Immune checkpoint inhibitors (ICI) represent a major advancement in cancer therapy by targeting a natural immune system mechanism. Under normal conditions, immune checkpoints act to prevent the immune system from attacking healthy cells. However, in the case of cancer, this protective mechanism can prevent the body from attacking tumor cells. ICI drugs work by disabling this checkpoint system, allowing the immune system to target and destroy cancerous cells.

Despite the success of ICIs in inhibiting tumor growth, they are only effective in less than 40% of patients. For the remaining patients, the drugs may cause side effects without offering any therapeutic benefit. While there have been efforts to determine in advance whether or not the drugs will be effective for specific patients, current tools for doing so – for example, based on a genetic signature or the amount of different cells – are not accurate.

The Technion researchers, led by Prof. Keren Yizhak and Ofir Shorer, have developed a new tool that predicts patient response to ICI drugs by analyzing the metabolism of immune cells in the tumor’s microenvironment. Since immune cells and cancer cells compete for resources within the same environment, understanding their metabolic demands can provide valuable predictions about treatment outcomes. The team used single-cell RNA-sequencing data from over one million immune cells in cancer patients treated with ICI, analyzing the expression of 1,700 metabolic genes to develop their prediction model.

The research, supported by the Ministry of Science and Technology, the Israel Science Foundation (ISF) and The Bruce & Ruth Rappaport Cancer Research Center and Rubenstein Fellowship, has been published in iScience