Compartment-specific activation of PPARγ governs breast cancer tumor growth, via metabolic reprogramming and symbiosis
Bibliographic record
Abstract
The role of PPARγ in cancer therapy is controversial, with studies showing either pro-tumorigenic or antineoplastic effects. This debate is very clinically relevant, because PPARγ agonists are used as antidiabetic drugs. Here, we evaluated if the effects of PPARγ on tumorigenesis are determined by the cell type in which PPARγ is activated. Second, we examined if the metabolic changes induced by PPARγ, such as glycolysis and autophagy, play any role in the tumorigenic process. To this end, PPARγ was overexpressed in breast cancer cells or in stromal cells. PPARγ-overexpressing cells were examined with respect to (1) their tumorigenic potential, using xenograft models, and (2) regarding their metabolic features. In xenograft models, we show that when PPARγ is activated in cancer cells, tumor growth is inhibited by 40%. However, when PPARγ is activated in stromal cells, the growth of co-injected breast cancer cells is enhanced by 60%. Thus, the effect(s) of PPARγ on tumorigenesis are dependent on the cell compartment in which PPARγ is activated. Mechanistically, stromal cells with activated PPARγ display metabolic features of cancer-associated fibroblasts, with increased autophagy, glycolysis and senescence. Indeed, fibroblasts overexpressing PPARγ show increased expression of autophagic markers, increased numbers of acidic autophagic vacuoles, increased production of L-lactate, cell hypertrophy and mitochondrial dysfunction. In addition, PPARγ fibroblasts show increased expression of CDKs (p16/p21) and β-galactosidase, which are markers of cell cycle arrest and senescence. Finally, PPARγ induces the activation of the two major transcription factors that promote autophagy and glycolysis, i.e., HIF-1α and NFκB, in stromal cells. Thus, PPARγ activation in stromal cells results in the formation of a catabolic pro-inflammatory microenvironment that metabolically supports cancer growth. Interestingly, the tumor inhibition observed when PPARγ is expressed in epithelial cancer cells is also associated with increased autophagy, suggesting that activation of an autophagic program has both pro- or antitumorigenic effects depending on the cell compartment in which it occurs. Finally, when PPARγ is expressed in epithelial cancer cells, the suppression of tumor growth is associated with a modest inhibition of angiogenesis. In conclusion, these data support the "two-compartment tumor metabolism" model, which proposes that metabolic coupling exists between catabolic stromal cells and oxidative cancer cells. Cancer cells induce autophagy, glycolysis and senescence in stromal cells. In return, stromal cells generate onco-metabolites and mitochondrial fuels (L-lactate, ketones, glutamine/aminoacids and fatty acids) that are used by cancer cells to enhance their tumorigenic potential. Thus, as researchers design new therapies, they must be conscious that cancer is not a cell-autonomous disease, but rather a tumor is an ecosystem of many different cell types, which engage in metabolic symbiosis.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".