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

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.

Tumor Reliance on Cytosolic versus Mitochondrial One-Carbon Flux Depends on Folate Availability.

Folate metabolism supplies one-carbon (1C) units for biosynthesis and methylation and has long been a target for cancer chemotherapy. Mitochondrial serine catabolism is considered the sole contributor of folate-mediated 1C units in proliferating cancer cells. Here, we show that under physiological folate levels in the cell environment, cytosolic serine-hydroxymethyltransferase (SHMT1) is the predominant source of 1C units in a variety of cancers, while mitochondrial 1C flux is overly repressed. Tumor-specific reliance on cytosolic 1C flux is associated with poor capacity to retain intracellular folates, which is determined by the expression of SLC19A1, which encodes the reduced folate carrier (RFC). We show that silencing SHMT1 in cells with low RFC expression impairs pyrimidine biosynthesis and tumor growth in vivo. Overall, our findings reveal major diversity in cancer cell utilization of the cytosolic versus mitochondrial folate cycle across tumors and SLC19A1 expression as a marker for increased reliance on SHMT1.

Fast and sensitive flow-injection mass spectrometry metabolomics by analyzing sample-specific ion distributions.

Mass spectrometry based metabolomics is a widely used approach in biomedical research. However, current methods coupling mass spectrometry with chromatography are time-consuming and not suitable for high-throughput analysis of thousands of samples. An alternative approach is flow-injection mass spectrometry (FI-MS) in which samples are directly injected to the ionization source. Here, we show that the sensitivity of Orbitrap FI-MS metabolomics methods is limited by ion competition effect. We describe an approach for overcoming this effect by analyzing the distribution of ion m/z values and computationally determining a series of optimal scan ranges. This enables reproducible detection of ~9,000 and ~10,000 m/z features in metabolomics and lipidomics analysis of serum samples, respectively, with a sample scan time of ~15 s and duty time of ~30 s; a ~50% increase versus current spectral-stitching FI-MS. This approach facilitates high-throughput metabolomics for a variety of applications, including biomarker discovery and functional genomics screens.