2. Academic Validation
  3. Computational framework for prioritizing candidate compounds overcoming the resistance of pancancer immunotherapy

Computational framework for prioritizing candidate compounds overcoming the resistance of pancancer immunotherapy

  • Cell Rep Med. 2025 Aug 19;6(8):102276. doi: 10.1016/j.xcrm.2025.102276.
Fangyoumin Feng 1,  Tian He 2,  Ping Lin 1,  Jinwu Hu 3,  Bihan Shen 1,  Zhixuan Tang 1,  Jian Zhou 2,  Jia Fan 2,  Bo Hu 4,  Hong Li 5
Affiliations

Affiliations

  • 1 Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China.
  • 2 Department of Hepatobiliary Surgery and Liver Transplantation, Liver Cancer Institute, Zhongshan Hospital, Fudan University, and Key Laboratory of Carcinogenesis and Cancer Invasion, Ministry of Education, Shanghai 20032, China.
  • 3 Department of Liver Cancer, Shanghai Geriatric Medical Center, Shanghai 20032, China.
  • 4 Department of Hepatobiliary Surgery and Liver Transplantation, Liver Cancer Institute, Zhongshan Hospital, Fudan University, and Key Laboratory of Carcinogenesis and Cancer Invasion, Ministry of Education, Shanghai 20032, China. Electronic address: hu.bo@zs-hospital.sh.cn.
  • 5 Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China. Electronic address: lihong01@sinh.ac.cn.
PMID: 40769146 DOI: 10.1016/j.xcrm.2025.102276
Abstract

Combination therapy has emerged as an effective approach to overcome resistance to immunotherapy. However, only a small number of drugs have been identified with synergistic effects with immunotherapy. Here, we develop a computational framework (IGeS-BS) to recommend compounds that potentially overcome resistance to immunotherapy. A meta-analysis of approximately 1,000 transcriptomes from immunotherapy patients revealed 33 tumor microenvironment (TME) signatures that can robustly and accurately estimate immunotherapy responses. An immuno-boosting landscape for more than 10,000 compounds and 13 Cancer types was subsequently generated on The Cancer Genome Atlas (TCGA) and The Library of Integrated Network-Based Cellular Signatures (LINCS) datasets. Furthermore, the immuno-boosting effects of several high-scoring compounds were evaluated by in vitro and in vivo experiments in hepatocellular carcinoma and other Cancer types. The results showed that the two best compounds (SB-366791 and CGP-60474) significantly alleviate the resistance of hepatocellular carcinoma to anti-PD1 therapy by activating immune cells. Collectively, our research provides an efficient framework for discovering compounds that enhance immunotherapy responses.

Keywords

drug; hepatocellular carcinoma; immuno-boosting score; immunotherapy; pancancer.

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