Key Points

  • VenG was superior to GClb across different genetic subgroups, but del(17p) and mutated TP53 remain as adverse prognostic markers.

  • Unmutated IGHV is a predictive factor for particular benefit from venetoclax and obinutuzumab.

Abstract

Genetic parameters are established prognostic factors in chronic lymphocytic leukemia (CLL) treated with chemoimmunotherapy, but are less well studied with novel compounds. We assessed immunoglobulin heavy variable chain (IGHV) mutation status, common genomic aberrations, and gene mutations in 421 untreated patients within the CLL14 trial (NCT02242942), comparing obinutuzumab+chlorambucil (GClb) vs obinutuzumab+venetoclax (VenG). The incidences of genomic aberrations considering the hierarchical model were del(17p) 7%, del(11q) 18%, +12 18%, and del(13q) 35%, whereas IGHV was unmutated in 60% of patients. NOTCH1 mutations were most common (23%), followed by SF3B1 (16%), ATM (13%), and TP53 (10%). Although the overall response rate (ORR) for GClb was lower in patients with del(17p), del(11q), mutated TP53, ATM, and BIRC3, none of these parameters reduced complete remission (CR) rate and ORR with VenG. At a median follow-up of 28 months, del(17p) and mutated TP53 were the only abnormalities with an effect on progression-free survival (PFS) for both treatment groups: GClb (hazard ratio [HR], 4.6 [P < .01]; HR, 2.7 [P < .01], respectively) and VenG (HR, 4.4 [P < .01]; HR, 3.1 [P < .01], respectively). No other factors affected outcome with VenG, whereas for GClb del(11q), BIRC3, NOTCH1, and unmutated IGHV were associated with shorter PFS. Multivariable analysis identified del(17p), del(11q), unmutated IGHV, and mutated TP53, BIRC3, and SF3B1 as independent prognostic factors for PFS with GClb, whereas for VenG, only del(17p) was significant. VenG was superior to GClb across most genetic subgroups. Patients with adverse genetic markers had the strongest benefit from VenG, particularly subjects with unmutated IGHV, which was identified as a predictive factor in a multivariable treatment-interaction analysis.

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