A Data-Driven Approach to Define Parsimonious Eligibility Criteria in First-Line Clinical Trials for Diffuse Large B-Cell Lymphoma Pubblico
Harkins, Robert (Spring 2020)
Published
Abstract
Background: Diffuse large B-cell lymphoma (DLBCL) is clinically and genetically heterogeneous. Forty percent of patients relapse or are refractory to first-line therapy and have inferior outcomes, indicating unmet treatment needs for high-risk disease. Randomized controlled trials (RCTs) designed to improve outcomes for these high-risk groups have been largely unsuccessful, potentially due to restrictive eligibility criteria that in fact limit enrollment of high-risk patients. We define evidence-based methods to streamline enrollment of patients with high-risk disease for first-line DLBCL clinical trials incorporating novel therapeutics. Methods: We enumerated enrollment criteria from 19 first-line DLBCL RCTs. We proposed eligibility criteria for four eligibility criterion categories: International Prognostic Index (IPI) score, age at diagnosis, Eastern Cooperative Oncology Group (ECOG) performance status (PS), and Ann Arbor stage. Using study-specific eligibility criteria and proposed criteria, we identified eligible patients in eight DLBCL data sets representing institutional, regional, and national populations. We performed survival analysis according to eligibility status to determine whether prior RCTs and proposed criteria targeted high-risk groups. We calculated sensitivity and specificity of combinations of proposed criteria to identify patients meeting eligibility criteria for prior studies and developed receiver operating characteristic plots to identify optimal combinations. We characterized the mutational profile of the eligible patient population. Results: We identified 52 eligibility criterion categories across 19 trials. We proposed the inclusion criteria IPI score ≥ 2, age at diagnosis ≥ 18, ECOG PS 0–2, and stage II–IV. Proposed criteria risk-stratified patients with hazard ratios for eligible versus ineligible patients of 1.37–3.58 for overall survival across data sets and defined eligibility for high-risk subgroups. Subsets of the proposed criteria lacking full IPI factors identified patients who were eligible for prior RCTs with sensitivity ≥ 0.75 for at least 14 of 19 RCTs when using data sets containing data for all data types included in analysis. We described patterns of DLBCL mutations for high-risk, eligible patients. Conclusion: Subsets of modernized eligibility criteria for first-line DLBCL RCTs identified high-risk, eligible patient groups with high sensitivity. We identified relationships between eligibility status and cohort genomics that facilitate precision medicine RCT design for DLBCL.
Table of Contents
Introduction 1
Background 2
Methods 9
Results 18
Discussion 28
References 36
Tables 42
Table 1. Patient demographics and baseline disease characteristics from all data sets 42
Table 2. Randomized controlled trials included in analysis (n = 19) 43
Table 3. Criterion categories in 19 diffuse large B-cell lymphoma randomized controlled trials 44
Figures 45
Figure 1. Eligibility criteria trends for IPI score, age at diagnosis, ECOG PS, and Ann Arbor stage in DLBCL RCTs spanning the R-CHOP era 45
Figure 2. Study eligibility in the Emory University and Schmitz et al. DLBCL cohorts using study-specific and proposed criteria 46
Figure 3. Study specific HRs for OS and PFS comparing eligible and ineligible groups across 19 DLBCL RCTs and using proposed criteria 47
Figure 4. Cox proportional hazards results comparing eligible groups from 19 DLBCL RCTs with eligible groups defined using proposed criteria 48
Figure 5. ROC plot illustrating the capacity of combinations of proposed criteria to identify patients eligible for the PHOENIX trial in the Schmitz et al. cohort 49
Figure 6. Impact of eligibility criteria on genetic alteration prevalence in the Reddy et al. data set 50
Figure 7. Impact of eligibility criteria on genetic alteration prevalence in the Chapuy et al. data set 51
Figure 8. Impact of eligibility criteria on genetic alteration prevalence for patients in genetic Cluster 1 in the Chapuy et al. data set 52
Figure 9. Impact of eligibility criteria on genetic alteration prevalence for patients in genetic Cluster 2 in the Chapuy et al. data set 53
Figure 10. Impact of eligibility criteria on genetic alteration prevalence for patients in genetic Cluster 3 in the Chapuy et al. data set 54
Figure 11. Impact of eligibility criteria on genetic alteration prevalence for patients in genetic Cluster 4 in the Chapuy et al. data set 55
Figure 12. Impact of eligibility criteria on genetic alteration prevalence for patients in genetic Cluster 5 in the Chapuy et al. data set 56
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