1Department of Biochemistry, Schulich School of Medicine and Dentistry, Western University, London, ON N6A 2C1 Canada.
2Department of Computer Science, Faculty of Science, Western University, London, ON N6A 2C1 Canada.
Signal Transduct Target Ther. 2019 Jan 11;4:1. doi: 10.1038/s41392-018-0034-5. eCollection 2019.
The selection of effective genes that accurately predict chemotherapy responses might improve cancer outcomes. We compare optimized gene signatures for cisplatin, carboplatin, and oxaliplatin responses in the same cell lines and validate each signature using data from patients with cancer. Supervised support vector machine learning is used to derive gene sets whose expression is related to the cell line GI values by backwards feature selection with cross-validation. Specific genes and functional pathways distinguishing sensitive from resistant cell lines are identified by contrasting signatures obtained at extreme and median GI thresholds. Ensembles of gene signatures at different thresholds are combined to reduce the dependence on specific GI values for predicting drug responses. The most accurate gene signatures for each platin are: cisplatin: , , , , , , , , , , , , , , and ; carboplatin: , , , , , , , , , , , , , , and and oxaliplatin: , , , , , , , , , , and . Data from The Cancer Genome Atlas (TCGA) patients with bladder, ovarian, and colorectal cancer were used to test the cisplatin, carboplatin, and oxaliplatin signatures, resulting in 71.0%, 60.2%, and 54.5% accuracies in predicting disease recurrence and 59%, 61%, and 72% accuracies in predicting remission, respectively. One cisplatin signature predicted 100% of recurrence in non-smoking patients with bladder cancer (57% disease-free; = 19), and 79% recurrence in smokers (62% disease-free; = 35). This approach should be adaptable to other studies of chemotherapy responses, regardless of the drug or cancer types.
选择能够准确预测化疗反应的有效基因可能会改善癌症的预后。我们比较了同一细胞系中顺铂、卡铂和奥沙利铂反应的优化基因特征，并使用癌症患者的数据验证了每个特征。使用带有交叉验证的反向特征选择的监督支持向量机学习来推导基因集，其表达通过 GI 值与细胞系相关联。通过对比在极端和中位数 GI 阈值下获得的特征来识别区分敏感和耐药细胞系的特定基因和功能途径。在不同阈值下的基因特征集的组合用于减少对特定 GI 值预测药物反应的依赖。每种铂类药物最准确的基因特征为：顺铂： ， ， ， ， ， ， ， ， ， ， ， 和 ；卡铂： ， ， ， ， ， ， ， ， ， ， ， 和 ；奥沙利铂： ， ， ， ， ， ， ， ， 和 。来自膀胱癌、卵巢癌和结直肠癌的癌症基因组图谱（TCGA）患者的数据用于测试顺铂、卡铂和奥沙利铂特征，分别导致疾病复发预测的准确率为 71.0%、60.2%和 54.5%，缓解预测的准确率为 59%、61%和 72%。一个顺铂特征预测了膀胱癌非吸烟者中 100%的复发（57%无疾病； = 19），以及吸烟者中 79%的复发（62%无疾病； = 35）。这种方法应该适用于其他化疗反应研究，无论药物或癌症类型如何。
