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. MillsLeng Han

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

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.

Original languageEnglish (US)
Article number100194
JournalInnovation(China)
Volume3
Issue number1
DOIs
StatePublished - Jan 25 2022
Externally publishedYes

Keywords

  • cancer immunotherapy
  • immune activation score
  • immune cell population
  • immune checkpoints
  • noninvasive biomarker

ASJC Scopus subject areas

  • General

Fingerprint

Dive into the research topics of 'Profiling of immune features to predict immunotherapy efficacy'. Together they form a unique fingerprint.

Cite this