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Pan-cancer analysis reveals synergistic effects of CDK4/6i and PARPi combination treatment in RB-proficient and RB-deficient breast cancer cells

机译:泛癌分析揭示了CDK4 / 6i和PARPi联合治疗对RB缺陷型和RB缺陷型乳腺癌细胞的协同作用

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摘要

Diagram showing the possible relationships among DNA damage, PARPs, DRPCC, and mutation. In detail, DNA damage and mutations are closely related (Formula 1). As both DRPCC(s) and PARP are involved in DNA repair, it is logical to suppose that cancer cells with decreased activity of DRPCC(s) and/or PARP should be biased toward acquiring more mutations (Formula 2 and 3). Moreover, cancer cells are often reliant on increased activity of PARP and/or DRPCC(s) to survive extra DNA damage from enhanced mutagenic pathway(s) and/or decreased DNA repair pathway(s) (Formula 4 and 5). Considering that PARPs are sensors of DNA damage and broadly involved in multiple DNA repair pathways, we reasoned that the combined inhibition of PARPs and DRPCC(s) should show synergy (Formula 7–9). Schematic showing the pipeline used for the prediction of DRPCC(s) and drug target(s). Summary statistics for the 27 different cancer types in this study. , All mutations ( ), missense mutations ( ), and sense mutations ( ) across 27 cancer types. Each data point represents one tumor sample, and the axis is log transformed for better data visualization. Red, orange, or blue horizontal bars indicate the average mutation load. The -scores for mRNA expression were calculated for each sample by comparing them RNA expression of a gene to the distribution in a reference population that represents typical expression for the gene. The returned value ( -score) indicates the number of standard deviations away from the mean of expression in the reference population. Scatter plots showing the correlation between the ranks of mutation load and gene expression. Each data point represents one tumor sample. Data of TP53, BRCA2, and PAPR2 are from BLCA, LGG, and ACC, respectively. The red dashed line is the best fit for visualization. Summary statistics for positive and negative genes across 27 cancer types. Heatmap depicting the enrichment of positive (left) and negative (right) genes, respectively.
机译:该图显示了DNA损伤,PARP,DRPCC和突变之间的可能关系。详细而言,DNA损伤和突变密切相关(公式1)。由于DRPCC和PARP都参与DNA修复,因此可以合理地假设具有DRPCC和/或PARP活性降低的癌细胞应偏向于获得更多的突变(公式2和3)。此外,癌细胞通常依赖于增强的诱变途径和/或减少的DNA修复途径而使PARP和/或DRPCC的活性增加以抵抗额外的DNA损伤(式4和5)。考虑到PARPs是DNA损伤的传感器并且广泛参与多种DNA修复途径,我们认为PARPs和DRPCC的联合抑制作用应显示协同作用(公式7–9)。示意图显示用于预测DRPCC和药物靶标的管线。这项研究中27种不同癌症类型的摘要统计。 ,27种癌症类型中的所有突变(),错义突变()和有义突变()。每个数据点代表一个肿瘤样本,并且对数轴进行了对数转换,以实现更好的数据可视化。红色,橙色或蓝色水平条表示平均突变负荷。通过将每个样品的基因RNA表达与代表该基因典型表达的参考群体中的分布进行比较,计算出每个样品的mRNA表达-得分。返回值(-score)表示远离参考总体中表达平均值的标准偏差数。散点图显示突变负荷等级与基因表达之间的相关性。每个数据点代表一个肿瘤样品。 TP53,BRCA2和PAPR2的数据分别来自BLCA,LGG和ACC。红色虚线最适合可视化。 27种癌症类型中阳性和阴性基因的摘要统计。热图分别描述了正(左)和负(右)基因的富集。

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