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

Modeling of Mouse Experiments Suggests that Optimal Anti-Hormonal Treatment for Breast Cancer is Diet-Dependent

Estrogen receptor positive breast cancer is frequently treated with anti-hormonal treatment such as aromatase inhibitors (AI). Interestingly, a high body mass index has been shown to have a negative impact on AI efficacy, most likely due to disturbances in steroid metabolism and adipokine production. Here, we propose a mathematical model based on a system of ordinary differential equations to investigate the effect of high-fat diet on tumor growth. We inform the model with data from mouse experiments, where the animals are fed with high-fat or control (normal) diet. By incorporating AI treatment with drug resistance into the model and by solving optimal control problems we found differential responses for control and high-fat diet. To the best of our knowledge, this is the first attempt to model optimal anti-hormonal treatment for breast cancer in the presence of drug resistance. Our results underline the importance of considering high-fat diet and obesity as factors influencing clinical outcomes during anti-hormonal therapies in breast cancer patients.

Modeling breast cancer proliferation, drug synergies, and alternating therapies

Estrogen receptor positive (ER+) breast cancer is responsive to a number of targeted therapies used clinically. Unfortunately, the continuous application of targeted therapy often results in resistance, driving the consideration of combination and alternating therapies. Toward this end, we developed a mathematical model that can simulate various mono, combination, and alternating therapies for ER + breast cancer cells at different doses over long time scales. The model is used to look for optimal drug combinations and predicts a significant synergism between Cdk4/6 inhibitors in combination with the anti-estrogen fulvestrant, which may help explain the clinical success of adding Cdk4/6 inhibitors to anti-estrogen therapy. Furthermore, the model is used to optimize an alternating treatment protocol so it works as well as monotherapy while using less total drug dose.

Mathematically Modeling the Effect of Endocrine and Cdk4/6 Inhibitor Therapies on Breast Cancer Cells

Mathematical modeling of cancer systems is beginning to be used to design better treatment regimens, especially in chemotherapy and radiotherapy. The effectiveness of mathematical modeling to inform treatment decisions and identify therapy protocols, some of which are highly nonintuitive, is because it enables the exploration of a huge number of therapeutic possibilities. Considering the immense cost of laboratory research and clinical trials, these nonintuitive therapy protocols would likely never be found by experimental approaches. While much of the work to date in this area has involved high-level models, which look simply at overall tumor growth or the interaction of resistant and sensitive cell types, mechanistic models that integrate molecular biology and pharmacology can contribute greatly to the discovery of better cancer treatment regimens. These mechanistic models are better able to account for the effect of drug interactions and the dynamics of therapy. The aim of this chapter is to demonstrate the use of ordinary differential equation-based mechanistic models to describe the dynamic interactions between the molecular signaling of breast cancer cells and two key clinical drugs. In particular, we illustrate the procedure for building a model of the response of MCF-7 cells to standard therapies used in the clinic. Such mathematical models can be used to explore the vast number of potential protocols to suggest better treatment approaches.

Facing the CDK4/6i resistance dilemma in patients with breast cancer, exploration of the resistance mechanism and possible reverse strategy: A narrative review

Breast cancer is one of the highest rates of malignancy of women, approximate 70% metastatic breast cancer are hormone receptor positive (HR+) and human epidermal growth factor receptor 2 negative (HER2-). Hormone therapy is the primary strategy of HR+/HER2- metastatic breast cancer. With the permission of cyclin-dependent kinase 4 and 6 inhibitors (CDK4/6i), progress free survival and overall survival were significantly licensed. However, inevitable outcome of CDK4/6i resistance has become the main reason that restricts the clinical benefit of patients. In recent years, the research on dealing with drug resistance has become a hot topic, a large number of molecular mechanisms have been focused, and a lot of experiments have been carried out at the preclinical level. This review summarizes the current knowledge of CDK4/6i resistance mechanism, systematically expounds the signaling pathways and targets leading to CDK4/6i resistance, analyzes different ways and mechanisms, and provides theoretical guidance for the clinical reversal of endocrine therapy resistance.

Can Systems Biology Advance Clinical Precision Oncology?

Precision oncology is perceived as a way forward to treat individual cancer patients. However, knowing particular cancer mutations is not enough for optimal therapeutic treatment, because cancer genotype-phenotype relationships are nonlinear and dynamic. Systems biology studies the biological processes at the systems' level, using an array of techniques, ranging from statistical methods to network reconstruction and analysis, to mathematical modeling. Its goal is to reconstruct the complex and often counterintuitive dynamic behavior of biological systems and quantitatively predict their responses to environmental perturbations. In this paper, we review the impact of systems biology on precision oncology. We show examples of how the analysis of signal transduction networks allows to dissect resistance to targeted therapies and inform the choice of combinations of targeted drugs based on tumor molecular alterations. Patient-specific biomarkers based on dynamical models of signaling networks can have a greater prognostic value than conventional biomarkers. These examples support systems biology models as valuable tools to advance clinical and translational oncological research.

Expanding control of the tumor cell cycle with a CDK2/4/6 inhibitor

The CDK4/6 inhibitor, palbociclib (PAL), significantly improves progression-free survival in HR⁺/HER2^- breast cancer when combined with anti-hormonals. We sought to discover PAL resistance mechanisms in preclinical models and through analysis of clinical transcriptome specimens, which coalesced on induction of MYC oncogene and Cyclin E/CDK2 activity. We propose that targeting the G₁ kinases CDK2, CDK4, and CDK6 with a small-molecule overcomes resistance to CDK4/6 inhibition. We describe the pharmacodynamics and efficacy of PF-06873600 (PF3600), a pyridopyrimidine with potent inhibition of CDK2/4/6 activity and efficacy in multiple in vivo tumor models. Together with the clinical analysis, MYC activity predicts (PF3600) efficacy across multiple cell lineages. Finally, we find that CDK2/4/6 inhibition does not compromise tumor-specific immune checkpoint blockade responses in syngeneic models. We anticipate that (PF3600), currently in phase 1 clinical trials, offers a therapeutic option to cancer patients in whom CDK4/6 inhibition is insufficient to alter disease progression.

Personalized Medicine for Neuroblastoma: Moving from Static Genotypes to Dynamic Simulations of Drug Response

High-risk neuroblastoma is an aggressive childhood cancer that is characterized by high rates of chemoresistance and frequent metastatic relapse. A number of studies have characterized the genetic and epigenetic landscape of neuroblastoma, but due to a generally low mutational burden and paucity of actionable mutations, there are few options for applying a comprehensive personalized medicine approach through the use of targeted therapies. Therefore, the use of multi-agent chemotherapy remains the current standard of care for neuroblastoma, which also conceptually limits the opportunities for developing an effective and widely applicable personalized medicine approach for this disease. However, in this review we outline potential approaches for tailoring the use of chemotherapy agents to the specific molecular characteristics of individual tumours by performing patient-specific simulations of drug-induced apoptotic signalling. By incorporating multiple layers of information about tumour-specific aberrations, including expression as well as mutation data, these models have the potential to rationalize the selection of chemotherapeutics contained within multi-agent treatment regimens and ensure the optimum response is achieved for each individual patient.