Bin Zhang1, Xueliang Niu2, Qiang Zhang1, Chunli Wang2, Bo Liu2, Dongsheng Yue1, Chenguang Li1, Giuseppe Giaccone3, Shiyong Li4, Liuwei Gao1, Hua Zhang1, Jian Wang5, Huanming Yang5, Renhua Wu2, Peixiang Ni2, Changli Wang6, Mingzhi Ye7, Weiran Liu8. 1. Department of Lung Cancer, Tianjin Lung Cancer Center, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, China. 2. Tianjin Medical Laboratory, BGI-Tianjin, BGI-Shenzhen, Tianjin, China; BGI Genomics, BGI-Shenzhen, Shenzhen, China. 3. Department of Lung Cancer, Tianjin Lung Cancer Center, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, China; Georgetown University, Washington, District of Columbia, USA. 4. BGI Genomics, BGI-Shenzhen, Shenzhen, China; BGI-Guangzhou Medical Laboratory, BGI-Shenzhen, Guangzhou, China. 5. BGI-Shenzhen, Shenzhen, China; James D. Watson Institute of Genome Sciences, Hangzhou, China. 6. Department of Lung Cancer, Tianjin Lung Cancer Center, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, China. Electronic address: wangchangli@tjmuch.com. 7. BGI Genomics, BGI-Shenzhen, Shenzhen, China; BGI-Guangzhou Medical Laboratory, BGI-Shenzhen, Guangzhou, China; BGI-Guangzhou, Guangzhou Key Laboratory of Cancer Trans-Omics Research, Guangzhou, China. Electronic address: yemingzhi@genomics.cn. 8. Department of Anesthesiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, China. Electronic address: wrliu@tmu.edu.cn.
Abstract
OBJECTIVES: Circulating tumor DNA (ctDNA) testing in plasma in patients with non-small-cell lung cancer (NSCLC) has the potential to be a supplemental or surrogate tool for tissue biopsy. Detection of genomic abnormalities in ctDNA and their association with clinical characteristics in early-stage NSCLC need to be clarified. MATERIALS AND METHODS: Here, we comprehensively analyzed gene variations of 48 tumor tissues and 48 matched preoperative (pre-op) plasma and 25 postoperative (post-op) plasma from early-stage NSCLC patients using a targeted 546 genes capture-based next generation sequencing (NGS) assay. RESULTS: In early-stage NSCLC, the average mutation allele frequency (MAF) in pre-op plasma ctDNA was lower than that in tissue DNA (tDNA). The concordant gene variations between pre-op ctDNA and tDNA were difficult to detect. However, we found the tissue- pre-op plasma concordant ctDNA mutation detection ratio in lung squamous cell carcinoma (LUSC) was much higher than that in lung adenocarcinoma (LUAD). We also established a LUSC-LUAD classification model by a least absolute shrinkage and selection operator (LASSO) based approach to help separate LUAD from LUSC based on ctDNA profiling. This model included 14 gene mutations and extracted an accuracy of 89.2% in the training set and 91.5% in the testing set. Correlation analysis showed tDNA-ctDNA concordant ratio was related to histologic subtype, gene mutations and tumor size in early-stage NSCLC. CONCLUSION: This study suggests histology subtype and gene mutations could affect ctDNA detection in early-stage NSCLC. NGS-based ctDNA profile has the potential utility in LUSC-LUAD classification.
OBJECTIVES: Circulating tumor DNA (ctDNA) testing in plasma in patients with non-small-cell lung cancer (NSCLC) has the potential to be a supplemental or surrogate tool for tissue biopsy. Detection of genomic abnormalities in ctDNA and their association with clinical characteristics in early-stage NSCLC need to be clarified. MATERIALS AND METHODS: Here, we comprehensively analyzed gene variations of 48 tumor tissues and 48 matched preoperative (pre-op) plasma and 25 postoperative (post-op) plasma from early-stage NSCLCpatients using a targeted 546 genes capture-based next generation sequencing (NGS) assay. RESULTS: In early-stage NSCLC, the average mutation allele frequency (MAF) in pre-op plasma ctDNA was lower than that in tissue DNA (tDNA). The concordant gene variations between pre-op ctDNA and tDNA were difficult to detect. However, we found the tissue- pre-op plasma concordant ctDNA mutation detection ratio in lung squamous cell carcinoma (LUSC) was much higher than that in lung adenocarcinoma (LUAD). We also established a LUSC-LUAD classification model by a least absolute shrinkage and selection operator (LASSO) based approach to help separate LUAD from LUSC based on ctDNA profiling. This model included 14 gene mutations and extracted an accuracy of 89.2% in the training set and 91.5% in the testing set. Correlation analysis showed tDNA-ctDNA concordant ratio was related to histologic subtype, gene mutations and tumor size in early-stage NSCLC. CONCLUSION: This study suggests histology subtype and gene mutations could affect ctDNA detection in early-stage NSCLC. NGS-based ctDNA profile has the potential utility in LUSC-LUAD classification.
Authors: G M Walls; L McConnell; J McAleese; P Murray; T B Lynch; K Savage; G G Hanna; D Gonzalez de Castro Journal: Radiat Oncol Date: 2020-05-29 Impact factor: 3.481
Authors: Hang Li; Ze-Lin Ma; Bin Li; Yun-Jian Pan; Jia-Qing Xiang; Ya-Wei Zhang; Yi-Hua Sun; Ting Hou; Analyn Lizaso; Yan Chen; Xi Li; Hong Hu Journal: Cancer Med Date: 2021-10-19 Impact factor: 4.452
Authors: Christopher S Trethewey; Harriet S Walter; Abdullah N M Alqahtani; Ralf Schmid; David S Guttery; Yvette Griffin; Matthew J Ahearne; Gerald S Saldanha; Sandrine P N Jayne; Martin J S Dyer Journal: Hemasphere Date: 2022-03-01