CD44v6 Dependence of Premetastatic Niche Preparation by Exosomes
Bibliographic record
Abstract
The metastasizing capacity of the rat pancreatic adenocarcinoma BSp73ASML (ASML(wt)) is strikingly reduced by a knockdown of CD44v4-v7 (ASML(kd)). We used this model to analyze the role of the CD44 variant isoform (CD44v) in (pre)metastatic niche formation. Intrafootpad injections of ASML(wt)-, but not ASML(kd)-conditioned medium (CM), strongly promote settlement of ASML(kd) cells in lymph nodes and lung. Fractionation of CM revealed a contribution by a soluble matrix and exosomes, where the CD44v6-containing ASML(wt)-soluble fraction can complement ASML(kd)-exosomes, but not vice versa. This implies that exosomes are the final actors, are CD44v-independent, but require a soluble matrix, which depends on CD44v. Analyzing the composition revealed that only the ASML(wt)-matrix contains c-Met and urokinase-type plasminogen activator receptor. In vitro, mostly ASML(wt)-exosomes promote proliferation and induce gene expression in metastatic organ cells. However, in vivo corresponding changes in the (pre) metastatic organ are only observed when both, exosomes plus the soluble matrix, are provided. Thus, neither CD44v nor exosomes alone suffice for (pre)metastatic niche formation. Instead, CD44v suffices for assembling a soluble matrix, which allows exosomes, independent of their origin from poorly or highly metastatic cells, to modulate (pre) metastatic organ cells for tumor cell embedding and growth.
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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".