In silico analysis reveals EP300 as a panCancer inhibitor of anti-tumor immune response via metabolic modulation.

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Large, publicy and freely available databases have revolutionized oncological research. Especially, immune-oncological studies have greatly benefited from bioinformatics-driven approaches combining genomic and gene expression profiling. The Cancer Genome Atlas (TCGA), for example, greatly facilitated the definition of the tumor mutational status and the consecutive expression of tumor neoantigens as determinants of anti-tumor immune response across many different malignancies. Head and neck squamous cell carcinoma (HNSCC) is one cancer type, in which the tumor immune microenvironment (TIME) is a key determinant of therapy response and prognosis. Additionally, HNSCC frequently carry a high tumor mutational burden and defects in DNA repair enzymes. Their response to immune-oncological therapeutics is, however, limited. 


We started our study with the aim to use the TCGA database for the definition of specific, altered genes associated with different TIME types. Our goal was to find new predictive markers or targets for immuno-therapeutic approaches in HNSCC. The platform cBioPortal (www.cbioportal.org) enabled easy access and manageable data acquisitions for our bioinformatic analyses. First, upregulation frequencies of different immune-related genes according to mRNA expression levels were used to define 3 TIME types (immune-activated, immune-suppressed, immune-absent). The TCGA database also includes an extensive clinical characterization of the included tumor patients. Therefore we could test the clinical relevance of the 3 TIME types by assessing their impact on prognosis. In accordance with other studies overall survival was best in the immune-activated group. The Cancer Digital Slide Archive (cancer.digitalslidearchive.org) allowed the correlation of the molecular TIME types with the histological appearances of the scanned tissue slides. 

 

When we compared mutation frequencies to the 3 TIME types, we found 9 genes significantly differentially mutated in the 3 TIME types with strongest differences for TP53 and the histone-acetyltransferase EP300. Mutations in EP300 correlated with an immune-activated TIME. We focused our next analyses on EP300, as it has not drawn much attention to its role in tumor immunity. In panCancer analyses anti-tumor immune activity was also increased in EP300 mutated esophageal, stomach and prostate cancers. Downregulation of EP300 gene expression was associated with higher anti-tumor immunity in most solid malignancies. Since EP300 is a promoter of glycolysis, which negatively affects anti-tumor immune response, we analyzed the association of EP300 with tumor metabolism. PanCancer tumor metabolism was strongly shifted towards oxidative phosphorylation in EP300 downregulated tumors. 

 

At this point we needed in vitro studies to further substantiate our findings. By accessing cell line data of the Connectivity Map (CMap) provided by the Broad Institute we were able to model an inhibition of EP300 with the small molecule C646 for several cell lines. Glycolysis-associated genes were downregulated in 5 of 8 cell lines after C646 treatment according to the publicy availbale in vitro data of the CMap.  

 

Our study reveals associations of specific gene alterations with different TIME types. In detail, we defined EP300 as a panCancer inhibitor of the TIME most likely via metabolic modulation. In this context EP300 represents a promising predictive biomarker and an immuno-therapeutic target. Based on our in silico  analyses we hope to path the way for preclinical studies testing EP300 as an immuno-therapeutic target.  

Rosemarie Krupar

Medical Doctor, Pathology of the Research Center Borstel, Leibniz Lung Center

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