Modeling chemotherapy-induced stress to identify rational combination therapies in the DNA damage response pathway

Area of Science:

• Cell Biology
• Computational Biology
• Pharmacology

Background:

• Cells activate complex signaling networks to manage DNA damage, influencing repair, survival, or cell death.
• Understanding the precise mechanisms linking DNA damage response (DDR) to cell fate is crucial for effective cancer therapy.

Purpose of the Study:

• To develop a multiscale computational model quantitatively linking chemotherapy-induced DNA damage response signaling to cell fate.
• To predict cellular responses to chemotherapy and targeted DDR inhibitors.

Main Methods:

• Developed and calibrated a multiscale computational model using data from U2OS osteosarcoma cells.
• Utilized data including cell cycle distribution, Western blotting for signaling, and time-lapse microscopy for cell fate.
• Experimentally validated model predictions for chemotherapy and drug combinations.

Main Results:

• The computational model quantitatively linked DNA damage response signaling to cell fate.
• The model successfully predicted cellular responses to chemotherapy alone and in combination with targeted inhibitors.
• Experimental validation confirmed the model's predictive accuracy.

Conclusions:

• Multiscale computational models can elucidate the molecular basis of cell survival and death decisions.
• Such models are valuable tools for rationally identifying chemotherapy-potentiating drug combinations.
• This approach offers a pathway to optimize cancer treatment strategies.