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.
Highlights
The objective of anti-cancer therapeutics is to kill cancer cells with minimum damage to the healthy cells
N1 was constructed directly from literature; N2 consisted of edges added and deleted from N1 by Genetic Algorithm; N3 consisted of edges added and deleted from N2 by Genetic Algorithm
The Partial least Square Regression (PLSR) plot for the simulation data from the basic network from N1, is given in Figure 2, which partial least-squares regression (PLSR) plot shows the close association between apoptosis and several apoptotic proteins
Summary
The objective of anti-cancer therapeutics is to kill cancer cells with minimum damage to the healthy cells. To this end, a solid understanding of the cell fate decisions (e.g. apoptosis, proliferation) of different cells under various conditions would be required. It is challenging to understand the dynamics of signal transduction at systems level, due to non-linearity of the network dynamics, e.g. feedback and crosstalk. In cancer cells, this becomes even more complicated due to various types of alterations (e.g. DNA mutations, genome rearrangement, epigenetic changes, and pathway alterations). These alterations allow cancer cells to adapt to new conditions and evolve drug resistance
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