Clonal hematopoiesis and therapy-related myeloid neoplasms following neuroblastoma treatment
Bibliographic record
Abstract
Therapy-related myeloid neoplasms (TMN) constitute one of the most challengingcomplications of cancer treatment.1 Whilst understanding of TMN pathogenesis remains fragmentary, genomic studies in adults have thus far refuted the notion that TMN simply result from cytotoxin-induced DNA damage.2–4 Analysis of the preclinical evolution of a limited number of adult TMN have retraced the majority of cases to clonal haematopoiesis (CH) that predates cytotoxic treatment and lacks the mutational footprint of genotoxic therapies.2–6 Balanced translocations, generally attributed to treatment with topoisomerase II inhibitors, are implicated in a minority of TMN.1 TMN is a leading cause of premature death in childhood cancer survivors, and affects 7-11% of children treated for high-risk neuroblastoma and sarcoma.7,8 However, the origin of pediatric TMN remains unclear. Targeted sequencing of known cancer genes detects CH in ~4% of children following cytotoxic treatment,6,9 whereas CH is vanishingly rare in young individuals in the general population.10,11 Moreover, to our knowledge, no cases of childhood TMN have been retraced to pretreatment CH. In light of these observations, we asked whether a broader driver landscape had eluded targeted CH screens in pediatric cancer patients and/or whether therapy-induced mutagenesis may be an under-recognised catalyst of CH and TMN in this patient group.
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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 | 0.002 |
| 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) | 0.002 | 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".