Rab35-dependent extracellular nanovesicles are required for induction of tumour supporting stroma
Bibliographic record
Abstract
Communication between diseased cells and the microenvironment is a complex yet crucial element in progression of varied pathological processes. Recent studies in cancer highlight an important role for small extracellular nanovesicles secreted by cancer cells as modulators of cancer-associated stroma, leading to enhanced angiogenesis and metastatic priming. The intrinsic factors regulating extracellular nanovesicle biogenesis and secretion are therefore relevant in studies of nano-communication in the cancer milieu. We generated prostate cancer cells bearing stable knockdown of several candidate vesicle regulating factors and examined the impact on cell health, vesicle secretion and on communication with fibroblastic stromal cells. We highlight that RAB11B and RAB35 regulate phenotypically distinct nanovesicle populations, each accounting for only around 20% of the total. Depleting RAB35, but not RAB11B leaves a remaining population of vesicles whose phenotype is insufficient for driving fibroblast to myofibroblast differentiation, leading to attenuated motile behaviours in 3D in vitro models. Co-implantation of tumour cells with stromal fibroblasts in xenografts similarly showed that RAB11B knockdown had little effect on growth rates in vivo. In contrast, significant attenuation in growth, and attenuation of myofibroblasts at the tumour site was evident when using RAB35-knockdown cells. The study concludes that a RAB35 regulated nanovesicle sub-population is particularly important for communication between cancer and stromal cells, and is required for generating a tumour-supportive microenvironment.
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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".