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Decoding Functional Impact of Epigenetic Regulator Mutations on Ligand-receptor Interaction Perturbations for Evaluation of Cancer Immunotherapy

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Journal J Cell Mol Med
Date 2024 Sep 26
PMID 39323009
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Abstract

Cellular crosstalk mediated by ligand-receptor interactions largely complicates the tumour ecosystem, resulting in heterogeneous tumour microenvironments that affect immune response and clinical benefits from immunotherapy. Epigenetic mechanisms are pivotal to expression changes of immune-related genes and can modulate the anti-tumour immune response. However, the functional consequences of disrupted epigenetic regulators (ERs) on ligand-receptor interactions in the tumour microenvironment remain largely unexplored. Here, we proposed mutations of ERs in perturbed interactions (MERIN), a molecular network-based approach that incorporates multi-omics data, to infer the potential consequences of ER mutations on ligand-receptor interaction perturbations. Leveraging cancer genomic profiles and molecular interaction data, we comprehensively decoded the functional consequences of ER mutations on dysregulated ligand-receptor interactions across 33 cancers. The dysregulated ligand-receptor genes were indeed enriched in cancer and immune-related function. We demonstrated the potential significance of PD1-PDL1 interaction-related ER mutations in stratifying cancer patients from multiple independent data cohorts. The ER mutation group showed distinct immunological characterizations and prognoses. Furthermore, we highlighted that the ER mutations could potentially predict clinical outcomes of immunotherapy. Our computational and clinical assessment underscore the utility of MERIN for elucidating the functional relevance of ER mutations in cancer immune response, potentially aiding patients' stratification for immunotherapy.

Citing Articles

Decoding functional impact of epigenetic regulator mutations on ligand-receptor interaction perturbations for evaluation of cancer immunotherapy.

Shi A, Lin C, Lyu J J Cell Mol Med. 2024; 28(18):e70009.

PMID: 39323009 PMC: 11424496. DOI: 10.1111/jcmm.70009.

References
1.
Herbst R, Soria J, Kowanetz M, Fine G, Hamid O, Gordon M . Predictive correlates of response to the anti-PD-L1 antibody MPDL3280A in cancer patients. Nature. 2014; 515(7528):563-7. PMC: 4836193. DOI: 10.1038/nature14011. View

2.
Li Y, Burgman B, Khatri I, Pentaparthi S, Su Z, McGrail D . e-MutPath: computational modeling reveals the functional landscape of genetic mutations rewiring interactome networks. Nucleic Acids Res. 2020; 49(1):e2. PMC: 7797045. DOI: 10.1093/nar/gkaa1015. View

3.
Subramanian A, Tamayo P, Mootha V, Mukherjee S, Ebert B, Gillette M . Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A. 2005; 102(43):15545-50. PMC: 1239896. DOI: 10.1073/pnas.0506580102. View

4.
Wang Z, Zhao J, Wang G, Zhang Z, Zhang F, Zhang Y . Comutations in DNA Damage Response Pathways Serve as Potential Biomarkers for Immune Checkpoint Blockade. Cancer Res. 2018; 78(22):6486-6496. DOI: 10.1158/0008-5472.CAN-18-1814. View

5.
Woods D, Sodre A, Villagra A, Sarnaik A, Sotomayor E, Weber J . HDAC Inhibition Upregulates PD-1 Ligands in Melanoma and Augments Immunotherapy with PD-1 Blockade. Cancer Immunol Res. 2015; 3(12):1375-85. PMC: 4674300. DOI: 10.1158/2326-6066.CIR-15-0077-T. View