Utilizing restricted mean duration of response for efficacy evaluation of cancer treatments

Efficacy and safety of single-agent belantamab mafodotin versus pomalidomide plus low-dose dexamethasone in patients with relapsed or refractory multiple myeloma (DREAMM-3): a phase 3, open-label, randomised study

BACKGROUND

Multiple myeloma remains incurable, and heavily pretreated patients with relapsed or refractory disease have few good treatment options. Belantamab mafodotin showed promising results in a phase 2 study of patients with relapsed or refractory multiple myeloma at second or later relapse and a manageable adverse event profile. We aimed to assess the safety and efficacy of belantamab mafodotin in a phase 3 setting.

METHODS

In the DREAMM-3 open-label phase 3 study, conducted at 108 sites across 18 countries, adult patients were enrolled who had confirmed multiple myeloma (International Myeloma Working Group criteria), ECOG performance status of 0-2, had received two or more previous lines of therapy, including two or more consecutive cycles of both lenalidomide and a proteasome inhibitor, and progressed on, or within, 60 days of completion of the previous treatment. Participants were randomly allocated using a central interactive response technology system (2:1) to receive belantamab mafodotin 2·5 mg/kg intravenously every 21 days, or oral pomalidomide 4·0 mg daily (days 1-21) and dexamethasone 40·0 mg (20·0 mg if >75 years) weekly in a 28-day cycle. Randomisation was stratified by previous anti-CD38 therapy, International Staging System stage, and number of previous therapies. The primary endpoint was progression-free survival in all patients who were randomly allocated. The safety population included all randomly allocated patients who received one or more doses of study treatment. This trial is registered with ClinicalTrials.gov, NCT04162210, and is ongoing. Data cutoff for this analysis was Sept 12, 2022.

FINDINGS

Patients were recruited between April 2, 2020, and April 18, 2022. As of September, 2022, 325 patients were randomly allocated (218 to the belantamab mafodotin group and 107 to the pomalidomide-dexamethasone group); 184 (57%) of 325 were male and 141 (43%) of 325 were female, 246 (78%) of 316 were White. Median age was 68 years (IQR 60-74). Median follow-up was 11·5 months (5·5-17·6) for belantamab mafodotin and 10·8 months (5·6-17·1) for pomalidomide-dexamethasone. Median progression-free survival was 11·2 months (95% CI 6·4-14·5) for belantamab mafodotin and 7·0 months (4·6-10·6) for pomalidomide-dexamethasone (hazard ratio 1·03 [0·72-1·47]; p=0·56). Most common grade 3-4 adverse events were thrombocytopenia (49 [23%] of 217) and anaemia (35 [16%]) for belantamab mafodotin, and neutropenia (34 [33%] of 102) and anaemia (18[18%]) for pomalidomide-dexamethasone. Serious adverse events occurred in 94 (43%) of 217 and 40 (39%) of 102 patients, respectively. There were no treatment-related deaths in the belantamab mafodotin group and one (1%) in the pomalidomide-dexamethasone group due to sepsis.

INTERPRETATION

Belantamab mafodotin was not associated with statistically improved progression-free survival compared with standard-of-care, but there were no new safety signals associated with its use. Belantamab mafodotin is being tested in combination regimens for relapsed or refractory multiple myeloma.

FUNDING

GSK (study number 207495).

Avelumab First-Line Maintenance for Advanced Urothelial Carcinoma: Results From the JAVELIN Bladder 100 Trial After ≥2 Years of Follow-Up

Clinical trials frequently include multiple end points that mature at different times. The initial report, typically based on the primary end point, may be published when key planned coprimary or secondary analyses are not yet available. Clinical trial updates provide an opportunity to disseminate additional results from studies, published in JCO or elsewhere, for which the primary end point has already been reported.Initial results from the phase III JAVELIN Bladder 100 trial (ClinicalTrials.gov identifier: NCT02603432) showed that avelumab first-line (1L) maintenance plus best supportive care (BSC) significantly prolonged overall survival (OS) and progression-free survival (PFS) versus BSC alone in patients with advanced urothelial carcinoma (aUC) who were progression-free after 1L platinum-containing chemotherapy. Avelumab 1L maintenance treatment is now a standard of care for aUC. Here, we report updated data with ≥ 2 years of follow-up in all patients, including OS (primary end point), PFS, safety, and additional novel analyses. Patients were randomly assigned 1:1 to receive avelumab plus BSC (n = 350) or BSC alone (n = 350). At data cutoff (June 4, 2021), median follow-up was 38.0 months and 39.6 months, respectively; 67 patients (19.5%) had received ≥2 years of avelumab treatment. OS remained longer with avelumab plus BSC versus BSC alone in all patients (hazard ratio, 0.76 [95% CI, 0.63 to 0.91]; 2-sided P = .0036). Investigator-assessed PFS analyses also favored avelumab. Longer-term safety was consistent with previous analyses; no new safety signals were identified with longer treatment duration. In conclusion, longer-term follow-up continues to show clinically meaningful efficacy benefits with avelumab 1L maintenance plus BSC versus BSC alone in patients with aUC. An interactive visualization of data reported in this article is available.

Assessment of added activity of an antitumor agent

An unprecedented number of novel oncology drugs are under preclinical and clinical development, and nearly all are developed in combinations. With an over-reliance on biological hypotheses, there is less effort to establish single agent activity before initiating late clinical development. This may be contributing to a decreased success rate going from phase 1 to approval in the immunotherapy era. Growing evidence in clinical trial data shows that the treatment benefit from most approved combination therapies can be explained by the independent drug action model. Using this working model, we develop a simple index to measure the added antitumor activity of a new drug based on mean response duration, an endpoint that naturally combines both response status and duration information for all patients, which is shown to be highly predictive of clinical benefit of FDA-approved anti-PD-(L)1 immunotherapies. This index sheds light on challenges and opportunities in contemporary oncology drug development and provides a practical tool to assist with decision-making in early clinical trials.

Evidence synthesis analysis with prioritized benefit outcomes in oncology clinical trials

Overall survival, progression-free survival, objective response/complete response, and duration of (complete) response are frequently used as the primary and secondary efficacy endpoints for designs and analyses of oncology clinical trials. However, these endpoints are typically analyzed separately. In this article, we introduce an evidence synthesis approach to prioritize the benefit outcomes by applying the generalized pairwise comparisons (GPC) method, and use win statistics (win ratio, win odds and net benefit) to quantify treatment benefit. Under the framework of GPC, the main advantage of this evidence synthesis approach is the ability to combine relevant outcomes of various types into a single summary statistic without relying on any parametric assumptions. It is particularly relevant since health authorities and the pharmaceutical industry are increasingly incorporating structured quantitative methodologies in their benefit-risk assessment. We apply this evidence synthesis approach to an oncology phase 3 study in first-line renal cell carcinoma to assess the overall effect of an investigational treatment by ranking the most clinically relevant endpoints in cancer drug development. This application and a simulation study demonstrate that the proposed approach can synthesize the evidence of treatment effect from multiple prioritized benefit outcomes, and has substantial advantage over conventional methods that analyze each individual endpoint separately. We also introduce a newly developed R package WINS for statistical inference based on win statistics.