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Updated: Oct 7, 2025
Predictive Immune Modeling of Solid Tumors
Published on: February 25, 2020
Prediction of biomarkers and therapeutic combinations for anti-PD-1 immunotherapy using the global gene network
Chia-Chin Wu1, Y Alan Wang2, J Andrew Livingston3,4
1Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. perwu777@gmail.com.
Abstract:
Owing to a lack of response to the anti-PD1 therapy for most cancer patients, we develop a network approach to infer genes, pathways, and potential therapeutic combinations that are associated with tumor response to anti-PD1. Here, our prediction identifies genes and pathways known to be associated with anti-PD1, and is further validated by 6 CRISPR gene sets associated with tumor resistance to cytotoxic T cells and targets of the 36 compounds that have been tested in clinical trials for combination treatments with anti-PD1. Integration of our top prediction and TCGA data identifies hundreds of genes whose expression and genetic alterations that could affect response to anti-PD1 in each TCGA cancer type, and the comparison of these genes across cancer types reveals that the tumor immunoregulation associated with response to anti-PD1 would be tissue-specific. In addition, the integration identifies the gene signature to calculate the MHC I association immunoscore (MIAS) that shows a good correlation with patient response to anti-PD1 for 411 melanoma samples complied from 6 cohorts. Furthermore, mapping drug target data to the top genes in our association prediction identifies inhibitors that could potentially enhance tumor response to anti-PD1, such as inhibitors of the encoded proteins of CDK4, GSK3B, and PTK2.
Insights
A network approach identifies key genes and pathways influencing anti-PD1 therapy response in cancer. This research reveals tissue-specific immune regulation and suggests combination therapies to improve patient outcomes.
Area of Science:
- Oncology
- Immunotherapy
- Computational Biology
Background:
- Most cancer patients do not respond to anti-PD1 therapy.
- Predicting response to anti-PD1 therapy is crucial for effective cancer treatment.
Purpose of the Study:
- To develop a network approach for inferring genes, pathways, and therapeutic combinations associated with anti-PD1 therapy response.
- To identify predictive biomarkers and potential combination strategies to enhance anti-PD1 efficacy.
Main Methods:
- Network-based analysis to predict genes and pathways related to anti-PD1 response.
- Validation using CRISPR gene sets, clinical trial compound targets, and TCGA data.
- Development of the MHC I association immunoscore (MIAS) using a gene signature.
Main Results:
- Identified known and novel genes/pathways associated with anti-PD1 response.
- Demonstrated tissue-specific tumor immunoregulation patterns.
- MIAS showed good correlation with patient response in melanoma.
- Identified potential drug targets (e.g., CDK4, GSK3B, PTK2 inhibitors) for combination therapy.
Conclusions:
- A network approach can effectively predict factors influencing anti-PD1 response.
- Tumor immunoregulation is tissue-specific, requiring tailored therapeutic strategies.
- Combination therapies targeting identified pathways and genes may improve anti-PD1 efficacy.
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