Clinical Significance of a Prospective Large Genomic Screening for SCLC: The Genetic Classification and a Biomarker-Driven Phase 2 Trial of Gedatolisib

Abstract

IntroductionSCLC has been treated as a single entity resulting in limited survival improvement. Developing effective tools for guiding appropriate therapeutic strategies is crucial. MethodsA total of 1035 SCLCs were prospectively analyzed by a genomic screening platform: LC-SCRUM-Asia. Fresh frozen tumor samples were subjected to a next-generation sequencing system enabling the integrative analysis of cancer-related genes. A phase 2 trial of gedatolisib for SCLC with PI3K/AKT/mTOR pathway mutations was conducted based on this screening. ResultsOn the basis of the treatment outcomes and therapeutic targets, the following five distinct genetic subgroups were identified in SCLC: NSCLC-subgroup (genetic alterations associated with NSCLC, 8.5%); Hotspot-subgroup (targetable hotspot mutations common in tumors, 3.0%); PI3K-subgroup (PI3K/AKT/mTOR pathway mutations, 7.4%); MYC-subgroup (MYC family amplifications, 13.0%); and HME-subgroup (mutations in the histone-modifying enzymes, 17.6%). The NSCLC-subgroup (hazard ratio = 1.57; 95% confidence interval: 1.22–2.03) and MYC-subgroup (hazard ratio = 1.56; 95% confidence interval: 1.26–1.93) had significantly shorter progression-free survivals after first-line platinum-based treatment. The Hotspot-subgroup and MYC-subgroup were candidates for novel targeted therapies. The HME-subgroup had a favorable survival in patients who received programmed cell death protein-1/programmed death-ligand 1 inhibitor-based therapies (p = 0.005, log-rank test) regardless of some overlap with other subgroups. There were 15 patients enrolled into the phase 2 trial of gedatolisib in the PI3K-subgroup, and the overall response rate and the disease control rate were 6.7% and 20%, respectively. The MYC-subgroup or NSCLC-subgroup was associated with unfavorable clinical outcomes in this trial. ConclusionsMolecular classification of SCLC by genetic approach is beneficial for predicting the treatment outcomes and effectively guiding the clinical choices.