Proteostasis control via HSP90α sustains YAP activity to drive aggressive behaviours in cancer-associated fibroblasts
Bibliographic record
Abstract
ABSTRACT Cancers adapt proteostasis to cope with the burden of misfolded proteins, stabilize key signalling nodes and sustain their malignant behaviour. Tumour stroma is subjected to similar stresses, but how they influence its aberrant status remains unclear. We show that tumour stroma presents consistent upregulation of target genes associated to the major misfolding regulator HSP90 in cancer-associated fibroblasts (CAFs), and that HSP90α is required for CAFs to remodel the extracellular matrix (ECM) and promote cancer cell motility and growth. Mechanistically, HSP90α sustains TGFβ responses and YAP protein levels required for CAF functionality. In vivo, stromal or fibroblast-specific loss of HSP90α results in reduced ECM deposition, angiogenesis, growth and dissemination of breast tumours. Clinical analyses reveal a correlation between HSP90-dependent programs and YAP activity in CAFs, that are also associated with poor patient prognosis. Our findings uncover a link between proteostasis, mechanotransduction and generation of aggressive tumour microenvironments through HSP90α.
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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 | 0.000 | 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".