Work

Protocol for using scCURE to construct an immunotherapy outcome prediction model

Year: 2024

Type: article

Abstract: It is challenging to predict the immunotherapy outcome from the baseline status of patients with cancer. Here, we introduce a protocol for constructing an immunotherapy prediction model utilizing single-cell RNA sequencing (scRNA-seq)-based changed and unchanged cell recognition during immunotherapy (scCURE). Initially, we describe the steps for using scCURE to discriminate unchanged cells with similar cellular and molecular functions from whole scRNA-seq data. Subsequently, we demonstrate ways to construct immunotherapy outcome prediction models using either scRNA-seq or bulk RNA sequencing (RNA-seq) data, predicated on the unchanged cells identified by scCURE. For complete details on the use and execution of this protocol, please refer to Zou et al. 1 • Guidance on identifying cell types that are changed or unchanged during immunotherapy • Construction of reliable immunotherapy outcome prediction models using unchanged cells • Instructions for analyzing unmatched pre- and post-treatment samples Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics. It is challenging to predict the immunotherapy outcome from the baseline status of patients with cancer. Here, we introduce a protocol for constructing an immunotherapy prediction model utilizing single-cell RNA sequencing (scRNA-seq)-based changed and unchanged cell recognition during immunotherapy (scCURE). Initially, we describe the steps for using scCURE to discriminate unchanged cells with similar cellular and molecular functions from whole scRNA-seq data. Subsequently, we demonstrate ways to construct immunotherapy outcome prediction models using either scRNA-seq or bulk RNA sequencing (RNA-seq) data, predicated on the unchanged cells identified by scCURE.

Source: STAR Protocols

DOI: 10.1016/j.xpro.2024.103476

Authors: Yujun Liu, Xin Zou, Henry H.Y. Tong, Yuan Ji, Lianjiang Tan, Jie Hao