Metabolic reprogramming, malignant transformation and metastasis: Lessons from chronic lymphocytic leukaemia and prostate cancer
Bibliographic record
Abstract
Metabolic reprogramming is a hallmark of cancer, crucial for malignant transformation and metastasis. Chronic lymphocytic leukaemia (CLL) and prostate cancer exhibit similar metabolic adaptations, particularly in glucose and lipid metabolism. Understanding this metabolic plasticity is crucial for identifying mechanisms contributing to metastasis. This review considers glucose and lipid metabolism in CLL and prostate cancer, exploring their roles in healthy and malignant states and during disease progression. In CLL, lipid metabolism supports cell survival and migration, with aggressive disease characterised by increased fatty acid oxidation and altered sphingolipids. Richter's transformation and aggressive lymphoma, however, exhibit a metabolic shift towards increased glycolysis. Similarly, prostate cell metabolism is unique, relying on citrate production in the healthy state and undergoing metabolic reprogramming during malignant transformation. Early-stage prostate cancer cells increase lipid synthesis and uptake, and decrease glycolysis, whereas metastatic cells re-adopt glucose metabolism, likely driven by interactions with the tumour microenvironment. Genetic drivers including TP53 and ATM mutations connect metabolic alterations to disease severity in these two malignancies. The bone microenvironment supports the metabolic demands of these malignancies, serving as an initiation niche for CLL and a homing site for prostate cancer metastases. By comparing these malignancies, this review underscores the importance of metabolic plasticity in cancer progression and highlights how CLL and prostate cancer may be models of circulating and solid tumours more broadly. The metabolic phenotypes throughout cancer cell transformation and metastasis, and the microenvironment in which these processes occur, present opportunities for interventions that could disrupt metastatic processes and improve patient outcomes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".