Tumour-derived extracellular vesicles within the therapy-induced senescent secretome distinctly suppress breast cancer via DKK1-mediated inflammatory response
Bibliographic record
Abstract
ABSTRACT Triple-negative breast cancers (TNBC), associated with poor prognosis and high tumour recurrence, are often-treated with taxanes in first-line treatment regimens. However, acquired disease resistance can often set in, hampering clinical efficacy. One avenue that could engender therapy resistance is therapy-induced senescence (TIS), as they represent a population of residual disease and are highly secretory. Although it is known that TIS can contribute to tumour development and therapy resistance via the therapy-induced secretome, the underlying molecular mechanisms are not fully understood. In this study, we sought to dissect the role of the TNBC-derived TIS-associated secretome in chemoresponse. We found that paclitaxel-treated cells induced mitotic slippage and entered senescence as tetraploid cells. The therapy-induced SASP was found to be enriched in soluble cytokines and other pro-tumorigenic factors linked to tumour recurrence and distant metastasis. Interestingly, we found that senescence-associated small extracellular vesicles (sEVs) or exosomes, an underappreciated component of SASP, increased genomic instability, ROS and anti-tumour activity. Exosomal proteomic and transcriptomic profiling further revealed DKK1, a negative regulator of WNT signalling, to be enriched in TIS-sEVs. Further investigation demonstrated DKK1-control of inflammatory cytokines production to confer reduced tumour activity in recipient TNBC cancer cells. Taken together, this study revealed unexpected findings where TIS-sEVs confer opposing tumourigenic outcomes to that elicited by TIS-SASP, indicating that sEVs should be considered as distinct SASP entities.
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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 | 0.000 |
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".