Receptor for hyaluronan‐mediated motility ( <scp>RHAMM</scp> ) 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 synthesis, processing, and signaling of hyaluronan (HA). Hyaluronan‐mediated motility receptor (RHAMM, CD168; HMMR ) is an HA receptor enabling tumor cells to sense and respond to this aberrant TME during breast cancer progression. Previous studies have associated RHAMM expression with breast tumor progression; however, cause and effect mechanisms are incompletely established. Focused gene expression analysis of an internal breast cancer patient cohort confirmed that increased RHAMM expression correlates with aggressive clinicopathological features. To probe mechanisms, we developed a novel 27‐gene RHAMM‐related signature (RRS) by intersecting differentially expressed genes in lymph node (LN)‐positive patient cases with the transcriptome of a RHAMM‐dependent model of cell transformation, which we validated in an independent cohort. We demonstrate that the RRS predicts for poor survival and is enriched for cell cycle and TME‐interaction pathways. Further analyses using CRISPR/Cas9‐generated RHAMM −/− breast cancer cells provided direct evidence that RHAMM promotes invasion in vitro and in vivo . Immunohistochemistry studies highlighted heterogeneous RHAMM protein expression, and spatial transcriptomics associated the RRS with RHAMM‐high microanatomic foci. We conclude that RHAMM upregulation leads to the formation of ‘invasive niches’, which are enriched in RRS‐related pathways that drive invasion and could be targeted to limit invasive progression and improve patient outcomes. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| 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) | 0.000 | 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 teacher head, 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".