Metabolic predictors of response to immune checkpoint blockade therapy

Metabolism of immune cells in the tumor microenvironment (TME) plays a critical role in cancer patient response to immune checkpoint inhibitors (ICI). Yet, a metabolic characterization of immune cells in the TME of patients treated with ICI is lacking. To bridge this gap we performed a semi-supervised analysis of similar to 1700 metabolic genes using single-cell RNA-seq data of > 1 million immune cells from similar to 230 samples of cancer patients treated with ICI. When clustering cells based on their metabolic gene expression, we found that similar immunological cellular states are found in different metabolic states. Most importantly, we found metabolic states that are significantly associated with patient response. We then built a metabolic predictor based on a dozen gene signature, which significantly differentiates between responding and non-responding patients across different cancer types (AUC = 0.8-0.92). Taken together, our results demonstrate the power of metabolism in predicting patient response to ICI.

Automated summary

The metabolism of immune cells in the tumor microenvironment plays a crucial role in cancer patient response to immune checkpoint inhibitors (ICI). This study used single-cell RNA-seq data to analyze the metabolic genes of immune cells in the TME of cancer patients treated with ICI. The results showed that metabolic states are associated with patient response, and a metabolic predictor based on gene signatures could effectively differentiate responding and non-responding patients.