KRAS is a regulator of the nutrient stress response in non-small-cell lung cancer (NSCLC). Induction of the ATF4 pathway during nutrient depletion requires AKT and NRF2 downstream of KRAS. The tumor suppressor KEAP1 strongly influences the outcome of activation of this pathway during nutrient stress; loss of KEAP1 in KRAS mutant cells leads to apoptosis. Through ATF4 regulation, KRAS alters amino acid uptake and asparagine biosynthesis. The ATF4 target asparagine synthetase (ASNS) contributes to apoptotic suppression, protein biosynthesis, and mTORC1 activation. Inhibition of AKT suppressed ASNS expression and, combined with depletion of extracellular asparagine, decreased tumor growth. Therefore, KRAS is important for the cellular response to nutrient stress, and ASNS represents a promising therapeutic target in KRAS mutant NSCLC.

KRAS mutated tumours represent a large fraction of human cancers, but the vast majority remains refractory to current clinical therapies. Thus, a deeper understanding of the molecular mechanisms triggered by KRAS oncogene may yield alternative therapeutic strategies. Here we report the identification of a common transcriptional signature across mutant KRAS cancers of distinct tissue origin that includes the transcription factor FOSL1. High FOSL1 expression identifies mutant KRAS lung and pancreatic cancer patients with the worst survival outcome. Furthermore, FOSL1 genetic inhibition is detrimental to both KRAS-driven tumour types. Mechanistically, FOSL1 links the KRAS oncogene to components of the mitotic machinery, a pathway previously postulated to function orthogonally to oncogenic KRAS. FOSL1 targets include AURKA, whose inhibition impairs viability of mutant KRAS cells. Lastly, combination of AURKA and MEK inhibitors induces a deleterious effect on mutant KRAS cells. Our findings unveil KRAS downstream effectors that provide opportunities to treat KRAS-driven cancers.

Activating mutations in RAS GTPases drive many cancers, but limited understanding of less-studied RAS interactors, and of the specifi c roles of different RAS interactor paralogs, continues to limit target discovery. We developed a multistage discovery and screening process to systematically identify genes conferring RAS-related susceptibilities in lung adenocarcinoma. Using affi nity purifi cation mass spectrometry, we generated a protein-protein interaction map of RAS interactors and pathway components containing hundreds of interactions. From this network, we constructed a CRISPR dual knockout library targeting 119 RAS-related genes that we screened for KRAS -dependent genetic interactions (GI). This approach identifi ed new RAS effectors, including the adhesion controller RADIL and the endocytosis regulator RIN1, and >250 synthetic lethal GIs, including a potent KRAS -dependent interaction between RAP1GDS1 and RHOA. Many GIs link specifi c paralogs within and between gene families. These fi ndings illustrate the power of multiomic approaches to uncover synthetic lethal combinations specifi c for hitherto untreatable cancer genotypes. SIGNIFICANCE:We establish a deep network of protein-protein and genetic interactions in the RAS pathway. Many interactions validated here demonstrate important specifi cities and redundancies among paralogous RAS regulators and effectors. By comparing synthetic lethal interactions across KRAS -dependent and KRAS -independent cell lines, we identify several new combination therapy targets for RAS-driven cancers.

In this issue of Molecular Cell, Reid et al. (2013) show that glutamine withdrawal causes PP2A-mediated activation of p53 through its regulator EDD, linking levels of a critical metabolite to an important regulator of cell survival and proliferation.

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<p>Results from the "GI-Directed" sublibrary screen. Different metrics are reported on each of the three tabs of the spreadsheet as indicated. GI-T-Score : GIT Score; MWU Adj Pval: FDR of Mann-Whitney U Test p-value after Benjamini-Hochberg correction; Double KO Phenotype : phenotype Z-score (pZ) of double knockout pair. Columns correspond to individual replicates in the indicated cell line.</p>

<p>Table of sgRNA sequences.</p>

<p>This table lists genes whose protein products were identified by AP/MS experiments in HEK293 cells or A549 cells on the indicated sheets. The columns are as follows. Bait: bait protein (gene-wise); Prey, prey protein (gene-wise); Background Values : NSAFs for other experiments, used to infer the background distributions as described above; Qualifier: mutation state of bait protein; FDR: false discovery rate; NSAF : the log10 transform of the Normalized Spectral Abundance Factor (see Methods), with greater numbers indicating stronger signals.</p>