作者
Jennifer L. Cotton,Javier Estrada,Vivek Sagar,Julie Chen,Michelle Piquet,John Alford,Youngchul Song,Xiaoyan Li,Markus Riester,Matthew T. DiMare,Katja Schumacher,Gaylor Boulay,Kathleen Sprouffske,Lin Fan,Tyler Burks,Leandra Mansur,Joel P. Wagner,Hyo‐eun C. Bhang,Oleg Iartchouk,John Reece-Hoyes,Erick J. Morris,Peter S. Hammerman,David A. Ruddy,Joshua M. Korn,Jeffrey A. Engelman,Matthew J. Niederst
摘要
Abstract For a majority of patients with non–small cell lung cancer with EGFR mutations, treatment with EGFR inhibitors (EGFRi) induces a clinical response. Despite this initial reduction in tumor size, residual disease persists that leads to disease relapse. Elucidating the preexisting biological differences between sensitive cells and surviving drug-tolerant persister cells and deciphering how drug-tolerant cells evolve in response to treatment could help identify strategies to improve the efficacy of EGFRi. In this study, we tracked the origins and clonal evolution of drug-tolerant cells at a high resolution by using an expressed barcoding system coupled with single-cell RNA sequencing. This platform enabled longitudinal profiling of gene expression and drug sensitivity in response to EGFRi across a large number of clones. Drug-tolerant cells had higher expression of key survival pathways such as YAP and EMT at baseline and could also differentially adapt their gene expression following EGFRi treatment compared with sensitive cells. In addition, drug combinations targeting common downstream components (MAPK) or orthogonal factors (chemotherapy) showed greater efficacy than EGFRi alone, which is attributable to broader targeting of the heterogeneous EGFRi-tolerance mechanisms present in tumors. Overall, this approach facilitates thorough examination of clonal evolution in response to therapy that could inform the development of improved diagnostic approaches and treatment strategies for targeting drug-tolerant cells. Significance: The evolution and heterogeneity of EGFR inhibitor tolerance are identified in a large number of clones at enhanced cellular and temporal resolution using an expressed barcode technology coupled with single-cell RNA sequencing.