Sahu Divya, Shi Jeffrey, Segura Rueda Isaac Andres, Chatrath Ajay, Dutta Anindya
Department of Genetics, University of Alabama at Birmingham, Birmingham, AL, 35294, USA.
Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, VA, 22903, USA.
NPJ Precis Oncol. 2024 Oct 2;8(1):219. doi: 10.1038/s41698-024-00714-7.
Gene expression profiles of hundreds of cancer cell-lines and the cell-lines' response to drug treatment were analyzed to identify genes whose expression correlated with drug resistance. In the GDSC dataset of 809 cancer cell lines, expression of 36 genes were associated with drug resistance (increased IC50) to many anti-cancer drugs. This was validated in the CTRP dataset of 860 cell lines. A polygenic score derived from the correlation coefficients of the 36 genes in cancer cell lines, UAB36, predicted resistance of cell lines to Tamoxifen. Although the 36 genes were selected from cell line behaviors, UAB36 successfully predicted survival of breast cancer patients in three different cohorts of patients treated with Tamoxifen. UAB36 outperforms two existing predictive gene signatures and is a predictor of outcome of breast cancer patients independent of the known clinical co-variates that affect outcome. This approach should provide promising polygenic biomarkers for resistance in many cancer types against specific drugs.
分析了数百种癌细胞系的基因表达谱以及这些细胞系对药物治疗的反应，以确定其表达与耐药性相关的基因。在包含809个癌细胞系的GDSC数据集中，36个基因的表达与对多种抗癌药物的耐药性（IC50升高）相关。这在包含860个细胞系的CTRP数据集中得到了验证。从癌细胞系中36个基因的相关系数得出的多基因评分UAB36，可预测细胞系对他莫昔芬的耐药性。尽管这36个基因是从细胞系行为中挑选出来的，但UAB36成功预测了在三个接受他莫昔芬治疗的不同患者队列中乳腺癌患者的生存期。UAB36优于两种现有的预测基因特征，并且是独立于影响预后的已知临床协变量的乳腺癌患者预后预测指标。这种方法应该能为许多癌症类型针对特定药物的耐药性提供有前景的多基因生物标志物。
