Hepburn Anastasia C, Lazzarini Nicola, Veeratterapillay Rajan, Wilson Laura, Bacardit Jaume, Heer Rakesh
Translational and Clinical Research Institute, Newcastle University Centre for Cancer, Newcastle University, Newcastle upon Tyne NE2 4HH, UK.
ICOS Research Group, School of Computing, Newcastle University, Newcastle upon Tyne NE4 5TG, UK.
Cancers (Basel). 2021 Aug 2;13(15):3903. doi: 10.3390/cancers13153903.
Cisplatin-based neoadjuvant chemotherapy (NAC) is recommended prior to radical cystectomy for muscle-invasive bladder cancer (MIBC) patients. Despite a 5-10% survival benefit, some patients do not respond and experience substantial toxicity and delay in surgery. To date, there are no clinically approved biomarkers predictive of response to NAC and their identification is urgently required for more precise delivery of care. To address this issue, a multi-methods analysis approach of machine learning and differential gene expression analysis was undertaken on a cohort of 30 MIBC cases highly selected for an exquisitely strong response to NAC or marked resistance and/or progression (discovery cohort). RGIFE (ranked guided iterative feature elimination) machine learning algorithm, previously demonstrated to have the ability to select biomarkers with high predictive power, identified a 9-gene signature (, , , , , , , , ) able to select responders from non-responders with 100% predictive accuracy. This novel signature correlated with overall survival in meta-analysis performed using published NAC treated-MIBC microarray data (validation cohort 1, = 26, Log rank test, = 0.02). Corroboration with differential gene expression analysis revealed cyclic nucleotide-gated channel, , as the top ranked upregulated gene in non-responders to NAC. A higher CNGB1 immunostaining score was seen in non-responders in tissue microarray analysis of the discovery cohort ( = 30, = 0.02). Kaplan-Meier analysis of a further cohort of MIBC patients (validation cohort 2, = 99) demonstrated that a high level of CNGB1 expression associated with shorter cancer specific survival ( < 0.001). Finally, in vitro studies showed siRNA-mediated CNGB1 knockdown enhanced cisplatin sensitivity of MIBC cell lines, J82 and 253JB-V. Overall, these data reveal a novel signature gene set and as a simpler proxy as a promising biomarker to predict chemoresponsiveness of MIBC patients.
对于肌层浸润性膀胱癌（MIBC）患者，推荐在根治性膀胱切除术之前进行基于顺铂的新辅助化疗（NAC）。尽管有5%-10%的生存获益，但一些患者无反应，并经历严重毒性和手术延迟。迄今为止，尚无临床批准的预测NAC反应的生物标志物，迫切需要识别这些标志物以更精准地提供治疗。为解决这一问题，对一组30例MIBC病例采用机器学习和差异基因表达分析的多方法分析方法，这些病例是因对NAC有强烈反应或显著耐药和/或进展而被高度选择的（发现队列）。RGIFE（排序引导迭代特征消除）机器学习算法先前已证明有能力选择具有高预测能力的生物标志物，该算法识别出一个9基因特征（，，，，，，，，），能够以100%的预测准确性从无反应者中筛选出反应者。在使用已发表的NAC治疗的MIBC微阵列数据进行的荟萃分析中（验证队列1，=26，对数秩检验，=0.02），这一新型特征与总生存相关。与差异基因表达分析的相互印证显示，环核苷酸门控通道，，是NAC无反应者中上调排名最高的基因。在发现队列的组织微阵列分析中，无反应者的CNGB1免疫染色评分更高（=30，=0.02）。对另一组MIBC患者（验证队列2，=99）的Kaplan-Meier分析表明，高水平的CNGB1表达与较短的癌症特异性生存相关（<0.001）。最后，体外研究表明，siRNA介导的CNGB1敲低增强了MIBC细胞系J82和253JB-V对顺铂的敏感性。总体而言，这些数据揭示了一个新型特征基因集，并且作为一个更简单的替代指标，是预测MIBC患者化疗反应性的有前景的生物标志物。
