Shoko Nakasone1, Ayako Suzuki2, Hitomi Okazaki3, Keiichi Onodera4, Junko Zenkoh5, Genichiro Ishii6, Yutaka Suzuki7, Masahiro Tsuboi8, Katsuya Tsuchihara9. 1. Department of Thoracic Surgery, National Cancer Center Hospital East, 6-5-1 Kashiwanoha, Kashiwa, Chiba, 277-8577, Japan; Course of Advanced Clinical Research of Cancer, Juntendo University Graduate School of Medicine, 2-1-1 Hongo, Bunkyo-ku, Tokyo, 113-8421, Japan. Electronic address: snakason@east.ncc.go.jp. 2. Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba, 277-8561, Japan. Electronic address: asuzuki@edu.k.u-tokyo.ac.jp. 3. Division of Translational Informatics, Exploratory Oncology Research and Clinical Trial Center, National Cancer Center, 6-5-1 Kashiwanoha, Kashiwa, Chiba, 277-8577, Japan. Electronic address: hitomi.nose.o@gmail.com. 4. Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba, 277-8561, Japan. Electronic address: 3950929256@edu.k.u-tokyo.ac.jp. 5. Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba, 277-8561, Japan. Electronic address: zenkoh@edu.k.u-tokyo.ac.jp. 6. Course of Advanced Clinical Research of Cancer, Juntendo University Graduate School of Medicine, 2-1-1 Hongo, Bunkyo-ku, Tokyo, 113-8421, Japan; Division of Pathology, Exploratory Oncology Research and Clinical Trial Center, National Cancer Center, 6-5-1 Kashiwanoha, Kashiwa, Chiba, 277-8577, Japan. Electronic address: gishii@east.ncc.go.jp. 7. Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba, 277-8561, Japan. Electronic address: ysuzuki@hgc.jp. 8. Department of Thoracic Surgery, National Cancer Center Hospital East, 6-5-1 Kashiwanoha, Kashiwa, Chiba, 277-8577, Japan. Electronic address: mtsuboi@east.ncc.go.jp. 9. Division of Translational Informatics, Exploratory Oncology Research and Clinical Trial Center, National Cancer Center, 6-5-1 Kashiwanoha, Kashiwa, Chiba, 277-8577, Japan. Electronic address: ktsuchih@east.ncc.go.jp.
Abstract
OBJECTIVES: Microtubule inhibitors (MTIs) are widely used as anti-cancer drugs for various types of tumors. Vinorelbine, an MTI, is utilized in postoperative adjuvant chemotherapy, especially for lung adenocarcinoma. However, no molecular markers are able to identify patients for whom MTIs would be effective. In this study, we attempted to identify practical markers to predict the efficacy of MTI-based adjuvant chemotherapy. MATERIALS AND METHODS: We explored a novel combination of molecular marker candidates, based on gene expression network analysis constructed using an omics panel of 26 lung adenocarcinoma cell lines. We then applied the obtained classification method to predict the efficacy of MTI treatment in patients who received adjuvant chemotherapy. RNA sequencing (RNA-seq) analysis was conducted using surgical specimens from 24 Japanese lung adenocarcinoma patients treated postoperatively with vinorelbine. RESULTS: We identified four modules within the network with module activities that were significantly associated with sensitivity to MTIs. Two modules were associated with high sensitivity to MTIs: genes with low differentiation or transdifferentiation of lung adenocarcinomas. On the other hand, MTI-low sensitivity modules were enriched in common epithelial genes and markers of well-differentiated lung adenocarcinomas. We also classified lung adenocarcinoma cases using the module activities associated with MTI efficacy and stratify the cases with MTI resistance. CONCLUSION: We demonstrate that the constructed classification method is useful for identifying patients with MTI resistance which results in a high risk of cancer relapse.
OBJECTIVES: Microtubule inhibitors (MTIs) are widely used as anti-cancer drugs for various types of tumors. Vinorelbine, an MTI, is utilized in postoperative adjuvant chemotherapy, especially for lung adenocarcinoma. However, no molecular markers are able to identify patients for whom MTIs would be effective. In this study, we attempted to identify practical markers to predict the efficacy of MTI-based adjuvant chemotherapy. MATERIALS AND METHODS: We explored a novel combination of molecular marker candidates, based on gene expression network analysis constructed using an omics panel of 26 lung adenocarcinoma cell lines. We then applied the obtained classification method to predict the efficacy of MTI treatment in patients who received adjuvant chemotherapy. RNA sequencing (RNA-seq) analysis was conducted using surgical specimens from 24 Japanese lung adenocarcinomapatients treated postoperatively with vinorelbine. RESULTS: We identified four modules within the network with module activities that were significantly associated with sensitivity to MTIs. Two modules were associated with high sensitivity to MTIs: genes with low differentiation or transdifferentiation of lung adenocarcinomas. On the other hand, MTI-low sensitivity modules were enriched in common epithelial genes and markers of well-differentiated lung adenocarcinomas. We also classified lung adenocarcinoma cases using the module activities associated with MTI efficacy and stratify the cases with MTI resistance. CONCLUSION: We demonstrate that the constructed classification method is useful for identifying patients with MTI resistance which results in a high risk of cancer relapse.
Authors: Xinjia Ruan; Yuqing Ye; Wenxuan Cheng; Li Xu; Mengjia Huang; Yi Chen; Junkai Zhu; Xiaofan Lu; Fangrong Yan Journal: Front Med (Lausanne) Date: 2022-06-03