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Computational cell fate modelling for discovery of rewiring in apoptotic network for enhanced cancer drug sensitivity
Abstract:
The ongoing cancer research has shown that malignant tumour cells have highly disrupted signalling transduction pathways. In cancer cells, signalling pathways are altered to satisfy the demands of continuous proliferation and survival. The changes in signalling pathways supporting uncontrolled cell growth, termed as rewiring, can lead to dysregulation of cell fates e.g. apoptosis. Hence comparative analysis of normal and oncogenic signal transduction pathways may provide insights into mechanisms of cancer drug-resistance and facilitate the discovery of novel and effective anti-cancer therapies. Here we propose a hybrid modelling approach based on ordinary differential equation (ODE) and machine learning to map network rewiring in the apoptotic pathways that may be responsible for the increase of drug sensitivity of tumour cells in triple-negative breast cancer. Our method employs Genetic Algorithm to search for the most likely network topologies by iteratively generating simulated protein phosphorylation data using ODEs and the rewired network and then fitting the simulated data with real data of cancer signalling and cell fate. Most of our predictions are consistent with experimental evidence from literature. Combining the strengths of knowledge-driven and data-driven approaches, our hybrid model can help uncover molecular mechanisms of cancer cell fate at systems level.
Insights
Researchers developed a hybrid model to understand how cancer cells rewire signaling pathways, potentially improving drug sensitivity in triple-negative breast cancer. This approach combines ordinary differential equations and machine learning for novel cancer therapy discovery.
Area of Science:
- Systems Biology
- Computational Biology
- Cancer Research
Background:
- Malignant tumor cells exhibit disrupted signaling transduction pathways essential for proliferation and survival.
- Pathway rewiring in cancer supports uncontrolled cell growth and can dysregulate cell fates like apoptosis.
- Understanding these alterations is crucial for discovering novel anti-cancer therapies and overcoming drug resistance.
Purpose of the Study:
- To map network rewiring in apoptotic pathways of triple-negative breast cancer.
- To investigate how pathway alterations influence tumor cell drug sensitivity.
- To develop a hybrid modeling approach for uncovering cancer cell fate mechanisms.
Main Methods:
- Utilized a hybrid modeling approach combining ordinary differential equations (ODEs) and machine learning.
- Employed a Genetic Algorithm to identify optimal network topologies.
- Generated simulated protein phosphorylation data using ODEs and iteratively fitted it with real cancer signaling and cell fate data.
Main Results:
- The hybrid model successfully mapped network rewiring in apoptotic pathways.
- Predictions derived from the model showed consistency with existing experimental evidence.
- The approach integrated knowledge-driven and data-driven methodologies.
Conclusions:
- The developed hybrid model offers a systems-level understanding of cancer cell fate mechanisms.
- This approach can facilitate the discovery of novel and effective anti-cancer therapies.
- The study provides insights into mechanisms of cancer drug resistance by analyzing pathway rewiring.
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