Lenvatinib in Patients With Advanced Grade 1/2 Pancreatic and Gastrointestinal Neuroendocrine Tumors: Results of the Phase II TALENT Trial (GETNE1509)

In the July 10, 2021 edition of Journal of Clinical Oncology, Jaume Capdevila and colleagues report on the results of the TALENT (GETNE 1509) trial


The purpose of the study was to investigate the efficacy and safety of lenvatinib in patients with previously treated advanced gastroentero-pancreatic-neuroendocrine tumor


The study was a a open label, single-arm, multicenter, phase 2 trial with 2 parallel cohorts involving 21 institutions in 4 European countries. Eligible patients were required to have histologically confirmed advanced grade 1-2 pancreatic (panNET) or GI (GI-NET) NETs with tumor progression after treatment with a targeted agent (panNET) or somatostatin analogs (GI-NET). Patients were treated with lenvatinib 24 mg administered orally once daily until disease progression or treatment intolerance. The primary end point was ORR with secondary end points including PFS, OS, DOR and safety.


Between September 2015 and March 2017, 111 patients were enrolled, with 55 (panNET) and 56 (GI-NET) patients in each arm. The median follow-up was 23 months. The O RR was 29.9% (95% CI, 21.6 to 39.6): 44.2% (panNET) and 16.4% (GI-NET). The median DOR of response was 19.9 (8.4-30.8) and 33.9 (10.6-38.3) months in the panNET and GI-NET groups, respectively. The median PFS was 15.7 months (95% CI, 14.1 to 19.5).  Reported adverse events included fatigue, hypertension, and diarrhea; it is important to note that, 93.7% of patients required dose reductions or interruptions.


The authors concluded ” We report the highest centrally confirmed response reported to date with a multikinase inhibitor in advanced GEP-NETs, with a particularly strong response in the panNET cohort. This study provides novel evidence for the efficacy of lenvatinib in patients with disease progression following treatment with other TAs, suggesting the potential value of lenvatinib in the treatment of advanced GEP-NETs”