ssGSEA score-based Ras dependency indexes derived from gene expression data reveal potential Ras addiction mechanisms with possible clinical implications.

For nearly a decade, the difficulties associated with both the determination and reproducibility of Ras-dependency indexes (RDIs) have limited their application and further delineation of the biology underlying Ras dependency. In this report, we describe the application of a computational single sample gene set enrichment analysis (ssGSEA) method to derive RDIs with gene expression data. The computationally derived RDIs across the Cancer Cell Line Encyclopedia (CCLE) cell lines show excellent agreement with the experimentally derived values and high correlation with a previous in-house siRNA effector node (siREN) study and external studies. Using EMT signature-derived RDIs and data from cell lines representing the extremes in RAS dependency, we identified enriched pathways distinguishing these classes, including the Fas signaling pathway and a putative Ras-independent pathway first identified in NK cells. Importantly, extension of the method to patient samples from The Cancer Genome Atlas (TCGA) showed the same consensus differential expression patterns for these two pathways across multiple tissue types. Last, the computational RDIs displayed a significant association with TCGA cancer patients' survival outcomes. Together, these lines of evidence confirm that our computationally derived RDIs faithfully represent a measure of Ras dependency in both cancer cell lines and patient samples. The application of such computational RDIs can provide insights into Ras biology and potential clinical applications.