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Enregistrement W4405034729 · doi:10.1182/blood-2024-205681

Retrospective Study to Compare Treatment Outcomes of Asciminib Vs. Ponatinib in 99 Patients with T315I Mutated Chronic Myeloid Leukemia

2024· article· en· W4405034729 sur OpenAlexaffabout
María Agustina Perusini, Camille Kockerols, Daniela Žáčková, Franck E. Nicolini, Fausto Castagnetti, Carolina Pavlovsky, Massimo Breccia, Delphine Réa, Carmen Fava, Lynn Savoie, Christophe Bouvier, Petra Čičátková, Julien Bollard, Swe Mar Linn, Gopila Gupta, Emilia Scalzulli, Tomoiku Takaku, Hiroshi Ureshino, Shinya Kimura, Sung‐Eun Lee, Dragana Milojković, Andrew J. Innes, Simone Claudiani, Valentín García‐Gutiérrez, Jiří Mayer, Peter E. Westerweel, Dennis Dong Hwan Kim

Notice bibliographique

RevueBlood · 2024
Typearticle
Langueen
DomaineMedicine
ThématiqueChronic Myeloid Leukemia Treatments
Établissements canadiensUniversity of CalgaryPrincess Margaret Cancer CentreUniversity Health Network
Organismes subventionnairesnon disponible
Mots-clésMedicinePonatinibInternal medicineDiscontinuationMyeloid leukemiaOncologyNilotinibImatinib

Résumé

récupéré en direct d'OpenAlex

Introduction. Patients (pts) with T315I-mutated chronic myeloid leukemia (CML) experience poor outcomes due to refractoriness and resistance to most approved tyrosine kinase inhibitors (TKIs). While ponatinib (PON) has demonstrated efficacy in T315-mutated CML pts, it is also known to be associated with an increased rate of cardiovascular disease (CVD). Asciminib (ASC) targets the ABL kinase myristoyl-binding pocket with high specificity and limited off-target activity, remaining effective against BCR::ABL1 kinase domain (KD) mutations, including T315I. It is debated if the efficacy of ASC is comparable to PON in this subgroup of pts. Methods & patients. Data from 607 CML pts treated with ASC or PON across 9 countries (Canada, the Netherlands, Czech Republic, France, Argentina, Italy, Japan, South Korea, and Spain) were retrospectively analyzed. PSM analysis from this cohort was presented at EHA 2024. For the current research we focused exclusively on pts with T315I mutations. Primary endpoints were event-free survival (EFS) and failure-free survival (FFS). FFS was calculated from the start date of the TKI of interest until treatment failure or last follow-up. EFS included discontinuation events. Major molecular response (MMR) was defined as BCR::ABL <0.1%IS. Treatment failure was defined as loss of complete hematologic response, loss of major cytogenetic response, transformation to accelerated or blast phase (A/BP), or death. Results. 99 pts with T315I-mutated CML were included: 35 treated with ASC and 64 with PON. The ASC group was older than the PON group (median age, 62 vs 50 years; p<0.001). Disease phase at diagnosis was comparable between the ASC and PON groups (20% vs 15% of A/BP; p=0.475). Sokal risk score was also similar: high risk in 47%, int risk in 38%, and low risk in 15%, (p=0.188). There were no differences in additional cytogenetic abnormalities or compound KD mutations between groups. Resistance was the primary cause of treatment failure in 79% of pts overall, with 59% in ASC group and 90% in PON group (p<0.001). Intolerance was the second most common cause, occurring in 20% of cases overall, with 41% in ASC group and 10% in PON group (p<0.001). Notably, 91% of pts in the ASC group had received at least 2 lines of TKI therapy prior (including PON in 28 pts (80%) compared to 53% in PON group (p<0.001). No PON-treated pts had previously received ASC. History of CVD including coronary artery disease (n=9), myocardial infarction (n=25), stroke (n=3) or peripheral artery occlusive disease (n=10) was present in 29% of pts overall, with 40% in ASC and 22% in PON group (p=0.06). Median starting dose (mg/day-range) of ASC was 400(80-400) and for PON, 45(15-45). With a median follow-up duration of 507 days in the ASC group and 2027 days in the PON group (p<0.001), MMR at 12 months was 56.6% (95% CI [45.4-68.4]). There were no significant differences between ASC (49.7%, [30.3-73.0]) and PON (59.3%, [46.1-72.9]) in terms of MMR (p = 0.38). Similarly, including only the 76 resistant pts, MMR was 52.1% (95% CI [27.3-80.02]) and 61.5% (95% CI [47.9-75.3]) for ASC and PON treated pts respectively (p=0.43). Overall EFS at 12 months was 19.1% [11.8-27.8%]), 27.4% [13.3-43.6%] for the ASC group, and 14.8% [7.2-24.8%] for the PON group (p=0.575). The FFS at 12 months was 24.0% [15.8-33.2%] overall, 33.9% [18.4-50.2%] for the ASC group, and 19.7% [10.9-30.5%] for the PON group (p=0.68). The OS was 80.8% [71.0-87.6%] overall, 80.1% [60.8-90.6%] in the ASC group, and 81.6% [69.2-89.3%] in the PON group (p=0.33). For pts with CVD, a key subgroup of interest, EFS at 12 months was 42.8% [14.8-68.6%] for ASC and 21.4% [5.2-44.8%] for PON (p = 0.23). FFS at 12 months was 53.5% [23.3-65.5%] for ASC and 21.4% [5.2-44.7%] for PON (p = 0.16). Adjusted for CVD, failure cause to previous TKI (i.e. intolerance vs. resistance), and line of therapy (as 2nd vs. beyond), ASC was not inferior to PON with HR for EFS: 0.94 [0.53-1.6] (p=0.85), FFS: 0.91 [0.53-1.56] (p=0.75), and OS: 0.41 [0.16-1.09] (p=0.07). Conclusion. ASC and PON appear to offer at least equivalent outcomes in terms of MMR, EFS, FFS, and OS. ASC and PON had similar outcomes in pts with CVD. This is noteworthy given that ASC-treated pts were heavily pretreated, older, and had a higher prevalence of CVD compared to PON-treated pts; PON-treated pts had higher resistance rates and longer follow-up. Further studies with larger sample size and extended follow-up are needed to confirm these findings.

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)0,002
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 enseignants0,002
Score d'incertitude au seuil0,007

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,002
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,001
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)0,0020,000

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 Opus0,013
Tête enseignante GPT0,277
Écart entre enseignants0,265 · 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

Citations2
Publié2024
Routes d'admission2
Résumé présentoui

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