Optimal sequencing of ibrutinib, idelalisib, and venetoclax in chronic lymphocytic leukemia: results from a multicenter study of 683 patients.
(Journal Article;Multicenter Study)
Background: Ibrutinib, idelalisib, and venetoclax are approved for treating CLL patients in the United States. However, there is no guidance as to their optimal sequence. Patients and methods: We conducted a multicenter, retrospective analysis of CLL patients treated with kinase inhibitors (KIs) or venetoclax. We examined demographics, discontinuation reasons, overall response rates (ORR), survival, and post-KI salvage strategies. Primary endpoint was progression-free survival (PFS). Results: A total of 683 patients were identified. Baseline characteristics were similar in the ibrutinib and idelalisib groups. ORR to ibrutinib and idelalisib as first KI was 69% and 81%, respectively. With a median follow-up of 17 months (range 1-60), median PFS and OS for the entire cohort were 35 months and not reached. Patients treated with ibrutinib (versus idelalisib) as first KI had a significantly better PFS in all settings; front-line [hazard ratios (HR) 2.8, CI 1.3-6.3, P = 0.01], relapsed-refractory (HR 2.8, CI 1.9-4.1, P < 0.001), del17p (HR 2.0, CI 1.2-3.4, P = 0.008), and complex karyotype (HR 2.5, CI 1.2-5.2, P = 0.02). At the time of initial KI failure, use of an alternate KI or venetoclax had a superior PFS when compared with chemoimmunotherapy. Furthermore, patients who discontinued ibrutinib due to progression or toxicity had marginally improved outcomes if they received venetoclax (ORR 79%) versus idelalisib (ORR 46%) (PFS HR .6, CI.3-1.0, P = 0.06). Conclusions: In the largest real-world experience of novel agents in CLL, ibrutinib appears superior to idelalisib as first KI. Furthermore, in the setting of KI failure, alternate KI or venetoclax therapy appear superior to chemoimmunotherapy combinations. The use of venetoclax upon ibrutinib failure might be superior to idelalisib. These data support the need for trials testing sequencing strategies to optimize treatment algorithms.
Mato, AR; Hill, BT; Lamanna, N; Barr, PM; Ujjani, CS; Brander, DM; Howlett, C; Skarbnik, AP; Cheson, BD; Zent, CS; Pu, JJ; Kiselev, P; Foon, K; Lenhart, J; Henick Bachow, S; Winter, AM; Cruz, A-L; Claxton, DF; Goy, A; Daniel, C; Isaac, K; Kennard, KH; Timlin, C; Fanning, M; Gashonia, L; Yacur, M; Svoboda, J; Schuster, SJ; Nabhan, C
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