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

• 成果类型: Article
• 署名作者: Lim, Jina; Jung, Hae Deok; Park, Soo Young; Jeon, Moonhyeon; Kim, Da Sol; Cho, Ryeongeun; Han, Dohyun; Ryu, Han Suk; Kim, Yoosik; Kim, Hyun Uk
• 署名单位: Korea Advanced Institute of Science & Technology (KAIST); Seoul National University (SNU); Seoul National University Hospital; Seoul National University (SNU); Seoul National University Hospital; Seoul National University (SNU); Seoul National University Hospital; Seoul National University (SNU); Seoul National University (SNU); Korea Advanced Institute of Science & Technology (KAIST); Korea Advanced Institute of Science & Technology (KAIST); Korea Advanced Institute of Science & Technology (KAIST); Korea Advanced Institute of Science & Technology (KAIST)
• 刊物名称: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
• ISSN/ISSBN: 0027-11004
• DOI: 10.1073/pnas.2425384122
• 发表日期: 2025-07-01
• 关键词: multidrug-resistance; metabolism; abcb1; polymorphisms; synthase; models

摘要

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.