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.
Optimizing compilers make significant contribution to the performance of modern computer systems. Among them VLIW architecture processors are the most compilerdependent, since their performance is ensured by effective compile time scheduling of multiple commands in a single clock. This leads to an eventual complication of VLIW compilers. Taking as an example optimizing compiler developed for the Elbrus family processors, it runs consequently over 300 stages of code optimization in basic mode. Such an amount of stages is needed to obtain decent performance, but it also makes compilation quite time consuming. It turns out that the main reason for compilation time increase when using high level compilation is applying some aggressive unreversable code transformations, which eventually leads to code size increase that is also unwanted. In addition, there remains the problem of using a number of optimizations that are useful for rare contexts. To reach the objectives, namely increasing performance, decreasing compilation time and code size, it is reasonable to choose an appropriate strategy on an early compilation stage according to some procedure specific characteristics. This paper discusses the procedures classification problems for this task and suggests several possible solutions.
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