Résumé
Simple Summary PRC2 (Polycomb repressive complex 2) is a catalytic multi-subunit complex involved in transcriptional repression through the methylation of lysine 27 at histone 3 (H3K27me1/2/3). Dysregulation of PRC2 has been linked to tumor development and progression. Here, we performed a comprehensive analysis of data in the genomic and transcriptomic (cBioPortal, KMplot) database portals of clinical tumor samples and evaluated clinical correlations of EZH2, SUZ12, and EED. Next, we developed an original Python application enabling the identification of genes cooperating with PRC2 in oncogenic processes for the analysis of the DepMap CRISPR knockout database. Our study identified cancer types that are most likely to be responsive to PRC2 inhibitors. By analyzing co-dependencies with other genes, this analysis also provides indications of prognostic biomarkers and new therapeutic regimens. PRC2 (Polycomb repressive complex 2) is an evolutionarily conserved protein complex required to maintain transcriptional repression. The core PRC2 complex includes EZH2, SUZ12, and EED proteins and methylates histone H3K27. PRC2 is known to contribute to carcinogenesis and several small molecule inhibitors targeting PRC2 have been developed. The present study aimed to identify the cancer types in which PRC2 targeting drugs could be beneficial. We queried genomic and transcriptomic (cBioPortal, KMplot) database portals of clinical tumor samples to evaluate clinical correlations of PRC2 subunit genes. EZH2, SUZ12, and EED gene amplification was most frequently found in prostate cancer, whereas lymphoid malignancies (DLBCL) frequently showed EZH2 mutations. In both cases, PRC2 alterations were associated with poor prognosis. Moreover, higher expression of PRC2 subunits was correlated with poor survival in renal and liver cancers as well as gliomas. Finally, we generated a Python application to analyze the correlation of EZH2/SUZ12/EED gene knockouts by CRISPR with the alterations detected in the cancer cell lines using DepMap data. As a result, we were able to identify mutations that correlated significantly with tumor cell sensitivity to PRC2 knockout, including SWI/SNF, COMPASS/COMPASS-like subunits and BCL2, warranting the investigation of these genes as potential markers of sensitivity to PRC2-targeting drugs.