Centre for Molecular Medicine and Therapeutics, The University of British Columbia, Vancouver, British Columbia, Canada.
British Columbia Children's Hospital Research Institute, Vancouver, British Columbia, Canada.
Brief Bioinform. 2022 May 13;23(3). doi: 10.1093/bib/bbac090.
Survival analysis is a technique for identifying prognostic biomarkers and genetic vulnerabilities in cancer studies. Large-scale consortium-based projects have profiled >11 000 adult and >4000 pediatric tumor cases with clinical outcomes and multiomics approaches. This provides a resource for investigating molecular-level cancer etiologies using clinical correlations. Although cancers often arise from multiple genetic vulnerabilities and have deregulated gene sets (GSs), existing survival analysis protocols can report only on individual genes. Additionally, there is no systematic method to connect clinical outcomes with experimental (cell line) data. To address these gaps, we developed cSurvival (https://tau.cmmt.ubc.ca/cSurvival). cSurvival provides a user-adjustable analytical pipeline with a curated, integrated database and offers three main advances: (i) joint analysis with two genomic predictors to identify interacting biomarkers, including new algorithms to identify optimal cutoffs for two continuous predictors; (ii) survival analysis not only at the gene, but also the GS level; and (iii) integration of clinical and experimental cell line studies to generate synergistic biological insights. To demonstrate these advances, we report three case studies. We confirmed findings of autophagy-dependent survival in colorectal cancers and of synergistic negative effects between high expression of SLC7A11 and SLC2A1 on outcomes in several cancers. We further used cSurvival to identify high expression of the Nrf2-antioxidant response element pathway as a main indicator for lung cancer prognosis and for cellular resistance to oxidative stress-inducing drugs. Altogether, these analyses demonstrate cSurvival's ability to support biomarker prognosis and interaction analysis via gene- and GS-level approaches and to integrate clinical and experimental biomedical studies.
生存分析是一种在癌症研究中识别预后生物标志物和遗传脆弱性的技术。大型基于联盟的项目已经对超过 11000 例成人和超过 4000 例儿科肿瘤病例进行了临床结局和多组学方法的分析。这为使用临床相关性研究分子水平的癌症病因提供了资源。尽管癌症通常源于多种遗传脆弱性和失调的基因集（GS），但现有的生存分析协议只能报告单个基因。此外，没有系统的方法将临床结果与实验（细胞系）数据联系起来。为了解决这些差距，我们开发了 cSurvival（https://tau.cmmt.ubc.ca/cSurvival）。cSurvival 提供了一个用户可调整的分析管道，带有经过精心整理的集成数据库，并提供了三个主要优势：（i）联合分析两个基因组预测因子以识别相互作用的生物标志物，包括用于识别两个连续预测因子最佳截止值的新算法；（ii）不仅在基因水平，而且在 GS 水平进行生存分析；和（iii）整合临床和实验细胞系研究以产生协同的生物学见解。为了展示这些优势，我们报告了三个案例研究。我们证实了自噬依赖性生存在结直肠癌中的发现，以及 SLC7A11 和 SLC2A1 高表达对几种癌症结局的协同负面影响。我们进一步使用 cSurvival 来识别 Nrf2-抗氧化反应元件通路的高表达作为肺癌预后和细胞对氧化应激诱导药物的抗性的主要指标。总之，这些分析表明 cSurvival 能够通过基因和 GS 水平的方法支持生物标志物预后和相互作用分析，并整合临床和实验生物医学研究。
