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Updated: Feb 28, 2026

Predictive Immune Modeling of Solid Tumors

Predictive Immune Modeling of Solid Tumors

Published on: February 25, 2020

ICIsc: A Deep Learning Framework for Predicting Immune Checkpoint Inhibitor Response by Integrating scRNA-Seq and

Zhenyu Jin¹, Di Zhang¹, Luonan Chen^1,2

¹Key Laboratory of Systems Health Science of Zhejiang Province, School of Life Science, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Hangzhou 310024, China.

Bioengineering (Basel, Switzerland)

|February 27, 2026

Summary

This summary is machine-generated.

A new deep learning framework, ICIsc, accurately predicts patient response to cancer immunotherapy by integrating single-cell RNA sequencing data with protein language models. This approach enhances treatment prediction and identifies key genes for better patient outcomes.

Keywords:

attention network checkpoint inhibitors deep learning immunotherapy response single simple network single-cell RNA sequencing

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Area of Science:

Computational biology
Immunology
Oncology

Background:

Immune checkpoint inhibitors (ICIs) like PD-1/PD-L1 and CTLA-4 have improved cancer survival but many patients do not respond.
Accurate prediction of ICI response is crucial for effective cancer treatment.

Purpose of the Study:

To develop a deep learning framework (ICIsc) for predicting patient response to ICIs.
To integrate single-cell RNA sequencing (scRNA-seq) data with protein large language models for enhanced prediction.

Main Methods:

ICIsc constructs patient representations from transcriptomic profiles and immune gene set scores.
Drug representations are derived from ICI amino acid sequences using Evolutionary Scale Modeling 2 (ESM2).
A bilinear attention module fuses patient and drug embeddings for bulk data; for scRNA-seq, a single-sample network (SSN) and GATv2 model immune microenvironment heterogeneity.

Main Results:

ICIsc significantly outperforms baseline models in predicting ICI response.
The framework demonstrates robust generalization performance on benchmark evaluations and independent validation.
SHAP analysis identified key genes, such as GAPDH, linked to immunotherapy response and prognosis.

Conclusions:

ICIsc offers an accurate and interpretable computational framework for predicting immunotherapy outcomes.
The model aids in understanding the mechanisms underlying patient response to cancer immunotherapies.
This approach has the potential to optimize clinical decision-making for cancer patients undergoing immunotherapy.