A study published in Cell Reports Medicine on Aug. 5 and led by Prof. LI Hong from the Shanghai Institute of Nutrition and Health of the Chinese Academy of Sciences, and Assoc. Prof. HU Bo from the Zhongshan Hospital, Fudan University, reported a scalable, data-driven computational framework for designing combinatorial immunotherapies, offering hope for patients with poor responses to current immunotherapies.
Immunotherapy, particularly immune checkpoint blockade (ICB), has revolutionized cancer treatment. Widespread resistance to ICB is a major challenge in clinical practice. To enhance treatment efficacy and overcome resistance, combining ICB therapy with chemotherapy or targeted therapy has become an important research direction. However, candidate combinations rely on empirical selection from existing drugs, and it is difficult to discover new candidates.
To make large-scale and automatic prediction of the candidates with the potential to be combined with ICB therapy, the researchers developed a novel computational framework named IGeS-BS.
The IGeS-BS first integrated transcriptomic data from thousands of patients who had received immunotherapy, and identified 33 robust signatures predictive of immune response. Then, it used these signatures to define a boosting score which quantified the compound-induced changes in the tumor microenvironment. Finally, the 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 has 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 provides a powerful computational framework for discovering compounds that enhance the efficacy or overcome the resistance of immunotherapy.
