Receptor for Hyaluronan-Mediated Motility (RHAMM) defines an invasive niche associated with tumor progression and predicts poor outcomes in breast cancer patients
Bibliographic record
Abstract
ABSTRACT Breast cancer invasion and metastasis result from a complex interplay between tumor cells and the tumor microenvironment (TME). Key oncogenic changes in the TME include aberrant metabolism and subsequent signaling of hyaluronan (HA). Hyaluronan Mediated Motility Receptor (RHAMM, HMMR ) is a HA receptor that enables tumor cells to sense and respond to the TME during breast cancer progression. Focused gene expression analysis of an internal breast cancer patient cohort demonstrates increased RHAMM expression correlates with aggressive clinicopathological features. We also develop a 27-gene RHAMM-dependent signature (RDS) by intersecting differentially expressed genes in lymph node positive cases with the transcriptome of a RHAMM-dependent model of cell transformation, which we validate in an independent cohort. We demonstrate RDS predicts for poor survival and associates with invasive pathways. Further analyses using CRISPR/Cas9 generated RHAMM -/- breast cancer cells provide direct evidence that RHAMM promotes invasion in vitro and in vivo . Additional immunohistochemistry studies highlight heterogeneous RHAMM expression, and spatial transcriptomics confirms the RDS emanates from RHAMM-high invasive niches. We conclude RHAMM upregulation leads to the formation of ‘invasive niches’, which are enriched in RDS-related pathways that drive invasion and could be targeted to limit invasive progression and improve patient outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 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".