About this trial
This phase III trial studies how well lenalidomide and dexamethasone works with or without daratumumab in treating patients with high-risk smoldering myeloma. Drugs used in chemotherapy, such as lenalidomide and dexamethasone, work in different ways to stop the growth of tumor cells, either by killing the cells, by stopping them from dividing, or by stopping them from spreading. Immunotherapy with monoclonal antibodies, such as daratumumab, may induce changes in the body's immune system and may interfere with the ability of tumor cells to grow and spread. Giving lenalidomide and dexamethasone with daratumumab may work better in treating patients with smoldering myeloma.
Eligibility criteria
This trial does not accept healthy volunteersQualifiers
Abnormal serum free light chain ratio of involved to uninvolved >20, but less than 100 if the involved FLC is >= 10 mg/dL by serum free light chain (FLC) assay
Serum M-protein level >= 2 gm/dL
Presence of t(4;14) or del 17p, del 13q or 1q gain by conventional cytogenetics or fluorescence in situ hybridization (FISH) studies.
>20% plasma cells on biopsy or aspirate
Disqualifiers
None
Trial design
Parallel
Treatments tested in this trial
Daratumumab
Biological/VaccineGiven IV
Dexamethasone
DrugGiven PO
Lenalidomide
DrugGiven PO
Quality-of-Life Assessment
Other interventionAncillary studies
Questionnaire Administration
Other interventionAncillary studies
Treatment groups
Trial outcomes
Primary outcomes
Overall survival (OS)
Will be estimated using the Kaplan-Meier (KM) method and compared using a stratified log-rank test. Stratified cox proportional hazards regression will produce a treatment hazard ratio estimate.
Functional Assessment of Cancer Therapy-General (FACT-G) score
A general effect size measure of half standard deviation will be used to establish the minimally important difference. Descriptive statistics (mean, standard deviation \[SD\], median, range) will be used to evaluate the distribution of levels and changes for the set of health-related quality of life (QOL) evaluations. Levels and changes will also be assessed graphically. Changes from baseline will be analyzed using linear mixed models based on restricted maximum likelihood estimation with covariance matrix maximizing Akaike information criteria. Models with treatment, assessment time, and treatment by assessment time interaction with and without other predictors will be fit. If there is substantial missingness, will analyze the data according to the methods described in Schluchter and Schluchter, Greene and Beck.
Secondary outcomes
Progression-free survival (PFS)
Will be estimated using the KM method and compared using a stratified log-rank test. Stratified cox proportional hazards regression will produce a treatment hazard ratio estimate.
Best response on treatment based on International Myeloma Working Group (IMWG) criteria
Response will be tabulated by category. Response rates of very good partial response (VGPR) or better and partial response (PR) or better will be compared using the Fisher's exact test. Ineligible patients are excluded from the analysis and unevaluable patients are counted in the denominator.
Incidence of adverse events by worst grade and type for treated patients determined using Common Terminology Criteria for Adverse Events (CTCAE) version 5.0
Will monitor the toxicities experienced by all treated patients. Will compare rates of worst grade 3 or higher non-hematologic treatment-related events using the Fisher's exact test.
Incidence of grade 3 or higher infusion-related reactions over course 1 determined based on CTCAE
Will monitor the toxicities experienced by all treated patients. Will compare rates of worst grade 3 or higher non-hematologic treatment-related events using the Fisher's exact test.
Other outcomes
Cumulative dose calculated as the sum of all doses taken across all cycles
Cumulative dose will be calculated overall and by cycle. Data will be summarized by treatment arm with descriptive statistics and graphically over time. Patients will be classified into dichotomous groups based on a 75% relative dose intensity cutoff (\< 75% versus \[vs\] \>= 75% represents poor vs good treatment adherence). The proportion of patients with poor lenalidomide treatment adherence will be compared between arms using the Fisher's exact test. In addition, multi-variable logistic regression analysis will be conducted to identify the baseline factors associated with calculated good treatment adherence.
Dose intensity calculated as cumulative dose received divided by treatment duration
Dose intensity will be calculated overall and by cycle. Data will be summarized by treatment arm with descriptive statistics and graphically over time. Patients will be classified into dichotomous groups based on a 75% relative dose intensity cutoff (\< 75% vs \>= 75% represents poor vs good treatment adherence). The proportion of patients with poor lenalidomide treatment adherence will be compared between arms using the Fisher's exact test. In addition, multi-variable logistic regression analysis will be conducted to identify the baseline factors associated with calculated good treatment adherence.
Relative dose intensity calculated as the dose intensity divided by planned dose intensity
Dose intensity will be calculated overall and by cycle. Data will be summarized by treatment arm with descriptive statistics and graphically over time. Patients will be classified into dichotomous groups based on a 75% relative dose intensity cutoff (\< 75% vs \>= 75% represents poor vs good treatment adherence). The proportion of patients with poor lenalidomide treatment adherence will be compared between arms using the Fisher's exact test. In addition, multi-variable logistic regression analysis will be conducted to identify the baseline factors associated with calculated good treatment adherence.
Duration of treatment
Treatment duration in each arm will be estimated using Kaplan-Meier methods and compared between arms with the log-rank test.
Sponsors and contacts
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ECOG-ACRIN Cancer Research Group
Lead sponsor
National Cancer Institute (NCI)
Collaborator