The EACR’s Top 10 Cancer Research Publications is a regular summary of the most interesting and impactful recent papers in cancer research. It is curated by the Board of the European Association for Cancer Research (EACR).
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Z. Xiao et al., Nature Communications volume 10, Article number: 3763 (2019)
Summary of the findings
Solid tumors consist of malignant cells along with stromal and immune cells that together form the tumor microenvironment (TME). Each of these cells faces a unique nutritional environment and has a unique genotypic background, resulting in cell-specific wiring of metabolism.
In this study, we characterized the metabolic landscape of the TME using single-cell RNA-seq data from two human cancers, melanoma and head and neck. The goal of this study was to answer two important questions: (1) what is the nature of metabolic reprogramming in single malignant and non-malignant cells in the TME; (2) what processes contribute most to the metabolic heterogeneity in each cell type. Neither could be answered with bulk measurements.
We found that single malignant cells exhibit higher metabolic plasticity and activity compared to non-malignant cells, possibly enabling these cells to adapt to specific genotypic and environmental contexts and satisfy demands for biomass and energy. Notably, the metabolic features of single malignant cells differ greatly from those of bulk tumors. We also find that mitochondrial activity is the major contributor to the metabolic heterogeneity in all cell types. Finally, we identify the metabolic signatures that distinguish different immune and stromal cell subtypes.
Future impact of the findings
This study advances our understanding of the metabolic heterogeneity of the TME to reach single-cell resolution in vivo, highlights the importance of single-cell measurements in understanding cancer cell metabolism, and unravels the essential role of mitochondria in mediating intratumoral metabolic heterogeneity. It is thus intriguing to determine whether these properties are applicable to other tumor types and altered by cancer therapeutics. The computational framework can also be applied in studying other tumor types and physiological processes. One limitation is that metabolic features are inferred from gene expression. While it has some predictive capacity for estimating metabolic flux, single cell metabolomics technologies will ultimately be needed to get further insights into metabolism at the single-cell level.
