B3GALT6 Promotes Dormant Breast Cancer Cell Survival and Recurrence by Enabling Heparan Sulfate-Mediated FGF Signaling
Bibliographic record
Abstract
Summary Breast cancer mortality results primarily from incurable recurrent tumors seeded by dormant, therapy-refractory residual tumor cells (RTCs). Understanding the mechanisms enabling dormant RTC survival is therefore essential for improving patient outcomes. We derived a dormancy-associated RTC signature that mirrors the transcriptional response to neoadjuvant chemotherapy in patients and is enriched for extracellular matrix-related pathways. In vivo CRISPR-Cas9 screening of dormancy-associated candidate genes identified the galactosyltransferase B3GALT6 as a functional regulator of RTC fitness. B3GALT6 covalently attaches glycosaminoglycans (GAGs) to proteins to generate proteoglycans and its germline loss-of-function causes skeletal dysplasias. We determined that B3GALT6-mediated biosynthesis of the GAG heparan sulfate predicts poor patient outcomes, promotes tumor recurrence by enhancing dormant RTC survival in multiple contexts, and does so via a B3GALT6-heparan sulfate/HS6ST1-heparan 6- O -sulfation/FGF1-FGFR2 signaling axis. These findings identify a role for B3GALT6 in cancer and suggest targeting FGF signaling as a novel approach to preventing recurrence by eradicating dormant RTCs.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".