Protocol for using scCURE to construct an immunotherapy outcome prediction model

Area of Science:

• Computational Biology
• Immunology
• Genomics

Background:

• Predicting patient response to cancer immunotherapy remains a significant clinical challenge.
• Baseline patient status is insufficient for accurate immunotherapy outcome prediction.
• Novel computational methods are needed to analyze complex biological data for predictive modeling.

Purpose of the Study:

• To introduce a novel protocol, scCURE (single-cell RNA sequencing-based changed and unchanged cell recognition during immunotherapy), for predicting immunotherapy outcomes.
• To detail the methodology for identifying unchanged cells from scRNA-seq data using scCURE.
• To demonstrate the construction of prediction models using scCURE-identified cells from both scRNA-seq and bulk RNA sequencing (RNA-seq) data.

Main Methods:

• Utilized single-cell RNA sequencing (scRNA-seq) data to identify cells with stable functions during immunotherapy.
• Developed the scCURE algorithm to discriminate between changed and unchanged cells based on cellular and molecular profiles.
• Constructed predictive models for immunotherapy outcomes leveraging the identified unchanged cells from scRNA-seq and bulk RNA-seq datasets.

Main Results:

• Successfully demonstrated a protocol for recognizing unchanged cells crucial for immunotherapy response.
• Established a framework for building immunotherapy prediction models based on scCURE-identified cell populations.
• Showcased the applicability of the scCURE method for both scRNA-seq and bulk RNA-seq data analysis.

Conclusions:

• The scCURE protocol provides a robust method for identifying key cellular populations that can improve immunotherapy outcome prediction.
• This approach offers a valuable tool for enhancing personalized cancer treatment strategies.
• Further research and validation of scCURE are warranted for broader clinical application.