An Integrated Metabolic Atlas of Clear Cell Renal Cell Carcinoma

By A. Hakimi, Ed Reznik, Chung-Han Lee, Chad Creighton, A. Brannon, Augustin Luna, B. A. Aksoy, E. Liu, Ronglai Shen, William Lee and 9 more. Cancer Cell, 2016

687 citations recorded by the source corpus.

Summary Dysregulated metabolism is a hallmark of cancer, manifested through alterations in metabolites. We performed metabolomic profiling on 138 matched clear cell renal cell carcinoma (ccRCC)/normal tissue pairs and found that ccRCC is characterized by broad shifts in central carbon metabolism, one-carbon metabolism and anti-oxidant response. Tumor progression and metastasis were associated with metabolite increases in glutathione and cysteine/methionine metabolism pathways. We develop an analytic pipeline and visualization tool (“metabolograms”) to bridge the gap between TCGA transcriptomic profiling and our metabolomic data, which enables us to assemble an integrated pathway-level metabolic atlas and to demonstrate discordance between transcriptome and metabolome. Lastly, expression profiling was performed on a high-glutathione cluster, which correspond to a poor-survival subgroup in the ccRCC TCGA cohort.

Subfields: Cancer Research, Molecular Biology, Pulmonary and Respiratory Medicine

View Paper: https://www.semanticscholar.org/paper/07f39b5984045fdb972666d7239fb84d06b1d2f6

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In Pulmonary and Respiratory Medicine

Citations in the corpus

Understanding the intersections between metabolism and cancer biology (2017, neutral): “Finally, the heterogeneity of metabolic phenotypes among human cancer patients may provide biomarkers that correlate with disease progression (Hakimi et al., 2016).”
Glutathione metabolism in cancer progression and treatment resistance (2018, background): “GSH is also one of the most overrepresented metabolites in ccRCC, lung cancer, and melanoma patient samples relative to normal tissues (Gamcsik et al., 2012; Li et al., 2014a; Hakimi et al., 2016).”
Pan-cancer immunogenomic analyses reveal genotype-immunophenotype relationships and predictors of response to checkpoint blockade (2016, methodology): “The immunophenogram was constructed similar to the recently proposed metabologram (Hakimi et al., 2016).”
Integrated Proteogenomic Characterization of Clear Cell Renal Cell Carcinoma (2019, background): “mRNA levels and corresponding glycolytic and OXPHOS metabolites in ccRCC (Hakimi et al., 2016).”
Tumor immune microenvironment characterization in clear cell renal cell carcinoma identifies prognostic and immunotherapeutically relevant messenger RNA signatures (2016, neutral): “number GSE74734 [80].”