scCURE identifies cell types responding to immunotherapy and enables outcome prediction

Abstract

Summary: A deep understanding of immunotherapy response/resistance mechanisms and a highly reliable therapy response prediction are vital for cancer treatment. Here, we developed scCURE (single-cell RNA sequencing [scRNA-seq] data-based Changed and Unchanged cell Recognition during immunotherapy). Based on Gaussian mixture modeling, Kullback-Leibler (KL) divergence, and mutual nearest-neighbors criteria, scCURE can faithfully discriminate between cells affected or unaffected by immunotherapy intervention. By conducting scCURE analyses in melanoma and breast cancer immunotherapy scRNA-seq data, we found that the baseline profiles of specific CD8+ T and macrophage cells (identified by scCURE) can determine the way in which tumor microenvironment immune cells respond to immunotherapy, e.g., antitumor immunity activation or de-activation; therefore, these cells could be predictive factors for treatment response. In this work, we demonstrated that the immunotherapy-associated cell-cell heterogeneities revealed by scCURE can be utilized to integrate the therapy response mechanism study and prediction model construction. Motivation: Single-cell RNA sequencing (scRNA-seq) has significantly advanced our understanding of how different types of cells respond to immunotherapy. However, inconsistencies persist in our understanding of the mechanistic underpinnings and in predictive models of immunotherapy, with studies reporting different cell subtypes contributing to mechanisms of response and prediction models without clear explanation. Such inconsistencies might originate from uncharacterized cellular heterogeneity. To tackle this issue, we present scCURE, which leverages pattern recognition modeling to differentiate between changed and unchanged cells during the course of immunotherapy.

Keywords

• CP: Systems biology