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Expert consensus on the detection and clinical application of tumor mutational burden
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Zhenying Guo1, Chunwei Xu2, Shirong Zhang3, Yue Hao4, Xiaotong Hu5, Ming Zhao6, Chan Xiang7, Yingshi Piao8, Pingli Sun9, Xueping Xiang10, Jing Zhao11, Huanwen Wu12, Weixing Li13, Jinpu Yu14, Jingping Yuan15, Shuangshuang Wang16, Cong Wang17, Yun Gu18, Bingjian Lv19, Liping Zhang20, Yueping Liu21, Xiaobin Cui22, Weizhong Gu23, Yining Li24, Wei Wang25, Wenjun Yang26, Weiguo Long27, Jingjing Xiang28, Hong Mou29, Biao Liu30, Huajuan Ruan31, Yubin Wang32, Yongjie Zhu33, Feng Wang34, Zhonghua Wang35, Xiaomin Feng18, Xing Liu36, Peng Li37, Min Deng38, Bin Lian39, Lili Mao39, Qian Wang40, Wenxian Wang4, Zhengbo Song4, Ziming Li41, Wenzhao Zhong42, Zhijie Wang43, Shengxiang Ren44, Wenfeng Fang45, Yongchang Zhang46, Jingjing Liu47, Xiuyu Cai48, Anwen Liu49, Wen Li50, Ping Zhan51, Hongbing Liu51, Tangfeng Lv51, Liyun Miao52, Lingfeng Min53, Yu Chen54, Yu Zhang55, Feng Wang56, Zhansheng Jiang57, Gen Lin54, Long Huang49, Xingxiang Pu58, Rongbo Lin54, Weifeng Liu59, Chuangzhou Rao60, Dongqing Lv61, Zongyang Yu62, Peng Shen63, Xiaoyan Li64, Chuanhao Tang65, Chengzhi Zhou66, Junping Zhang67, Junli Xue68, Hui Guo69, Qian Chu70, Rui Meng71, Jingxun Wu72, Rui Zhang73, Jin Zhou74, Zhengfei Zhu75, Yongheng Li76, Hong Qiu70, Fan Xia75, Yuanyuan Lu77, Xiaofeng Chen78, Rui Ge79, Enyong Dai80, Yu Han81, Jian Zhang82, Yinghua Ji83, Xianbin Liang84, Hongmei Zhang85, Xuelei Ma85, Xuewen Liu86, Yu Yao87, Peng Luo82, Weiwei Pan88, Fei Pang89, Fan Wu90, Dejian Gu91, Li Wang91, Liping Wang92, Youcai Zhu93, Li Lin65, Weiwen Li94, Xinqing Lin66, Jing Cai49, Ling Xu95, Jisheng Li96, Xiaodong Jiao97, Kainan Li98, Jia Wei99, Huijing Feng67, Lin Wang100, Yingying Du101, Wang Yao102, Xuefei Shi103, Xiaomin Niu41, Dongmei Yuan51, Yanwen Yao51, Yinbin Zhang69, Binbin Song104, Wenfeng Li105, Jianfei Fu106, Hong Wang107, Mingxiang Ye51, Dong Wang51, Zhaofeng Wang51, Qing Ji4, Yuan Fang108, Qing Wei4, Zhen Wang109, Bin Wan110, Donglai Lv111, Xiaofeng Li93, Shengjie Yang112, Jing Kang42, Jiatao Zhang42, Chao Zhang42, Lin Shi113, Yina Wang114, Bihui Li115, Zhang Zhang116, Ke Wang117, 118, Zhefeng Liu107, Nong Yang46, Lin Wu58, Xiaobing Chen119, Gu Jin120, Zhongwu Li121, Miao Li122, Guansong Wang123, Jiandong Wang124, Meiyu Fang4, Yong Fang125, Xiaojia Wang4, Jing Chen71, Yiping Zhang4, Xixu Zhu109, Yi Shen126, Shenglin Ma127, Biyun Wang128, Lu Si39, Yong Song51, Yuanzhi Lu129, Aijun Liu130, Yuchen Han7
Cancer Biology & Medicine | 2026, 23(2) : 218 - 246
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Cancer Biology & Medicine | 2026, 23(2): 218-246
• Consensus •
Expert consensus on the detection and clinical application of tumor mutational burden
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Zhenying Guo1, Chunwei Xu2, Shirong Zhang3, Yue Hao4, Xiaotong Hu5, Ming Zhao6, Chan Xiang7, Yingshi Piao8, Pingli Sun9, Xueping Xiang10, Jing Zhao11, Huanwen Wu12, Weixing Li13, Jinpu Yu14, Jingping Yuan15, Shuangshuang Wang16, Cong Wang17, Yun Gu18, Bingjian Lv19, Liping Zhang20, Yueping Liu21, Xiaobin Cui22, Weizhong Gu23, Yining Li24, Wei Wang25, Wenjun Yang26, Weiguo Long27, Jingjing Xiang28, Hong Mou29, Biao Liu30, Huajuan Ruan31, Yubin Wang32, Yongjie Zhu33, Feng Wang34, Zhonghua Wang35, Xiaomin Feng18, Xing Liu36, Peng Li37, Min Deng38, Bin Lian39, Lili Mao39, Qian Wang40, Wenxian Wang4, Zhengbo Song4, Ziming Li41, Wenzhao Zhong42, Zhijie Wang43, Shengxiang Ren44, Wenfeng Fang45, Yongchang Zhang46, Jingjing Liu47, Xiuyu Cai48, Anwen Liu49, Wen Li50, Ping Zhan51, Hongbing Liu51, Tangfeng Lv51, Liyun Miao52, Lingfeng Min53, Yu Chen54, Yu Zhang55, Feng Wang56, Zhansheng Jiang57, Gen Lin54, Long Huang49, Xingxiang Pu58, Rongbo Lin54, Weifeng Liu59, Chuangzhou Rao60, Dongqing Lv61, Zongyang Yu62, Peng Shen63, Xiaoyan Li64, Chuanhao Tang65, Chengzhi Zhou66, Junping Zhang67, Junli Xue68, Hui Guo69, Qian Chu70, Rui Meng71, Jingxun Wu72, Rui Zhang73, Jin Zhou74, Zhengfei Zhu75, Yongheng Li76, Hong Qiu70, Fan Xia75, Yuanyuan Lu77, Xiaofeng Chen78, Rui Ge79, Enyong Dai80, Yu Han81, Jian Zhang82, Yinghua Ji83, Xianbin Liang84, Hongmei Zhang85, Xuelei Ma85, Xuewen Liu86, Yu Yao87, Peng Luo82, Weiwei Pan88, Fei Pang89, Fan Wu90, Dejian Gu91, Li Wang91, Liping Wang92, Youcai Zhu93, Li Lin65, Weiwen Li94, Xinqing Lin66, Jing Cai49, Ling Xu95, Jisheng Li96, Xiaodong Jiao97, Kainan Li98, Jia Wei99, Huijing Feng67, Lin Wang100, Yingying Du101, Wang Yao102, Xuefei Shi103, Xiaomin Niu41, Dongmei Yuan51, Yanwen Yao51, Yinbin Zhang69, Binbin Song104, Wenfeng Li105, Jianfei Fu106, Hong Wang107, Mingxiang Ye51, Dong Wang51, Zhaofeng Wang51, Qing Ji4, Yuan Fang108, Qing Wei4, Zhen Wang109, Bin Wan110, Donglai Lv111, Xiaofeng Li93, Shengjie Yang112, Jing Kang42, Jiatao Zhang42, Chao Zhang42, Lin Shi113, Yina Wang114, Bihui Li115, Zhang Zhang116, Ke Wang117, 118, Zhefeng Liu107, Nong Yang46, Lin Wu58, Xiaobing Chen119, Gu Jin120, Zhongwu Li121, Miao Li122, Guansong Wang123, Jiandong Wang124, Meiyu Fang4, Yong Fang125, Xiaojia Wang4, Jing Chen71, Yiping Zhang4, Xixu Zhu109, Yi Shen126, Shenglin Ma127, Biyun Wang128, Lu Si39, Yong Song51, Yuanzhi Lu129, Aijun Liu130, Yuchen Han7
Affiliations
  • 1Cancer Center, Department of Pathology, Zhejiang Provincial People’s Hospital (Affiliated People’s Hospital), Hangzhou Medical College, Hangzhou 310014, China
  • 2Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou 310022, China
  • 3Translational Medicine Research Center, Key Laboratory of Clinical Cancer Pharmacology and Toxicology Research of Zhejiang Province, Affiliated Hangzhou First People’s Hospital, Cancer Center, Westlake University School of Medicine, Hangzhou 310006, China
  • 4Department of Chemotherapy, Chinese Academy of Sciences University Cancer Hospital (Zhejiang Cancer Hospital), Hangzhou 310022, China
  • 5Department of Pathology, Sir Run Run Shaw Hospital, Zhejiang University, Hangzhou 310016, China
  • 6Ningbo Clinical Pathology Diagnosis Center, Ningbo 315100, China
  • 7Department of Pathology, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai 200030, China
  • 8Department of Pathology, Beijing Tongren Hospital, Capital Medical University, Beijing 100730, China
  • 9Department of Pathology, The Second Hospital of Jilin University, Changchun 130041, China
  • 10Department of Pathology, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310009, China
  • 11Department of Pathology, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou 310003, China
  • 12Department of Pathology, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100730, China
  • 13Laboratory Medicine Center, Zhejiang Center for Clinical Laboratory, Zhejiang Provincial People’s Hospital, Affiliated People’s Hospital, Hangzhou Medical College, Hangzhou 310014, China
  • 14Department of Cancer Molecular Diagnostics Core, Tianjin Medical University Cancer Institute and Hospital, Tianjin 300060, China
  • 15Department of Pathology, Renmin Hospital of Wuhan University, Wuhan 430060, China
  • 16Department of Pathology, Affiliated Hospital of Nanjing University of Chinese Medicine, Jiangsu Province Hospital of Chinese Medicine, Nanjing 210029, China
  • 17Department of Pathology, The First Affiliated Hospital of Nanjing Medical University (Jiangsu Province Hospital), Nanjing 210029, China
  • 18Department of Pathology, Women’s Hospital of Nanjing Medical University, Nanjing Women and Children’s Healthcare Hospital, Nanjing 210004, China
  • 19Department of Surgical Pathology Women’s Hospital, School of Medicine, Zhejiang University, Hangzhou 310006, of China
  • 20Department of Pathology, Shanghai Pudong Hospital, School of Medicine, Fudan University, Shanghai 201399, China
  • 21Department of Pathology, The Fourth Hospital of Hebei Medical University, Shijiazhuang 050011, China
  • 22Department of Pathology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing 210008, China
  • 23Department of Pathology, Children’s Hospital Zhejiang University School of Medicine, Hangzhou 310005, China
  • 24Department of Pathology, Stomatology Hospital, School of Stomatology, Zhejiang University, Hangzhou 310000, China
  • 25Department of Pathology, Tongde Hospital of Zhejiang Province, Hangzhou 310012, China
  • 26Department of Pathology, Affiliated Hospital of Hangzhou Normal University, Hangzhou 310000, China
  • 27Department of Pathology, The People’s Hospital of Leshan, Leshan 614000, China
  • 28Department of Pathology, Affiliated Hangzhou First People’s Hospital, Westlake University School of Medicine, Hangzhou 310006, China
  • 29Department of Pathology, Chun’an First People’s Hospital, Hangzhou 311700, China
  • 30Department of Pathology, The Affiliated Suzhou Hospital, Nanjing Medical University, Suzhou 215006, China
  • 31Department of Pathology, The First People’s Hospital of Hangzhou Lin’an District, Affiliated Lin’an People’s Hospital, Hangzhou Medical College, Hangzhou 311300, China
  • 32Department of Pathology, Tiantai County People’s Hospital, Taizhou 317200, China
  • 33Department of Pathology, First People’s Hospital of Aksu, Aksu 843000, China
  • 34Department of Pathology, Xianju People’s Hospital, Zhejiang Southeast Campus of Zhejiang Provincial People’s Hospital, Affiliated Xianju’s Hospital, Hangzhou Medical College, Taizhou 317300, China
  • 35Department of Pathology, Dinghai Branch of Zhejiang Provincial People’s Hospital, Zhoushan 316000, China
  • 36Department of Pathology, Shulan (Hangzhou) Hospital, Hangzhou 310022, China
  • 37Department of Pathology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China
  • 38Department of Pathology, Zhejiang Provincial People’s Hospital (Fuyang Campus), Hangzhou 311400, China
  • 39Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Melanoma and Sarcoma, Peking University Cancer Hospital & Institute, Beijing 100142, China
  • 40Department of Respiratory Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Jiangsu Province Hospital of Chinese Medicine, Nanjing 210029, China
  • 41Department of Shanghai Lung Cancer Center, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai 200030, China
  • 42Guangdong Lung Cancer Institute, Guangdong Provincial Laboratory of Translational Medicine in Lung Cancer, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, School of Medicine, Guangzhou 510080, China
  • 43State Key Laboratory of Molecular Oncology, Department of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
  • 44Department of Medical Oncology, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai 200433, China
  • 45Department of Medical Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, China
  • 46Department of Medical Oncology, Lung Cancer and Gastrointestinal Unit, Hunan Cancer Hospital/The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha 410013, China
  • 47Department of Thoracic Cancer, Jilin Cancer Hospital, Changchun 130012, China
  • 48Department of VIP Inpatient, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, China
  • 49Department of Oncology, Second Affiliated Hospital of Nanchang University, Nanchang 330006, China
  • 50Key Laboratory of Respiratory Disease of Zhejiang Province, Department of Respiratory and Critical Care Medicine, Second Affiliated Hospital of Zhejiang University School of Medicine, Cancer Center, Zhejiang University, Hangzhou 310009, China
  • 51Department of Respiratory Medicine, Affiliated Jinling Hospital, Medical School of Nanjing University, Nanjing 210002, China
  • 52Department of Respiratory Medicine, Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing 210008, China
  • 53Department of Respiratory Medicine, Clinical Medical School of Yangzhou University, Subei People’s Hospital of Jiangsu Province, Yangzhou 225001, China
  • 54Department of Medical Oncology, Fujian Medical University Cancer Hospital & Fujian Cancer Hospital, Fuzhou 350014, China
  • 55Department of Oncology, Guizhou Provincial People’s Hospital, Guiyang 550001, China
  • 56Department of Internal Medicine, Cancer Center of PLA, Qinhuai Medical Area, Affiliated Jinling Hospital, Medical School of Nanjing University, Nanjing 210002, China
  • 57Department of Integrative Oncology, Tianjin Medical University Cancer Institute and Hospital, Tianjin 300060, China
  • 58The Second Department of Thoracic Oncology, Hunan Cancer Hospital/The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha 410013, China
  • 59Department of Orthopedic Oncology Surgery, Beijing Ji shui tan Hospital, Peking University, Beijing 100035, China
  • 60Department of Radiotherapy and Chemotherapy, Hwamei Hospital, University of Chinese Academy of Sciences, Ningbo 315010, China
  • 61Department of Pulmonary Medicine, Taizhou Hospital of Wenzhou Medical University, Taizhou 317000, China
  • 62Department of Respiratory Medicine, the 900th Hospital of the Joint Logistics Team (the Former Fuzhou General Hospital), Fujian Medical University, Fuzhou 350025, China
  • 63Department of Oncology, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China
  • 64Department of Oncology, Beijing Tiantan Hospital, Capital Medical University, Beijing 100700, China
  • 65Department of Medical Oncology, Peking University International Hospital, Beijing 102206, China
  • 66State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou 510300, China
  • 67Department of Thoracic Oncology, Shanxi Academy of Medical Sciences, Shanxi Bethune Hospital, Taiyuan 030032, China
  • 68Department of Oncology, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai 200123, China
  • 69Department of Oncology, The Second Affiliated Hospital of Medical College, Xi’an Jiaotong University, Xi’an 710004, China
  • 70Department of Oncology, Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
  • 71Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
  • 72Department of Medical Oncology, The First Affiliated Hospital of Medicine, Xiamen University, Xiamen 361003, China
  • 73Department of Medical Oncology, Cancer Hospital of China Medical University, Shenyang 110042, China
  • 74Department of Medical Oncology, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology, Chengdu 610041, China
  • 75Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai 200032, China
  • 76Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital & Institute, Beijing 100142, China
  • 77State Key Laboratory of Cancer Biology, National Clinical Research Center for Digestive Diseases and Xijing Hospital of Digestive Diseases, Fourth Military Medical University, Xi’an 710032, China
  • 78Department of Oncology, Jiangsu Province Hospital and Nanjing Medical University First Affiliated Hospital, Nanjing 210029, China
  • 79Department of General Surgery, Huadong Hospital Affiliated to Fudan University, Shanghai 200040, China
  • 80Department of Oncology and Hematology, China-Japan Union Hospital of Jilin University, Changchun 130033, China
  • 81Department of Gastrointestinal Oncology, Harbin Medical University Cancer Hospital, Harbin 150081, China
  • 82Department of Oncology, Zhujiang Hospital, Southern Medical University, Guangzhou 510282, China
  • 83Department of Oncology, The First Affiliated Hospital of Xinxiang Medical University, Xinxiang 453000, China
  • 84Department of Oncology, The Third People’s Hospital of Zhengzhou, Zhengzhou 450000, China
  • 85Department of Biotherapy, State Key Laboratory of Biotherapy, Cancer Center, West China Hospital, Sichuan University, Chengdu 610041, China
  • 86Department of Oncology, the Third Xiangya Hospital, Central South University, Changsha 410013, China
  • 87Department of Medical Oncology, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an 710061, China
  • 88Department of Cell Biology, College of Medicine, Jiaxing University, Jiaxing 314001, China
  • 89Department of Medical, Shanghai OrigiMed Co, Ltd, Shanghai 201114, China
  • 90Department of Medical, Menarini Silicon Biosystems SpA, Shanghai 200333, China
  • 91Department of Medical, Geneplus-Beijing, Beijing 102200, China
  • 92Department of Oncology, Baotou Cancer Hospital, Baotou 014000, China
  • 93Department of Thoracic Disease Diagnosis and Treatment Center, Zhejiang Rongjun Hospital, The Third Affiliated Hospital of Jiaxing University, Jiaxing 314000, China
  • 94Department of Oncology, Lishui Municipal Central Hospital, Lishui 323000, China
  • 95Department of Interventional Pulmonary Diseases, Anhui Chest Hospital, Hefei 230011, China
  • 96Department of Medical Oncology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan 250012, China
  • 97Department of Medical Oncology, Shanghai Changzheng Hospital, Naval Medical University, Shanghai 200070, China
  • 98Department of Oncology, Shandong Provincial Third Hospital, Cheeloo College of Medicine, Shandong University, Jinan 250031, China
  • 99Department of The Comprehensive Cancer Center, Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing 210008, China
  • 100Department of Pathology, Shanxi Academy of Medical Sciences, Shanxi Bethune Hospital, Taiyuan 030032, China
  • 101Department of Oncology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China
  • 102Department of Interventional Oncology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510060, China
  • 103Department of Respiratory Medicine, Huzhou Hospital, Zhejiang University School of Medicine, Huzhou 313000, China
  • 104Department of Medical Oncology, The Affiliated Hospital of Jiaxing University, Jiaxing 314000, China
  • 105Department of Radiation Oncology, First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China
  • 106Department of Medical Oncology, Affiliated Jinhua Hospital, Zhejiang University School of Medicine, Jinhua 321000, China
  • 107Senior Department of Oncology, The 5th Medical Center of PLA General Hospital, Beijing 100071, China
  • 108Department of Pathology, Anhui Provincial Children’s Hospital, Hefei 230051, China
  • 109Department of Radiation Oncology, Affiliated Jinling Hospital, Medical School of Nanjing University, Nanjing 210002, China
  • 110Department of Respiratory Medicine, The Affiliated Jiangning Hospital of Nanjing Medical University, Nanjing 210002, China
  • 111Department of Clinical Oncology, The 901 Hospital of Joint Logistics Support Force of People Liberation Army, Hefei 230031, China
  • 112Department of Thoracic Surgery, Chuxiong Yi Autonomous Prefecture People’s Hospital, Chuxiong 675000, China
  • 113Department of Respiratory Medicine, Zhongshan Hospital, Fudan University, Shanghai 200032, China
  • 114Department of Oncology, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou 310000, China
  • 115Department of Oncology, The Second Affiliated Hospital of Guilin Medical University, Guilin 541199, China
  • 116International Cooperative Laboratory of Traditional Chinese Medicine Modernization and Innovative Drug Discovery of Chinese Ministry of Education (MOE), Guangzhou City Key Laboratory of Precision Chemical Drug Development, School of Pharmacy, Jinan University, Guangzhou 510632, China
  • 117National Health Commission (NHC) Key Laboratory of Nuclear Medicine, Jiangsu Key Laboratory of Molecular Nuclear Medicine, Jiangsu Institute of Nuclear Medicine, Wuxi 214063, China
  • 118Department of Radiopharmaceuticals, School of Pharmacy, Nanjing Medical University, Nanjing 210000, China
  • 119Department of Oncology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou 450008, China
  • 120Department of Bone and Soft-tissue Surgery, Chinese Academy of Sciences University Cancer Hospital (Zhejiang Cancer Hospital), Hangzhou 310022, China
  • 121Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Pathology, Peking University Cancer Hospital & Institute, Beijing 100142, China
  • 122Department of Oncology, The Cancer Hospital of Qinghai Province, Xining 810001, China
  • 123Institute of Respiratory Diseases, Xinqiao Hospital, Third Military Medical University, Chongqing 400037, China
  • 124Department of Pathology, Affiliated Jinling Hospital, Medical School of Nanjing University, Nanjing 210002, China
  • 125Department of Medical Oncology, Sir Run Run Shaw Hospital, Zhejiang University, Hangzhou 310016, China
  • 126Department of Thoracic Surgery, Affiliated Jinling Hospital, Medical School of Nanjing University, Nanjing 210002, China
  • 127Department of Oncology, Key Laboratory of Clinical Cancer Pharmacology and Toxicology Research of Zhejiang Province, Affiliated Hangzhou Cancer Hospital, Cancer Center, Westlake University School of Medicine, Hangzhou 310006, China
  • 128Department of Breast Cancer and Urological Medical Oncology, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
  • 129Department of Clinical Pathology, The First Affiliated Hospital of Jinan University, Guangzhou 510630, China
  • 130Senior Department of Pathology, the 7th Medical Center of PLA General Hospital, Beijing 100700, China
About Author:

Author contributions

Designed the expert consensus: Zhenying Guo, Chunwei Xu, Shirong Zhang, Yue Hao, Yuanzhi Lu, Aijun Liu, Yuchen Han.

Conceived and drafted the expert consensus: Zhenying Guo, Chunwei Xu, Shirong Zhang, Yue Hao.

Coordinated with other authors: Zhenying Guo, Chunwei Xu, Shirong Zhang, Yue Hao, Xiaotong Hu, Yuanzhi Lu, Aijun Liu, Yuchen Han.

Discussed, read, and approved the final manuscript: All authors.

Published: 2026-02-15 doi: 10.20892/j.issn.2095-3941.2025.0351
Outline
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As an emerging biomarker, tumor mutational burden (TMB) has attracted increasing attention from clinicians in predicting the efficacy of tumor immunotherapy. Currently, TMB is detected primarily by whole-exome sequencing or targeted panel sequencing on high-throughput sequencing platforms. However, the lack of uniformity in detection methods, threshold settings, and reporting formats, as well as the significant differences in TMB values among different cancer types, have hindered the standardized application of this biomarker in clinical practice. This consensus focuses on the definition, standardization of detection, clinical significance, and limitations of TMB, and provides consensus recommendations for the clinical application of TMB in real-world practice in China. This consensus is aimed at helping clinicians and laboratory personnel understand the clinical significance and testing standards of TMB, promoting more accurate interpretation of test results, and improving patient care.

Biomarkers  /  tumor mutational burden  /  tumor immunotherapy  /  targeted panel sequencing  /  whole-exome sequencing
Zhenying Guo, Chunwei Xu, Shirong Zhang, Yue Hao, Xiaotong Hu, Ming Zhao, Chan Xiang, Yingshi Piao, Pingli Sun, Xueping Xiang, Jing Zhao, Huanwen Wu, Weixing Li, Jinpu Yu, Jingping Yuan, Shuangshuang Wang, Cong Wang, Yun Gu, Bingjian Lv, Liping Zhang, Yueping Liu, Xiaobin Cui, Weizhong Gu, Yining Li, Wei Wang, Wenjun Yang, Weiguo Long, Jingjing Xiang, Hong Mou, Biao Liu, Huajuan Ruan, Yubin Wang, Yongjie Zhu, Feng Wang, Zhonghua Wang, Xiaomin Feng, Xing Liu, Peng Li, Min Deng, Bin Lian, Lili Mao, Qian Wang, Wenxian Wang, Zhengbo Song, Ziming Li, Wenzhao Zhong, Zhijie Wang, Shengxiang Ren, Wenfeng Fang, Yongchang Zhang, Jingjing Liu, Xiuyu Cai, Anwen Liu, Wen Li, Ping Zhan, Hongbing Liu, Tangfeng Lv, Liyun Miao, Lingfeng Min, Yu Chen, Yu Zhang, Feng Wang, Zhansheng Jiang, Gen Lin, Long Huang, Xingxiang Pu, Rongbo Lin, Weifeng Liu, Chuangzhou Rao, Dongqing Lv, Zongyang Yu, Peng Shen, Xiaoyan Li, Chuanhao Tang, Chengzhi Zhou, Junping Zhang, Junli Xue, Hui Guo, Qian Chu, Rui Meng, Jingxun Wu, Rui Zhang, Jin Zhou, Zhengfei Zhu, Yongheng Li, Hong Qiu, Fan Xia, Yuanyuan Lu, Xiaofeng Chen, Rui Ge, Enyong Dai, Yu Han, Jian Zhang, Yinghua Ji, Xianbin Liang, Hongmei Zhang, Xuelei Ma, Xuewen Liu, Yu Yao, Peng Luo, Weiwei Pan, Fei Pang, Fan Wu, Dejian Gu, Li Wang, Liping Wang, Youcai Zhu, Li Lin, Weiwen Li, Xinqing Lin, Jing Cai, Ling Xu, Jisheng Li, Xiaodong Jiao, Kainan Li, Jia Wei, Huijing Feng, Lin Wang, Yingying Du, Wang Yao, Xuefei Shi, Xiaomin Niu, Dongmei Yuan, Yanwen Yao, Yinbin Zhang, Binbin Song, Wenfeng Li, Jianfei Fu, Hong Wang, Mingxiang Ye, Dong Wang, Zhaofeng Wang, Qing Ji, Yuan Fang, Qing Wei, Zhen Wang, Bin Wan, Donglai Lv, Xiaofeng Li, Shengjie Yang, Jing Kang, Jiatao Zhang, Chao Zhang, Lin Shi, Yina Wang, Bihui Li, Zhang Zhang, Ke Wang, Zhefeng Liu, Nong Yang, Lin Wu, Xiaobing Chen, Gu Jin, Zhongwu Li, Miao Li, Guansong Wang, Jiandong Wang, Meiyu Fang, Yong Fang, Xiaojia Wang, Jing Chen, Yiping Zhang, Xixu Zhu, Yi Shen, Shenglin Ma, Biyun Wang, Lu Si, Yong Song, Yuanzhi Lu, Aijun Liu, Yuchen Han. Expert consensus on the detection and clinical application of tumor mutational burden[J]. Cancer Biology & Medicine, 2026 , 23 (2) : 218 -246 . DOI: 10.20892/j.issn.2095-3941.2025.0351
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Introduction
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Immunotherapy based on immune checkpoint inhibitors (ICIs) has significantly improved objective response rates (ORRs) and overall survival (OS) in patients with advanced malignancies. However, the lack of effective population-based biomarkers has resulted in an overall response rate below 20% for ICI monotherapy, which is often accompanied by immune-related adverse events. Therefore, an urgent need exists to identify accurate and reliable biomarkers for screening patients who might potentially benefit from immunotherapy. Tumor mutational burden (TMB), defined by the number of tumor genetic variants, correlates with ICI efficacy. The concept of TMB originated from a study published in Nature in 2013 examining more than 7,000 specimens from 30 cancer types1. That study analyzed mutation profiles through whole genome sequencing (WGS) and whole-exome sequencing (WES) technologies and described the number of mutations per megabase (Mb) in various cancer samples. In 2015, the first study exploring the correlation between TMB and immunotherapy efficacy in non-small cell lung cancer (NSCLC) was published in Science2. In that study, patients with NSCLC with above-median TMB showed prolonged progression-free survival (PFS). Since then, multiple large studies have confirmed the predictive role of TMB in ICI treatment. In 2017, a study in Genome Medicine involving 100,000 patients with solid tumors explored the correlation between targeted capture sequencing panel and WES results for TMB detection, and confirmed the feasibility and reliability of panel detection of TMB3. The 2019 Chinese Society of Clinical Oncology guidelines and the National Comprehensive Cancer Network guidelines for molecular pathological testing in advanced lung cancer both included TMB. In 2020, the U.S. Food and Drug Administration (FDA) approved pembrolizumab as a monotherapy for patients with inoperable or metastatic tumor mutational burden-high (TMB-H) solid tumors that progressed on prior therapy. Pembrolizumab became the first anti-tumor drug using TMB as a biomarker to be approved worldwide. However, the lack of unified standards for TMB detection and evaluation in clinical practice markedly limits its clinical application. To promote a unified understanding and standardization of TMB testing and its clinical application, the East China Lung Cancer Group, Youth Committee (ECLUNG YOUNG, also known as the Yangtze River Delta Lung Cancer Collaboration Group) has developed this expert consensus on the clinical use of TMB, based on the characteristics and practices in the field of thoracic oncology in China, and jointly formulated an expert consensus on the clinical application of TMB. To enable the highest possible accuracy and reliability of ICI treatment prediction, this consensus provides definitions for TMB, sample type, detection method, gene panel inclusion criteria, bioinformatics analysis methods, threshold setting, and the clinical significance of TMB testing. Additionally, the expert group provides opinions regarding the selection of various efficacy prediction biomarkers including TMB in tumor immunotherapy. The recommendation grades were used in the principles and methods for developing the ECLUNG consensuses/guidelines4.
TMB definition
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Consensus 1: TMB is defined as the average number of somatic nonsynonymous mutations contained in a specific genomic region per megabase. TMB reflects not only tumor genomic mutations but also neoantigen production ability, which is associated with immunotherapy efficacy. However, TMB evaluation is affected by a variety of factors, and clarifying the scope of its use for precise application is important (strongly recommended).
TMB, the number of somatic nonsynonymous mutations contained in a given genomic region per Mb3,5,6, indirectly reflects a tumor’s ability to produce neoantigens3,7,8. When tumor cells undergo somatic nonsynonymous mutations, new antigens may be produced and recognized by the body’s immune system, thus triggering an immune response. TMB-H typically indicates that tumor cells carry large numbers of somatic nonsynonymous mutations and are therefore likely to produce cancer-specific neoantigens3,8. These neoantigens are processed and loaded onto major histocompatibility complexes, where they are recognized by the immune system and trigger a T cell-mediated antitumor immune response8. Therefore, for some tumor types, the neoantigen burden or TMB is expected to be an important clinical biomarker for guiding ICI therapy decisions9–12. A growing body of clinical data suggest that patients with TMB-H cancers are relatively likely to benefit from ICI therapy. However, many challenges exist in the TMB evaluation process. A variety of factors, such as sample type, sample quality and quantity, genome coverage, sequencing platform, bioinformatics analysis procedures, and threshold setting, can significantly affect the evaluation process and experimental results13. Different types of samples, such as tumor tissue samples and blood samples, may have different TMB test results. Poor sample quality, such as low proportions of tumor cells or nucleic acid degradation, interferes with test accuracy. The number of detected mutations varies according to genome coverage. Differences in sequencing platforms and bioinformatics analysis procedures may lead to bias in the identification and calculation of mutations. The change in the threshold setting directly affects the determination of TMB-High or TMB-low (TMB-L). In addition, tumors with different organ and tissue origins significantly differ in the magnitude (i.e., mutations per megabase) and nature of TMB14. Therefore, clarifying TMB application scenarios and scope is important to ensure accurate clinical application of this biomarker for predicting the efficacy of tumor immunotherapy.
Standardization of TMB assays
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Consensus 2: We recommend using recently paraffin-embedded tumor tissue samples for TMB detection. The tissue to be tested should first undergo pathological quality control to ensure that it contains sufficient malignant tumor cells (strongly recommended).
Consensus 3: We recommend prioritizing use of nucleic acid extraction kits approved by the National Medical Products Administration (NMPA) or qualified by laboratory performance verification for genomic DNA extraction. Additionally, TMB testing laboratories should establish appropriate quality control standards and operating procedures for DNA samples according to actual needs, and should strictly control the purity, concentration, and degree of fragmentation of DNA samples to be tested (recommended).
Consensus 4: When targeted sequencing panels are used for TMB evaluation, we recommend evaluating their consistency with WES results. In principle, the coverage of the targeted sequencing panel should not be below 1.0 Mb, and the minimum effective sequencing depth is 200×. We recommend that targeted sequencing panels for TMB testing maximally cover the patient’s other molecular genetic information, including driver mutations that could guide targeted therapy, positive predictors of immunotherapy associated with the development of gene variants, and possibly negative predictors of immunotherapy (recommended).
Consensus 5: For TMB detection based on targeted sequencing panels, WES should be used as the gold standard. Additionally, somatic mutations that affect protein coding should be included, and somatic mutations with a detection mutation frequency >5% should be ensured to achieve accurate, stable TMB detection values. The ICI efficacy follow-up database should be used to standardize genome alignment and mutation detection algorithms. We recommend using control samples or Chinese population databases to rule out germline variants (strongly recommended).
Consensus 6: Significant differences exist in TMB values among cancer types, and the threshold should be determined according to the clinical efficacy of ICIs, to maximize the identification of patients who might potentially benefit from ICI treatment. Importantly, TMB thresholds vary among targeted sequencing panels and detection systems (recommended).
Consensus 7: In addition to focusing on TMB calculation principles and values, the TMB report should interpret the significance of immunotherapy for specific cancer types. Systematic evaluation of mutations in various driver genes detected by the panel is also necessary to comprehensively interpret patients’ tumor biological characteristics. Application of the Molecular Tumor Board (MTB) for clinical decision-making is strongly recommended (strongly recommended).
Principles of sample collection and processing
In clinical practice, tissue TMB (tTMB) testing is widely used, whereas the accuracy of blood TMB (bTMB) testing requires further verification15. Therefore, this consensus primarily establishes principles for TMB sample collection and processing according to the published literature and consensus.
Specimen types and recommended requirements for TMB testing
(a)

Formalin-fixed, paraffin-embedded (FFPE) tumor tissue: FFPE tissue is a commonly used specimen type in TMB testing. We recommend using 10% neutral formalin fixative solution for FFPE tissue. The volume of the fixation fluid should be more than 10 times that of the sample tissue. The optimal fixation time is approximately 24 h for surgical specimens and 12 h for needle biopsies. For TMB evaluation, recent tissue blocks within 3 years, preferably within 1 year, or paraffin sections prepared within 6–8 weeks, with a section thickness of 4–5 μm, are recommended5,10. At least 5 sections are necessary for surgical tissue specimens. A minimum of 10 sections is necessary for biopsies or small specimens. The tissue specimen should contain at least 200 viable tumor cells, or 10% of the tumor, to ensure sufficient material for testing and quality control. Insufficient numbers of tumor cells can affect the accuracy of the test results. If necessary, techniques such as microdissection can be used to enrich the tumor cell population or remove necrotic tissue, thereby enhancing the sensitivity of TMB detection, depending on patient consent.

(b)

Fresh tumor tissue: Fresh tissue, although usable, is not routinely recommended, because of difficulties in assessing the tumor cell proportion13. Frozen sections can be used for initial quality control. For surgical specimens, a minimum of 10 mg (approximately the size of a grain of rice) is required. For biopsies or small specimens, at least one piece of tumor tissue that is visible to the naked eye is needed. Importantly, the accuracy of TMB detection and calculation is affected by the proportion of tumor cells, and FDA-approved TMB assays require a tumor cell proportion of at least 20%16–19.

(c)

Normal control: Most TMB assays require a normal control to provide and filter out germline variation information20. Normal tissue or peripheral blood can be used as a control. If a peripheral blood sample is used as a control, 2–5 mL EDTA anticoagulated peripheral blood should be collected. After collection, the sample should be gently inverted and mixed 8–10 times to prevent blood clotting. We recommend that specimens be transported to the testing laboratory at a low temperature of 4°C and processed within 2 h to avoid hemolysis. If processing is not possible within 2 h, or long-distance transportation is required, we recommend storing or transporting samples at room temperature in a dedicated cell-free DNA collection tube.

(d)

Lesion selection: Both the primary tumor lesion and distant metastatic lesion tissue can be used for TMB evaluation21. Given tumor heterogeneity, several studies have explored the detection of TMB in paired primary lesions, distant metastases, and lymph node metastases of lung adenocarcinoma22. TMB is significantly lower in lymph node metastases than in primary lesions, whereas the difference between distant metastases and primary lesions is relatively small. In addition, significant intratumoral gene mutation heterogeneity may be present among regions of the same tumor, thus potentially leading to large variations in TMB values23,24. Currently, no studies have specifically explored heterogeneity in TMB samples and correlations with clinical outcomes. Therefore, we recommend preferential use of primary tumor lesion tissue and distant metastatic lesion tissue for TMB detection.

Nucleic acid extraction and quality control
In TMB detection, nucleic acid extraction is a key step. We recommend using nucleic acid extraction kits that have been approved by the NMPA or whose performance has been validated by laboratories. Nucleic acid extraction should be performed in laboratories meeting the stringent requirements for high-throughput sequencing5,10, to ensure the quality and stability of nucleic acid extraction, and to prevent problematic kits or laboratory conditions from affecting subsequent test results25. DNA purity, concentration, and fragmentation should be rigorously evaluated before TMB detection. The quality of the tumor tissue DNA library is critical. Insufficient concentrations, particularly those below 5.0 nmol/L, can lead to inflated or TMB-L and consequently result in false-positive or false-negative results. Clinical genetic testing laboratories should select an appropriate platform for nucleic acid quality control. We recommend using micronucleic acid fluorescence quantification to determine nucleic acid concentrations, and evaluating nucleic acid fragmentation with agarose gel electrophoresis or a nucleic acid fragment bioanalyzer. The amount of nucleic acid required for TMB detection with large panel genetic testing is 20–200 ng, depending on the sequencing method. For WES-based TMB assays, we recommend a nucleic acid amount of at least 250 ng. Clarifying the nucleic acid requirements would help ensure test accuracy and reliability.
Panel design and platform selection for TMB assays
Method selection for TMB detection
TMB assays are important for evaluating the efficacy of tumor immunotherapy. Currently, 2 main methods are used, WES and targeted panel sequencing, each with its own advantages and limitations5,10,26. WES directly sequences the entire exome region of the tumor genome and therefore accurately reflects TMB and is considered the gold standard. However, complexity and cost limit widespread use of WES in routine clinical practice27–29. Targeted panel sequencing focuses on a comprehensive set of tumor-associated genes, combined with bioinformatics algorithms, to quickly and accurately identify alterations in the tumor genome16,24. Several studies have confirmed that targeted panel sequencing results show high consistency with WES results; this method therefore provides a viable alternative for TMB detection30,31.
Panel size in TMB detection
The size of the targeted sequencing panel is an important factor influencing several key metrics in TMB assessment, including confidence intervals, thresholds, assay sensitivity, specificity, and positive and negative predictive values. Different panels cover different genomic regions, thus resulting in variations in TMB values32. The Dana-Farber Cancer Institute has constructed large (300-gene), moderately sized (48-gene), and small (15-gene) panels, and has found that small panels are poor predictors of overall mutational load, whereas large panels successfully recapitulate the WES mutational load33. Some research institutions have used random mutation models to mimic non-random mutations in real-world cancer genomes and intratumoral heterogeneity in public databases, and have found that the coefficient of variation (CV) of TMB assessed with targeted sequencing panels is inversely proportional to the square root of the panel size and the square root of the TMB. When the tumor TMB threshold was set to 10 mutations per megabase (mut/Mb), CV increased with decreasing panel coverage (from 4 Mb to 2 Mb, 1 Mb, 0.5 Mb, and 0.25 Mb, with CVs of 22%, 26%, 32%, 45%, and 63%, respectively). When the panel size was less than 0.5 Mb, the CV sharply rose. At less than 1.0 MB, TMB assessment accuracy significantly decreased, thus hindering effective identification of patients who might benefit from immunotherapy34–36. To study the effects of selected genes on the CV of TMB, researchers in Germany, the United Kingdom, and Switzerland have simulated 3 panels: panel A, containing oncogenes and tumor suppressor genes; panel B consisting of randomly selected genes; and panel C excluding oncogenes and tumor suppressor genes32. Analysis of The Cancer Genome Atlas (TCGA) data indicated a 6% greater TMB variance for panels B and C, and a 15% greater TMB variance for panel A, than observed with a random mutation model. Therefore, the panel design should cover a wide range of genetic elements and should not be limited to oncogenes and tumor suppressor genes. The Genomics Evidence Neoplasia Information Exchange, initiated by the American Association for Cancer Research, has analyzed real-world TMB data from approximately 19,000 patients with cancer. When the TMB was 0–5 mutations per Mb, approximately two-thirds of the samples could not be accurately assessed with small panels. Panel sizes greater than 1.0 Mb are therefore necessary to accurately assess TMB37. Researchers in the Cheng laboratory31 at Jilin Cancer Hospital have evaluated panels covering more than 1.0 Mb and verified 98.7% concordance in TMB calculations by using their OncoTOP algorithm and WES across 2,864 samples of 18 cancer types. In a study by Zhang’s team38 at Sun Yat-sen University, large-panel-based TMB assessment findings were highly concordant with WES TMB detection results in lung cancer populations (Spearman R = 0.81). In the first one-third of the TMB-H population, both panel and WES TMB were predictive of the response to ICI therapies (PFS HR: 0.45/0.43). In an analysis of 3 FDA-approved targeted sequencing panels, the panel with ≥1.0 Mb coverage met the clinical requirements for TMB evaluation. Therefore, we recommend an ideal coverage of targeted sequencing panels for the evaluation of TMB between 1.0 and 3.0 Mb39. In addition, targeted sequencing panels can introduce bias arising from changes in the covered genomic regions. The TMB panel should contain comprehensive molecular genetic information from the patient, such as driver gene mutations (e.g., EGFR, ALK, ROS1, RET, NTRK, BRAF, ERBB2, KRAS, and MET), to guide targeted therapy. Additionally, both positive predictors of immunotherapy response [e.g., deficient mismatch repair (dMMR)/microsatellite instability (MSI), POLE, POLD1, and BRCA1/2] and potential negative predictors [e.g., programmed death receptor-1 ligand 1 (PD-L1), PIK3CA, JAK1/2, PTEN, and STK11] should be considered, because they are associated with mutation-driven mechanisms40. Rebiopsy and genetic testing should be minimized to avoid delays in treatment41–46.
Sequencing coverage depth
Sequencing depth is critical for both panel-based and WES-based TMB assays. The standard sequencing depth for WES is typically 100×, at which allele mutations with frequencies greater than 15% can be reliably detected, thus ensuring TMB assessment accuracy. In contrast, panels can achieve higher sequencing depths, thus resulting in detection of more low-frequency variants42–44,47. To effectively increase the ability to detect low-frequency variants at specific loci, we recommend a sequencing depth of at least 200× for targeted sequencing combinations4. The average depth of coverage for the FDA-approved MSK-IMPACT sequencing panel is 200×. In 2019, the European Society for Medical Oncology (ESMO) guidelines clearly stated that the depth of coverage required for accurate assessment of TMB should be greater than 200×13. A Chinese research team has shown that panel assays show higher consistency with WES at 500× sequencing depths and significantly correlate with patient immunotherapy efficacy45–47,48.
Cost-effectiveness analyses
Targeted panels outperform WES in cost-effectiveness, with lower costs, higher sequencing depth, faster turnaround, and better coverage of disease-relevant genes, thus enhancing variant detection, and conferring advantages in routine clinical use and large-scale screening. WES offers broader coverage for novel variants in complex cases but has higher costs, more generated data (increasing analysis expenses), and potentially lower gene-specific coverage. This method might save costs by replacing multiple sequential tests or as prices decline to near-panel levels.
In advanced NSCLC, WGS-TMB (with cost and scope similar to those of WES) is less cost-effective than PD-L1 testing for immunotherapy selection. For WGS-TMB to achieve comparable cost-effectiveness to PD-L1 testing, it would require either a ≥24% reduction in sequencing costs or an improvement in TMB’s predictive value for immunotherapy response. Combined TMB-PD-L1 testing would require a 40%–50% reduction in sequencing and drug costs to achieve cost-effectiveness comparable to that of single PD-L1 testing49. Decision-tree models favor PD-L1 over TMB/no testing; tissue-based TMB is the most economical TMB method but remains more expensive than no testing, and its economic value depends on its costs and utility50.
Standardization requirements for TMB algorithms
The core aim of the TMB algorithm is to accurately align and calculate somatic mutations that affect protein-coding sequences. To ensure high accuracy and reliability of TMB calculation results, several potential problems in sequencing data processing and mutation analysis at the algorithm level remain to be solved.
Quality control of sequencing data
From the generation of raw data by the sequencing platform to the completion of somatic mutation detection, data typically undergo 4 critical stages: quality control, data filtering, genome alignment, and mutation detection. Because raw sequencing data often include problems such as low-quality readouts, linker contamination, and insertion and deletion errors, data quality must be evaluated through bioinformatics analysis. Moreover, strict quality control standards must be established for parameters such as data efficiency, error rate, Q30, GC content, mapping rate, average sequencing depth and uniformity of the target region, to ensure that these problems do not interfere with TMB detection. Specifically, these criteria include data availability of >99%, error rate <0.1%, Q20 score >90%, Q30 score >85%, GC content of 42–55%, and mapping rate >95%4.
Upstream data preprocessing workflows
Standardization of upstream data preprocessing is crucial for ensuring the accuracy, reliability, and reproducibility of subsequent variant calling. Eliminating technical biases (e.g., sequencing errors, adapter contamination, and PCR duplicates) and correcting data quality through standardized steps are essential to generate high-quality BAM files suitable for variant calling. Relevant workflows adhere to internationally recognized protocols, such as GATK Best Practices and ENCODE standards51, while adapting to different sequencing types (e.g., WGS, WES, or targeted region sequencing). Key steps in sequence include quality control of raw sequencing data (FASTQ) by using FastQC to evaluate metrics such as the Phred quality score distribution, GC content, and adapter contamination rate, combined with MultiQC for aggregating multi-sample quality reports and defining thresholds (e.g., average Phred score ≥20)52; sequence trimming and filtering via Trimmomatic or Cutadapt to remove adapter sequences, trim low-quality ends (e.g., LEADING:3 or TRAILING:3), and filter short reads (e.g., length <36 bp) to decrease alignment noise53; alignment to the reference genome (e.g., hg38 or GRCh38) with tools such as BWA-MEM (for WES) with specified parameters (e.g., seed length and mismatch penalties) to generate SAM files; conversion of SAM to BAM format with SAMtools (to reduce storage), and sorting by chromosomal position to facilitate subsequent steps54; marking PCR duplicates via Picard’s Mark Duplicates to avoid bias in variant calling from duplicate counts; base quality score recalibration (BQSR) with GATK’s BaseRecalibrator with known variant databases (e.g., dbSNP or 1000 Genomes) to correct for the inherent base quality biases of sequencers, thus yielding recalibrated BAM files; and post-preprocessing quality validation with Qualimap or GATK’s Collect HsMetrics (for WES) to assess key indicators (e.g., alignment rate ≥95%, duplicate rate <10%, and average coverage depth meeting experimental design) to ensure suitability for variant calling. Additionally, we discuss standardization principles for reproducibility, including selecting tool versions (e.g., GATK 4.2.6 or BWA 0.7.17) to avoid version-related discrepancies, transparently reporting parameters with clear justifications for adjustments (e.g., for tumor vs. normal tissues), recommending workflow management tools (e.g., Snakemake or Nextflow) for automation, and providing reusable scripts (e.g., via GitHub repositories) to facilitate replication54,55.
TMB algorithm and mutation analysis
After the initial filtering of raw sequencing data, a variety of bioinformatics analysis tools play integral roles in genome sequence alignment and mutation detection. Commonly used software for next-generation sequencing (NGS) alignment includes BWA55, Bowtie56, SOAP57, BFAST58, ELAND59, MAQ60, and SHRiMP61, among which the BWA software with the SMEM algorithm for alignment with the human reference genome is the most widely used. For mutation detection, commonly used algorithms include VarScan262, which relies on statistical methods, and Strelka63 and Mutect264, which are based on Bayesian models. In addition, the acceleration algorithm Genomic Variant Caller (GVC)65, developed with artificial intelligence (AI) technology, can adapt to data from various platforms through the AI model, thereby ensuring efficient and accurate detection results. Moreover, the detection speed is 4–8 times faster than that of traditional software, thus providing strong support for accelerating the application of NGS in clinical practice (Supplementary material).
Evaluation of TMB algorithm accuracy
Panel-based TMB detection is performed by calculating the number of somatic mutations in the exons covered by the panel divided by the exon coverage area of the panel. However, a discrepancy exists between calculated TMB values and TMB values obtained through WES detection66–69. Tumor purity is a non-negligible factor in the calculation of TMB values. Anagnostou et al.70 have found that with decreasing tumor purity, the frequency of somatic mutation sites also decreases. Correction of TMB values by tumor purity correction parameters enables better differentiation of efficacy in patients treated with ICIs. In general, the purity of tumor cells should not be below 20%. When the purity of tumor cells is low, a larger tumor purity correction parameter is required.
Filtering out germline variants is essential for accurately determining TMB. Ideally, all germline variants, including single nucleotide polymorphisms (SNPs; population allele frequency ≥1%) and mutations (population allele frequency <1%), should be excluded from the TMB calculation, to avoid overestimating the TMB values. Consequently, a normal sample to serve as a control must often be obtained from the patient, most commonly by collecting paracancerous tissue or peripheral blood71. For example, among the 4 FDA-approved methods for tumor TMB detection, MSK-Impact, Omics Core, and PGDx elio tissue complete all filter the germline mutations detected in tumor samples by detecting the patient’s leukocyte control, to ensure that the mutations used to calculate the TMB are all somatic mutations. However, this approach not only increases the cost of testing but also poses ethical issues associated with germline genetic information. Some laboratories that have developed TMB panels capable of screening germline variants by using only tumor tissue samples, often filtered with the help of population-based databases, such as the Genome Aggregation Database (gnomAD), TCGA, Exome Aggregation Consortium (ExAC), 1000 Genomes Project, and Single Nucleotide Polymorphism Database (dbSNP). However, most of these public population databases are based on European and American population data, which are not fully applicable to the Chinese population and therefore might lead to inaccuracies in TMB estimation for Asian and African population. The OncoTOP algorithm, published by Cheng’s team31, solves the technical bottleneck of traditional TMB detection based on paired samples (Supplementary material). By integrating multi-dimensional information such as CNV, MSI, and SNP and mutation frequency, the algorithm achieves ultra-high-accuracy germline variant identification and TMB calculation without control samples. Moreover, the algorithm was developed completely on the basis of a Chinese population database and therefore has wide application prospects31. Therefore, we recommend using control samples (peripheral blood or normal tissue) to remove germline variants when determining somatic mutations in TMB or to optimize germline variant filtering by laboratory-constructed Chinese population databases or algorithms71,72.
Exploration and clinical application of TMB thresholds
Distribution of TMB across cancer types
In a WES analysis of 27 tumor types based on TCGA database, the median nonsynonymous mutation frequency among tumor types significantly differed, by more than 1,000-fold. The TMB in pediatric tumors was low, at approximately 0.1 mut/Mb, whereas some patients with malignant melanoma and NSCLC had more than 100 mut/Mb1. In addition, high heterogeneity in TMB values was observed among patients with the same cancer type. In malignant melanoma and lung cancer, the TMB values ranged from 0.1 to 100 mut/Mb across patients, and a DNA-targeted sequencing study based on a pan-cancer cohort of 10,000 patients has yielded similar results73. A Chinese team has found similar results in a sample of 2,864 cases from 18 cancer types, with differences in the distribution of TMB among different cancer types45.
TMB threshold
The median TMB and its range markedly differ across cancer types11. A fixed TMB threshold may capture a higher proportion of TMB-H patients with cancer types with inherently higher TMB levels but a lower proportion of TMB-H patients with cancer types with lower TMB levels. Therefore, a unified screening strategy should be applied for all cancer types. Specifically, cases ranking in the top 20% are defined as the TMB-H group. However, the TMB-H thresholds identified across different cancer types substantially vary, from 4.4 to 52.2 mut/Mb11. Consequently, establishing cancer type-specific thresholds for defining TMB-H will be essential.
The classification of TMB-H thresholds should not be based solely on population distribution, and whether this classification can correctly indicate immunotherapy efficacy is the key criterion34. Budczies et al.34 have used the WES TMB results from TCGA database as a reference standard and set the cut-off value of TMB at 199 mut/Mb. Correspondingly, panel-based TMB thresholds were defined for 10–12% of the population with lung adenocarcinoma and 17%–19% of the population with lung squamous cell carcinoma (LUSC). The retrospective FIR, BIRCH, and POPLAR studies initially used the Foundation One LDT method based on genomic DNA targeted sequencing, with thresholds of either 9.9 mut/Mb or the top 75% of the population. These thresholds partially distinguished populations benefiting from ICI treatment45. However, 2 prospective clinical trials, CheckMate 568 and CheckMate 227, using the parallel-validated FoundationOne CDx assay, identified 10 mut/Mb as the optimal TMB threshold for predicting immunotherapy efficacy in NSCLC74. In addition, a Chinese team led by Liang45 has found that patients with lung cancer with TMB > 10 mut/Mb had a higher benefit rate from ICI treatment compared to those with TMB ≤ 10 mut/Mb. Zou’s team46 has indicated that patients with melanoma whose TMB exceeds 8 mut/Mb have the highest benefit rate of ICI treatment, and Lin’ s team found that patients with gastric cancer with TMB exceeding 8 mut/Mb have significantly higher ORR in ICI treatment than those with TMB ≤ 8 mut/Mb75. Therefore, the clinical efficacy of ICIs is the best criterion for determining the TMB threshold.
Methods frequently used for TMB detection
Since TMB was first identified as a potential predictive biomarker for immunotherapy efficacy, multiple testing laboratories and companies have developed TMB detection solutions. Internationally recognized commercial panels include TEMPUS (648 genes, 2.4 Mb genome coverage), Guardant OMNI (550 genes, 2.2 Mb genome coverage), Caris SureSelect XT (592 genes, 1.6 Mb genome coverage), and F1CDx (324 genes, 1.1 Mbgenome coverage), as well as institutionally validated platforms such as MSK-IMPACT (468 genes, 1.2 Mb genome coverage) from the Memorial Sloan Kettering Cancer Center and OncoPanel (447 genes, 1.3 Mb genome coverage) from the Dana-Farber Cancer Institute. In China, several providers offer similar commercial tests, including GenePlus (1,021 genes, 1.6 Mb genome coverage), Burning Rock (520 genes, 1.26 Mb genome coverage), and Geneseeq (425 genes, 1.26 Mb genome coverage)76. Notably, Geneseeq’s large gene panel has been approved by China’s NMPA for TMB testing in formalin-fixed tissue samples from patients with EGFR/ALK-negative non-squamous NSCLC. However, significant discrepancies exist in TMB definitions across experimental and bioinformatics methods (Table 1). For instance, F1CDx incorporates synonymous mutations and short intronic indels into TMB calculations, whereas these features are typically excluded by other algorithms. Emerging evidence suggests that indels might generate novel open reading frames capable of producing immunogenic neoantigens; therefore, intronic region analysis and indel inclusion might potentially enhance TMB estimation accuracy. Nevertheless, F1CDx’s lack of paired germline sequencing risks overestimating TMB because of incomplete germline variant filtering. In contrast, the FDA-approved MSK-IMPACT platform uses matched blood sequencing to eliminate germline variants and focuses solely on somatic exonic mutations across approximately 500 cancer-related genes. These methodological heterogeneities highlight the challenges in achieving reproducible TMB measurements across testing platforms.
Recommendations for TMB report templates
Information to include in TMB reports
(a)

Basic information: Information should include the examinee, including name, sex, age, clinical and pathological diagnosis, genetic testing history, family medical history, and clinical treatment history; sample information, including sample type, date of collection, collection institution, and date of receipt by the testing laboratory; and descriptions of the project, including the assay method, platform used, assay range (e.g., number of genes and panel size), library construction method, and reference genome used.

(b)

Test results: Information should include the total number of somatic coding mutations, TMB value, tumor type, TCGA database ranking, and summary of the interpretation of results.

(c)

Interpretation of results: Information should include TMB evaluation, such as mutation type, minimum detection frequency, and consistency with WES, as well as interpretation of the results, including whether a clear threshold statement is present. For the various immunotherapies, relevant thresholds and clinical trial evidence should be clearly stated.

(d)

Signature: Testing technicians and auditors are required to sign. The final report must be reviewed by a qualified physician or authorized signatory who holds a senior title or MD degree, has a background in pathology, has at least a master’s degree, and has received relevant training.

(e)

Limitation description: The limitations of TMB in clinical applications should be explained, with full consideration of factors such as tumor type, sample type, the testing panel, and relevant clinical research results.

In summary, we recommend that TMB reports document the above information in detail for clinical use. Information on the recommended report template is presented below (Table 2).
Recommendations for clinical interpretation of TMB reports
We recommend interpreting TMB results according to the specific indications and therapeutic agents. Samstein et al.11 have demonstrated that the TMB thresholds for benefit from immunotherapy vary across cancer types. For example, in IMvigor210, a clinical trial of atezolizumab for bladder cancer, the TMB threshold detected by the FoundationOne product was ≥16 mut/Mb17. In the clinical trial BIRCH/FIR for NSCLC treatment, the TMB threshold detected by the Foundation One product was ≥13.5 mut/Mb in first-line patients and ≥17.1 mut/Mb in second-line patients. The TMB threshold in the POPLAR clinical trial was ≥15.8 mut/Mb77. In the CheckMate 012 study of nivolumab combined with ipilimumab for patients with NSCLC, the CheckMate 032 study for small cell lung cancer (SCLC), and the CheckMate 038 study for malignant melanoma, the TMB thresholds detected by WES were ≥307 mut/Mb, ≥248 mut/Mb, and ≥100 mut/Mb78, respectively. Therefore, as a biomarker for predicting ICI efficacy, the TMB threshold for companion diagnostic products of different drugs has relatively limited reference value. A unified TMB threshold should not be used to assess the therapeutic effects of different ICIs on patients.
Clinical significance of TMB
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Consensus 8: TMB, an emerging independent predictor of ICI therapeutic efficacy, is associated with the efficacy of monotherapy or a combination of 2 ICIs in multiple tumor types, and has been demonstrated to be a predictive marker for pan-cancer immunotherapy efficacy. TMB testing is recommended for patients with solid tumors that have progressed on prior standard therapy for whom no better alternative therapy is available, particularly patients with pre-relapse TMB-H, to help expand the population benefiting from immunotherapy. In cases in which tumor tissue is difficult to obtain or the amount of tumor tissue in paraffin-embedded samples is insufficient, blood may be considered for bTMB evaluation based on circulating tumor DNA. However, the value of bTMB must be clarified by more prospective studies (recommended).
Consensus 9: TMB is a validated predictive biomarker for the efficacy of pan-cancer immunotherapy. However, the level of evidence currently varies across cancer types. We recommend using TMB in cancers that have been widely studied and validated (recommended).
tTMB
TMB has been well established in several studies as a predictive biomarker of ICI efficacy in the treatment of solid tumors10,16–18. In 2014, Snyder et al.7 first reported TMB as a potential biomarker for ICIs in melanoma. Subsequently, the predictive value of TMB in predicting the response to pembrolizumab (an anti-PD-1 antibody) was validated in patients with advanced NSCLC2.
In the CheckMate 568 study, which evaluated the efficacy of nivolumab in combination with ipilimumab in metastatic NSCLC, higher tumor tTMB (≥10 mut/Mb) was associated with higher ORR (44% vs. 12.0%) and longer PFS (7.1 vs. 2.6 months)9. In the prospective CheckMate 227 trial, PFS was significantly longer in patients with NSCLC with TMB-H (≥10 mut/Mb) than in patients with TMB-L (7.2 vs. 5.5 months; OR 0.58; 95% CI 0.41 to 0.81; P <0.001), independently of PD-L1 expression levels13. However, the efficacy of TMB-guided monoclonal antibodies is limited, because nivolumab monotherapy showed a trend toward improved outcomes in patients with TMB-H, whereas no significant benefit was observed in patients with TMB-L. In LUSC, an important NSCLC subtype whose genomic characteristics are distinct from those of lung adenocarcinoma, TMB also serves as a predictive marker of immunotherapy efficacy13. In a CheckMate 227 subgroup analysis, nivolumab plus ipilimumab, compared with chemotherapy, significantly prolonged PFS in patients with TMB-H (≥10 mut/mb), including those with LUSC13. The CheckMate 9LA study confirmed that nivolumab plus ipilimumab and short-course chemotherapy resulted in significantly longer PFS and OS in patients with TMB-H LUSC than in patients with TMB-L79. Beyond NSCLC, the efficacy of TMB and immunotherapy has been demonstrated in other cancer types. TMB-H has been associated with immunotherapy efficacy in SCLC, head and neck, breast, and colorectal cancers23,24,80. In the CheckMate 275 study, nivolumab-treated patients with urothelial carcinoma with TMB-H (≥13 mut/Mb) had higher ORR and longer PFS/OS than patients with TMB-L81. In the KEYNOTE-158 study, which included 10 tumors (e.g., SCLC and endometrial cancer), pembrolizumab-treated patients with tTMB-H (≥10 mut/Mb) had a higher ORR than patients with TMB-L (29% vs. 6%)82. On the basis of those results, the FDA approved pembrolizumab as a monotherapy for patients with advanced TMB-H (≥10 mut/mb) unresectable and/or metastatic solid tumors that progress after standard therapy. For patients with cancer of unknown primary (CUP), TMB testing can help guide treatment decisions. Historical data have indicated that approximately 10%–20% of patients with CUP have TMB-H83. In a study by Maria et al.,26 in 31 patients with CUP treated with nivolumab plus ipilimumab, the TMB-H (≥12 mut/Mb) patients had a significantly higher ORR than the TMB-L patients (60% vs. 7.7%), thus suggesting that patients with CUP with TMB-H are more likely to have a favorable response after receiving ICIs. However, the predictive value of TMB in clinical applications varies across studies. In the KEYNOTE-021 and KEYNOTE-189 studies, TMB was not associated with ICI efficacy in combination with chemotherapy84,85; therefore, the predictive role of TMB may vary by treatment regimen and tumor type. In Chinese cohorts, TMB’s predictive role has also been validated. A study by Tian’s team86 at Sichuan University has suggested significant improvements in ORR in patients with advanced NSCLC with TMB ≥8 mut/Mb and higher immune diversity than those with TMB <8 mut/Mb. A team led by Zhang38 at Sun Yat-sen University has demonstrated a significant increase in PFS in patients with advanced NSCLC with TMB-H (HR: 0.43–0.45). In the future, further in-depth research is needed to optimize the clinical application of TMB and better provide guidance for the treatment of patients with cancer (Table 3).
bTMB
With advances in liquid biopsy technology, assessing bTMB provides a new avenue for predicting response to immunotherapy and dynamically monitoring treatment changes in patients with difficult tissue specimen access24,112. The use of circulating tumor DNA to detect bTMB has an advantage of high sequencing coverage. In a retrospective study in patients with NSCLC treated with atezolizumab, patients with bTMB ≥16 mut/Mb experienced prolonged PFS with this therapy24. In addition, combined screening with PD-L1 expression analysis enables more accurate screening of patients who have achieved prolonged PFS and OS from treatment. In a recent study of advanced NSCLC at Union Medical College Hospital, patients with high bTMB have been found to benefit from ICIs and to potentially achieve better prognostic outcomes with combined testing of other blood parameters48. In the TRACELib002 study conducted by Lu’s team40 at Shanghai Chest Hospital, patients with bTMB ≥16 mut/Mb had a median OS of 24.5 months after immunotherapy, which was significantly longer than the 14.6 months observed for patients with bTMB <16 mut/Mb. However, studies have also shown that although bTMB and tTMB are correlated, most of the mutations detected by each method are inconsistent. Moreover, this heterogeneity becomes more pronounced with greater intervals between tissue and blood sample collection112. Currently, the clinical value of bTMB has not been fully clarified, and further in-depth exploration will be necessary in future research.
Current limitations and future directions of bTMB
bTMB is a promising non-invasive surrogate for tTMB, yet its clinical implementation is hampered by substantial technical and biological hurdles. Contemporary trials and meta-analyses are actively examining its utility as a predictive biomarker for immunotherapy response. Findings have highlighted both its potential and limitations. Notable limitations include suboptimal concordance with tTMB—concordance that is highly contingent on technical variables such as cell-free DNA input, sequencing depth, and maximum somatic allele frequency113–115. Insufficient cell-free DNA yield or inadequate sequencing depth frequently results in poor correlation and false-negative outcomes, particularly in early-stage malignancies or tumors with low shedding propensity114,115. Additionally, the absence of standardized bTMB thresholds and the varying cut-off values used across investigative platforms and studies obscure clinical interpretation and impede cross-study comparability91,114,116–118. Biological confounders, including low tumor burden, intratumoral heterogeneity, and clonal hematopoiesis, further introduce biases leading to bTMB underestimation or overestimation, thus compromising its reliability as a biomarker114,115,118. Economically, the high sequencing depth required for accurate bTMB quantification elevates costs, although emerging evidence suggests that targeted panels might mitigate expenses without sacrificing analytical accuracy91,114,119.
The MYSTIC trial, B-F1RST trial, and meta-analyses have prospectively evaluated bTMB as a predictive biomarker for immunotherapy and demonstrated its potential as well as needs for assay optimization and further validation91,117,120–122. Future advancements in this field are expected to involve multiple fronts, such as (1) assay optimization and harmonization, including refinement of panel design; enhancement of bioinformatics pipelines; and standardization of thresholds to increase reproducibility and clinical translatability114,115,116,118; (2) integration with complementary biomarkers, e.g., PD-L1 expression, genomic aberrations, and immune microenvironment signatures, to augment predictive precision for immunotherapy response; (3) implementation of dynamic monitoring via serial bTMB measurements, to enable real-time surveillance of treatment efficacy and disease progression116,118; and (4) expansion of large-scale prospective trials and meta-analyses115,116,118,122. In summary, bTMB has emerged as a compelling minimally invasive biomarker for immunotherapy stratification, yet its clinical integration necessitates resolution of technical, biological, and standardization challenges. Sustained efforts in trial advancement and harmonization will be critical to its successful translation into routine clinical practice.
Evidence of TMB in various cancer types
TMB has been incorporated into multiple guidelines as a predictive biomarker for immunotherapy efficacy in solid tumors (Table 3). In an exploratory analysis of the CheckMate 026 trial comparing nivolumab monotherapy vs. platinum-based chemotherapy as a first-line treatment for PD-L1-positive (≥5%) NSCLC, patients with TMB-H (>243 mutations/exome) achieved the highest ORR and longest PFS with nivolumab88. The phase III CheckMate 227 trial prospectively evaluated nivolumab plus ipilimumab vs. chemotherapy in patients with TMB-H (≥10 mut/Mb), as assessed by FoundationOne CDx, and indicated significantly improved PFS under combination therapy vs. standard chemotherapy in patients with TMB elevation, although no OS benefit was reported10. In SCLC, the CheckMate 032 study confirmed that TMB-H patients (>248 mutations/exome) treated with nivolumab monotherapy or nivolumab-ipilimumab combination therapy exhibited higher ORR and longer PFS/OS than patients with TMB-L8. In melanoma, the phase III IMspire150 trial demonstrated that atezolizumab combined with vemurafenib and cobimetinib significantly improved PFS vs. placebo, vemurafenib, and cobimetinib in patients with BRAF V600-mutant TMB-H (≥10 mut/Mb) cancer101. Similarly, in the CheckMate 067 trial evaluating nivolumab alone or combined with ipilimumab vs. ipilimumab monotherapy, TMB-H (≥50th percentile) was associated with significantly improved clinical outcomes across all 3 treatment arms98. Growing evidence supports TMB’s predictive value for immunotherapy benefits in other tumor types, including urothelial carcinoma81,107, head and neck cancer23, and gastrointestinal cancers (including microsatellite-stable tumors)105,123. On the basis of these findings, the KEYNOTE-158 study investigated the association between high tTMB (≥10 mut/Mb by F1CDx) and immunotherapy outcomes across 10 tumor types82. Among 790 patients, only a small subset (n = 102, 13%) met TMB-H criteria. The most common TMB-H tumor types included SCLC (33%), cervical squamous cell carcinoma (16%), and anal squamous cell carcinoma (14%). TMB-H patients showed superior ORR and PFS to TMB-low patients (ORR: 29% vs. 6%; 12-month PFS: 26% vs. 13%). These data led to FDA approval of pembrolizumab monotherapy for patients with TMB-H (≥10 mut/Mb) solid tumors that progress after prior therapies, for whom no alternative treatment options are available. Although KEYNOTE-158 supported pembrolizumab use in cancers with ≥10 mut/Mb, the therapy remains ineffective for some tumor types; therefore, a single TMB threshold cannot universally identify responders across all cancers (Figure 1).
Limitations of clinical applications and future exploration
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Consensus 10: TMB has high heterogeneity among cancer types and has limitations in predicting immunotherapy effectiveness. We recommend combining TMB with other biomarkers to aid in judging the potential benefits of immunotherapy (recommended).
Limitations in clinical applications
TMB has many limitations in clinical applications. Although multiple studies have shown a positive correlation between TMB and ICI efficacy, in some tumor types, a low TMB may also indicate benefit from ICI therapy. For example, in patients with recurrent glioblastoma, a very low mutational burden is an important feature indicating benefit from PD-1/PD-L1 blockade therapy124. CD8+ T cell infiltration of tumors underpins favorable response to immunotherapy, yet in one study, CD8+ T cell counts were not found to be elevated in all cancers with TMB levels, and the ORR of patients with TMB-H tumors did not reach 20% in cancer types (e.g., breast cancer, prostate cancer, and glioma) in which CD8+ T cell levels were not significantly associated with neoantigen burden (ORR = 15.3%, 95% CI 9.2 to 23.4, P = 0.95)19. In addition, owing to factors such as the immunosuppressive tumor environment and poor immune cell infiltration, TMB-H might not be suitable as a predictor of efficacy in all cancer immunotherapies125,126.
TMB values significantly differ among cancer types. Cancers associated with chronic mutagen exposure, such as lung cancer and melanoma, typically have a high TMB, whereas leukemia has a low TMB. The KEYNOTE-158 study has demonstrated a durable monotherapy benefit of pembrolizumab in patients with TMB ≥10 mut/Mb; however, cancer type representation was limited, and some common cancer types, such as colorectal cancer, were not included in the study. Moreover, a single TMB threshold is not applicable to all tumor types. TMB-H may be associated with high microsatellite instability in colorectal cancer. Schrock et al.21 have estimated an optimal predictive threshold of 37–41 mut/Mb for TMB-H in patients with high microsatellite instability metastatic colorectal cancer treated with ICIs. Therefore, a reasonable TMB threshold should vary depending on the tumor type127.
Moreover, the spatiotemporal heterogeneity of TMB is a major challenge in clinical applications. In a study by Kazdal128, using a targeted sequencing panel to sequence multiple regions of lung adenocarcinoma and calculate TMB values, 30% of patients showed TMB differences among regions within the same tumor, and the maximum absolute TMB difference was 14.13 mut/Mb. Moreover, the TMB values changed over time. In an analysis of pre- and post-treatment paired data in 63 patients, TMB-L levels showed 73% concordance between primary and metastatic lesions, and only 1 of the 2 primary TMB-H lesions (50%) remained TMB-H after treatment (P = 0.32)36.
Combined use of TMB with other biomarkers
Although TMB has emerged as an independent biomarker for immunotherapy response, combining TMB assessment with other biomarkers might be used to further stratify patients for clinical decision-making and potentially overcome several inherent limitations of using TMB alone as an ICI biomarker. Several biomarkers have been investigated alongside TMB, including PD-L1 expression, tumor-infiltrating lymphocytes, RNA gene expression profiles, blood-derived neutrophil-to-lymphocyte ratio (dNLR), and HLA variations (Table 4).
Generally, TMB and PD-L1 expression show weak but positive correlations131,132. However, this association may vary across tumor types131,132. Because both are potential independent predictors of immunotherapy efficacy, patients with tumors with elevated TMB and high PD-L1 expression levels might derive greater therapeutic benefits. In the CheckMate 026 study among patients with advanced NSCLC, those with TMB-H (≥243 somatic missense mutations/exome) and a PD-L1 tumor proportion score (TPS) ≥50% demonstrated the highest radiographic response probability to nivolumab, whereas patients with low/intermediate TMB and low PD-L1 expression (1–49%) showed the lowest ORR88. Subsequent randomized clinical trials, including KEYNOTE-010, KEYNOTE-042, IMpower 110, CheckMate 227, and MYSTIC, reported similar findings indicating improved outcomes with ICIs in patients exhibiting both TMB-H and PD-L1 expression133,134. However, variable definitions of TMB-H across those studies and uncertainty regarding optimal integration of these 2 biomarkers for predicting immunotherapy efficacy in NSCLC remain unresolved.
Beyond PD-L1 expression, TMB-H is often associated with elevated neoantigen burden and CD8+ tumor-infiltrating lymphocytes, and consequently, immunotherapy responsiveness19,135. In a recent pan-cancer analysis, in cancer types in which CD8+ T-cell levels positively correlate with neoantigen load (e.g., melanoma, lung, and bladder cancers), TMB-H correlated with a 39.8% ICI ORR, a value significantly higher than that observed for TMB-L (OR: 4.1, P <0.0001). In contrast, in cancers lacking this relationship (e.g., breast cancer, prostate cancer, and glioma), TMB-H tumors showed a lower ORR than observed in TMB-L tumors (OR: 0.46, P = 0.02)135. These data suggest that TMB’s predictive accuracy partly depends on baseline immune cell infiltration levels, which vary across cancer types.
Emerging evidence highlights how germline and somatic HLA variations determine antigen presentation and cancer susceptibility to ICIs. Integrating HLA status with TMB might therefore enhance predictive capabilities. Somatic HLA-I loss of heterozygosity (LOH) exhibits a nonlinear relationship with TMB, thus suggesting alternative immune evasion mechanisms in TMB-H tumors136. In an ICI-treated non-squamous NSCLC cohort, intact HLA-I has been associated with significantly prolonged OS (HR: 0.65, P = 0.01), and outcome prediction improved in combination with TMB137. Studies on germline HLA polymorphisms have yielded mixed results: HLA-A*03 correlates with attenuated OS (HR: 1.48 per allele, P <0.001) across ICIs137, whereas HLA-B44 supertypes are associated with prolonged OS, and HLA-B62 subtypes/somatic HLA-I LOH are associated with poor outcomes138. However, in a meta-analysis of 17 pembrolizumab trials (n >3,500), germline HLA profiles alone were insufficient to predict pembrolizumab efficacy, thus underscoring the complexity of HLA contributions to immune responses139.
dNLR has emerged as a clinically accessible biomarker reflecting systemic inflammation-host immunity balance. A recent study has demonstrated that combining TMB with dNLR enhances predictive accuracy. Patients with high dNLR (top 20th percentile) and TMB-L (below median) showed the poorest outcomes, whereas those with low dNLR (bottom 80th percentile) and TMB-H (above median) achieved the highest ORR and survival, thus suggesting dNLR/TMB integration might optimize PD-(L)1 therapy selection140.
Gene expression profiles (GEPs) and immune cell signatures are gaining traction as predictive tools. In a retrospective analysis of CheckMate 066/067 trials in melanoma, the highest ORR was observed with nivolumab (75.0%) and nivolumab-ipilimumab (66.7%) in patients with TMB-H and an elevated 4-gene inflammatory signature (CD274/CD8A/LAG3/STAT1), whereas 27.6% was observed with ipilimumab alone98. Similarly, biomarker analyses across 22 tumor types in pembrolizumab trials have indicated the highest response likelihood in tumors with TMB-H plus elevated T-cell-inflamed GEP or PD-L1 expression110,111.
Collectively, these findings highlight the promise of combining TMB with complementary biomarkers, including PD-L1 expression, MSI status, immune infiltration, HLA variations, neoantigen landscape, and ploidy, to refine immunotherapy response prediction. Comprehensive multi-biomarker evaluation might guide treatment decisions, identify optimal beneficiaries, and advance personalized cancer care.
International vs. Chinese guidelines for TMB assessment
A comparative analysis of TMB assessment guidelines revealed both alignment and divergence between the Chinese consensus and major international standards, including those from ASCO, ESMO, FDA, and collaborative initiatives such as Friends of Cancer Research and QuIP (Table 5).
Regarding aligned consensus points, the International Consensus and the Chinese Expert Consensus are highly consistent, with core alignment in the following 5 aspects. First, both emphasize the urgent need for standardization of TMB testing, by clearly stating that TMB calculation, reporting, and analytical validation must be uniformly standardized to ensure consistency and reliability of results across laboratories and platforms15,68,69,141. Second, both recognize WES as the gold standard for TMB detection, while acknowledging that targeted NGS panels are more clinically practical alternative solutions. The international consensus indicates that 1–2 Mb is a common coverage range for panels, whereas the Chinese consensus further specifies that panel coverage must be ≥1.0 Mb (because coverage <1.0 Mb significantly decreases accuracy); moreover, both indicate that the gene content and coverage range of panels must be sufficient to generate reliable results;15,68,69,141. Third, both prioritize FFPE tumor tissue as the testing sample and indicate that the tumor cell content of samples must meet standards—with the threshold implicitly set at ≥20% in the international consensus and explicitly specified in the Chinese consensus—while also recommending strict quality control for DNA purity, concentration, and fragmentation degree. Fourth, both recommend detailed reporting of experimental parameters (including panel name/version, sequencing platform, and library construction method), types of variants included/excluded (e.g., whether nonsynonymous mutations or insertions/deletions are included), germline variant filtering criteria, and reference bases (e.g., reference genome version) to improve testing transparency and the comparability of the results.15,68,69,141. Fifth, both recognize that TMB cutoffs require clinical validation. Although both clearly state that cutoffs vary by cancer type and testing platform, both oppose the use of a unified pan-cancer TMB cutoff15,68,69,141.
However, significant differences in key technical details and regulatory practices persist between guidelines. In terms of bioinformatics and variant filtering, the international consensus recommends relying on international public databases (e.g., gnomAD or 1000 Genomes) for germline variant filtering, and only mentions general variant calling tools such as MuTect/Mutect2, but does not address the limitations of population-specific databases or recommend tumor purity correction for TMB values. The Chinese consensus clearly indicates that international databases “are dominated by European populations and are not suitable for the Chinese population,” thus emphasizing the need to use laboratory-built Chinese population databases or dedicated algorithms (e.g., the OncoTOP algorithm) to achieve high-precision germline filtering (without a need for paired tumor-normal samples). Simultaneously, the Chinese consensus recommends tumor purity correction for TMB values (which is necessary when the purity is <20%, to avoid underestimation/overestimation) and explicitly includes somatic variants with a detection frequency >5% to ensure accuracy141. In terms of practical clinical standards, the Chinese consensus focuses more on implementation details. First, it recommends use of nucleic acid extraction kits approved by the NMPA or verified by laboratories, and clearly specifies a DNA concentration ≥5.0 nmol/L. Second, it explicitly specifies the sequencing depth (≥200×), and it lists domestic panels approved by the NMPA or commonly used in clinical practice, including recommending panels covering both driver genes for targeted therapy (e.g., EGFR and ALK) and positive/negative predictors of ICI efficacy (e.g., PD-L1 and STK11). In contrast, the international consensus only mentions methodological differences between hybrid capture and amplicon sequencing, without specifying such practical standards. In terms of regulatory endorsement, in the international practice described in the international consensus, the U.S. FDA has approved specific TMB testing products (e.g., FoundationOne CDx) as companion diagnostic tools for pan-cancer ICI therapy; the corresponding regulatory practice in the Chinese consensus is that, although the NMPA has approved some domestic panels (e.g., Geneseeq PRIME) for TMB testing in specific cancer types (e.g., EGFR/ALK-negative non-squamous NSCLC), unlike the FDA, it has not yet approved pan-cancer TMB companion diagnostic products, thus resulting in differences in regulatory pathways compared with international practices109.
Future exploration
Despite extensive discussions regarding the variability of TMB-H thresholds, primary data validating the efficacy of proposed cutoffs specifically in Chinese patient populations remain lacking. To establish reliable, clinically relevant TMB-H thresholds for specific tumor types, large-scale, prospective, multicenter studies should be conducted across China. These studies should enroll patients with diverse tumor types, use standardized TMB assessment methods (including WES and validated large gene panels), and correlate TMB values with clinical outcomes (e.g., response to immunotherapy, PFS, and OS). This approach would help identify tumor-specific TMB-H cutoffs applicable to the Chinese population that can predict treatment efficacy.
Research has indicated significant differences in the distribution of TMB and optimal cutoffs across cancer types (e.g., the median TMB in most solid tumors among Chinese patients is 4–6 mutations per megabase, and higher values are observed in colorectal cancer and lung cancer)46. Therefore, validation studies must be stratified by tumor type to ensure clinical relevance.
Communicating TMB results and their significance to patients
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Effectively communicating TMB results is critical for patient selection, education, and informed consent in the context of immunotherapy. Given the complexity and evolving nature of TMB as a biomarker, clinicians must ensure that patients understand both the potential and limitations of TMB-guided treatment decisions. Key considerations in this communication include (1) clarifying the fundamental nature of TMB, specifically, that it quantifies the number of mutations in tumor DNA and might predict responsiveness to certain immunotherapies, and (2) elucidating its predictive utility, including that although TMB-H can indicate elevated likelihood of ICI benefit, it does not ensure a response, because outcomes are influenced by multiple additional factors15,142. An equally important consideration is addressing limitations and uncertainties, such as the lack of standardization in TMB testing, discrepancies between tissue- and blood-based assays, and the ongoing need for validation of cut-offs and methods. Furthermore, TMB results should be contextualized within the broader framework of other biomarkers (e.g., PD-L1 expression)15,142, tumor type, and available treatment options. Practical aspects, including the turnaround time for TMB testing, the potential need for repeat biopsies, and implications for treatment timing, must also be discussed. Finally, supporting informed consent requires ensuring that patients understand the potential benefits, risks, and uncertainties associated with TMB-guided therapy, to facilitate shared decision-making.
Funding
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  • This research was supported by grants from the National Natural Science Foundation of China (Grant No. 82072577) and from the International Medical Association (Grant No. IMA-F-2025-001).
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Year 2026 volume 23 Issue 2
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doi: 10.20892/j.issn.2095-3941.2025.0351
  • Receive Date：2025-06-28
  • Online Date：2026-11-07
  • Published：2026-02-15
Article Data
Affiliations
History
  • Received：2025-06-28
  • Accepted：2025-12-17
Funding
This research was supported by grants from the National Natural Science Foundation of China (Grant No. 82072577) and from the International Medical Association (Grant No. IMA-F-2025-001).
Affiliations
    1Cancer Center, Department of Pathology, Zhejiang Provincial People’s Hospital (Affiliated People’s Hospital), Hangzhou Medical College, Hangzhou 310014, China
    2Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou 310022, China
    3Translational Medicine Research Center, Key Laboratory of Clinical Cancer Pharmacology and Toxicology Research of Zhejiang Province, Affiliated Hangzhou First People’s Hospital, Cancer Center, Westlake University School of Medicine, Hangzhou 310006, China
    4Department of Chemotherapy, Chinese Academy of Sciences University Cancer Hospital (Zhejiang Cancer Hospital), Hangzhou 310022, China
    5Department of Pathology, Sir Run Run Shaw Hospital, Zhejiang University, Hangzhou 310016, China
    6Ningbo Clinical Pathology Diagnosis Center, Ningbo 315100, China
    7Department of Pathology, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai 200030, China
    8Department of Pathology, Beijing Tongren Hospital, Capital Medical University, Beijing 100730, China
    9Department of Pathology, The Second Hospital of Jilin University, Changchun 130041, China
    10Department of Pathology, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310009, China
    11Department of Pathology, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou 310003, China
    12Department of Pathology, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100730, China
    13Laboratory Medicine Center, Zhejiang Center for Clinical Laboratory, Zhejiang Provincial People’s Hospital, Affiliated People’s Hospital, Hangzhou Medical College, Hangzhou 310014, China
    14Department of Cancer Molecular Diagnostics Core, Tianjin Medical University Cancer Institute and Hospital, Tianjin 300060, China
    15Department of Pathology, Renmin Hospital of Wuhan University, Wuhan 430060, China
    16Department of Pathology, Affiliated Hospital of Nanjing University of Chinese Medicine, Jiangsu Province Hospital of Chinese Medicine, Nanjing 210029, China
    17Department of Pathology, The First Affiliated Hospital of Nanjing Medical University (Jiangsu Province Hospital), Nanjing 210029, China
    18Department of Pathology, Women’s Hospital of Nanjing Medical University, Nanjing Women and Children’s Healthcare Hospital, Nanjing 210004, China
    19Department of Surgical Pathology Women’s Hospital, School of Medicine, Zhejiang University, Hangzhou 310006, of China
    20Department of Pathology, Shanghai Pudong Hospital, School of Medicine, Fudan University, Shanghai 201399, China
    21Department of Pathology, The Fourth Hospital of Hebei Medical University, Shijiazhuang 050011, China
    22Department of Pathology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing 210008, China
    23Department of Pathology, Children’s Hospital Zhejiang University School of Medicine, Hangzhou 310005, China
    24Department of Pathology, Stomatology Hospital, School of Stomatology, Zhejiang University, Hangzhou 310000, China
    25Department of Pathology, Tongde Hospital of Zhejiang Province, Hangzhou 310012, China
    26Department of Pathology, Affiliated Hospital of Hangzhou Normal University, Hangzhou 310000, China
    27Department of Pathology, The People’s Hospital of Leshan, Leshan 614000, China
    28Department of Pathology, Affiliated Hangzhou First People’s Hospital, Westlake University School of Medicine, Hangzhou 310006, China
    29Department of Pathology, Chun’an First People’s Hospital, Hangzhou 311700, China
    30Department of Pathology, The Affiliated Suzhou Hospital, Nanjing Medical University, Suzhou 215006, China
    31Department of Pathology, The First People’s Hospital of Hangzhou Lin’an District, Affiliated Lin’an People’s Hospital, Hangzhou Medical College, Hangzhou 311300, China
    32Department of Pathology, Tiantai County People’s Hospital, Taizhou 317200, China
    33Department of Pathology, First People’s Hospital of Aksu, Aksu 843000, China
    34Department of Pathology, Xianju People’s Hospital, Zhejiang Southeast Campus of Zhejiang Provincial People’s Hospital, Affiliated Xianju’s Hospital, Hangzhou Medical College, Taizhou 317300, China
    35Department of Pathology, Dinghai Branch of Zhejiang Provincial People’s Hospital, Zhoushan 316000, China
    36Department of Pathology, Shulan (Hangzhou) Hospital, Hangzhou 310022, China
    37Department of Pathology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China
    38Department of Pathology, Zhejiang Provincial People’s Hospital (Fuyang Campus), Hangzhou 311400, China
    39Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Melanoma and Sarcoma, Peking University Cancer Hospital & Institute, Beijing 100142, China
    40Department of Respiratory Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Jiangsu Province Hospital of Chinese Medicine, Nanjing 210029, China
    41Department of Shanghai Lung Cancer Center, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai 200030, China
    42Guangdong Lung Cancer Institute, Guangdong Provincial Laboratory of Translational Medicine in Lung Cancer, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, School of Medicine, Guangzhou 510080, China
    43State Key Laboratory of Molecular Oncology, Department of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
    44Department of Medical Oncology, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai 200433, China
    45Department of Medical Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, China
    46Department of Medical Oncology, Lung Cancer and Gastrointestinal Unit, Hunan Cancer Hospital/The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha 410013, China
    47Department of Thoracic Cancer, Jilin Cancer Hospital, Changchun 130012, China
    48Department of VIP Inpatient, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, China
    49Department of Oncology, Second Affiliated Hospital of Nanchang University, Nanchang 330006, China
    50Key Laboratory of Respiratory Disease of Zhejiang Province, Department of Respiratory and Critical Care Medicine, Second Affiliated Hospital of Zhejiang University School of Medicine, Cancer Center, Zhejiang University, Hangzhou 310009, China
    51Department of Respiratory Medicine, Affiliated Jinling Hospital, Medical School of Nanjing University, Nanjing 210002, China
    52Department of Respiratory Medicine, Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing 210008, China
    53Department of Respiratory Medicine, Clinical Medical School of Yangzhou University, Subei People’s Hospital of Jiangsu Province, Yangzhou 225001, China
    54Department of Medical Oncology, Fujian Medical University Cancer Hospital & Fujian Cancer Hospital, Fuzhou 350014, China
    55Department of Oncology, Guizhou Provincial People’s Hospital, Guiyang 550001, China
    56Department of Internal Medicine, Cancer Center of PLA, Qinhuai Medical Area, Affiliated Jinling Hospital, Medical School of Nanjing University, Nanjing 210002, China
    57Department of Integrative Oncology, Tianjin Medical University Cancer Institute and Hospital, Tianjin 300060, China
    58The Second Department of Thoracic Oncology, Hunan Cancer Hospital/The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha 410013, China
    59Department of Orthopedic Oncology Surgery, Beijing Ji shui tan Hospital, Peking University, Beijing 100035, China
    60Department of Radiotherapy and Chemotherapy, Hwamei Hospital, University of Chinese Academy of Sciences, Ningbo 315010, China
    61Department of Pulmonary Medicine, Taizhou Hospital of Wenzhou Medical University, Taizhou 317000, China
    62Department of Respiratory Medicine, the 900th Hospital of the Joint Logistics Team (the Former Fuzhou General Hospital), Fujian Medical University, Fuzhou 350025, China
    63Department of Oncology, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China
    64Department of Oncology, Beijing Tiantan Hospital, Capital Medical University, Beijing 100700, China
    65Department of Medical Oncology, Peking University International Hospital, Beijing 102206, China
    66State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou 510300, China
    67Department of Thoracic Oncology, Shanxi Academy of Medical Sciences, Shanxi Bethune Hospital, Taiyuan 030032, China
    68Department of Oncology, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai 200123, China
    69Department of Oncology, The Second Affiliated Hospital of Medical College, Xi’an Jiaotong University, Xi’an 710004, China
    70Department of Oncology, Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
    71Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
    72Department of Medical Oncology, The First Affiliated Hospital of Medicine, Xiamen University, Xiamen 361003, China
    73Department of Medical Oncology, Cancer Hospital of China Medical University, Shenyang 110042, China
    74Department of Medical Oncology, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology, Chengdu 610041, China
    75Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai 200032, China
    76Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital & Institute, Beijing 100142, China
    77State Key Laboratory of Cancer Biology, National Clinical Research Center for Digestive Diseases and Xijing Hospital of Digestive Diseases, Fourth Military Medical University, Xi’an 710032, China
    78Department of Oncology, Jiangsu Province Hospital and Nanjing Medical University First Affiliated Hospital, Nanjing 210029, China
    79Department of General Surgery, Huadong Hospital Affiliated to Fudan University, Shanghai 200040, China
    80Department of Oncology and Hematology, China-Japan Union Hospital of Jilin University, Changchun 130033, China
    81Department of Gastrointestinal Oncology, Harbin Medical University Cancer Hospital, Harbin 150081, China
    82Department of Oncology, Zhujiang Hospital, Southern Medical University, Guangzhou 510282, China
    83Department of Oncology, The First Affiliated Hospital of Xinxiang Medical University, Xinxiang 453000, China
    84Department of Oncology, The Third People’s Hospital of Zhengzhou, Zhengzhou 450000, China
    85Department of Biotherapy, State Key Laboratory of Biotherapy, Cancer Center, West China Hospital, Sichuan University, Chengdu 610041, China
    86Department of Oncology, the Third Xiangya Hospital, Central South University, Changsha 410013, China
    87Department of Medical Oncology, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an 710061, China
    88Department of Cell Biology, College of Medicine, Jiaxing University, Jiaxing 314001, China
    89Department of Medical, Shanghai OrigiMed Co, Ltd, Shanghai 201114, China
    90Department of Medical, Menarini Silicon Biosystems SpA, Shanghai 200333, China
    91Department of Medical, Geneplus-Beijing, Beijing 102200, China
    92Department of Oncology, Baotou Cancer Hospital, Baotou 014000, China
    93Department of Thoracic Disease Diagnosis and Treatment Center, Zhejiang Rongjun Hospital, The Third Affiliated Hospital of Jiaxing University, Jiaxing 314000, China
    94Department of Oncology, Lishui Municipal Central Hospital, Lishui 323000, China
    95Department of Interventional Pulmonary Diseases, Anhui Chest Hospital, Hefei 230011, China
    96Department of Medical Oncology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan 250012, China
    97Department of Medical Oncology, Shanghai Changzheng Hospital, Naval Medical University, Shanghai 200070, China
    98Department of Oncology, Shandong Provincial Third Hospital, Cheeloo College of Medicine, Shandong University, Jinan 250031, China
    99Department of The Comprehensive Cancer Center, Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing 210008, China
    100Department of Pathology, Shanxi Academy of Medical Sciences, Shanxi Bethune Hospital, Taiyuan 030032, China
    101Department of Oncology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China
    102Department of Interventional Oncology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510060, China
    103Department of Respiratory Medicine, Huzhou Hospital, Zhejiang University School of Medicine, Huzhou 313000, China
    104Department of Medical Oncology, The Affiliated Hospital of Jiaxing University, Jiaxing 314000, China
    105Department of Radiation Oncology, First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China
    106Department of Medical Oncology, Affiliated Jinhua Hospital, Zhejiang University School of Medicine, Jinhua 321000, China
    107Senior Department of Oncology, The 5th Medical Center of PLA General Hospital, Beijing 100071, China
    108Department of Pathology, Anhui Provincial Children’s Hospital, Hefei 230051, China
    109Department of Radiation Oncology, Affiliated Jinling Hospital, Medical School of Nanjing University, Nanjing 210002, China
    110Department of Respiratory Medicine, The Affiliated Jiangning Hospital of Nanjing Medical University, Nanjing 210002, China
    111Department of Clinical Oncology, The 901 Hospital of Joint Logistics Support Force of People Liberation Army, Hefei 230031, China
    112Department of Thoracic Surgery, Chuxiong Yi Autonomous Prefecture People’s Hospital, Chuxiong 675000, China
    113Department of Respiratory Medicine, Zhongshan Hospital, Fudan University, Shanghai 200032, China
    114Department of Oncology, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou 310000, China
    115Department of Oncology, The Second Affiliated Hospital of Guilin Medical University, Guilin 541199, China
    116International Cooperative Laboratory of Traditional Chinese Medicine Modernization and Innovative Drug Discovery of Chinese Ministry of Education (MOE), Guangzhou City Key Laboratory of Precision Chemical Drug Development, School of Pharmacy, Jinan University, Guangzhou 510632, China
    117National Health Commission (NHC) Key Laboratory of Nuclear Medicine, Jiangsu Key Laboratory of Molecular Nuclear Medicine, Jiangsu Institute of Nuclear Medicine, Wuxi 214063, China
    118Department of Radiopharmaceuticals, School of Pharmacy, Nanjing Medical University, Nanjing 210000, China
    119Department of Oncology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou 450008, China
    120Department of Bone and Soft-tissue Surgery, Chinese Academy of Sciences University Cancer Hospital (Zhejiang Cancer Hospital), Hangzhou 310022, China
    121Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Pathology, Peking University Cancer Hospital & Institute, Beijing 100142, China
    122Department of Oncology, The Cancer Hospital of Qinghai Province, Xining 810001, China
    123Institute of Respiratory Diseases, Xinqiao Hospital, Third Military Medical University, Chongqing 400037, China
    124Department of Pathology, Affiliated Jinling Hospital, Medical School of Nanjing University, Nanjing 210002, China
    125Department of Medical Oncology, Sir Run Run Shaw Hospital, Zhejiang University, Hangzhou 310016, China
    126Department of Thoracic Surgery, Affiliated Jinling Hospital, Medical School of Nanjing University, Nanjing 210002, China
    127Department of Oncology, Key Laboratory of Clinical Cancer Pharmacology and Toxicology Research of Zhejiang Province, Affiliated Hangzhou Cancer Hospital, Cancer Center, Westlake University School of Medicine, Hangzhou 310006, China
    128Department of Breast Cancer and Urological Medical Oncology, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
    129Department of Clinical Pathology, The First Affiliated Hospital of Jinan University, Guangzhou 510630, China
    130Senior Department of Pathology, the 7th Medical Center of PLA General Hospital, Beijing 100700, China

Corresponding:

Correspondence to: Yuanzhi Lu, Aijun Liu and Yuchen Han, E-mail: , and
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表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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