Growth and adaptation mechanisms of tumour spheroids with time-dependent oxygen availability

Developing New Peptides and Peptide-Drug Conjugates for Targeting the FGFR2 Receptor-Expressing Tumor Cells and 3D Spheroids

In this work, we utilized a biomimetic approach for targeting KATO (III) tumor cells and 3D tumoroids. Specifically, the binding interactions of the bioactive short peptide sequences ACSAG (A-pep) and LPHVLTPEAGAT (L-pep) with the fibroblast growth factor receptor (FGFR2) kinase domain was investigated for the first time. Both peptides have been shown to be derived from natural resources previously. We then created a new fusion trimer peptide ACSAG-LPHVLTPEAGAT-GASCA (Trimer-pep) and investigated its binding interactions with the FGFR2 kinase domain in order to target the fibroblast growth factor receptor 2 (FGFR2), which is many overexpressed in tumor cells. Molecular docking and molecular dynamics simulation studies revealed critical interactions with the activation loop, hinge and glycine-rich loop regions of the FGFR2 kinase domain. To develop these peptides for drug delivery, DOX (Doxorubicin) conjugates of the peptides were created. Furthermore, the binding of the peptides with the kinase domain was further confirmed through surface plasmon resonance studies. Cell studies with gastric cancer cells (KATO III) revealed that the conjugates and the peptides induced higher cytotoxicity in the tumor cells compared to normal cells. Following confirmation of cytotoxicity against tumor cells, the ability of the conjugates and the peptides to penetrate 3D spheroids was investigated by evaluating their permeation in co-cultured spheroids grown with KATO (III) and colon tumor-associated fibroblasts (CAFs). Results demonstrated that Trimer-pep conjugated with DOX showed the highest permeation, while the ACSAG conjugate also demonstrated reasonable permeation of the drug. These results indicate that these peptides may be further explored and potentially utilized to create drug conjugates for targeting tumor cells expressing FGFR2 for developing therapeutics.

Real-Time Cell Cycle Imaging in a 3D Cell Culture Model of Melanoma, Quantitative Analysis, Optical Clearing, and Mathematical Modeling

Aberrant cell cycle progression is a hallmark of solid tumors. Therefore, cell cycle analysis is an invaluable technique to study cancer cell biology. However, cell cycle progression has been most commonly assessed by methods that are limited to temporal snapshots or that lack spatial information. In this chapter, we describe a technique that allows spatiotemporal real-time tracking of cell cycle progression of individual cells in a multicellular context. The power of this system lies in the use of 3D melanoma spheroids generated from melanoma cells engineered with the fluorescent ubiquitination-based cell cycle indicator (FUCCI). This technique, combined with mathematical modeling, allows us to gain further and more detailed insight into several relevant aspects of solid cancer cell biology, such as tumor growth, proliferation, invasion, and drug sensitivity.

Formation and Growth of Co-Culture Tumour Spheroids: New Compartment-Based Mathematical Models and Experiments

Co-culture tumour spheroid experiments are routinely performed to investigate cancer progression and test anti-cancer therapies. Therefore, methods to quantitatively characterise and interpret co-culture spheroid growth are of great interest. However, co-culture spheroid growth is complex. Multiple biological processes occur on overlapping timescales and different cell types within the spheroid may have different characteristics, such as differing proliferation rates or responses to nutrient availability. At present there is no standard, widely-accepted mathematical model of such complex spatio-temporal growth processes. Typical approaches to analyse these experiments focus on the late-time temporal evolution of spheroid size and overlook early-time spheroid formation, spheroid structure and geometry. Here, using a range of ordinary differential equation-based mathematical models and parameter estimation, we interpret new co-culture experimental data. We provide new biological insights about spheroid formation, growth, and structure. As part of this analysis we connect Greenspan's seminal mathematical model to co-culture data for the first time. Furthermore, we generalise a class of compartment-based spheroid mathematical models that have previously been restricted to one population so they can be applied to multiple populations. As special cases of the general model, we explore multiple natural two population extensions to Greenspan's seminal model and reveal biological mechanisms that can describe the internal dynamics of growing co-culture spheroids and those that cannot. This mathematical and statistical modelling-based framework is well-suited to analyse spheroids grown with multiple different cell types and the new class of mathematical models provide opportunities for further mathematical and biological insights.

Profile-Wise Analysis: A profile likelihood-based workflow for identifiability analysis, estimation, and prediction with mechanistic mathematical models

Interpreting data using mechanistic mathematical models provides a foundation for discovery and decision-making in all areas of science and engineering. Developing mechanistic insight by combining mathematical models and experimental data is especially critical in mathematical biology as new data and new types of data are collected and reported. Key steps in using mechanistic mathematical models to interpret data include: (i) identifiability analysis; (ii) parameter estimation; and (iii) model prediction. Here we present a systematic, computationally-efficient workflow we call Profile-Wise Analysis (PWA) that addresses all three steps in a unified way. Recently-developed methods for constructing 'profile-wise' prediction intervals enable this workflow and provide the central linkage between different workflow components. These methods propagate profile-likelihood-based confidence sets for model parameters to predictions in a way that isolates how different parameter combinations affect model predictions. We show how to extend these profile-wise prediction intervals to two-dimensional interest parameters. We then demonstrate how to combine profile-wise prediction confidence sets to give an overall prediction confidence set that approximates the full likelihood-based prediction confidence set well. Our three case studies illustrate practical aspects of the workflow, focusing on ordinary differential equation (ODE) mechanistic models with both Gaussian and non-Gaussian noise models. While the case studies focus on ODE-based models, the workflow applies to other classes of mathematical models, including partial differential equations and simulation-based stochastic models. Open-source software on GitHub can be used to replicate the case studies.