journal article·2026-05Open access

Single‐Cell Computational Frameworks for Quantifying BET Bromodomain Inhibitor Resistance and Screening Re‐Sensitizer Drugs in Triple‐Negative Breast Cancer

Haizhou Liu, Mengqin Yuan, Yini Shang, Jiahao Chen, Fei Hou, Lihong Wang, Wei Jiang

Advanced Science· Volume 13· Issue 29

Abstract

ABSTRACT Triple‐negative breast cancer (TNBC) is an aggressive subtype characterized by rapid proliferation and a great propensity for metastasis. Therapeutic options for TNBC remain limited due to the absence of targetable hormone receptors. While BET bromodomain inhibitors (BBDIs) exhibit promising anticancer potential, the emergence of drug resistance presents a major challenge. Here, through leveraging single‐cell RNA sequencing (scRNA‐seq) data across continuous states of BBDI treatment in TNBC, this study conducts an extensive investigation into BBDI resistance, and develops two computational frameworks, FR20 and D‐FR20, to quantify BBDI resistance at single‐cell resolution and to screen potential BBDI re‐sensitizer drugs, respectively. The accuracy and scalability of FR20 are confirmed through rigorous evaluation in nine independent datasets. In addition, cellular dynamic changes and ferroptosis inhibition are revealed in the evolution of BBDI resistance. Experimental validation demonstrates that GPX 4 overexpression significantly reduces drug sensitivity in TNBC cells. Furthermore, in vitro and in vivo experiments validate the ability of the small molecule filgotinib, identified by D‐FR20, to re‐sensitize BBDI and effectively eliminate resistant TNBC cells. Collectively, this study provides two computational frameworks for predicting BBDI resistance and candidate re‐sensitizer, as well as demonstrates the roles of ferroptosis in BBDI resistance, offering a promising avenue for TNBC treatment.
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Research context

OpenAlex topics: Protein Degradation and Inhibitors · Single-cell and spatial transcriptomics · Ferroptosis and cancer prognosis

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Fujian Key Laboratory of Tumor Immunotherapy The First Affiliated Hospital Fujian Medical University Fuzhou China

National Regional Medical Center Binhai Campus of the First Affiliated Hospital Fujian Medical University Fuzhou China

Department of Bioinformatics Fujian Key Laboratory of Medical Bioinformatics School of Medical Technology and Engineering Fujian Medical University Fuzhou China

Department of Pathophysiology School of Medicine Southeast University Nanjing China

Department of Biomedical Engineering Nanjing University of Aeronautics and Astronautics Nanjing China

Funding

National Natural Science Foundation of China

References (40)

Show source-provided references
1. 10.1016/S0140-6736(16)32454-010.1016/S0140-6736(16)32454-0 ↗
2. 10.1038/nrclinonc.2016.6610.1038/nrclinonc.2016.66 ↗
3. 10.1038/nature1650810.1038/nature16508 ↗
4. 10.1038/nature0950410.1038/nature09504 ↗
5. 10.4155/fsoa-2018-011510.4155/fsoa-2018-0115 ↗
6. 10.1038/s41467-022-29358-610.1038/s41467-022-29358-6 ↗
7. 10.3390/genes1402026810.3390/genes14020268 ↗
8. 10.1016/j.cmet.2022.09.02110.1016/j.cmet.2022.09.021 ↗
9. 10.1016/j.cell.2018.03.04110.1016/j.cell.2018.03.041 ↗
10. 10.1038/s41467-021-21884-z10.1038/s41467-021-21884-z ↗
11. 10.1016/j.ccell.2021.09.01010.1016/j.ccell.2021.09.010 ↗
12. 10.1186/s13073-024-01327-210.1186/s13073-024-01327-2 ↗
13. 10.1158/0008-5472.CAN-23-131310.1158/0008-5472.CAN-23-1313 ↗
14. 10.1016/j.canlet.2014.10.01710.1016/j.canlet.2014.10.017 ↗
15. 10.1016/j.cell.2017.10.04910.1016/j.cell.2017.10.049 ↗
16. 10.1016/j.cels.2017.11.00110.1016/j.cels.2017.11.001 ↗
17. 10.1093/bioinformatics/btv69310.1093/bioinformatics/btv693 ↗
18. 10.1186/s13287-016-0278-310.1186/s13287-016-0278-3 ↗
19. Bromodomain Inhibitor jq1 Induces Cell Cycle Arrest and Apoptosis of Glioma Stem Cells through the VEGF/PI3K/AKT Signaling Pathway
20. 10.3389/fnins.2019.0119510.3389/fnins.2019.01195 ↗
21. 10.1016/j.freeradbiomed.2020.02.02710.1016/j.freeradbiomed.2020.02.027 ↗
22. 10.1016/j.molcel.2020.04.02710.1016/j.molcel.2020.04.027 ↗
23. 10.1038/s41389-021-00316-z10.1038/s41389-021-00316-z ↗
24. 10.1038/nature1100310.1038/nature11003 ↗
25. 10.1038/nature1100510.1038/nature11005 ↗
26. 10.1186/s13073-021-01000-y10.1186/s13073-021-01000-y ↗
27. 10.1093/bib/bbab16110.1093/bib/bbab161 ↗
28. 10.1038/s41416-020-01137-410.1038/s41416-020-01137-4 ↗
29. 10.1056/NEJMoa151374910.1056/NEJMoa1513749 ↗
30. 10.1016/j.cell.2021.04.04810.1016/j.cell.2021.04.048 ↗
31. 10.6026/9732063000200510.6026/97320630002005 ↗
32. 10.1016/j.xinn.2021.10014110.1016/j.xinn.2021.100141 ↗
33. 10.1038/s41467-019-09234-610.1038/s41467-019-09234-6 ↗
34. 10.1186/1471-2105-14-710.1186/1471-2105-14-7 ↗
35. 10.1038/nmeth.440210.1038/nmeth.4402 ↗
36. 10.1126/science.aax024910.1126/science.aax0249 ↗
37. 10.1038/nature2012310.1038/nature20123 ↗
38. 10.1093/nar/gkac93510.1093/nar/gkac935 ↗
39. 10.1073/pnas.050658010210.1073/pnas.0506580102 ↗
40. 10.1093/bioinformatics/btac75710.1093/bioinformatics/btac757 ↗