A Multi-Omics Investigation Unveiling the Tiered Regulation of Breast Cancer Cell Metabolism

Altered metabolism is a hallmark of cancer, but little is still known about its regulation. In this study, we
measure transcriptomic, proteomic, phospho-proteomic and fluxomics data in a breast cancer cell-line
(MCF7) across three different growth conditions. Integrating these multiomics data within a genome
scale human metabolic model in combination with machine learning we systematically chart the
different layers of metabolic regulation in breast cancer cells, predicting which enzymes and pathways
are regulated at which level. We distinguish between two types of reactions, directly or indirectly
regulated. Directly-regulated reactions include those whose flux is regulated by transcriptomic
alterations (~890) or via proteomic or phospho-proteomics alterations (~140) in the enzymes catalyzing
them. Potentially indirectly regulated reactions are those that currently lack evidence for direct
regulation (~930). Many metabolic pathways are predicted to be regulated at different levels, and those
may change at different media conditions. Remarkably, we find that the flux of predicted indirectly
regulated reactions is strongly coupled to the flux of the predicted directly regulated ones, uncovering a
hierarchical organization of breast cancer cell metabolism. Furthermore, the predicted indirectly
regulated reactions are predominantly reversible. Taken together, this architecture may facilitate the
rapid and efficient metabolic reprogramming in response to the varying environmental conditions
incurred by the tumor cells. The approach presented lays a conceptual and computational basis for a
more complete mapping of metabolic regulation in different cancers with incoming additional data.