether metabolic checkpoints serve “suppressive” or “supportive” roles in cancer development and/or progression. This work was supported by grants from the CIHR (MOP‐93799) and CCSRI (700586).
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.033 | 0.016 |
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".