UCSF

The design of clinical trials with outcomes reported in cohorts including nested subgroups is common in novel agents seeking new indications for approval. This structure represents a tension between drug companies that have an incentive to pursue broad biomarker-agnostic approvals and patients whose best interest is to identify the subgroup(s) most likely to benefit from the drug. Programmed death ligand 1 (PD-L1) and checkpoint inhibitors are a prominent example with early trials reporting efficacy of checkpoint inhibitors in cohorts with high levels of PD-L1. Subsequent analyses incrementally report outcomes in broader patient cohorts that include the nested subgroup of high PD-L1 expression which drives the positive outcome in the entire cohort. Comparing aggregate outcomes between groups of patients with known heterogeneous outcomes deters the effective analysis of all available data. Exploring the optimal treatment for individual patients with different levels of PD-L1 expression, whether it is checkpoint inhibitors only, checkpoint inhibitors combined with chemotherapy or chemotherapy only, requires a granular approach to trial design and reporting. Such grouping of patients with different biomarker findings is increasingly seen in the setting of adjuvant therapy, as well as in targeted therapies that show efficacy in a single gene mutation which however are studied in the setting of panels of mutations. Here we discuss the difference between nested and adjacent subgroups in oncology.