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Synthetic lethal combinations of low-toxicity drugs for breast cancer identified in silico by genetic screens in yeast

机译:通过酵母基因筛查在计算机模拟中鉴定出的低毒性乳腺癌合成药物致命组合

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摘要

In recent years, the concept of synthetic lethality, describing a cellular state where loss of two genes leads to a non-viable phenotype while loss of one gene can be compensated, has emerged as a novel strategy for cancer therapy. Various compounds targeting synthetic lethal pathways are either under clinical investigation or are already routinely used in multiple cancer entities such as breast cancer. Most of them target the well-described synthetic lethal interplay between PARP1 and BRCA1/2. In our study, we investigated, using an in silico methodological approach, clinically utilized drug combinations for breast cancer treatment, by correlating their known molecular targets with known homologous interaction partners that cause synthetic lethality in yeast. Further, by creating a machine-learning algorithm, we were able to suggest novel synthetic lethal drug combinations of low-toxicity drugs in breast cancer and showed their negative effects on cancer cell viability in vitro. Our findings foster the understanding of evolutionarily conserved synthetic lethality in breast cancer cells and might lead to new drug combinations with favorable toxicity profile in this entity.
机译:近年来,合成致死性的概念已成为一种新型的癌症治疗策略,这种概念描述了一种细胞状态,其中两个基因的丢失导致一个无法生存的表型,而一个基因的丢失可以得到补偿。靶向合成致死途径的各种化合物正在临床研究中,或已在多种癌症实体(例如乳腺癌)中常规使用。它们大多数针对的是PARP1和BRCA1 / 2之间的合成杀伤相互作用。在我们的研究中,我们通过计算机方法学的方法,通过将其已知的分子靶标与导致酵母菌致死的已知同源相互作用伙伴相关联,研究了临床上用于乳腺癌治疗的药物组合。此外,通过创建机器学习算法,我们能够提出乳腺癌中低毒性药物的新型合成致死药物组合,并显示它们对体外癌细胞生存能力的负面影响。我们的发现促进了对乳腺癌细胞中进化保守的合成致死性的理解,并可能导致在此实体中具有有利毒性特征的新药组合。

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