Mathematical modelling of breast cancer cells in response to endocrine therapy and Cdk4/6 inhibition.

Wei He,Diane M Demas,Isabel P Conde,William T Baumann,Ayesha N Shajahan-Haq

doi:10.1098/rsif.2020.0339

Wei He, Diane M Demas + Show 3 more

Open Access

https://doi.org/10.1098/rsif.2020.0339

Copy DOI

Abstract

Oestrogen receptor (ER)-positive breast cancer is responsive to a number of targeted therapies used clinically. Unfortunately, the continuous application of any targeted therapy often results in resistance to the therapy. Our ultimate goal is to use mathematical modelling to optimize alternating therapies that not only decrease proliferation but also stave off resistance. Toward this end, we measured levels of key proteins and proliferation over a 7-day time course in ER+ MCF-7 breast cancer cells. Treatments included endocrine therapy, either oestrogen deprivation, which mimics the effects of an aromatase inhibitor, or fulvestrant, an ER degrader. These data were used to calibrate a mathematical model based on key interactions between ER signalling and the cell cycle. We show that the calibrated model is capable of predicting the combination treatment of fulvestrant and oestrogen deprivation. Further, we show that we can add a new drug, palbociclib, to the model by measuring only two key proteins, cMyc and hyperphosphorylated RB1, and adjusting only parameters associated with the drug. The model is then able to predict the combination treatment of oestrogen deprivation and palbociclib. We illustrate the model's potential to explore protocols that limit proliferation and hold off resistance by not depending on any one therapy.

Highlights

Despite advances in treatment options, it is estimated that 42 260 women and men will die as a result of breast cancer in the USA this year [1]
MCF-7 cells depend on E2 for growth, and to enable us to control the level of E2 in the cell culture medium, cells were grown in phenol red-free 2 improved minimal essential medium (Life Technologies, Grand Island, NY; A10488-01) with 10% charcoal-stripped calf serum (CCS) and supplemented with 10 nM 17β-oestradiol
The training data for estimating the model parameters consisted of time-course measurements of the proteins in red in figure 1b for the –E2 and +E2 + ICI treatment conditions. These data are shown in figure 2, and it can be seen that the majority of measurements are statistically significant, the p21 measurements are quite noisy, and there is no real trend in the cyclinD1 data for the –E2 case

Summary

Introduction

Despite advances in treatment options, it is estimated that 42 260 women and men will die as a result of breast cancer in the USA this year [1]. Endocrine therapies act to decrease ER signalling in a variety of ways: (i) by depriving the ER of its ligand oestrogen (17β-oestradiol, hereafter referred to as E2) via aromatase inhibitors (e.g. letrozole), (ii) by competitively inhibiting the binding of E2 to the ER (the mode of action of tamoxifen), or (iii) by increasing the degradation of the ER via the proteasome (the mode of action of ICI 182 780 (ICI; Faslodex/fulvestrant)). This inhibition of ER signalling can halt proliferation by arresting cells in G1 and inducing cell death [2,3,4]. Such a model would allow for optimizing sequential therapies, subject to numerous constraints, to propose therapeutic protocols that can be evaluated experimentally

Objectives

Methods

Results

Conclusion