MétaCan
Menu
Retour à la cohorte
Enregistrement 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 sur 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

Notice bibliographique

RevueBlood · 2025
Typearticle
Langueen
DomaineMedicine
ThématiqueChronic Myeloid Leukemia Treatments
Établissements canadiensOntario Institute for Cancer ResearchPrincess Margaret Cancer CentreUniversity Health Network
Organismes subventionnairesnon disponible
Mots-clésSomatic cellGermline mutationEpigeneticsRUNX1Myeloid leukemiaSomatic evolution in cancerTrisomy 8SpliceosomeMyeloidImatinib mesylate

Résumé

récupéré en direct d'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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,001
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,001
Score d'incertitude au seuil0,004

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,000
Communication savante0,0010,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

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.

Tête enseignante Opus0,017
Tête enseignante GPT0,298
Écart entre enseignants0,281 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2025
Routes d'admission2
Résumé présentoui

Explorer davantage

Même revueBloodMême sujetChronic Myeloid Leukemia TreatmentsTravaux en français237 207