cSurvival: a web resource for biomarker interactions in cancer outcomes

Survival analysis is a technique to identify prognostic biomarkers and genetic vulnerabilities in cancer studies. Large-scale consortium-based projects have profiled >11,000 adult and >4,000 paediatric tumor cases with clinical outcomes and multi-omics 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: (a) joint analysis with two genomic predictors to identify interacting biomarkers, including new algorithms to identify optimal cutoffs for two continuous predictors; (b) survival analysis not only at the gene, but also the GS level; and (c) 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. Together, these analyses demonstrate cSurvivals ability to support biomarker prognosis and interaction analysis via gene- and GS-level approaches and to integrate clinical and experimental biomedical studies. Key pointsO_LIWe developed cSurvival, an advanced framework using clinical correlations to study biomarker interactions in cancers, with source code and curated datasets freely available for all C_LIO_LIcSurvival includes new algorithms to identify optimal cutoffs for two continuous predictors to stratify patients into risk groups, enabling for the first time joint analysis with two genomic predictors; C_LIO_LIcSurvival allows survival analysis at the gene set (GS) level with comprehensive and up-to-date GS libraries C_LIO_LIThe cSurvival pipeline integrates clinical outcomes and experimental cancer cell line data to generate synergistic biological insights and to mine for appropriate preclinical cell line tools C_LIO_LIcSurvival is built on a manually curated cancer outcomes database C_LI