Characterization of Mechanisms of Resistance in Previously Treated Chronic Lymphocytic Leukemia (CLL) From a Head‐to‐Head Trial of Acalabrutinib Versus Ibrutinib
Notice bibliographique
Résumé
Introduction: Acalabrutinib (Acala) is a highly selective, next-generation covalent Bruton tyrosine kinase inhibitor (BTKi) approved for CLL. In ELEVATE-RR (NCT02477696) at a median follow-up of 41 mo, Acala demonstrated noninferior progression-free survival with fewer cardiovascular adverse events versus ibrutinib (Ibr) in patients (pts) with relapsed/refractory (R/R) CLL. Disease progression on covalent BTKis is often characterized by acquisition of B-cell receptor pathway mutations, but no data have compared mutational profiles of Acala versus Ibr. We report clonal evolution data in pts with CLL progressing on Acala versus Ibr in ELEVATE-RR. Methods: Peripheral blood samples at baseline and relapse from pts in ELEVATE-RR were used. DNA was extracted from enriched CD19+ cells (RoboSep) and subjected to a 50-gene sequencing assay panel with a sensitivity cutoff for BTK and PLCG2 resistance–associated mutations at 0.5% variant allele fraction (VAF). Forty-eight other CLL-associated genes were assessed at 1%–2% VAF. Results: Paired (baseline and progression) samples were available for 47 (excluding 1 Richter) and 30 (excluding 6 Richter) pts in the Acala and Ibr groups, respectively. At progression, emergent BTK mutations were seen in 31 (66%) Acala versus 11 (37%) Ibr pts (P = 0.02) (Figure 1; median VAF: 5.7 vs. 5.8). Emergent PLCG2 mutations occurred in 3 (6%) Acala vs. 6 (20%) Ibr pts (P = 0.14). Only 1 Acala pt had co-occurrence of BTK and PLCG2 mutations versus 4 Ibr pts. BTK C481S, C481Y, and C481R mutations occurred at similar frequency in both groups; a novel E41V mutation within the pleckstrin homology domain of BTK (median VAF: 16%) was seen in 1 Acala pt. L528W and A428D co-mutations were observed in 1 Ibr pt. Six Acala pts had TP53 and BTK co-mutations. Emergent TP53 mutations were seen in both groups (13% [Acala] vs. 7% [Ibr], P = 0.47; median VAF: 5% [Acala] versus 37% [Ibr]). Only 2 Ibr pts had TP53 mutations (1 had TP53/BTK co-mutation). No statistical difference was seen in the proportions of Acala versus Ibr pts who acquired BTK mutations among pts with del(17p) (39% vs. 64%; P = 0.18), del(11q) (77% vs. 46%; P = 0.07), complex karyotype (58% vs. 73%; P = 0.48), unmutated IGHV (90% vs. 100%; P = 0.55), or trisomy 12 positivity (3% vs. 18%; P = 0.16). Additional mutations (Acala vs. Ibr) included DNMT3A (5 vs. 1 pts), TET2 (1 pt for each), and NRAS (1 pt; Acala only). The research was funded by: AstraZeneca Keywords: Chronic Lymphocytic Leukemia (CLL), Genomics, Epigenomics, and Other -Omics, Molecular Targeted Therapies Conflicts of interests pertinent to the abstract. J. A. Woyach Consultant or advisory role: Abbvie, AstraZeneca, BeiGene, Genentech, Janssen, Merck, Loxo/Lilly, Newave, Pharmacyclics Research funding: Abbvie, Janssen, Karyopharm Therapeutics, Loxo/Lilly, Pharmacyclics, Schrodinger D. Jones Research funding: Abbvie, Acerta/AstraZeneca, Pharmacyclics, Novartis, MingSight, Other remuneration: The Ohio State University: High sensitivity BTK mutation profiling W. Jurczak Consultant or advisory role: Abbvie, AstraZeneca, BeiGene, Lilly, Roche, Takeda Research funding: Abbvie, AstraZeneca, BeiGene, Janssen, Lilly, Roche, Takeda T. Robak Consultant or advisory role: AstraZeneca, BeiGene, Janssen Oncology Honoraria: AstraZeneca, BeiGene, Janssen Research funding: AstraZeneca, BeiGene, Janssen A. Illés Employment or leadership position: University of Debrecen Honoraria: Janssen, Celgene, Novartis, Pfizer, Takeda, Roche Research funding: Takeda, Seattle Genetics A. P. Kater Consultant or advisory role: AstraZeneca, BMS, Roche/Genentech, Janssen, AbbVie, LAVA Research funding: AstraZeneca, BMS, Roche/Genentech, Janssen, AbbVie Other remuneration: Janssen, LAVA, AbbVie, AstraZeneca P. Ghia Honoraria: AbbVie, AstraZeneca, Janssen, BMS, MSD, Loxo Oncology/Lilly, and Roche Research funding: AbbVie, AstraZeneca, Janssen, and BMS J. C. Byrd Consultant or advisory role: Janssen, Novartis, Syndax, Newave, AstraZeneca, Kura, Vincerx, Trilrom, Abbvie Stock ownership: Vincerx Research funding: Zencor, Pharmacyclics Educational grants: Janssen, Novartis Other remuneration: Ohio State University J. F. Seymour Consultant or advisory role: AbbVie, AstraZeneca, Celgene, Genentech, Genor Bio, Gilead, Janssen, Morphosys, Roche, Sunesis, TG Therapeutics Research funding: Abbvie, Celgene, Janssen, Roche Other remuneration: Abbvie, Celgene, Roche, TG Therapetics G. De Jesus Employment or leadership position: AstraZeneca Stock ownership: AstraZeneca R. Lai Employment or leadership position: AstraZeneca G. de Bruin Employment or leadership position: Acerta Pharma B.V. A. Butturini Employment or leadership position: AstraZeneca Stock ownership: AstraZeneca, Amgen, Roche S. Rule Employment or leadership position: AstraZeneca V. Munugalavadla Employment or leadership position: AstraZeneca Stock ownership: AstraZeneca; Gilead Sciences
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,000 |
| Bibliométrie | 0,000 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».