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Updated: Jul 10, 2025
Predictive Immune Modeling of Solid Tumors
Published on: February 25, 2020
scCURE identifies cell types responding to immunotherapy and enables outcome prediction
Xin Zou1, Yujun Liu2, Miaochen Wang3
1Center for Tumor Diagnosis & Therapy, Jinshan Hospital, Fudan University, Shanghai 201508, China; Department of Pathology, Jinshan Hospital, Fudan University, Shanghai 201508, China.
Insights
We developed scCURE to identify cells responding to cancer immunotherapy. This method helps predict treatment effectiveness by analyzing cell profiles in tumor microenvironments.
Area of Science:
- Immunology
- Computational Biology
- Oncology
Background:
- Understanding cancer immunotherapy response and resistance is crucial for effective treatment.
- Reliable prediction of therapy response is needed to guide clinical decisions.
Purpose of the Study:
- To develop a novel computational method, scCURE (single-cell RNA sequencing data-based Changed and Unchanged cell Recognition during immunotherapy), for analyzing immunotherapy response.
- To identify cell populations and their baseline profiles that predict immunotherapy outcomes.
- To explore immunotherapy-associated cell-cell heterogeneities for mechanism studies and prediction models.
Main Methods:
- Utilized Gaussian mixture modeling, Kullback-Leibler (KL) divergence, and mutual nearest-neighbors criteria.
- Applied scCURE to single-cell RNA sequencing (scRNA-seq) data from melanoma and breast cancer immunotherapy studies.
- Discriminated between immunotherapy-affected and unaffected cells.
Main Results:
- scCURE successfully identified distinct cell populations based on their response to immunotherapy.
- Baseline profiles of specific CD8+ T and macrophage cells, identified by scCURE, were found to predict tumor microenvironment immune cell responses.
- These cell profiles correlate with immunotherapy treatment response, indicating potential as predictive factors.
Conclusions:
- scCURE provides a robust method for analyzing cellular responses to immunotherapy using scRNA-seq data.
- Specific baseline immune cell profiles within the tumor microenvironment can predict immunotherapy efficacy.
- The identified cell-cell heterogeneities offer insights into immunotherapy mechanisms and can be leveraged for treatment response prediction models.
Abstract:
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.
