Inferring cancer dependencies on metabolic genes from large-scale genetic screens

Abstract

Background Cancer cells reprogram their metabolism to survive and propagate. Thus, targeting metabolic rewiring in tumors is a promising therapeutic strategy. Genome-wide RNAi and CRISPR screens are powerful tools for identifying genes essential for cancer cell proliferation and survival. Integrating loss-of-function genetic screens with genomics and transcriptomics datasets reveals molecular mechanisms that underlie cancer cell dependence on specific genes; though explaining cell line-specific essentiality of metabolic genes was recently shown to be especially challenging. Results We find that variability in tissue culture medium between cell lines in a genetic screen is a major confounding factor affecting cell line-specific essentiality of metabolic genes—while, quite surprisingly, not being previously accounted for. Additionally, we find that altered expression level of a metabolic gene in a certain cell line is less indicative of its essentiality than for other genes. However, cell line-specific essentiality of metabolic genes is significantly correlated with changes in the expression of neighboring enzymes in the metabolic network. Utilizing a machine learning method that accounts for tissue culture media and functional association between neighboring enzymes, we generated predictive models for cancer cell line-specific dependence on 162 metabolic genes (representing a ~ 2.2-fold increase compared to previous studies). The generated predictive models reveal numerous novel associations between molecular features and cell line-specific dependency on metabolic genes. Specifically, we demonstrate how cancer cell dependence on one-carbon metabolic enzymes is explained based on cancer lineage, oncogenic mutations, and RNA expression of neighboring enzymes. Conclusions Considering culture media as well as accounting for molecular features of functionally related metabolic enzymes in a metabolic network significantly improves our understanding of cancer cell line-specific dependence on metabolic genes. We expect our approach and predictive models of metabolic gene essentiality to be a useful tool for investigating metabolic abnormalities in cancer. Electronic supplementary material The online version of this article (10.1186/s12915-019-0654-4) contains supplementary material, which is available to authorized users.

The Metabolic Heterogeneity and Flexibility of Cancer Stem Cells

“…Within such a complicated metabolic system, it is very difficult to identify potential targets in CSCs. However, recent studies have attempted to identify such targets by systematically linking the metabolic profiles of tumors with genome-wide transcriptome and proteomics analysis [ 142 , 199 , 200 , 201 ]. In the near future, by accumulating such research data, it is expected that the identification of the metabolic vulnerabilities of each tumor will be possible, which will enable the development of more efficient diagnostic techniques and therapies.…”

“…When cancer cells are taken out from a complicated microenvironment, it is difficult to think that those cancer cells can maintain the metabolic state originally exhibited in the tumor for a long period of time. In addition, conventional cell culture systems use culture media that contain nutrients, such as glucose and glutamine, at levels that exceed those found in physiological conditions, so there is a risk that cancer cells may shift to a metabolic system that depends on those nutrients [ 140 , 142 ]. Therefore, to more accurately elucidate the metabolism of CSCs, it may be necessary to perform quick analyses in fresh cells immediately after they are taken from the body, or to culture CSCs under conditions close to the original environment.…”

The landscape of metabolic pathway dependencies in cancer cell lines

“…To focus on metabolism, we queried 69 metabolic pathways from the Kyoto Encyclopedia of Genes and Genomes (KEGG) (Kanehisa et al, 2014). Because metabolism is influenced by culture type (Li et al, 2019) and culture medium (Lagziel et al, 2019), we first divided cancer cell lines by culture type (e.g., adherent v. suspension culture) and media (e.g., RPMI v. DMEM) ( Fig. 1A).…”

“…Recent developments in large scale CRISPR-based genetic (Meyers et al, 2017;Tsherniak et al, 2017) and pharmacologic screening (Corsello et al, 2020) along with large panels of comprehensively characterized cancer cell lines (Ghandi et al, 2019) have proved powerful tools for identification of genes essential for cancer cell survival (Lagziel et al, 2019), elucidation of drug mechanism-of-action (Gonçalves et al, 2020;Lin et al, 2019;Meyers et al, 2017), and discovery of novel candidate drug targets (Barretina et al, 2012;Garnett et al, 2012).…”

Differential Gene Set Enrichment Analysis: A statistical approach to quantify the relative enrichment of two gene sets

“…Recent developments in gene essentiality screening have enabled the identification of novel gene dependencies in cancer cell lines (22)(23)(24)(25). Because DGSEA can identify the metabolic tradeoff between glycolysis and oxidative phosphorylation, we hypothesized that DGSEA would identify different essential genes than GSEA using either glycolysis or oxidative phosphorylation alone.…”