Related Experiment Video
Updated: May 15, 2025
Predictive Immune Modeling of Solid Tumors
Published on: February 25, 2020
Alterations in genomic features and the tumour immune microenvironment predict immunotherapy outcomes in advanced
Chenjun Huang1, Zhiquan Qiu2, Honglian Huang1,3
1Department of Clinical Laboratory Medicine Center, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, 200437, China.
Background:
The response to immunotherapy is limited in advanced biliary tract cancer (BTC). Response prediction is a serious challenge in the clinic.
Methods:
This study included 60 patients with advanced BTC who received anti-PD-1 treatment. Among these patients, 30 were subjected to 520 gene panel sequencing, and 50 were subjected to multiplex circulating cytokine testing. The entropy and mutation features were analysed via the optimized pipeline based on our previous work. The repeated LASSO algorithm was used to identify the optimal features. The associations between sequence features and cell communications were explored by analysing single-cell transcriptome data from BTC (GSE125449). Cox regression was used to develop the integrated model. Time-dependent C-index, Kaplan‒Meier, and receiver operating characteristic (ROC) curves were used to assess the prediction performance.
Results:
TP53, NRAS, FBXW7, and APC were identified as prognosis-related genes. The average C-indices of sequence entropy (0.819) and mutation (0.817) for overall survival (OS) were significantly greater than those of tumour mutation burden (TMB, 0.392) and mutation score (0.638). Single-cell transcriptome data revealed that TP53, KRAS, and NRAS were enriched in plasmacytoid dendritic cells (pDCs) and that the communication between pDCs and macrophages was mediated through the CXCL signalling pathway. The integrated model (EM-CXCL10) showed powerful predictive performance for survival status (AUC: 0.863, 95% CI: 0.643-0.972) and objective response rate (AUC: 0.990, 95% CI: 0.822-1.000).
Conclusions:
This study constructed a multidimensional strategy that might indicate the prognosis of BTC immunotherapy, enabling the recognition of BTC patients who would benefit from immunotherapy, thereby guiding personalized clinical decision-making.
Insights
Predicting immunotherapy response in advanced biliary tract cancer (BTC) is challenging. This study developed an integrated model (EM-CXCL10) using gene mutations and cytokine signaling to identify patients who will benefit from anti-PD-1 therapy.
Area of Science:
- Oncology
- Immunotherapy
- Genomics
- Biomarker Discovery
Background:
- Response to immunotherapy in advanced biliary tract cancer (BTC) is limited.
- Predicting treatment response in BTC patients receiving immunotherapy remains a significant clinical challenge.
Purpose of the Study:
- To develop a predictive model for immunotherapy response in advanced BTC.
- To identify key genomic and cellular communication features associated with treatment outcomes.
Main Methods:
- Analyzed gene panel sequencing and multiplex circulating cytokine data from 60 advanced BTC patients treated with anti-PD-1 therapy.
- Utilized optimized pipelines for entropy and mutation feature analysis, with LASSO algorithm for feature selection.
- Explored cell communication pathways using single-cell transcriptome data and developed an integrated predictive model (EM-CXCL10) via Cox regression.
Main Results:
- Identified TP53, NRAS, FBXW7, and APC as prognosis-related genes.
- Sequence entropy and mutation features showed higher predictive accuracy for overall survival than TMB.
- The integrated EM-CXCL10 model demonstrated strong predictive performance for survival status (AUC: 0.863) and objective response rate (AUC: 0.990).
Conclusions:
- A multidimensional strategy integrating genomic and cellular communication data can predict immunotherapy prognosis in BTC.
- The developed EM-CXCL10 model aids in identifying BTC patients likely to benefit from immunotherapy, guiding personalized treatment decisions.
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