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

doi:10.1016/j.xinn.2021.100194

Profiling of immune features to predict immunotherapy efficacy

Innovation (Camb). 2021 Dec 2;3(1):100194. doi: 10.1016/j.xinn.2021.100194. eCollection 2022 Jan 25.

Authors

Youqiong Ye^{1

2}, Yongchang Zhang³, Nong Yang³, Qian Gao⁴, Xinyu Ding¹, Xinwei Kuang⁴, Rujuan Bao¹, Zhao Zhang², Chaoyang Sun⁵, Bingying Zhou⁶, Li Wang⁶, Qingsong Hu⁷, Chunru Lin⁷, Jianjun Gao⁸, Yanyan Lou⁹, Steven H Lin¹⁰, Lixia Diao¹¹, Hong Liu⁴, Xiang Chen⁴, Gordon B Mills¹², Leng Han^{2

13

14}

Affiliations

¹ Shanghai Institute of Immunology, Department of Immunology and Microbiology, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China.
² Department of Biochemistry and Molecular Biology, The University of Texas Health Science Center at Houston McGovern Medical School, Houston, TX 77030, USA.
³ 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.
⁴ Department of Dermatology, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China.
⁵ Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China.
⁶ 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.
⁷ Department of Molecular and Cellular Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
⁸ Department of Genitourinary Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
⁹ Division of Hematology and Oncology, Mayo Clinic, Jacksonville, FL 32224, USA.
¹⁰ Department of Radiation Oncology, Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
¹¹ Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
¹² Knight Cancer Institute, Oregon Health and Science University, Portland, OR 97239, USA.
¹³ Center for Epigenetics and Disease Prevention, Institute of Biosciences and Technology, Texas A&M University, Houston, TX 77030, USA.
¹⁴ Department of Translational Medical Sciences, College of Medicine, Texas A&M University, Houston, TX 77030 USA.

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 (IS _Δ) 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.

Keywords: cancer immunotherapy; immune activation score; immune cell population; immune checkpoints; noninvasive biomarker.

Grants and funding

P30 CA016672/CA/NCI NIH HHS/United States