Intratumoral heterogeneity and metabolic cross-feeding in a three-dimensional model of breast cancer: an in silico perspective.

Abstract Today, intra-tumoral composition is a relevant factor associated with the progression and aggresivity of cancer. Despite it suggests a metabolic interdependence among the subpopulations inside the tumor, a detailed map of how this interdependence contributes to malignant phenotype is still lacking. To address this question, we developed a systems biology approach integrating single-cell RNASeq and genome-scale metabolic reconstruction for mapping the metabolic cross-feeding among the subpopulations previously identified in spheroids of MCF7 breast cancer. By calibrating our model with expression profiles and the experimental growth rate, we concluded that the Reverse Warburg effect emerges as a mechanism to optimize the community growth. Furthermore, through an in silico analysis, we identified lactate, alpha-ketoglutarate, and some amino acids as key metabolites whose disponibility alters the growth rate of the spheroid. Altogether, this work contributes with a strategy for assessing how space and intra-tumoral heterogeneity influence the metabolic robustness of cancer, issues that claim computational strategies to move toward the design of optimized treatments.