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Enregistrement W2980964338 · doi:10.1182/blood-2018-99-110135

Early Prediction of Stable MR4.5 By Achievement of 2.8 Log Reduction in BCR-ABL1 qPCR Levels at 6 Months in Patients with Chronic Myeloid Leukemia Treated with Frontline Imatinib

2018· article· en· W2980964338 sur OpenAlexaff
Aisling Nee, Jeffrey H. Lipton, Dennis Dong Hwan Kim

Notice bibliographique

RevueBlood · 2018
Typearticle
Langueen
DomaineMedicine
ThématiqueChronic Myeloid Leukemia Treatments
Établissements canadiensPrincess Margaret Cancer CentreUniversity Health Network
Organismes subventionnairesnon disponible
Mots-clésDiscontinuationImatinibMedicineImatinib mesylateInternal medicineOncologyPopulationUnivariate analysisTyrosine-kinase inhibitorMyeloid leukemiaMultivariate analysisCancerEnvironmental health

Résumé

récupéré en direct d'OpenAlex

Abstract Introduction Tyrosine kinase inhibitor (TKI) therapy has dramatically improved the prognosis of CML, with life expectancy now approaching that of the general population. TKIs are, however, associated with impaired quality of life, toxicity and financial burden to the patient and economy. Treatment free remission (TFR) is achievable in approximately half of patients who attain a sustained deep molecular response (DMR), however, it is not yet fully elucidated how best to predict candidates for successful TFR attempt. It is even harder to predict which patients will achieve a sustained DMR for 2 years or longer, which is a pre-requisite for TKI discontinuation. If it were possible to identify the patients who will not achieve a sustained DMR with Imatinib, a TKI switch could be considered earlier in order to make them a candidate for TFR attempt. Aims We aimed to identify disease characteristics and molecular responses that can predict future achievement of Stable MR4.5 (defined as a reduction in BCR/ABL1 transcripts of 4.5 logs or deeper on repeated testing for 2 consecutive years) with frontline Imatinib. Patients and Methods We collected data on pre-TKI variables (baseline disease characteristics), post-TKI variables (molecular response at various timepoints) and outcomes in patients commencing frontline Imatinib in our institution from 1999 to 2014 (n=593). Statistical analysis was performed using EZR software. Univariate analysis was performed by cumulative incidence method considering competing events and Gray test. Cut-offs for continuous variables were determined by recursive partitioning (rpart). Multivariable analysis was performed using Fine-Gray model. Results With 8.9 years of median follow-up duration, the overall survival was 96.9% at 8 years. The median time to MR4.5 was 8.8 years. The rate of MR4.5 was 39.7% at 5 years and 48.3% at 8 years. The rate of Stable MR4.5 was 25.6% at 8 years. In the subset of patients achieving MR4.5, over 80% subsequently achieved Stable MR4.5 (82.4% at 8 years) (Fig. 1). The median time from achievement of first MR4.5 to Stable MR4.5 was 3.5 years. Univariate analyses of baseline variables (age, gender, disease phase, additional cytogenetic abnormalities and baseline blood counts) were performed, using rpart method to determine cut-offs for blood counts as follows: white cell count (WBC) ≥218x109/L, blast percentage ≥4%, hemoglobin (Hb) ≥88.5g/L and platelets ≥176x109/L. The only statistically significant pre-TKI variables on these analyses were WBC, blast percentage, Hb and platelet count. Univariate analyses of the following post-TKI variables were also performed: molecular response at 3, 6 and 12 months, time to complete cytogenetic response, major molecular response and MR4.5.Early molecular responses of ≥1 log reduction in transcripts at 3 months, ≥2 logs at 6 months and ≥3 logs at 12 months were tested. The following cut-offs for molecular response, as determined by rpart method, were also tested: ≥2.2 log reduction at 3 months, ≥2.8 logs at 6 months and ≥3 logs at 12 months.Univariate analysis showed statistical significance (p<0.0001) for all the post-TKI variables tested. Multivariable analyses of baseline blood counts and molecular response at 3 and 6 months were performed. The only variable that remained statistically significant was molecular response at 6 months using a cut-off of ≥2.8 log reduction in transcripts (HR 3.1, p<0.001) (Table 1). 44.4% of patients achieved ≥2.8 log reduction in transcripts at 6 months, with a rate of Stable MR4.5 at 8 years of 65.8%, compared to 17.2% for those with <2.8 log reduction at 6 months (Fig. 2). Conclusions In patients who achieved MR4.5, over 80% subsequently achieved Stable MR4.5, making them eligible for TKI discontinuation. In multivariable analysis, molecular response at 6 months was the only predictor for subsequent achievement of Stable MR4.5. Based on this data, a patient at high-risk of failing to attain Stable MR4.5 with Imatinib therapy can be identified if they fail to achieve a 2.8 log reduction or deeper within 6 months of Imatinib therapy. If a patient interested in TFR has a molecular response at 6 months of less than a 2.8 log reduction, then a switch in therapy to a second generation TKI may be considered. The optimal 6 month response to predict future Stable MR4.5 remains unclear, but our data suggest that a cut-off in transcripts of ≥ 2.8 log reduction may be a better predictor of future Stable MR4.5. Disclosures Lipton: ARIAD: Consultancy, Research Funding; Bristol-Myers Squibb: Consultancy, Research Funding; Novartis: Consultancy, Research Funding; Pfizer: Consultancy, Research Funding. Kim:BMS: Consultancy, Honoraria, Research Funding; Novartis: Consultancy, Honoraria, Research Funding; Pfizer: Consultancy; Paladin: Consultancy.

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 enseignants0,002
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 savante0,0000,000
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 Opus0,009
Tête enseignante GPT0,215
Écart entre enseignants0,206 · 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
Publié2018
Routes d'admission1
Résumé présentoui

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