Oncogenes induce the cancer-associated fibroblast phenotype: Metabolic symbiosis and “fibroblast addiction” are new therapeutic targets for drug discovery
Bibliographic record
Abstract
Metabolic coupling, between mitochondria in cancer cells and catabolism in stromal fibroblasts, promotes tumor growth, recurrence, metastasis, and predicts anticancer drug resistance. Catabolic fibroblasts donate the necessary fuels (such as L-lactate, ketones, glutamine, other amino acids, and fatty acids) to anabolic cancer cells, to metabolize via their TCA cycle and oxidative phosphorylation (OXPHOS). This provides a simple mechanism by which metabolic energy and biomass are transferred from the host microenvironment to cancer cells. Recently, we showed that catabolic metabolism and "glycolytic reprogramming" in the tumor microenvironment are orchestrated by oncogene activation and inflammation, which originates in epithelial cancer cells. Oncogenes drive the onset of the cancer-associated fibroblast phenotype in adjacent normal fibroblasts via paracrine oxidative stress. This oncogene-induced transition to malignancy is "mirrored" by a loss of caveolin-1 (Cav-1) and an increase in MCT4 in adjacent stromal fibroblasts, functionally reflecting catabolic metabolism in the tumor microenvironment. Virtually identical findings were obtained using BRCA1-deficient breast and ovarian cancer cells. Thus, oncogene activation (RAS, NFkB, TGF-β) and/or tumor suppressor loss (BRCA1) have similar functional effects on adjacent stromal fibroblasts, initiating "metabolic symbiosis" and the cancer-associated fibroblast phenotype. New therapeutic strategies that metabolically uncouple oxidative cancer cells from their glycolytic stroma or modulate oxidative stress could be used to target this lethal subtype of cancers. Targeting "fibroblast addiction" in primary and metastatic tumor cells may expose a critical Achilles' heel, leading to disease regression in both sporadic and familial cancers.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".