KDM5-driven transcriptional noise fuels plasticity-led awakening and relapse in paediatric cancer
Bibliographic record
Abstract
How drug-tolerant persister (DTP) cells escape quiescence to drive tumour relapse is a central unresolved question in cancer evolution. Here, we identify transcriptional noise (TN), defined as the stochastic variability in gene expression, as a latent property of paediatric cancer cells that becomes a driver of adaptive regrowth after treatment withdrawal. Using functional assays, lineage tracing, single-cell transcriptomics, and multiscale landscape modelling, we show that therapy enriches mesenchymal-like tolerant states in neuroblastoma without clonal selection, while post-treatment awakening is a stochastic process fuelled by noise-enabled plasticity in cell-identity programmes. The histone demethylase KDM5A relocates to noisy cell-state genes during awakening, promoting H3K4me3 removal and chromatin remodelling at these loci. KDM5 inhibition abrogates this process, and suppresses transcriptional noise, halts DTP exit, and prevents tumour recovery in both neuroblastoma and hepatoblastoma models. These results establish DTP as an exploitable evolutionary bottleneck, positioning KDM5-mediated transcriptional noise as an actionable therapeutic target to limit cancer adaptation and relapse.
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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) | 0.002 | 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".