High hyaluronan binding and RHAMM expression identify an invasive and metastatic subpopulation in androgen-resistant prostate cancer cells
Bibliographic record
Abstract
ABSTRACT Hyaluronan (HA) metabolism in prostate cancer associates with androgen resistance and metastasis. We showed that binding of low molecular weight HA (≤250kDa) to castration-resistant prostate cancer cells was heterogeneous with most cells binding low amounts of HA (HA low ) while a minor subset bound higher amounts of this polysaccharide (HA high ). HA high subsets, which were separated by FACS, were stably more metastatic in vivo than HA low comparators. Multiplexed flow cytometry analyses indicated that both subsets displayed similar expression of the HA receptor CD44 while an elevated RHAMM cell surface display was unique to HA high subsets. Genomic deletion of RHAMM using CRISPR-Cas9 editing reduced the detection of HA high subsets by 6mer but not 250kDa HA fluorescent probes, and phenocopied the lower aggressive properties of HA low tumor cells. Few differences in the mutation landscape of RHAMM +/+ vs. RHAMM -/- tumor cells were detected but pathway analyses of differentially expressed genes predicted RHAMM -loss altered extracellular matrix signaling. Transcriptomic analyses revealed that HA high subsets and RHAMM + /+ PC3MLN4 cells shared high expression of follistatin (FST), an activin member of the TGF-β family that is clinically linked to metastases in PCA patients. A causal role for FST in RHAMM +/+ tumor cell aggression was assessed using motility as a surrogate marker of invasive capability. FST antibodies blocked RHAMM +/+ PC3MLN4 cell migration while conversely, recombinant FST protein rescued the migration deficit of RHAMM -/- comparators. These results define a novel form of prostate cancer cell heterogeneity, identify a method for detecting and isolating highly metastatic subsets and highlight a novel RHAMM-regulated pathway that may be targeted to improve patient management by limiting metastasis.
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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".