Metabolic drug survey highlights cancer cell dependencies and vulnerabilities

Interrogation of cellular metabolism with high-throughput screening approaches can unravel contextual biology and identify cancer-specific metabolic vulnerabilities. To systematically study the consequences of distinct metabolic perturbations, we assemble a comprehensive metabolic drug library (CeMM Library of Metabolic Drugs; CLIMET) covering 243 compounds. We, next, characterize it phenotypically in a diverse panel of myeloid leukemia cell lines and primary patient cells. Analysis of the drug response profiles reveals that 77 drugs affect cell viability, with the top effective compounds targeting nucleic acid synthesis, oxidative stress, and the PI3K/mTOR pathway. Clustering of individual drug response profiles stratifies the cell lines into five functional groups, which link to specific molecular and metabolic features. Mechanistic characterization of selective responses to the PI3K inhibitor pictilisib, the fatty acid synthase inhibitor GSK2194069, and the SLC16A1 inhibitor AZD3965, bring forth biomarkers of drug response. Phenotypic screening using CLIMET represents a valuable tool to probe cellular metabolism and identify metabolic dependencies at large.

Metformin treatment response is dependent on glucose growth conditions and metabolic phenotype in colorectal cancer cells

Cancer cells exhibit altered metabolism, a phenomenon described a century ago by Otto Warburg. However, metabolic drug targeting is considered an underutilized and poorly understood area of cancer therapy. Metformin, a metabolic drug commonly used to treat type 2 diabetes, has been associated with lower cancer incidence, although studies are inconclusive concerning effectiveness of the drug in treatment or cancer prevention. The aim of this study was to determine how glucose concentration influences cancer cells’ response to metformin, highlighting why metformin studies are inconsistent. We used two colorectal cancer cell lines with different growth rates and clinically achievable metformin concentrations. We found that fast growing SW948 are more glycolytic in terms of metabolism, while the slower growing SW1116 are reliant on mitochondrial respiration. Both cell lines show inhibitory growth after metformin treatment under physiological glucose conditions, but not in high glucose conditions. Furthermore, SW1116 converges with SW948 at a more glycolytic phenotype after metformin treatment. This metabolic shift is supported by changed GLUT1 expression. Thus, cells having different metabolic phenotypes, show a clear differential response to metformin treatment based on glucose concentration. This demonstrates the importance of growth conditions for experiments or clinical studies involving metabolic drugs such as metformin.

An alternative food source for metabolism and longevity studies in Caenorhabditis elegans

Caenorhabditis elegans is an instrumental research model used to advance our knowledge in areas including development, metabolism, and aging. However, research on metabolism and/or other measures of health/aging are confounded by the nematode’s food source in the lab, live E. coli bacteria. Commonly used treatments, including ultraviolet irradiation and antibiotics, are successful in preventing bacterial replication, but the bacteria can remain metabolically active. The purpose of this study is to develop a metabolically inactive food source for the worms that will allow us to minimize the confounding effects of bacterial metabolism on worm metabolism and aging. Our strategy is to use a paraformaldehyde (PFA) treated E. coli food source and to determine its effects on worm health, metabolism and longevity. We initially determine the lowest possible concentrations of PFA necessary to rapidly and reproducibly kill bacteria. We then measure various aspects of worm behavior, healthspan and longevity, including growth rate, food attraction, brood size, lifespan and metabolic assessments, such as oxygen consumption and metabolomics. Our resulting data show that worms eat and grow well on these bacteria and support the use of 0.5% PFA-killed bacteria as a nematode food source for metabolic, drug, and longevity experiments.

The effect of L-carnitine depletion induced by long-term therapy of mice with meldonium on brain mitochondrial balance

Meldonium is a metabolic drug used for treatment of coronary heart disease. The effect of the drug lies in its ability to inhibit synthesis and transport of L-carnitine. At the same time, a long-term deficiency of L-carnitine can theoretically negatively affect the activity of the transcription factor Nrf2, which is extremely important for maintaining mitochondrial balance in cells. We have shown that meldonium therapy for 3 months at a dose of 100 mg/kg in mice causes a decrease in the expression of the Nrf2 gene in the brain. A decrease in the Nrf2 level causes suppression of mitochondrial biogenesis, which is manifested in a decrease in the level of mtDNA and the level of Cox1 expression. However, no negative effect of meldonium on the bioenergetics parameters of mitochondria was found, as evidenced by the maintenance of a stable mitochondrial potential and the level of production of reactive oxygen species. Jne mohth after the end of the meldonium therapy, expression of the genes responsible for mitochondrial biogenesis and mitophagy (p62, Pink1, Tfam) was observed and the expression level of genes responsible for mitochondrial fusion returned to control values. These changes may be associated with the normalization of the level of L-carnitine in brain cells.

Metabolic Drug Response Phenotyping in Colorectal Cancer Organoids by LC-QTOF-MS

As metabolic rewiring is crucial for cancer cell proliferation, metabolic phenotyping of patient-derived organoids is desirable to identify drug-induced changes and trace metabolic vulnerabilities of tumor subtypes. We established a novel protocol for metabolomic and lipidomic profiling of colorectal cancer organoids by liquid chromatography quadrupole time-of-flight mass spectrometry (LC-QTOF-MS) facing the challenge of capturing metabolic information from a minimal sample amount (<500 cells/injection) in the presence of an extracellular matrix (ECM). The best procedure of the tested protocols included ultrasonic metabolite extraction with acetonitrile/methanol/water (2:2:1, v/v/v) without ECM removal. To eliminate ECM-derived background signals, we implemented a data filtering procedure based on the p-value and fold change cut-offs, which retained features with signal intensities >120% compared to matrix-derived signals present in blank samples. As a proof-of-concept, the method was applied to examine the early metabolic response of colorectal cancer organoids to 5-fluorouracil treatment. Statistical analysis revealed dose-dependent changes in the metabolic profiles of treated organoids including elevated levels of 2′-deoxyuridine, 2′-O-methylcytidine, inosine and 1-methyladenosine and depletion of 2′-deoxyadenosine and specific phospholipids. In accordance with the mechanism of action of 5-fluorouracil, changed metabolites are mainly involved in purine and pyrimidine metabolism. The novel protocol provides a first basis for the assessment of metabolic drug response phenotypes in 3D organoid models.

Metabolic Drug Survey Highlights Cancer Cell Dependencies and Vulnerabilities

Here we assembled a novel metabolic drug library covering 243 compounds allowing to systematically identify metabolic dependencies in high-throughput phenotypic screens. The CeMM Library of Metabolic Drugs (CLIMET) was compiled in a stepwise fashion, starting from 8000 candidate compounds, after a survey of public drug-target databases, and ending with 243 highly-curated compounds including extensive crosschecking for approval status, structural information, compound's potency and selectivity for the intended target, pathway/target redundancy, and commercial availability. To assess the potency of the compounds in CLIMET, we screened the full collection against a panel of 15 diverse myeloid leukemia cell lines. Each compound was tested for its effect on cell growth and survival in a 10,000-fold concentration range (1nM to 10uM) enabling the generation of dose response curves and calculation of area under the curve values (AUC) for each drug. We, further, functionally grouped the cell lines and drugs based on metabolic drug efficacy patterns and associated them with distinct genomic and metabolic attributes. Analysis of the metabolic drug response profiles revealed that 77 compounds (32%) affected cell viability with the top effective compounds targeting nucleotide metabolism, oxidative stress, and the PI3K/mTOR pathway. Unsupervised hierarchical clustering of the drug sensitivity profiles stratified the cell lines in 5 functional taxonomic groups, with the activity of 19 compounds significantly contributing to the cell line grouping (e.g. PF-02545920, GW 4064, mTOR inhibitors, daporinad). Comparison of the oxygen consumption rate and extracellular acidification rate showed that the examined cell lines have analogous baseline metabolic phenotypes, suggesting that the mitochondrial function of the cells as assessed by Seahorse analysis did not significantly influence the clustering. Genotype to phenotype associations were identified between FLT3mutations and sensitivity to 5-FU, lestaurtinib, and PF-02545920. Moreover, RAS mutations negatively correlated to mTOR and mitochondrial respiration inhibitor sensitivity, whereas TP53 mutations conferred a resistance phenotype to PI3K pathway inhibitors and antineoplastic agents. Selective sensitivities were detected to the lactate transporter (SLC16A1) inhibitor AZD3965, the PI3K inhibitor pictilisib, and the fatty acid synthase inhibitor GSK2194069, which could be explained by varied gene expression in sensitive cell lines and target/process dependency. CLIMET allows for identification of metabolic susceptibilities, grouping of cancer cells based on metabolic dependencies, as well as understanding of context-dependent mechanism of action of drugs. Functional drug testing may provide a rapid and robust approach to identify metabolic vulnerabilities, responding patients, and prioritize compounds for clinical evaluation as illustrated with our study. Disclosures Staber: Janssen: Consultancy, Honoraria; AbbVie: Consultancy, Honoraria; Takeda: Consultancy, Honoraria; Astra Zeneca: Consultancy, Honoraria; Roche: Consultancy, Honoraria, Research Funding; Celgene/ BMS: Consultancy, Honoraria; Gilead: Consultancy, Honoraria; msd: Consultancy, Honoraria.