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 remains a central unresolved question in cancer evolution. Here, we use neuroblastoma and hepatoblastoma, paediatric cancers with low genetic instability, as tractable model systems to study the principles and molecular mechanisms key in the early stages of adaptation to treatment. We identify transcriptional noise, the stochastic variability in gene expression, as a latent cellular property amplified post-treatment, during awakening from persistence, which drives adaptive regrowth following therapy withdrawal. Through functional assays, lineage tracing and single-cell multiomics, we show that treatment enriches for progenitor-like tolerant states with limited detectable clonal selection. Upon drug withdrawal, awakening proceeds via a stochastic process driven by noise-enabled plasticity in cell identity programmes. Mechanistically, AP-1-bound chromatin states emerge post-treatment, and AP-1 activity primes KDM5A recruitment to cell-state identity genes during awakening, driving H3K4me3 peak narrowing at their promoters and an increase in transcriptional noise that enables MES-to-ADRN plasticity. Pharmacological or genetic inhibition of AP-1 or KDM5 activity suppresses transcriptional noise and phenotypic plasticity, blocks DTP exit and prevents tumour recovery in both neuroblastoma and hepatoblastoma models. Together, these results identify KDM5-mediated transcriptional noise, operating within AP-engaged chromatin landscapes, as a key driver of early adaptation to treatment. Targeting this axis offers a strategy to exploit DTP awakening as an evolutionary bottleneck to prevent treatment adaptation and cancer 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".