Xian-Ning Wu1, Dan Su2, Yi-De Mei3, Mei-Qing Xu1, Hao Zhang4, Ze-Yu Wang4, Li-Ling Li5,6, Li Peng7, Jun-Yi Jiang8, Jia-Yi Yang9, Dong-Jie Li10,11, Hui Cao12, Zhi-Wei Xia13, Wen-Jing Zeng14, Quan Cheng15,16,17, Nan Zhang18. 1. Department of Thoracic Surgery, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230001, Anhui, People's Republic of China. 2. School of Nursing, Anhui Medical University, Hefei, People's Republic of China. 3. School of Life Sciences, University of Science and Technology of China (USTC), Hefei, 230027, Anhui, People's Republic of China. 4. Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, 410008, People's Republic of China. 5. Department of Pathology, Xiangya Hospital, Central South University, Changsha, People's Republic of China. 6. Department of Pathology, Xiangya Medical School, Central South University, Changsha, People's Republic of China. 7. Department of Ophthalmology, Central South University Xiangya School of Medicine Affiliated Haikou Hospital, Haikou, People's Republic of China. 8. Aier School of Ophthalmology, Central South University, Changsha, People's Republic of China. 9. Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, People's Republic of China. 10. Department of Clinical Pharmacology, and Geriatric Urology, Xiangya International Medical Center, Xiangya Hospital, Central South University, Changsha, People's Republic of China. 11. National Clinical Research Center for Geriatric Disorders, Changsha, People's Republic of China. 12. Department of Psychiatry, The Second People's Hospital of Hunan Province, The Hospital of Hunan University of Chinese Medicine, Changsha, People's Republic of China. 13. Department of Neurology, Hunan Aerospace Hospital, Changsha, People's Republic of China. 14. Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, People's Republic of China. 15. Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, 410008, People's Republic of China. chengquan@csu.edu.cn. 16. National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, People's Republic of China. chengquan@csu.edu.cn. 17. Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, People's Republic of China. chengquan@csu.edu.cn. 18. One-Third Lab, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150000, Hei Longjiang, People's Republic of China. awekevin@onethird-lab.com.
Abstract
BACKGROUND: Lung adenocarcinoma (LUAD), a subtype of non-small cell lung cancer (NSCLC), causes high mortality around the world. Previous studies have suggested that the metabolic pattern of tumor is associated with tumor response to immunotherapy and patient's survival outcome. Yet, this relationship in LUAD is still unknown. METHODS: Therefore, in this study, we identified the immune landscape in different tumor subtypes classified by metabolism-related genes expression with a large-scale dataset (tumor samples, n = 2181; normal samples, n = 419). We comprehensively correlated metabolism-related phenotypes with diverse clinicopathologic characteristics, genomic features, and immunotherapeutic efficacy in LUAD patients. RESULTS: And we confirmed tumors with activated lipid metabolism tend to have higher immunocytes infiltration and better response to checkpoint immunotherapy. This work highlights the connection between the metabolic pattern of tumor and tumor immune infiltration in LUAD. A scoring system based on metabolism-related gene expression is not only able to predict prognosis of patient with LUAD but also applied to pan-cancer. LUAD response to checkpoint immunotherapy can also be predicted by this scoring system. CONCLUSIONS: This work revealed the significant connection between metabolic pattern of tumor and tumor immune infiltration, regulating LUAD patients' response to immunotherapy.
BACKGROUND:Lung adenocarcinoma (LUAD), a subtype of non-small cell lung cancer (NSCLC), causes high mortality around the world. Previous studies have suggested that the metabolic pattern of tumor is associated with tumor response to immunotherapy and patient's survival outcome. Yet, this relationship in LUAD is still unknown. METHODS: Therefore, in this study, we identified the immune landscape in different tumor subtypes classified by metabolism-related genes expression with a large-scale dataset (tumor samples, n = 2181; normal samples, n = 419). We comprehensively correlated metabolism-related phenotypes with diverse clinicopathologic characteristics, genomic features, and immunotherapeutic efficacy in LUAD patients. RESULTS: And we confirmed tumors with activated lipid metabolism tend to have higher immunocytes infiltration and better response to checkpoint immunotherapy. This work highlights the connection between the metabolic pattern of tumor and tumor immune infiltration in LUAD. A scoring system based on metabolism-related gene expression is not only able to predict prognosis of patient with LUAD but also applied to pan-cancer. LUAD response to checkpoint immunotherapy can also be predicted by this scoring system. CONCLUSIONS: This work revealed the significant connection between metabolic pattern of tumor and tumor immune infiltration, regulating LUAD patients' response to immunotherapy.
Authors: Rishab Ramapriyan; Mauricio S Caetano; Hampartsoum B Barsoumian; Ana Carolina P Mafra; Erika Pereira Zambalde; Hari Menon; Efrosini Tsouko; James W Welsh; Maria Angelica Cortez Journal: Pharmacol Ther Date: 2018-11-12 Impact factor: 12.310
Authors: Ping-Chih Ho; Jessica Dauz Bihuniak; Andrew N Macintyre; Matthew Staron; Xiaojing Liu; Robert Amezquita; Yao-Chen Tsui; Guoliang Cui; Goran Micevic; Jose C Perales; Steven H Kleinstein; E Dale Abel; Karl L Insogna; Stefan Feske; Jason W Locasale; Marcus W Bosenberg; Jeffrey C Rathmell; Susan M Kaech Journal: Cell Date: 2015-08-27 Impact factor: 41.582