Dong Siyao, Song Chengyan, Qi Baocui, Jiang Xiaochen, Liu Lu, Xu Yan
College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.
College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.
Genomics. 2021 May;113(3):1026-1036. doi: 10.1016/j.ygeno.2021.02.015. Epub 2021 Feb 26.
The existence and emergence of drug resistance in tumor cells is the main burden of cancer treatment. Most cancer drug resistance analyses are based entirely on cell line data and ignore the discordance between human tumors and cell lines, leading to biased preclinical model transformation. Based on cancer tissue data in TCGA and cancer cell line data in CCLE, this study identified and excluded non-preserved module (NP module) between cancer tissue and cell lines. We used strongly preserved module (SP module) for clinically relevant drug resistance analysis and identified 2068 "cancer-drug-module" pairs of 7 cancer types and 212 drugs based on data in GDSC. Furthermore, we identified potentially ineffective combination therapy (PICT) from multiple perspectives. Finally, we found 1608 sets of predictors that can predict drug response. These results provide insights and clues for the clinical selection of effective chemotherapy drugs to overcome cancer resistance in a new perspective.
肿瘤细胞中耐药性的存在和出现是癌症治疗的主要负担。大多数癌症耐药性分析完全基于细胞系数据，而忽略了人类肿瘤与细胞系之间的不一致性，从而导致临床前模型转化存在偏差。基于TCGA中的癌症组织数据和CCLE中的癌细胞系数据，本研究识别并排除了癌症组织与细胞系之间的非保守模块（NP模块）。我们使用强保守模块（SP模块）进行临床相关的耐药性分析，并基于GDSC中的数据确定了7种癌症类型和212种药物的2068个“癌症-药物-模块”对。此外，我们从多个角度识别了潜在无效的联合治疗（PICT）。最后，我们发现了1608组可预测药物反应的预测因子。这些结果从新的角度为临床选择有效的化疗药物以克服癌症耐药性提供了见解和线索。
