Division of Hematology and Oncology, School of Medicine, Indiana University, Indianapolis, Indiana, United States of America.
Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, Ohio, United States of America.
PLoS Comput Biol. 2022 Aug 19;18(8):e1009421. doi: 10.1371/journal.pcbi.1009421. eCollection 2022 Aug.
Cancer is a complex disease with usually multiple disease mechanisms. Target combination is a better strategy than a single target in developing cancer therapies. However, target combinations are generally more difficult to be predicted. Current CRISPR-cas9 technology enables genome-wide screening for potential targets, but only a handful of genes have been screend as target combinations. Thus, an effective computational approach for selecting candidate target combinations is highly desirable. Selected target combinations also need to be translational between cell lines and cancer patients. We have therefore developed DSCN (double-target selection guided by CRISPR screening and network), a method that matches expression levels in patients and gene essentialities in cell lines through spectral-clustered protein-protein interaction (PPI) network. In DSCN, a sub-sampling approach is developed to model first-target knockdown and its impact on the PPI network, and it also facilitates the selection of a second target. Our analysis first demonstrated a high correlation of the DSCN sub-sampling-based gene knockdown model and its predicted differential gene expressions using observed gene expression in 22 pancreatic cell lines before and after MAP2K1 and MAP2K2 inhibition (R2 = 0.75). In DSCN algorithm, various scoring schemes were evaluated. The 'diffusion-path' method showed the most significant statistical power of differentialting known synthetic lethal (SL) versus non-SL gene pairs (P = 0.001) in pancreatic cancer. The superior performance of DSCN over existing network-based algorithms, such as OptiCon and VIPER, in the selection of target combinations is attributable to its ability to calculate combinations for any gene pairs, whereas other approaches focus on the combinations among optimized regulators in the network. DSCN's computational speed is also at least ten times fast than that of other methods. Finally, in applying DSCN to predict target combinations and drug combinations for individual samples (DSCNi), DSCNi showed high correlation between target combinations predicted and real synergistic combinations (P = 1e-5) in pancreatic cell lines. In summary, DSCN is a highly effective computational method for the selection of target combinations.
癌症是一种复杂的疾病，通常有多种疾病机制。与单一靶点相比，靶点组合是开发癌症疗法的更好策略。然而，靶点组合通常更难预测。目前的 CRISPR-cas9 技术可用于对潜在靶点进行全基因组筛选，但只有少数基因被筛选为靶点组合。因此，非常需要一种有效的计算方法来选择候选靶点组合。选定的靶点组合也需要在细胞系和癌症患者之间具有转化性。因此，我们开发了 DSCN（基于 CRISPR 筛选和网络的双靶点选择），该方法通过谱聚类蛋白-蛋白相互作用（PPI）网络将患者的表达水平与细胞系中的基因必需性相匹配。在 DSCN 中，开发了一种抽样方法来模拟第一靶点敲低及其对 PPI 网络的影响，同时也便于选择第二个靶点。我们的分析首先证明了基于 DSCN 抽样的基因敲低模型及其预测的 22 个胰腺细胞系在 MAP2K1 和 MAP2K2 抑制前后的差异基因表达之间具有高度相关性（R2 = 0.75）。在 DSCN 算法中，评估了各种评分方案。在胰腺癌中，'扩散路径'方法在区分已知的合成致死（SL）与非-SL 基因对方面显示出最显著的统计学功效（P = 0.001）。DSCN 在选择靶点组合方面优于现有的基于网络的算法，如 OptiCon 和 VIPER，这归因于它能够计算任何基因对的组合，而其他方法则侧重于网络中优化调节剂的组合。DSCN 的计算速度也比其他方法快至少十倍。最后，在将 DSCN 应用于预测个体样本的靶点组合和药物组合（DSCNi）时，DSCNi 显示了在胰腺细胞系中预测的靶点组合与实际协同组合之间的高度相关性（P = 1e-5）。总之，DSCN 是一种非常有效的靶点组合选择计算方法。
