The Innovation (Jan 2022)
Profiling of immune features to predict immunotherapy efficacy
- Youqiong Ye,
- Yongchang Zhang,
- Nong Yang,
- Qian Gao,
- Xinyu Ding,
- Xinwei Kuang,
- Rujuan Bao,
- Zhao Zhang,
- Chaoyang Sun,
- Bingying Zhou,
- Li Wang,
- Qingsong Hu,
- Chunru Lin,
- Jianjun Gao,
- Yanyan Lou,
- Steven H. Lin,
- Lixia Diao,
- Hong Liu,
- Xiang Chen,
- Gordon B. Mills,
- Leng Han
Affiliations
- Youqiong Ye
- Shanghai Institute of Immunology, Department of Immunology and Microbiology, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China; Department of Biochemistry and Molecular Biology, The University of Texas Health Science Center at Houston McGovern Medical School, Houston, TX 77030, USA
- Yongchang Zhang
- Department of Medical Oncology, Lung Cancer and Gastrointestinal Unit, Hunan Cancer Hospital/The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, Hunan 410008, China
- Nong Yang
- Department of Medical Oncology, Lung Cancer and Gastrointestinal Unit, Hunan Cancer Hospital/The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, Hunan 410008, China
- Qian Gao
- Department of Dermatology, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
- Xinyu Ding
- Shanghai Institute of Immunology, Department of Immunology and Microbiology, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
- Xinwei Kuang
- Department of Dermatology, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
- Rujuan Bao
- Shanghai Institute of Immunology, Department of Immunology and Microbiology, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
- Zhao Zhang
- Department of Biochemistry and Molecular Biology, The University of Texas Health Science Center at Houston McGovern Medical School, Houston, TX 77030, USA
- Chaoyang Sun
- Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
- Bingying Zhou
- State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100037, China
- Li Wang
- State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100037, China
- Qingsong Hu
- Department of Molecular and Cellular Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- Chunru Lin
- Department of Molecular and Cellular Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- Jianjun Gao
- Department of Genitourinary Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- Yanyan Lou
- Division of Hematology and Oncology, Mayo Clinic, Jacksonville, FL 32224, USA
- Steven H. Lin
- Department of Radiation Oncology, Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- Lixia Diao
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- Hong Liu
- Department of Dermatology, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China; Corresponding author
- Xiang Chen
- Department of Dermatology, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China; Corresponding author
- Gordon B. Mills
- Knight Cancer Institute, Oregon Health and Science University, Portland, OR 97239, USA; Corresponding author
- Leng Han
- Department of Biochemistry and Molecular Biology, The University of Texas Health Science Center at Houston McGovern Medical School, Houston, TX 77030, USA; Center for Epigenetics and Disease Prevention, Institute of Biosciences and Technology, Texas A&M University, Houston, TX 77030, USA; Department of Translational Medical Sciences, College of Medicine, Texas A&M University, Houston, TX 77030 USA; Corresponding author
- Journal volume & issue
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Vol. 3,
no. 1
p. 100194
Abstract
Immune checkpoint blockade (ICB) therapies exhibit substantial clinical benefit in different cancers, but relatively low response rates in the majority of patients highlight the need to understand mutual relationships among immune features. Here, we reveal overall positive correlations among immune checkpoints and immune cell populations. Clinically, patients benefiting from ICB exhibited increases for both immune stimulatory and inhibitory features after initiation of therapy, suggesting that the activation of the immune microenvironment might serve as the biomarker to predict immune response. As proof-of-concept, we demonstrated that the immune activation score (ISΔ) based on dynamic alteration of interleukins in patient plasma as early as two cycles (4–6 weeks) after starting immunotherapy can accurately predict immunotherapy efficacy. Our results reveal a systematic landscape of associations among immune features and provide a noninvasive, cost-effective, and time-efficient approach based on dynamic profiling of pre- and on-treatment plasma to predict immunotherapy efficacy.
