Impact of cytotoxic therapy on clonal hematopoiesis and myeloid neoplasms in breast cancer patients
Bibliographic record
Abstract
Clonal hematopoiesis (CH), which is characterized by variants of hematopoietic stem cells, increases the risk of subsequent myeloid neoplasms (MNs). This study aimed to investigate the prevalence and characteristics of CH variants in breast cancer (BC) patients treated with cytotoxic therapy (CT), focusing on those who developed MNs after cytotoxic therapy (MN-pCT). We retrospectively analyzed 107 BC patients from a biobank and sequenced peripheral blood and bone marrow samples from 31 CH-associated genes at 2 time points. We analyzed changes in CH for paired samples: T0 to T1 (before and after CT) and T1 to T2 (after CT vs greater CT exposure). Additionally, we compared CH variants in patients with and without MN-pCT. 29% of patients harbored CH variants that were restricted to 8 genes and DNMT3A was the most frequent variant. Among 54 patients with paired samples (T1 to T2), the variant allele frequency (VAF) of CH variants significantly increased after greater CT exposure (P = .02). However, there were no significant changes in VAF before and after CT. Five of the 9 patients who developed MN-pCT harbored CH variants. TP53 was the most frequently mutated gene, but it did not significantly affect MN-pCT risk compared to patients without CH variants. Although the presence of CH did not directly predict MN-pCT development in patients with BC, CT induced changes in CH genes. Further studies are required to determine the role of specific CH variants in the risk of MN-pCT and their potential as predictive biomarkers.
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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 | 0.001 |
| 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".