"Integrative mathematical modelling unveils hidden mechanism of resistance to PI3K inhibition and identifies new effective combination therapies for breast cancer"
The phosphatidylinositol 3-kinase (PI3K)-AKT-mTOR signalling pathway is a master regulator of cell growth and its activation is frequently associated with cell transformation and cancer. This is particularly common in breast cancer, where alterations in members of this pathway occur in over 50% of patients, irrespective of tumour subtype. Over the last decade, targeted drugs directed at the PI3K pathway, particularly inhibitors directed at PI3K, have been under intense clinical development. However, the emergence of acquired and/or adaptive resistance to these agents, the latter involving dynamic rewiring of signalling networks and crosstalk, has presented major challenges for the delivery of impactful treatments. This highlights the critical need to identify the molecular mechanisms through which tumour cells rewire their signalling outputs and bypass the inhibitory effect of targeted therapies, and to develop more effective combination therapies. To address these challenges, we constructed a multi-pathway mechanistic model based on differential equations that integrates the PI3K-AKT signalling axis with key cancer-relevant pathways, incorporating known feedback and cross-talk mechanisms. We calibrated this model using time-course kinetic data in response to inhibition of PI3K by a selective and clinically-relevant inhibitor BYL719 (BYL), obtained from the T47D breast cancer cell lines. Integrative simulations/experimental analyses reveal an unexpected role for the cyclin-dependent kinase inhibitor p21, which in contrary to its known growth-inhibitory function, appears to promote resistance to PI3K inhibition. Consistent with this, model simulations further predict a dynamic and adaptive reactivation of p21 following acute BYL treatment, which we validated experimentally using immunoblotting and phosphoproteomic profiling in both parental T47D cells and cells that have become resistant to BYL. Next, following a similar approach we recently published, we simulated the effect of various potential drug combinations targeting pair-wise nodes within the PI3K integrative network to identify potential co-targets that can be effectively combined with PI3K inhibition for more anti-tumour benefit. Among these, we predict dual inhibition of PI3K and the kinase PDK1 displays the most potent synergistic effect in suppressing pro-growth signalling and cancer cell growth. Model predictions were subsequently validated using immunoblotting and cell viability assays. In addition, analysis of breast cancer patient data from TCGA demonstrates concomitant overexpression of the genes encoding PIK3 and PDK1 is associated with worse patient survival, further supporting their validity as co-targets. Collectively, our integrative analyses uncovered novel resistance mechanisms against PI3K inhibition, and identified effective combination therapeutic strategies that overcome such resistance, leading to better treatment for PI3K-driven breast cancer.