Prediction of biomarkers and therapeutic combinations for anti-PD-1 immunotherapy using the global gene network association

Chia-Chin Wu (), Y. Alan Wang (), J. Andrew Livingston, Jianhua Zhang and P. Andrew Futreal
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Chia-Chin Wu: The University of Texas MD Anderson Cancer Center
Y. Alan Wang: The University of Texas MD Anderson Cancer Center
J. Andrew Livingston: The University of Texas MD Anderson Cancer Center
Jianhua Zhang: The University of Texas MD Anderson Cancer Center
P. Andrew Futreal: The University of Texas MD Anderson Cancer Center

Nature Communications, 2022, vol. 13, issue 1, 1-14

Abstract: 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.

Date: 2022
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DOI: 10.1038/s41467-021-27651-4

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