Application of a Biphasic Mathematical Model of Cancer Cell Drug Response for Formulating Potent and Synergistic
Jinyan Shen1,2, Li Li1,3, Niall G Howlett1
1Department of Cell and Molecular Biology, University of Rhode Island, Kingston, RI 02881, USA.
Abstract:
Triple negative breast cancer is a collection of heterogeneous breast cancers that are immunohistochemically negative for estrogen receptor, progesterone receptor, and ErbB2 (due to deletion or lack of amplification). No dominant proliferative driver has been identified for this type of cancer, and effective targeted therapy is lacking. In this study, we hypothesized that triple negative breast cancer cells are multi-driver cancer cells, and evaluated a biphasic mathematical model for identifying potent and synergistic drug combinations for multi-driver cancer cells. The responses of two triple negative breast cancer cell lines, MDA-MB-231 and MDA-MB-468, to a panel of targeted therapy drugs were determined over a broad range of concentrations. The analyses of the drug responses by the biphasic mathematical model revealed that both cell lines were indeed dependent on multiple drivers, and inhibitors of individual drivers caused a biphasic response: a target-specific partial inhibition at low nM concentrations, and an off-target toxicity at μM concentrations. We further demonstrated that combinations of drugs, targeting each driver, cause potent, synergistic, and cell-specific cell killing. Immunoblotting analysis of the effects of the individual drugs and drug combinations on the signaling pathways supports the above conclusion. These results support a multi-driver proliferation hypothesis for these triple negative breast cancer cells, and demonstrate the applicability of the biphasic mathematical model for identifying effective and synergistic targeted drug combinations for triple negative breast cancer cells.
Insights
Triple negative breast cancer cells are multi-driver, requiring combinations of targeted therapies. A biphasic model identifies potent, synergistic drug combinations for effective cell killing.
Area of Science:
- Oncology
- Pharmacology
- Mathematical Biology
Background:
- Triple negative breast cancer (TNBC) lacks targeted therapy due to its heterogeneity and absence of identifiable dominant drivers.
- Existing treatments for TNBC are limited, necessitating novel therapeutic strategies.
Purpose of the Study:
- To test the hypothesis that TNBC cells are multi-driver cancer cells.
- To evaluate a biphasic mathematical model for identifying potent and synergistic drug combinations for TNBC.
Main Methods:
- Assessed drug responses of two TNBC cell lines (MDA-MB-231, MDA-MB-468) to targeted therapies across various concentrations.
- Applied a biphasic mathematical model to analyze drug response data.
- Utilized immunoblotting to analyze signaling pathway alterations induced by drug treatments.
Main Results:
- The biphasic model confirmed multi-driver dependence in both TNBC cell lines.
- Individual driver inhibitors showed biphasic responses: partial inhibition at low concentrations and off-target toxicity at higher concentrations.
- Combinations of drugs targeting multiple drivers demonstrated potent, synergistic, and cell-specific cancer cell killing.
Conclusions:
- TNBC cells exhibit multi-driver proliferation characteristics.
- The biphasic mathematical model is effective in identifying synergistic targeted drug combinations for TNBC.
- This approach offers a promising strategy for developing effective therapies for triple negative breast cancer.
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