Metabolic modelling-based in silico drug target prediction identifies six novel repurposable drugs for melanoma

Abstract

Despite high initial response rates to targeted kinase inhibitors, the majority of patients suffering from metastatic melanoma present with high relapse rates, demanding for alternative therapeutic options. We have previously developed a drug repurposing workflow to identify metabolic drug targets that, if depleted, inhibit the growth of cancer cells without harming healthy tissues. In the current study, we have applied a refined version of the workflow to specifically predict both, common essential genes across various cancer types, and melanoma-specific essential genes that could potentially be used as drug targets for melanoma treatment. The in silico single gene deletion step was adapted to simulate the knock-out of all targets of a drug on an objective function such as growth or energy balance. Based on publicly available, and in-house, large-scale transcriptomic data metabolic models for melanoma were reconstructed enabling the prediction of 28 candidate drugs and estimating their respective efficacy. Twelve highly efficacious drugs with low half-maximal inhibitory concentration values for the treatment of other cancers, which are not yet approved for melanoma treatment, were used for in vitro validation using melanoma cell lines. Combination of the top 4 out of 6 promising candidate drugs with BRAF or MEK inhibitors, partially showed synergistic growth inhibition compared to individual BRAF/MEK inhibition. Hence, the repurposing of drugs may enable an increase in therapeutic options e.g., for non-responders or upon acquired resistance to conventional melanoma treatments.

Epigenetic control of metabolic identity across cell types

“…Core genes and core metabolic genes together captured 60% of the common essential genes. The missing 31 essential metabolic genes were associated with active ChromGene states (1)(2)(3)(4)(5)(6) in most tissues and cell types, with one exception, gamma-glutamyl transferase (GGTLC2), which carried a repressive chromatin state across most samples (Supplementary Figure S4). We observed similar patterns for non-metabolic core essential genes: except for a few genes that are associated with a repressive state in most cell types, core essential genes carry activating states (ChromGene state 1-6) in most cell types (Supplementary Figure S4).…”

“…Metabolic network modelling, which integrates cell type-specific epigenomic maps with the biochemical reactome, is a powerful approach to capture metabolic identity across cell types and identity disruptions in disease states [4][5][6][7][8][9][10] . Metabolic models provide key insights into how the inactivation of rate-limiting enzymes controls metabolic flux, interactions between metabolic pathways and compartments, and the ultimate capacity of cells to perform metabolic functions 11 .…”

“…While ChromHMM and Segway define chromatin states at the level of individual loci, the recent ChromGene 20 method defines gene-level chromatin state by integrating histone modification signals throughout the gene body and flanking regions using a mixture of hidden Markov models 21 . ChromGene defines six active states (1)(2)(3)(4)(5)(6), one poised state (7), and five repressive states (8)(9)(10)(11)(12) (Supplementary Table 1), based on combinations of ten histone modifications. This gene-level view provides better insight into which genes encoding for enzymes and transporters in the metabolic networks are active at the chromatin level, and which are transcriptionally repressed.…”