Emergence of new somatic mutations in CML patients optimally responding to tyrosine kinase inhibitor therapy: Proposal of long-term genomic monitoring
Bibliographic record
Abstract
Abstract Introduction Next-generation sequencing (NGS) has revealed a broader range of somatic mutations in chronic myeloid leukemia (CML) patients (pts), which is associated with long-term outcomes, including ASXL1. The current ELN laboratory guideline recommend NGS profiling for pts progressing to blast phase (BP) but not at diagnosis or in optimally responding chronic phase (CP) CML pts. With effective suppression of the Philadelphia chromosome-positive (Ph+) clone, 5–15% of pts develop clonal evolution (CE) in Philadelphia-negative (Ph-) cells, such as monosomy 7 or trisomy 8, typically detected via metaphase cytogenetics. It is plausible to expect these clones to carry somatic mutation(s), which could be identified earlier by NGS. However, the dynamics of somatic mutation profile following long-term TKI therapy in CML pts is not well investigated. Also, the dynamics of somatic mutations in optimally responding pts remain underexplored, particularly for the emergence of new mutations. We hypothesized that such mutations may arise in Ph- cells. This study aimed to characterize these mutations, focusing on their genetic profiles and doubling times (DT), and to assess their clinical relevance. Methods We analyzed paired peripheral blood samples from 51 CML pts treated at Princess Margaret Cancer Centre (Toronto, Canada) and University Hospital Brno (Brno, Czech Republic), selected from a 254-pts' cohort (Blood Advances 2024). DNA was extracted from mononuclear cells and sequenced using a single-molecule-tagging, molecular inversion probe (smMIP)-based approach. A custom CML-specific smMIP panel targeting 37 genes (332 amplicons) was used, covering epigenetic regulators, signaling pathways, transcription factors, spliceosome components, tumor suppressors, and cohesion complex genes. The assay had a detection limit of 0.1%. Mutation doubling time (DT) was calculated using two time points (T1 and T2) with the formula: DT = (T2 − T1) × log(2) / [log(VAF2) − log(VAF1)]. Results Median age was 61 years (range: 17–80). Disease risk was stratified at diagnosis with 8 (16%), 27 (53%) 14 pts (26%) as low, intermediate and high Sokal risk group. First-line therapy included imatinib in 36 pts (71%) and second-generation TKIs in 15 pts (29%) (nilotinib n=12, dasatinib n=3). Sequencing was performed prior to TKI therapy and at a median of 398 days post-TKI therapy. At the time of 2nd sample collection, 42 pts (82%) had achieved an optimal response per 2020 ELN guidelines. Among them, somatic mutations were detected in 21 pts (50%). Eight pts (16%) had mutations at diagnosis with similar allele frequencies, while 13 (26%) acquired new mutations during TKI therapy. Frequently mutated genes among optimal responders with emerging mutations included DNMT3A (n=6), TET2 (n=5), ASXL1 (n=4), and EZH2 (n=2). Additional mutations in JAK2, SF3B1, U2AF1, PHF6, TP53, BRAF, and CBL were each seen in one patient. Median time to new mutation emergence was 227 days (range: 105–7578), with a median DT of 59 days (range: 22–1909). Among optimal responders with emerging mutations (n=13), median time to MR4 was 255 days (range: 168–776), compared to 469 days (range: 168–4008) in those without mutations (n=21; p=0.14). All pts achieving optimal responses with emerging mutations eventually achieved MR4 or deeper response with a median follow-up of 2.7 years (range: 294–7601 days) and none lost MR4 while on TKI therapy. Treatment-free remission (TFR) was attempted in 4 pts (31%), with 2 maintaining TFR at last follow-up. Conclusions We have observed a new pattern of somatic mutation dynamics in CML pts on TKI therapy. Emergence of new somatic mutations was observed in CML pts achieving optimal TKI response. Thus, their presence is not necessarily a marker of clonal evolution toward TKI resistance or disease progression. Current ELN laboratory guideline does not recommend baseline NGS in CP-CML, limiting proper interpretation of new mutations during follow-up. Our findings support baseline mutation profiling in all CML pts at diagnosis, regardless of clinical status. Many detected mutations are consistent with clonal hematopoiesis, potentially contributing to the development of clonal evolution in Ph- clone after optimal TKI response. We now recommend regular monitoring of these mutations during TKI therapy. Given the median DT of ~60 days, annual NGS surveillance may be a practical strategy for early detection of significant clonal changes.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".