Crosstalk between HER2 and PD-1/PD-L1 in Breast Cancer: From Clinical Applications to Mathematical Models.

Breast cancer is one of the major causes of mortality in women worldwide. The most aggressive breast cancer subtypes are human epidermal growth factor receptor-positive (HER2) and triple-negative breast cancers. Therapies targeting HER2 receptors have significantly improved HER2 breast cancer patient outcomes. However, several recent studies have pointed out the deficiency of existing treatment protocols in combatting disease relapse and improving response rates to treatment. Overriding the inherent actions of the immune system to detect and annihilate cancer via the immune checkpoint pathways is one of the important hallmarks of cancer. Thus, restoration of these pathways by various means of immunomodulation has shown beneficial effects in the management of various types of cancers, including breast. We herein review the recent progress in the management of HER2 breast cancer via HER2-targeted therapies, and its association with the programmed death receptor-1 (PD-1)/programmed death ligand-1 (PD-L1) axis. In order to link research in the areas of medicine and mathematics and point out specific opportunities for providing efficient theoretical analysis related to HER2 breast cancer management, we also review mathematical models pertaining to the dynamics of HER2 breast cancer and immune checkpoint inhibitors

Optimized scaling of translational factors in oncology: from xenografts to RECIST

Purpose: Tumor growth inhibition (TGI) models are regularly used to quantify the PK–PD relationship between drug concentration and\ua0in vivo\ua0efficacy in oncology. These models are typically calibrated with data from xenograft mice and before being used for clinical predictions, translational methods have to be applied. Currently, such methods are commonly based on replacing model components or scaling of model parameters. However, difficulties remain in how to accurately account for inter-species differences. Therefore, more research must be done before xenograft data can fully be utilized to predict clinical response. Method: To contribute to this research, we have calibrated TGI models to xenograft data for three drug combinations using the nonlinear mixed effects framework. The models were translated by replacing mice exposure with human exposure and used to make predictions of clinical response. Furthermore, in search of a better way of translating these models, we estimated an optimal way of scaling model parameters given the available clinical data. Results: The predictions were compared with clinical data and we found that clinical efficacy was overestimated. The estimated optimal scaling factors were similar to a standard allometric scaling exponent of − 0.25. Conclusions: We believe that given more data, our methodology could contribute to increasing the translational capabilities of TGI models. More specifically, an appropriate translational method could be developed for drugs with the same mechanism of action, which would allow for all preclinical data to be leveraged for new drugs of the same class. This would ensure that fewer clinically inefficacious drugs are tested in clinical trials

Heterogeneous Populations in B Cell Memory Responses and Glioblastoma Growth

Heterogeneity is a hallmark of biological systems at every conceivable scale. In this work, I develop computational methods for describing various interacting types of biological heterogeneity. I apply them to explore two scenarios of biomedical interest: the evocation of protective B cell responses by vaccination and the growth dynamics of an aggressive brain tumour. In the vast majority of currently licensed vaccines, antibody titres are strong correlates of vaccine-induced immunity. However, diseases like influenza, tuberculosis and malaria continue to escape efficient vaccination, and the mechanisms behind many established vaccines remain incompletely understood. In the first part of this work, I therefore develop a data-driven computational model of the B cell memory response to vaccination based on an ensemble of simulated germinal centres. This model can address immunisation problems of different difficulty levels by allowing both pathogen- and host-specific parameters to vary. Using this framework, I show that two distinct bottlenecks for successful vaccination exist: the availability of high-quality precursors for clonal selection and the efficiency of affinity maturation dependent on binding complexity. Together with experimental collaborators, we have used these results to interpret single-cell immunoglobulin sequencing data from a vaccination trial targeting the malaria parasite Plasmodium falciparum (Pf ). As predicted for a complex antigen, after repeated immunisation with Pf sporozoites, the clonal selection of potent germline and memory B cell precursors against a major surface protein outpaces affinity maturation because the majority of immunoglobulin gene mutations are affinity-neutral. These findings have implications for the design of potentially personalised vaccination strategies to induce potent B cell responses against structurally complex antigens. A quantitative understanding of functional cell heterogeneity in tumour growth promises insights into the fundamentals of cancer biology. In the second part of this work, I correspondingly develop mathematical models of glioblastoma growth. Employing a Bayesian approach to parameter estimation and incorporating a large body of experimental data from mouse models, I show that brain tumour stem cells drive exponential tumour growth while more differentiated tumour progenitor cells, although fast cycling, are unable to sustain expansion by themselves. Comparing a three-dimensional simulation of tumour growth to experimental growth curves, I derive that glioblastoma stem cells are highly migratory. Based on single-cell clonal tracing data and a combination of deterministic and stochastic modelling approaches, I identify their migration rate and explain experimentally observed clone size distributions. Finally, I employ the resulting fully quantified model of tumour growth to predict the response to two therapeutic interventions. These predictions were verified experimentally by our collaborators, suggesting that quantitative knowledge on the hierarchical subpopulation structure of a tumour may provide valuable guidance for treatment

Do we really understand how drug eluted from stents modulates arterial healing?

The advent of drug-eluting stents (DES) has revolutionised the treatment of coronary artery disease. These devices, coated with anti-proliferative drugs, are deployed into stenosed or occluded vessels, compressing the plaque to restore natural blood flow, whilst simultaneously combating the evolution of restenotic tissue. Since the development of the first stent, extensive research has investigated how further advancements in stent technology can improve patient outcome. Mathematical and computational modelling has featured heavily, with models focussing on structural mechanics, computational fluid dynamics, drug elution kinetics and subsequent binding within the arterial wall; often considered separately. Smooth Muscle Cell (SMC) proliferation and neointimal growth are key features of the healing process following stent deployment. However, models which depict the action of drug on these processes are lacking. In this article, we start by reviewing current models of cell growth, which predominantly emanate from cancer research, and available published data on SMC proliferation, before presenting a series of mathematical models of varying complexity to detail the action of drug on SMC growth in vitro. Our results highlight that, at least for Sodium Salicylate and Paclitaxel, the current state-of-the-art nonlinear saturable binding model is incapable of capturing the proliferative response of SMCs across a range of drug doses and exposure times. Our findings potentially have important implications on the interpretation of current computational models and their future use to optimise and control drug release from DES and drug-coated balloons

The Shaping of Cancer by the Tumour Microenvironment and Its Relevance for Cancer Therapy

In this book, we present a compilation of original research articles as well as review articles that are focused on improving our understanding of the molecular and cellular mechanisms by which cancer cells adapt to their microenvironment. These include the interplay between cancer cells and the surrounding microenvironmental cells (e.g., macrophages, tumor-infiltrating lymphocytes and myeloid cells) and microenvironmental environments (e.g., oxidative stress, pH, hypoxia) and the implications of this dynamic interaction to tumor radioresistance, chemoresistance, invasion and metastasis. Finally, the importance and relevance of these findings are translated to cancer therapy

Novel mechanisms of resistance to EGFR inhibitory drugs in non-small cell lung cancer

EGFR activating mutations are present in 10-40% of non-small cell lung cancer. Such mutations render tumour cells sensitive to EGFR tyrosine kinase inhibitors (EGFR TKIs), with responses of up to 80% in populations selected for the presence of an activating mutation. Unfortunately, almost all patients develop resistance after about a year. Clinically described mechanisms of resistance include the presence of a secondary mutation (T790M) in EGFR which prevents EGFR TKIs binding to the EGF receptor, and amplification MET which permits survival signalling via the ERBB3 receptor. However in 30% of cases, the mechanism of acquired resistance to EGFR TKIs is still unknown. My aim was to carry out a genome-wide siRNA screen to identify novel mechanisms of resistance to EGFR TKIs. I identified two genes that have not been implicated in EGFR TKI resistance previously, NF1 and DEPTOR, which are negative regulators of RAS and mTOR respectively. Depletion of NF1 or DEPTOR leads to increased resistance to EGFR TKIs via upregulation of MAPK signalling by direct and indirect mechanisms