Phosphoproteomics profiling of sorafenib-resistant hepatocellular carcinoma patient-derived xenografts reveals potential therapeutic strategies
Hepatocellular carcinoma (HCC) is the most common form of primary liver cancer with poor prognosis. Sorafenib, a first-line treatment for advanced HCC, has shown limited clinical benefits due to the onset of drug resistance. Thus, it is imperative to comprehend the mechanisms underlying sorafenib resistance and explore strategies to overcome or delay it. Here, we established HCC patient-derived xenograft (PDX) models with acquired resistance to sorafenib and performed comprehensive proteomic and phosphoproteomic analyses on these models. The active cell cycle pathway along with the active cyclin-dependent kinase CDK1 and DNA-dependent protein kinase PRKDC was identified through KEGG pathway enrichment and kinase substrate enrichment analyses. Upon investigating the potential of combining sorafenib with putative kinase inhibitors, we found that the combination displays synergistic anti-proliferative effects in the sorafenib-resistant liver cancer cell line, thus providing a proof of concept for phosphoproteomic-guided design of precision medicine.
Authors
- Changqing Wu (ORCID: https://orcid.org/0000-0003-4369-9045)
- Xin‐Rong Yang (ORCID: https://orcid.org/0000-0002-2716-9338)
- S. Wu (ORCID: https://orcid.org/0000-0002-1255-0347)
- Yang Liu (ORCID: https://orcid.org/0000-0002-5942-839X)
- Lei Zhang (ORCID: https://orcid.org/0000-0002-4880-824X)
- Haojie Lu (ORCID: https://orcid.org/0000-0003-3477-7662)
- Feng Li (ORCID: https://orcid.org/0000-0002-7063-9497)
- Bo Hu (ORCID: https://orcid.org/0000-0001-9213-1304)
- Ying Zhang (ORCID: https://orcid.org/0000-0002-1995-5494)
- Jun Wang (ORCID: https://orcid.org/0000-0002-7437-8503)
Institutions
- Tongji University (CN)
- Fudan University (CN)
- Zhongshan Hospital (CN)
- Shanghai Tenth People's Hospital (CN)
Publication Details
- Journal
- iScience
- Published
- 2024-12-20
- DOI
- https://doi.org/10.1016/j.isci.2024.111657
- Citations
- 2
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.81
Funders
- National Natural Science Foundation of China
- National Key Research and Development Program of China