Mutational dynamics of early and late relapsed childhood ALL: rapid clonal expansion and long-term dormancy
Bibliographic record
Abstract
Childhood acute lymphoblastic leukemia (cALL) is the most frequent pediatric cancer. Refractory/relapsed cALL presents a survival rate of ∼45% and is still one of the leading causes of death by disease among children. Mechanisms, such as clonal competition and evolutionary adaptation, govern treatment resistance. However, the underlying clonal dynamics leading to multiple relapses and differentiating early (<36 months postdiagnosis) from late relapse events remain elusive. Here, we use an integrative genome-based analysis combined with serial sampling of relapsed tumors (from primary tumor to ≤4 relapse events) from 19 pre-B-cell cALL patients (8 early and 11 late relapses) to assess the fitness of somatic mutations and infer their ancestral relationships. By quantifying both general clonal dynamics and newly acquired subclonal diversity, we show that 2 distinct evolutionary patterns govern early and late relapse: on one hand, a highly dynamic pattern, sustained by a putative defect of DNA repair processes, illustrating the quick emergence of fitter clones, and on the other hand, a quasi-inert evolution pattern, suggesting the escape from dormancy of leukemia stem cells likely spared from initial cytoreductive therapy. These results offer new insights into cALL relapse mechanisms and highlight the pressing need for adapted treatment strategies to circumvent resistance mechanisms.
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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 | 0.001 | 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 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".