bioRxivImmunology
Analysis of genetic signatures of tumor microenvironment yields insight into mechanisms of resistance to immunotherapy
Abstract
BackgroundTherapeutic intervention targeting immune cells have led to remarkable improvements in clinical outcomes of tumor patients. However, responses are not universal. The inflamed tumor microenvironment has been reported to correlate with response in tumor patients. However, due to the lack of appropriate experimental methods, the reason why the immunotherapeutic resistance still existed on the inflamed tumor microenvironment remains unclear. Materials and methodsHere, based on integrated single-cell RNA sequencing technology, we classified tumor microenvironment into inflamed immunotherapeutic responsive and inflamed non-responsive. Then, phenotype-specific genes were identified to show mechanistic differences between distant TME phenotypes. Finally, we screened for some potential favorable TME phenotypes transformation drugs to aid current immunotherapy. ResultsMultiple signaling pathways were phenotypes-specific dysregulated. For example, Interleukin signaling pathways including IL-4 and IL-13 were activated in inflamed TME across multiple tumor types. PPAR signaling pathways and multiple epigenetic pathways were respectively inhibited and activated in inflamed immunotherapeutic non-responsive TME, suggesting a potential mechanism of immunotherapeutic resistance and target for therapy. We also identified some genetic markers of inflamed non-responsive or responsive TME, some of which have shown its potentials to enhance the efficacy of current immunotherapy. ConclusionThese results may contribute to the mechanistic understanding of immunotherapeutic resistance and guide rational therapeutic combinations of distant targeted chemotherapy agents with immunotherapy.
- organism: human
- assay: scRNA-seq
- research focus: cancer & tumor microenvironment
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: statistical model
- approach: machine learning (classical)
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