Heterogeneity of the Cancer Cell Line Metabolic Landscape

Abstract

The unravelling of the complexity of cellular metabolism is in its infancy. Cancer-associated genetic alterations may result in changes to cellular metabolism that aid in understanding phenotypic changes, reveal detectable metabolic signatures, or elucidate vulnerabilities to particular drugs. To understand cancer-associated metabolic transformation we performed untargeted metabolite analysis of 173 different cancer cell lines from 11 different tissues under constant conditions for 1099 different species using liquid chromatography-mass spectrometry (LC-MS). We correlate known cancer-associated mutations and gene expression programs with metabolic signatures, generating novel associations of known metabolic pathways with known cancer drivers. We show that metabolic activity correlates with drug sensitivity and use metabolic activity to predict drug response and synergy. Finally, we study the metabolic heterogeneity of cancer mutations across tissues, and find that genes exhibit a range of context specific, and more general metabolic control.

Pathway metabolite ratios reveal distinctive glutamine metabolism in a subset of proliferating cells

“…However, there were some instances where cell lines from the same tissue origin clustered together, such as Cluster 3 that was enriched with prostate cancer cells and Cluster 4 with endometrial cancer cells (Fig 1B), which has been observed in another pan-cancer metabolome study (Shorthouse et al, 2022). Despite the identification of heterogeneous clusters of cells based upon the levels of metabolites of high flux pathways, there was no clear organization of these metabolites into pathways (Fig 1B), likely because strong metabolite interactions are often localized at the reaction level (Benedetti et al, 2023), that could underpin a testable hypothesis centered on differences and similarities of pathway activity.…”

“…More recently, several publications have reported the outcomes of high-throughput metabolomics using pan-cancer cell line panels, such as the NCI-60 (60 cell lines, Jain et al, 2012;Ortmayr et al, 2019) and CCLE panels (928 cell lines, Li et al, 2019), CAMP (988 tissue samples, Benedetti et al, 2023), and other panels containing 180 cell lines (Cherkaoui et al, 2022), and 173 cell lines (Shorthouse et al, 2022). These studies primarily aimed to identify links between cancer cell metabolic phenotypes and transcriptional regulation (Benedetti et al, 2023;Ortmayr et al, 2019), or genetic alterations and dependencies (Li et al, 2019;Mullen & Singh, 2023) that were associated with drug-sensitivities (Shorthouse et al, 2022). Notably, Cherkaoui and colleagues (2022) took a top-down approach by clustering the metabolome acquired by untargeted metabolomics across 49 KEGG metabolic pathways of 180 cancer cells.…”