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

Ming Yi,Frank Mccormick,Robert M Stephens,Dwight V Nissley

doi:10.1038/s41598-020-66986-8

Abstract

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.

Highlights

A decade ago, efforts were made to investigate the oncogene “addiction” phenomenon, whereby tumors require the sustained expression and activity of a single aberrantly activated oncogene[6]
Results single sample gene set enrichment analysis (ssGSEA) scores derived using the EMT signature are highly correlated with experimentally measured Ras-dependency indexes (RDIs) regardless of the data source and technology platform
The derived ssGSEA scores, from the EMT signature, showed excellent correlation with experimentally measured RDIs compared to other signatures that we evaluated

Summary

Introduction

Efforts were made to investigate the oncogene “addiction” phenomenon, whereby tumors require the sustained expression and activity of a single aberrantly activated oncogene[6]. The derived ssGSEA RDI scores, especially those derived using the EMT signature[7], show excellent correlation with experimentally measured RDIs independent of the technology platform (RNAseq and microarray) These ssGSEA RDI scores showed a close relationship with our previous in-house siREN study that examined oncogene dependency[14] and high-throughput RNAi (RNA interference) and CRISPR (clustered regularly interspaced short palindromic repeats) screening studies from the Broad Institute that defined a cancer dependency map and identified cancer-type-specific vulnerabilities on genes in the entire genome, respectively[15,16]. The ssGSEA scores of RAS dependency-related signature and Ras pathway genes showed a significant association with cancer patients’ survival outcome by our in-house survival analysis method[18] Together, these observations indicate that computationally derived ssGSEA scores faithfully represent the levels of Ras dependency of cancer cell lines and cancer patient samples, directly providing insights for cancer research and potential clinical applications. This is the first report of a computational method that uses genome-wide gene expression profiling to represent RDIs, and we assert that these findings constitute an opportunity to revitalize the RAS dependency discussion

Methods

Results

Conclusion