Lenalidomide promotes the development of <i>TP53</i>-mutated therapy-related myeloid neoplasms
Bibliographic record
Abstract
There is a growing body of evidence that therapy-related myeloid neoplasms (t-MNs) with driver gene mutations arise in the background of clonal hematopoiesis (CH) under the positive selective pressure of chemo- and radiation therapies. Uncovering the exposure relationships that provide selective advantage to specific CH mutations is critical to understanding the pathogenesis and etiology of t-MNs. In a systematic analysis of 416 patients with t-MN and detailed prior exposure history, we found that TP53 mutations were significantly associated with prior treatment with thalidomide analogs, specifically lenalidomide. We demonstrated experimentally that lenalidomide treatment provides a selective advantage to Trp53-mutant hematopoietic stem and progenitor cells (HSPCs) in vitro and in vivo, the effect of which was specific to Trp53-mutant HSPCs and was not observed in HSPCs with other CH mutations. Because of the differences in CK1α degradation, pomalidomide treatment did not provide an equivalent level of selective advantage to Trp53-mutant HSPCs, providing a biological rationale for its use in patients at high risk for t-MN. These findings highlight the role of lenalidomide treatment in promoting TP53-mutated t-MNs and offer a potential alternative strategy to mitigate the risk of t-MN development.
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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) | 0.001 | 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".