A collaborative study led by Prof. LI Hong from the Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences (CAS), and Associate Prof. HU Bo from the Zhongshan Hospital, Fudan University, has developed a scalable, data-driven computational framework for designing combinatorial immunotherapies—offering fresh hope for patients with poor responses to current immunotherapies. Entitled “Computational Framework for Prioritizing Candidate Compounds Overcoming the Resistance of Pancancer Immunotherapy”, these findings were published in Cell Reports Medicine on Aug. 5, 2025.

Immunotherapy, particularly immune checkpoint blockade (ICB), has revolutionized cancer treatment. However, widespread resistance to ICB remained a major challenge in clinical practice. The combination of ICB therapy with chemotherapy or targeted therapy has become an important research direction for enhancing treatment efficacy and overcoming resistance. However, the candidate combinations rely on empirical selection from existing drugs, and it is difficult to discover new candidates.

To achieve large-scale and automatic prediction of candidates with the potential to be combined with ICB therapy, the research team developed a novel computational framework named IGeS-BS. 

Firstly, IGeS-BS integrated transcriptomic data from thousands of patients receiving immunotherapy and identified 33 robust signatures predictive of immune response. Then, IGeS-BS used these signatures to define a boosting score, which quantifies the compound-induced changes in the tumor microenvironment. Finally, IGeS-BS ranked compounds based on their boosting scores, with top-ranked compounds being more likely to enhance ICB therapy efficacy.

Applying IGeS-BS to over 10,000 compounds across 13 cancer types has generated an immuno-response landscape and successfully prioritized candidates with synergistic potential. Experimental validation confirmed that two high-ranking compounds—SB-366791 and CGP-60474—could significantly reverse resistance to anti-PD-1 therapy in liver cancer.

This study provided a powerful computational framework for discovering compounds that enhance the efficacy or overcome the resistance of immunotherapy.

This work was supported by the National Natural Science Foundation of China, the Shanghai Natural Science Foundation, the Youth Innovation Promotion Association of CAS, and the Shanghai Sailing Program.

Computational framework for prioritizing candidate compounds overcoming the resistance of pancancer immunotherapy. (Image provided by Prof. LI Hong’s group)

Paper link: https://doi.org/10.1016/j.xcrm.2025.102276

Scientific Contact:
Prof. LI Hong
Shanghai Institute of Nutrition and Health,
Chinese Academy of Sciences
Email: lihong01@sinh.ac.cn

Media Contact:
WANG Jin
Shanghai Institute of Nutrition and Health,
Chinese Academy of Sciences
Email: wangjin01@sinh.ac.cn