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Record W2120002678 · doi:10.4161/cc.24289

Compartment-specific activation of PPARγ governs breast cancer tumor growth, via metabolic reprogramming and symbiosis

2013· article· en· W2120002678 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueCell Cycle · 2013
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPeroxisome Proliferator-Activated Receptors
Canadian institutionsInstitute of Cancer Research
Fundersnot available
KeywordsAutophagyBiologyStromal cellCarcinogenesisCancer cellCancer researchPeroxisome proliferator-activated receptorCell biologyCancerReceptorApoptosisBiochemistry

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.593

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.005
GPT teacher head0.209
Teacher spread0.204 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it