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Hunt for the tipping point during endocrine resistance process in breast cancer by dynamic network biomarkers
Rui Liu1, Jinzeng Wang2,3, Masao Ukai4,5
1School of Mathematics, South China University of Science and Technology, Guangzhou, China.
Abstract:
Acquired drug resistance is the major reason why patients fail to respond to cancer therapies. It is a challenging task to determine the tipping point of endocrine resistance and detect the associated molecules. Derived from new systems biology theory, the dynamic network biomarker (DNB) method is designed to quantitatively identify the tipping point of a drastic system transition and can theoretically identify DNB genes that play key roles in acquiring drug resistance. We analyzed time-course mRNA sequence data generated from the tamoxifen-treated estrogen receptor (ER)-positive MCF-7 cell line, and identified the tipping point of endocrine resistance with its leading molecules. The results show that there is interplay between gene mutations and DNB genes, in which the accumulated mutations eventually affect the DNB genes that subsequently cause the change of transcriptional landscape, enabling full-blown drug resistance. Survival analyses based on clinical datasets validated that the DNB genes were associated with the poor survival of breast cancer patients. The results provided the detection for the pre-resistance state or early signs of endocrine resistance. Our predictive method may greatly benefit the scheduling of treatments for complex diseases in which patients are exposed to considerably different drugs and may become drug resistant.
Insights
This study introduces a dynamic network biomarker (DNB) method to identify the tipping point for endocrine resistance in cancer. DNB genes were found to be crucial in developing drug resistance and predicting poor patient survival.
Area of Science:
- Oncology
- Systems Biology
- Genomics
Background:
- Acquired drug resistance is a primary cause of cancer therapy failure.
- Identifying the onset and molecular drivers of endocrine resistance remains challenging.
Purpose of the Study:
- To apply the dynamic network biomarker (DNB) method to quantitatively identify the tipping point of endocrine resistance.
- To detect key molecules and genes associated with the development of drug resistance.
Main Methods:
- Analysis of time-course mRNA sequencing data from tamoxifen-treated MCF-7 cells.
- Application of the dynamic network biomarker (DNB) method to identify tipping points and key genes.
- Survival analysis using clinical datasets to validate DNB gene associations.
Main Results:
- Identified the tipping point of endocrine resistance and associated leading molecules in ER-positive breast cancer cells.
- Revealed an interplay between gene mutations and DNB genes in driving transcriptional changes and resistance.
- Validated DNB genes as predictors of poor survival in breast cancer patients.
Conclusions:
- The DNB method can detect pre-resistance states and early signs of endocrine resistance.
- This predictive approach may improve treatment scheduling for drug-resistant cancers.
- Understanding DNB genes offers insights into mechanisms of acquired drug resistance.
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