Robust metabolic transcriptional components in 34,494 patient-derived cancer-related samples and cell lines

Abstract

Background Patient-derived bulk expression profiles of cancers can provide insight into the transcriptional changes that underlie reprogrammed metabolism in cancer. These profiles represent the average expression pattern of all heterogeneous tumor and non-tumor cells present in biopsies of tumor lesions. Hence, subtle transcriptional footprints of metabolic processes can be concealed by other biological processes and experimental artifacts. However, consensus independent component analyses (c-ICA) can capture statistically independent transcriptional footprints of both subtle and more pronounced metabolic processes. Methods We performed c-ICA with 34,494 bulk expression profiles of patient-derived tumor biopsies, non-cancer tissues, and cell lines. Gene set enrichment analysis with 608 gene sets that describe metabolic processes was performed to identify the transcriptional components enriched for metabolic processes (mTCs). The activity of these mTCs was determined in all samples to create a metabolic transcriptional landscape. Results A set of 555 mTCs was identified of which many were robust across different datasets, platforms, and patient-derived tissues and cell lines. We demonstrate how the metabolic transcriptional landscape defined by the activity of these mTCs in samples can be used to explore the associations between the metabolic transcriptome and drug sensitivities, patient outcomes, and the composition of the immune tumor microenvironment. Conclusions To facilitate the use of our transcriptional metabolic landscape, we have provided access to all data via a web portal (www.themetaboliclandscapeofcancer.com). We believe this resource will contribute to the formulation of new hypotheses on how to metabolically engage the tumor or its (immune) microenvironment.

Propionate reinforces epithelial identity and reduces aggressiveness of lung carcinoma

“…Assessing EMT state in patient‐derived samples is technically difficult due to its dynamic nature (Vasaikar et al , 2021) and a similar level of technical limitations also exists in detecting and quantifying the global altered metabolic state in tumors (Han et al , 2021; Rohatgi et al , 2022). Nevertheless, transcriptomic approaches can effectively portrait the metabolic reprogramming in cancer as the metabolic enzyme regulation also occurs at the transcriptional level (Leeuwenburgh et al , 2021; Rohatgi et al , 2022). On this basis, the present study conducted a comprehensive lung cancer transcriptome analysis in the context of EMT‐associated metabolic processes.…”

Multivariate analysis of metabolic state vulnerabilities across diverse cancer contexts reveals synthetically lethal associations

“…The search for novel anti-cancer metabolic targets has significantly benefited from recent developments in characterization of large panels of diverse cancer cell lines, including their genetic dependency maps, genomic, transcriptomic, and metabolomic features 11,12 . Systematic studies have generated and integrated such large-scale datasets to characterize the landscape of metabolic pathway alterations and dependencies across cancer cell lines, demonstrating the context-specific nature of metabolic dependencies, and identifying new metabolic vulnerabilities in cancer cells [13][14][15][16][17][18] . Building upon promising outcomes from these studies, a critical next step would be to develop computational models that reveal metabolic state-specific gene or pathway co-dependencies to discover potential synthetic lethalities imposed by the cancer genotype or tissue context 2 .…”

Gene expression is a poor predictor of steady‐state metabolite abundance in cancer cells

“…A more recent study found relationships between transcriptional regulator activity and metabolite levels, however, the associations were limited to 53 cancer cell lines 15 . On the other hand, numerous studies on cancer metabolism have solely used gene and protein expression to infer metabolic activity without direct profiling of metabolite levels 16–19 . However, gene expression patterns may not intuitively reflect the activity of metabolic pathways, 20 where the reaction rate or environmental factors may also have a substantial impact.…”

“…15 On the other hand, numerous studies on cancer metabolism have solely used gene and protein expression to infer metabolic activity without direct profiling of metabolite levels. [16][17][18][19] However, gene expression patterns may not intuitively reflect the activity of metabolic pathways, 20 where the reaction rate or environmental factors may also have a substantial impact. Another issue in previous cancer metabolism studies is that most research was focused on a specific cancer type [21][22][23][24] or metabolic pathway, 25,26 limiting a comprehensive understanding of metabolism reprogramming.…”