Isotope-Guided Analytical Strategies for Assessing Metabolic Networks in Cancer

Dear Colleagues,

The reprogramming of the cancer metabolism is central to the process of malignant transformation. A growing repertoire of flux analysis techniques have provided new insights into our understanding of the cancer metabolism in primary and metastatic settings, as well as revealing the complex metabolic interplay between cancer cells and other cell types in the tumor microenvironment. Stable isotope-guided analysis, whether targeted or untargeted, has enabled fundamental discoveries in the cancer metabolism. Hypothesis-driven approaches that interpret targeted metabolite ¹³C, ¹⁵N, ¹⁸O or ²H labeling patterns have uncovered key nutrient dependencies in tumor and immune cells, revealed metabolic reprogramming driven by oncogenic mutations, and led to the identification of unique metabolic adaptations that enable the continuity of energy generation, while fostering enhanced biosynthetic pathways. In contrast, unsupervised isotope-tracing metabolomics, especially with ultra-high resolution mass spectrometry systems, can measure metabolome-wide shifts in pathways, which is discovery-based and hypothesis-generating without a defined model. ¹³C metabolic flux analysis (¹³C-MFA) leverages measured extracellular uptake and secretion rates, as well as isotopic labeling with an assumed metabolic network model, in order to find the most likely solution of fluxes.¹³C-MFA can be aided by approaches that integrate the constraint-based modeling of metabolic flux, which itself can be used for flux prediction for cancer mechanisms using genome-scale human metabolic network reconstructions. This issue will cover how advances in stable isotope and computational flux methodologies have yielded new insights into metabolic networks within the tumor niche, and then detail how emerging flux methods at the cutting-edge of isotope flux analysis are being applied to examine compartmental and spatial metabolic interactions within tumors.

Dr. Irwin J. Kurland
Prof. Dr. Henri Brunengraber
Dr. Alison E. Ringel
Dr. Yunping Qiu
Guest Editors

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• spatially resolved metabolism
• metastatic flux adaptations
• deconvoluting tumor metabolic heterogeneity
• flux characterization of immunometabolism
• constraint-based flux modeling
• mass isotopomer distribution analysis
• ultra-high resolution flux characterization
• in vivo flux quantification
• system biology integration with flux analyses