Systematic RNA interference reveals that oncogenic KRAS-driven cancers require TBK1

Abstract

The proto-oncogene KRAS is mutated in a wide array of human cancers, most of which are aggressive and respond poorly to standard therapies. Although the identification of specific oncogenes has led to the development of clinically effective, molecularly targeted therapies in some cases, KRAS has remained refractory to this approach. A complementary strategy for targeting KRAS is to identify gene products that, when inhibited, result in cell death only in the presence of an oncogenic allele1,2. Here we have used systematic RNA interference (RNAi) to detect synthetic lethal partners of oncogenic KRAS and found that the non-canonical IκB kinase, TBK1, was selectively essential in cells that harbor mutant KRAS. Suppression of TBK1 induced apoptosis specifically in human cancer cell lines that depend on oncogenic KRAS expression. In these cells, TBK1 activated NF-κB anti-apoptotic signals involving cREL and BCL-XL that were essential for survival, providing mechanistic insights into this synthetic lethal interaction. These observations identify TBK1 and NF-κB signaling as essential in KRAS mutant tumors and establish a general approach for the rational identification of co-dependent pathways in cancer.

The SCN9A channel and plasma membrane depolarization promote cellular senescence through Rb pathway

“…An enrichment score was also computed in unique samples for a given gene set, using the ssGSEA tool (Barbie et al., 2009). We ran ssGSEA from GenePattern (Reich et al., 2006) to compute the enrichment score of a given pathway in each sample.…”

Hyperactive gp130/STAT3‐driven gastric tumourigenesis promotes submucosal tertiary lymphoid structure development

“…We used reads per kilobase of exon per million mapped reads (RPKM)32 to represent expression levels. Single‐sample Gene Set Enrichment Analysis (ssGSEA)33 was used to investigate a three‐gene signature ( CXCL13 , CCL19 , CCL21 ) linked with TLS development in gp130 F/F mice. This data was also validated in an independent cohort of intestinal‐type GC patients from the Asian Cancer Research Group (ACRG; GEO accession number GSE62254) 34…”

Immunological and classical subtypes of oral premalignant lesions

“…According to an external cross validation, the best gene classifier was selected and applied to the testing subset. In addition, based on differential gene expression between OPL subtypes, we computed a gene expression score for the OPL classification, using the single sample gene set enrichment analysis (ssGSEA) 24,25 from Gene Pattern. 26 We compared these methods of classification in terms of the accuracy percentage (= [number of samples misclassified/number of samples well-classified]*100).…”

“…Pathway-specific pathways were downloaded from the Molecular Signature Database (MSigDB database Molecular Signatures Database v5.2, 2016) and included a total of 1,329 canonical pathways. The ssGSEA was used to compute an enrichment score for each pathway in each sample 24,25 which was run from GenePattern. 26 Unlike GSEA which analyzes differential pathways between two phenotypical groups, the ssGSEA tool allows for computing an enrichment score (ES) of a given gene set in each sample.…”

“…The gene expression values for a given sample are rank-normalized, and an ES is produced using the empirical cumulative distribution functions of the genes in the gene set and the remaining genes. 24,25 When UP and DN versions of a gene set are available, a combined score was computed. Default parameters were used (weighting component of 0.75 and minimal gene set size of 10 genes).…”