한빛사논문
Su Bin Lima,b, Trifanny Yeob, Wen Di Leeb, Ali Asgar S. Bhagatb,c, Swee Jin Tand, Daniel Shao Weng Tane,f,g, Wan-Teck Lime,h,i, and Chwee Teck Lima,b,c,j,1
a NUS Graduate School for Integrative Sciences & Engineering, National University of Singapore, 117456 Singapore, Singapore; b Department of Biomedical Engineering, National University of Singapore, 117583 Singapore, Singapore; c Institute for Health Innovation and Technology (iHealthtech), National University of Singapore, 117599 Singapore, Singapore; d Regional Scientific Affairs, Sysmex Asia Pacific, 528735 Singapore, Singapore; e Division of Medical Oncology, National Cancer Centre Singapore, 169610 Singapore, Singapore; f Cancer Stem Cell Biology, Genome Institute of Singapore, 138672 Singapore, Singapore; g Cancer Therapeutics Research Laboratory, National Cancer Centre Singapore, 169610 Singapore, Singapore; h Office of Academic and Clinical Development, Duke-NUS Medical School, 169857 Singapore, Singapore; i Institute of Molecular and Cell Biology (IMCB) National Cancer Centre (NCC) Max Planck Institute (MPI) Singapore Oncogenome (INMSOG) Laboratory, Institute of Molecular and Cell Biology, 138673 Singapore, Singapore; and j Mechanobiology Institute, National University of Singapore, 117411 Singapore, Singapore
1To whom correspondence may be addressed.
Abstract
Despite pronounced genomic and transcriptomic heterogeneity in non–small-cell lung cancer (NSCLC) not only between tumors, but also within a tumor, validation of clinically relevant gene signatures for prognostication has relied upon single-tissue samples, including 2 commercially available multigene tests (MGTs). Here we report an unanticipated impact of intratumor heterogeneity (ITH) on risk prediction of recurrence in NSCLC, underscoring the need for a better genomic strategy to refine prognostication. By leveraging label-free, inertial-focusing microfluidic approaches in retrieving circulating tumor cells (CTCs) at single-cell resolution, we further identified specific gene signatures with distinct expression profiles in CTCs from patients with differing metastatic potential. Notably, a refined prognostic risk model that reconciles the level of ITH and CTC-derived gene expression data outperformed the initial classifier in predicting recurrence-free survival (RFS). We propose tailored approaches to providing reliable risk estimates while accounting for ITH-driven variance in NSCLC.
microfluidics, circulating biomarkers, tumor heterogeneity
논문정보
관련 링크
관련분야 연구자보기
소속기관 논문보기
관련분야 논문보기
해당논문 저자보기