Metabolic predictors of response to immune checkpoint blockade therapy

Ofir Shorer; Keren Yizhak

doi:10.1016/j.isci.2023.108188

Metabolic predictors of response to immune checkpoint blockade therapy

iScience. 2023 Oct 12;26(11):108188. doi: 10.1016/j.isci.2023.108188. eCollection 2023 Nov 17.

Authors

Ofir Shorer¹, Keren Yizhak^{1

2}

Affiliations

¹ Department of Cell Biology and Cancer Science, The Ruth and Bruce Rappaport Faculty of Medicine, Technion - Israel Institute of Technology, Haifa 3525422, Israel.
² The Taub Faculty of Computer Science, Technion - Israel Institute of Technology, Haifa 3200003, Israel.

Abstract

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 ∼1700 metabolic genes using single-cell RNA-seq data of > 1 million immune cells from ∼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.

Keywords: Cancer; Human metabolism; Immunology.