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Updated: Jul 24, 2025
Predictive Immune Modeling of Solid Tumors
Published on: February 25, 2020
Immune micro-environment analysis and establishment of response prediction model for PD-1 blockade immunotherapy in
1Department of Immunology, School of Basic Medical Sciences, NHC Key Laboratory of Medical Immunology, Peking University, Beijing, China.
Purpose:
Only a small proportion of patients obtain survival benefit from PD-1 blockade immunotherapy due to the highly heterogeneous and suppressed immune micro-environment of GBM. We aimed at revealing the characteristics of tumor micro-environment (TME) of GBM related to response to PD-1 inhibitors and constructing a response prediction model for screening patients possibly benefit from PD-1 inhibitors.
Methods:
Based on the composition and expression profiles of cell subpopulations calculated by deconvoluting the GBM bulk RNA-seq, differentially expressed gene analysis and gene set enrichment analysis (GSEA) were performed to explore genes and pathways related to response to PD-1 inhibitors. Further by combining least absolute shrinkage and selection operator (LASSO) regression and expression correlation with PD-L1, the response prediction genes of PD-1 inhibitors were identified and the response prediction model was constructed through binary logistic regression.
Results:
The comparison of abundances of tumor infiltrating immune cells showed that the abundance of M0 macrophages of responders was lower, while the abundance of activated dendritic cells (DCs) was higher before PD-1 inhibitors treatment; the abundances of plasma cells and M0 macrophages of responders were lower after PD-1 inhibitors treatment. In addition, GSEA showed that the main up-regulation pathways in the tumor micro-environment of responders before PD-1 inhibitors treatment included the regulation of T-helper 1 type immune response, the positive regulation of natural killer cell-mediated cytotoxicity, p53 signaling pathway, homotypic cell-cell adhesion, etc., the main down-regulation pathways included the activation of microglia and myeloid leukocytes, Ras signaling pathway, etc. Afterward, ITGAX, LRRFIP1 and FMN1 were identified as the key response prediction genes of PD-1 inhibitors and the response prediction model based on them showed good predictive performance with potential value of clinical application in its validation and verification.
Conclusions:
ITGAX, LRRFIP1 and FMN1 were identified as the response prediction genes of PD-1 inhibitors and the response prediction model based on them was proved to have potential clinical value.
