Advanced Computational Method Prioritizes Compounds to Boost Cancer Immunotherapy Outcomes
Researchers have developed a novel computational framework to identify compounds that enhance the effectiveness of cancer immunotherapy, potentially overcoming resistance and improving patient outcomes.
A recent breakthrough published in Cell Reports Medicine introduces a scalable, data-driven computational framework designed to identify promising compound combinations to enhance cancer immunotherapy effectiveness. Led by researchers from the Chinese Academy of Sciences and Fudan University, this innovative approach aims to overcome the widespread resistance seen with current immune checkpoint blockade (ICB) therapies.
Immunotherapy has transformed cancer treatment, especially with ICB methods that activate the immune system to target tumors. However, many patients exhibit resistance, limiting the success of these therapies. Combining ICB with other treatments like chemotherapy or targeted drugs is a common strategy, but the selection of combination candidates has traditionally relied on empirical methods, making the discovery process time-consuming and inefficient.
The new computational tool, named IGeS-BS, addresses this challenge by integrating transcriptomic data from thousands of patients treated with immunotherapy. It identifies 33 key gene signatures that predict immune response, which are then used to calculate a boosting score reflecting a compound's potential to modify the tumor microenvironment favorably. This ranking system allows for the automatic prediction of the most promising compounds to synergize with ICB therapies.
Applying IGeS-BS to over 10,000 compounds across 13 different cancer types, the researchers built an immuno-response landscape that highlights candidates with high synergistic potential. Among the top-ranked compounds, SB-366791 and CGP-60474 demonstrated significant ability to reverse resistance to anti-PD-1 therapy in liver cancer models, according to experimental validation.
This research presents a powerful tool that could significantly accelerate the discovery of therapeutic combinations, offering hope for improving outcomes for patients who otherwise respond poorly to existing immunotherapies. The framework is a step forward in the personalized approach to cancer treatment and may inform future clinical strategies.
Source: https://medicalxpress.com/news/2025-08-tool-compounds-cancer-immunotherapy-effectiveness.html
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