Single-Cell Computational Frameworks for Quantifying BET Bromodomain Inhibitor Resistance and Screening Re-Sensitizer...

Area of Science:

• Oncology
• Computational Biology
• Genomics

Background:

• Triple-negative breast cancer (TNBC) is aggressive with limited treatment options.
• BET bromodomain inhibitors (BBDIs) show promise but face drug resistance challenges.

Purpose of the Study:

• Investigate BBDI resistance mechanisms in TNBC using single-cell RNA sequencing (scRNA-seq).
• Develop computational frameworks (FR20, D-FR20) to quantify resistance and screen re-sensitizer drugs.
• Explore the role of ferroptosis in BBDI resistance.

Main Methods:

• Leveraged scRNA-seq data from TNBC cells under continuous BBDI treatment.
• Developed and validated FR20 for single-cell resistance quantification across nine datasets.
• Utilized D-FR20 to screen for BBDI re-sensitizer drugs.
• Conducted experimental validation of findings, including GPX4 overexpression and filgotinib efficacy.

Main Results:

• Identified cellular dynamic changes and ferroptosis inhibition during BBDI resistance evolution.
• GPX4 overexpression was shown to decrease drug sensitivity in TNBC cells.
• The D-FR20 framework identified filgotinib as a potent re-sensitizer for resistant TNBC cells.
• FR20 demonstrated accuracy and scalability in predicting BBDI resistance.

Conclusions:

• Computational frameworks FR20 and D-FR20 offer novel tools for predicting BBDI resistance and identifying re-sensitizers.
• Ferroptosis plays a significant role in BBDI resistance in TNBC.
• Filgotinib shows potential for overcoming BBDI resistance and treating TNBC.