Genome-scale knockout simulation and clustering analysis of drug-resistant breast cancer cells reveal drug sensitization targets

Anticancer chemotherapy is an essential part of cancer treatment, but the emergence of resistance remains a major hurdle. Metabolic reprogramming is a notable phenotype associated with the acquisition of drug resistance. Here, we develop a computational framework that predicts metabolic gene targets capable of reverting the metabolic state of drug-resistant cells to that of drug-sensitive parental cells, thereby sensitizing the resistant cells. The computational framework performs single-gene knockout simulation of genome-scale metabolic models that predicts genome-wide metabolic flux distribution in drug-resistant cells, and clusters the resulting knockout flux data using uniform manifold approximation and projection, followed by k -means clustering. From the clustering analysis, knockout genes that lead to the flux data near that of drug-sensitive cells are considered drug sensitization targets. This computational approach is demonstrated using doxorubicin- and paclitaxel-resistant MCF7 breast cancer cells. Drug sensitization targets are further refined based on proteome and metabolome data, which generate GOT1 for doxorubicin-resistant MCF7, GPI for paclitaxel-resistant MCF7, and SLC1A5 as a common target. These targets are experimentally validated where treating drug-resistant cancer cells with small-molecule inhibitors results in increased sensitivity of drug-resistant cells to doxorubicin or paclitaxel. The applicability of the developed framework is further demonstrated using drug-resistant triple-negative breast cancer cells. Taken together, the computational framework predicts drug sensitization targets in an intuitive and cost-efficient manner and can be applied to overcome drug-resistant cells associated with various cancers and other metabolic diseases.