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Proc. Natl. Acad. Sci. USA, Jul 2025, 122 (26) | https://doi.org/10.1073/pnas.2425384122 Genome-scale knockout simulation and clustering analysis of drug-resistant breast cancer cells reveal drug sensitization targets

JinA Lim^{1 †}, Hae Deok Jung ^{1 †}, Soo Young Park ², Moonhyeon Jeon ¹, Da Sol Kim ², Ryeongeun Cho ¹, Dohyun Han ^{3 4}, Han Suk Ryu ^{2 5 6 *}, Yoosik Kim ^{1 7 8 9 *}, Hyun Uk Kim ^{1 7 10 *}

¹Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, Republic of Korea.
²Department of Pathology, Seoul National University Hospital, Seoul 03080, Republic of Korea.
³Transdisciplinary Department of Medicine and Advanced Technology, Seoul National University Hospital, Seoul 03080, Republic of Korea.
⁴Proteomics Core Facility, Biomedical Research Institute, Seoul National University Hospital, Seoul 03080, Republic of Korea.
⁵Department of Pathology, Seoul National University College of Medicine, Seoul 03080, Republic of Korea.
⁶Cancer Research Institute, Seoul National University, Seoul 03080, Republic of Korea.
⁷Graduate School of Engineering Biology, Korea Advanced Institute of Science and Technology, Daejeon 34141, Republic of Korea.
⁸KAIST Institute for BioCentury, Korea Advanced Institute of Science and Technology, Daejeon 34141, Republic of Korea.
⁹KAIST Institute for Health Science and Technology, Korea Advanced Institute of Science and Technology, Daejeon 34141, Republic of Korea.
¹⁰BioProcess Engineering Research Center, Korea Advanced Institute of Science and Technology, Daejeon 34141, Republic of Korea.

^†These authors contributed equally

^*Corresponding authors: correspondence to Han Suk Ryu, Yoosik Kim or Hyun Uk Kim

Abstract

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.

논문정보

형식Research article
게재일2025년 07월 (BRIC 등록일 2025-06-26)
연구진국내 연구진
분야 의약학 > 종양의학

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