Interim Analysis of a Prospective Multicentre Study Using Next Generation Sequencing for Kinase Domain Mutational Analysis in CML Patients on First or Subsequent TKI Therapy
Notice bibliographique
Résumé
Introduction Kinase domain mutations in the BCR-ABL1 gene are associated with resistance to tyrosine kinase inhibitors (TKI) in chronic myeloid leukaemia (CML). Next-generation Sequencing (NGS) allows detection of low-level kinase domain mutations as well as quantification of Variant Allele Frequency (VAF).We have previously demonstrated that NGS consistently detects early appearance of kinase domain (KD) mutations in CML patients (Kizilors et al. Lancet Haematology 2019)and highlighted the need for regular monitoring for KD mutations.To that end a multicentre prospective non-interventional study of centralised NGS screening to detect KD mutations was launched in the UK and Republic of Ireland to evaluate the use of NGS in clinical practice (ClinicalTrials.gov, number NCT03647215, INCB 84344-401). Methods Patients with CML on first or subsequent TKI therapy who meet the ELN 2013 criteria for warning or failure are eligible for this prospective study. NGS assay was performed on illumnia MiSeq with a single round PCR using our ISO15189 accredited assay as previously described. Results were communicated to the treating haematologist within 10-12 working days. As this is a non-interventional study, clinical intervention was left to the discretion of treating physician. Repeat mutational analysis was/is encouraged until achievement of a sustained MR3/BCR-ABL<0.1%IS. Results Between December 2017 and June 2019, 192 CML patients from 31 institutions were included in this prospective study (median age 56 yrs, 19-91). 184 patients had chronic phase CML, 5 de novo accelerated phase and 2 de novo blastic phase (missing data n=1). 84 (43.8%) patients were on first line TKI (imatinib n=71, nilotinib n=9, dasatinib n=4), while 58 (30.2%) patients were treated with second line TKI (imatinib followed by 2GTKI n= 52, 2GTKI followed by another 2GTKI n=5), 27 (14.1%) patients were treated with 3rdline TKI (including 4/27 patients on ponatinib) and 23 (12.0%) patients were on 4thline (11/23 patients on ponatinib). At the time of study entry, 78 (40.6%) were in MR2 (BCR-ABL 0.1-1%IS), 50 (26.0%) were in MR1 only (BCR-ABL 1-10%IS), and 54 (28.1%) had a BCR-ABL>10%IS. Of note 10 (5.2%) had a BCR-ABL<0.1%IS. Only 93 (48.9%) patients had previous KD mutational analysis performed by Sanger Sequencing (SS) before study entry. 23 of 192 patients (12.0%) were found to have a KD mutation by NGS while in CP after one (n=12), two (n=3) three (n=2) or four (n=6) lines of TKI therapy. The ongoing TKI therapy was imatinib (n=10), dasatinib (n=4), nilotinib (n=2), bosutinib (n=3) and ponatinib (n=5). Incidence of KD mutation was 8/78 (10.2%) in the MR2 group, 8/50 (16%) in the MR1 and 7/54 (12.9%) in patient with BCR-ABL>10%IS. A single mutation was found in 19 patients while two patients had two mutations and two patients had 3 or more mutations. The most frequent mutations found were T315I (n=11), F317L (n=3), G250E (n=3), V299L (n=2) and E459K (n=2). A low-level mutation was found in 8/23 (35%) patients and would otherwise not be detectable by SS. A mutation conferring resistance to the ongoing TKI ('clinically relevant mutations') was found in 16/23 patients (69%). TKI switch was made in at least 7 patients with response obtained in 5/7 patients at last follow up. Update on the remaining 16 patients is currently being collected and interim updated results will be presented. Serial samples from patients tested negative on the first KD mutational analysis were obtained for 27 patients and remained negative on repeat analysis, of whom 7 patients had reduction in BCR-ABL transcript levels in the interim (2 increase and 21 without change). Four patients found with KD mutation(s) also underwent repeat testing for monitoring of VAF showing a reduction in clone size/VAF and BCR-ABL transcript levels in two patients. Conclusions This interim analysis demonstrates the clinical importance of KD mutational analysis using NGS. The high proportion of clinically relevant mutations -ie conferring resistance to the ongoing TKI treatment-highlights the potential clinical impact of early detection of KD mutation by NGS providing guidance for a rationale switch of TKI therapy. KD mutation distribution was similar in patients in MR2 compared to those with higher disease burden suggesting the importance of using NGS while disease burden is low in order to increase the success of early TKI switch. Interim updated results will be presented. Disclosures De Lavallade: BMS: Honoraria, Research Funding, Speakers Bureau; Pfizer: Honoraria, Speakers Bureau; Incyte biosciences: Honoraria, Research Funding, Speakers Bureau; Novartis: Honoraria, Speakers Bureau. Jackson:Incyte biosciences: Honoraria, Research Funding, Speakers Bureau. Oakervee:Pfizer: Honoraria; Novartis: Honoraria; Bristol Myers-Squibb: Honoraria. Ewing:Bristol Myers-Squibb: Other: Meeting attendance sponsorship ; Novartis: Honoraria, Other: Meeting attendance sponsorship . Byrne:Ariad/Incyte: Honoraria, Speakers Bureau. Rothwell:Novartis: Honoraria, Other: advisory board; Pfizer: Speakers Bureau; Incyte: Speakers Bureau. Pillai:Celgene: Honoraria. Mehta:Pfizer: Other: Advisory board. Copland:Bristol-Myers Squibb: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Novartis: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Cyclacel: Research Funding; Incyte: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Astellas: Honoraria, Speakers Bureau; Pfizer: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau. Ottmann:Roche: Honoraria; Pfizer: Honoraria; Fusion Pharma: Honoraria; Celgene: Honoraria, Research Funding; Incyte: Honoraria, Research Funding; Amgen: Honoraria, Research Funding; Novartis: Honoraria; Takeda: Honoraria. Radia:Blueprint Medicines: Consultancy; Novartis: Consultancy, Speakers Bureau. Harrington:Bristol-Myers Squibb: Research Funding. Greig:Incyte: Employment. Thompson:Incyte: Employment. Kizilors:Incyte biosciences: Research Funding.
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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 | 0,013 | 0,013 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,003 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,001 |
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 ».