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Record W4417021452 · doi:10.1182/blood-2025-3767

Emergence of new somatic mutations in CML patients optimally responding to tyrosine kinase inhibitor therapy: Proposal of long-term genomic monitoring

2025· article· en· W4417021452 on OpenAlexaffabout
Yael Morgenstern, Gopila Gupta, Flavia Patino, María Agustina Perusini, Daniela Žáčková, Ivana Ježíšková, Anežka Kvetková, Tomáš Jurček, Ali Keshavarz, Danielle Pyne, Oyeronke Ayansola, Amirthagowri Ambalavanan, Jiří Mayer, Andrea Arruda, Mark D. Minden, Jessie J.F. Medeiros, Sagi Abelson, Dennis Kim

Bibliographic record

VenueBlood · 2025
Typearticle
Languageen
FieldMedicine
TopicChronic Myeloid Leukemia Treatments
Canadian institutionsOntario Institute for Cancer ResearchPrincess Margaret Cancer CentreUniversity Health Network
Fundersnot available
KeywordsSomatic cellGermline mutationEpigeneticsRUNX1Myeloid leukemiaSomatic evolution in cancerTrisomy 8SpliceosomeMyeloidImatinib mesylate

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.017
GPT teacher head0.298
Teacher spread0.281 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2025
Admission routes2
Has abstractyes

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