Clinical Correlations of Polycomb Repressive Complex 2 in Different Tumor Types

Maksim Erokhin, Group of Chromatin Biology, Institute of Gene Biology, Russian Academy of Sciences
Olga Chetverina, Group of Chromatin Biology, Institute of Gene Biology, Russian Academy of Sciences
Balázs Győrffy, TTK Cancer Biomarker Research Group, Institute of Enzymology
Victor V. Tatarskiy, Laboratory of Molecular Oncobiology, Institute of Gene Biology, Russian Academy of Science
Vladic Mogila, Department of Control of Genetic Processes, Institute of Gene Biology, Russian Academy of Sciences
Alexander A. Shtil, Laboratory of Molecular Oncobiology, Institute of Gene Biology, Russian Academy of Sciences
Igor B. Roninson, Department of Drug Discovery and Biomedical Sciences, University of South Carolina
Jerome Moreaux, Institute of Human Genetics, UMR 9002 Centre National de la Recherche Scientifique, University of Montpellier
Pavel Georgiev, Department of Control of Genetic Processes, Institute of Gene Biology, Russian Academy of Sciences
Giacomo Cavalli, Institute of Human Genetics, UMR 9002 Centre National de la Recherche Scientifique, University of Montpellier
Darya Chetverina, Group of Epigenetics, Institute of Gene Biology, Russian Academy of Sciences

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Abstract

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. , , and gene amplification was most frequently found in prostate cancer, whereas lymphoid malignancies (DLBCL) frequently showed 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 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.