cSurvival

cSurvival performs survival analysis to identify prognostic biomarkers and genetic vulnerabilities in cancer by enabling joint and gene set–level analyses and integrating clinical and experimental cell line data.

Key Features:

• Joint analysis with two genomic predictors: Enables joint survival analysis of two genomic predictors and identification of interacting biomarkers, including novel algorithms to determine optimal cutoffs for continuous predictors.
• Gene set (GS) level survival analysis: Extends survival analysis to gene sets to assess the collective impact of deregulated pathways on clinical outcomes.
• Integration of clinical and experimental data: Integrates clinical outcomes with experimental cell line studies to link clinical observations and laboratory findings.
• Multiomics profiling from consortium datasets: Profiles multiomics data sourced from large-scale consortium-based projects for comprehensive molecular characterization.
• Detection of biomarker interactions and synergistic effects: Identifies interacting biomarkers and synergistic effects, exemplified by analyses of SLC7A11 and SLC2A1.
• Curated integrated database: Leverages a curated, integrated database to support analyses across datasets.
• Adjustable analytical pipeline: Provides an adjustable analytical pipeline to customize parameters and analysis strategies.
• Survival analysis at gene and gene-set levels: Performs survival analyses at both individual gene and gene set levels.

Scientific Applications:

• Investigating molecular etiologies: Correlates molecular features with clinical outcomes to investigate cancer etiologies at the molecular level.
• Exploring genetic vulnerabilities: Identifies genetic vulnerabilities and deregulated gene sets that contribute to cancer development and progression.
• Analyzing biomarker synergy: Generates insights into synergistic biomarker effects, including the negative prognostic impact of high SLC7A11 and SLC2A1 expression across cancers.
• Validating mechanistic hypotheses in colorectal cancer: Supports confirmation of autophagy-dependent survival mechanisms in colorectal cancers.
• Prognostic pathway discovery in lung cancer: Enables discovery of pathways such as the Nrf2–antioxidant response element pathway as indicators of lung cancer prognosis and resistance to oxidative stress–inducing drugs.

Methodology:

Implements joint survival analyses and gene-set level survival analysis, uses algorithms to determine optimal cutoffs for continuous predictors, integrates multiomics consortium datasets with experimental cell line data via a curated integrated database, and supports an adjustable analytical pipeline.