Emergence of new somatic mutations in CML patients optimally responding to tyrosine kinase inhibitor therapy: Proposal of long-term genomic monitoring
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».