Lessons learned from BATTLE-2 in the war on cancer: the use of Bayesian method in clinical trial design

In the past decade, the therapeutic landscape for non-small cell lung cancer (NSCLC) has evolved considerably with the advent of targeted therapy. It has become the standard of care to match patients with relevant targeted therapeutics according to their molecular abnormalities (1). Treatment of patients with EGFR-mutant NSCLC with EGFR tyrosine kinase inhibitors (EGFR-TKIs) serves as the paradigm of precision medicine in lung cancer. In the U.S., three first- or second-generation EGFR-TKIs (erlotinib, gefitinib, afatinib) are available for use in the first-line treatment of EGFR-mutant NSCLC (2). Osimertinib, a third-generation EGFR-TKI, is approved by the U.S. Food and Drug Administration (FDA) for patients whose disease has progressed after an earlier generation EGFR-TKI and whose tumors have a secondary mutation (T790M) (2). The list of genetic aberrations for which effective targeted therapeutics are available includes ALK and ROS1 translocations (3,4), and continues to expand due to an improved understanding of the molecular pathogenesis of NSCLC. However, due to the ever-growing number of targeted therapeutics and their putative targets, the simultaneous development of a molecularly targeted agent and a predictive biomarker is not often achievable using data from traditional non-randomized early-phase clinical trials (5), which raises the need for innovative approaches to clinical trial design