Protocol for decoding immune predictors of response to immunotherapy through pan-cancer multiomics analysis

TL;DR

This paper provides a detailed protocol for analyzing T cell behavior in various cancers using advanced sequencing and profiling techniques to identify immune responses and biomarkers for immunotherapy.

Contribution

The protocol integrates multi-omics data to identify T cell subsets and signaling patterns linked to immunotherapy response.

Findings

Steps for gene-regulatory network analysis and cell-cell interaction analysis are outlined.

The protocol helps identify T cell subsets associated with immune checkpoint blockade response.

It enables the development of predictive biomarkers for immunotherapy.

Abstract

Here, we present a protocol for analyzing T cell dynamics in multiple cancers through single-cell RNA sequencing (scRNA-seq), single-cell immune profiling, and mass cytometry/cytometry by time-of-flight (CyTOF). We describe steps for performing gene-regulatory network analysis to identify key transcription factors and cell-cell interaction analysis to explore immune signaling. This protocol facilitates the identification of T cell subsets associated with immune checkpoint blockade (ICB) response and the development of predictive biomarkers.

For complete details on the use and execution of this protocol, please refer to Li et al.1

•Steps for integrating multi-omics data to analyze T cell dynamics across cancers•Instructions for single-cell RNA sequencing from sample collection to data analysis•Guidance on TCR clonality analysis to track T cell response to ICB therapy•Steps for building a response prediction model and response index (RI) for ICB therapy

Steps for integrating multi-omics data to analyze T cell dynamics across cancers

Instructions for single-cell RNA sequencing from sample collection to data analysis

Guidance on TCR clonality analysis to track T cell response to ICB therapy

Steps for building a response prediction model and response index (RI) for ICB therapy

Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.

Here, we present a protocol for analyzing T cell dynamics in multiple cancers through single-cell RNA sequencing (scRNA-seq), single-cell immune profiling, and mass cytometry (CyTOF). We describe steps for performing gene-regulatory network analysis to identify key transcription factors and cell-cell interaction analysis to explore immune signaling. This protocol facilitates the identification of T cell subsets associated with immune checkpoint blockade (ICB) response and the development of predictive biomarkers.

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