The impact of AMPK signalling and clinical therapeutics on cancer metabolism
Bibliographic record
Abstract
Cancer metabolism is intricately re-wired to support tumour growth. Despite the initial discovery that cancers depend on aerobic glycolysis, it is well appreciated that cancer cells exploit numerous metabolic pathways, including those linked to mitochondrial metabolism, to help fuel tumorigenesis. Metabolic gene programs are controlled by transcriptional complexes, such as the peroxisome proliferator-activated gamma coactivator 1 (PGC-1) / estrogen-related receptor alpha (ERR) axis, which act as master orchestrators of metabolism. The activity of the PGC-1α/ERRα axis is upregulated by the AMP-activated protein kinase (AMPK), a central metabolic regulator that is triggered in response to energetic stress. The work in this thesis demonstrates that the AMPK/PGC-1α/ERRα axis increases the bioenergetic functions of cancer cells, but inhibits one-carbon metabolism and purine biosynthesis, resulting in improved sensitivity to methotrexate (MTX), a chemotherapeutic drug widely used in the clinic. MTX treatment promotes bioenergetic functions and has antiproliferative effects that are dependent on AMPK. As a result, the combination of MTX with AMPK activators can improve chemotherapeutic response. Recently, there is increased interest in repurposing metabolic drugs to treat cancer. We show that the antidiabetic drug canagliflozin decreases cancer cell proliferation and reduces the activity of the citric acid cycle through perturbation of glutamine metabolism. The work in this thesis unravels the role of the AMPK/PGC-1α/ERRα pathway in controlling antifolate response and reinforces the premise of pharmacologically targeting cancer metabolism to impede tumourigenesis.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".