Cell cycle-tailored targeting of metastatic melanoma: Challenges and opportunities

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.

Fluorescence-Based Quantitative and Spatial Analysis of Tumour Spheroids: A Proposed Tool to Predict Patient-Specific Therapy Response

Tumour spheroids are widely used to pre-clinically assess anti-cancer treatments. They are an excellent compromise between the lack of microenvironment encountered in adherent cell culture conditions and the great complexity of in vivo animal models. Spheroids recapitulate intra-tumour microenvironment-driven heterogeneity, a pivotal aspect for therapy outcome that is, however, often overlooked. Likely due to their ease, most assays measure overall spheroid size and/or cell death as a readout. However, as different tumour cell subpopulations may show a different biology and therapy response, it is paramount to obtain information from these distinct regions within the spheroid. We describe here a methodology to quantitatively and spatially assess fluorescence-based microscopy spheroid images by semi-automated software-based analysis. This provides a fast assay that accounts for spatial biological differences that are driven by the tumour microenvironment. We outline the methodology using detection of hypoxia, cell death and PBMC infiltration as examples, and we propose this procedure as an exploratory approach to assist therapy response prediction for personalised medicine.

Estimating parameters of a stochastic cell invasion model with fluorescent cell cycle labelling using approximate Bayesian computation

We develop a parameter estimation method based on approximate Bayesian computation (ABC) for a stochastic cell invasion model using fluorescent cell cycle labelling with proliferation, migration and crowding effects. Previously, inference has been performed on a deterministic version of the model fitted to cell density data, and not all parameters were identifiable. Considering the stochastic model allows us to harness more features of experimental data, including cell trajectories and cell count data, which we show overcomes the parameter identifiability problem. We demonstrate that, while difficult to collect, cell trajectory data can provide more information about the parameters of the cell invasion model. To handle the intractability of the likelihood function of the stochastic model, we use an efficient ABC algorithm based on sequential Monte Carlo. Rcpp and MATLAB implementations of the simulation model and ABC algorithm used in this study are available at https://github.com/michaelcarr-stats/FUCCI.

Mathematical Model of Tumour Spheroid Experiments with Real-Time Cell Cycle Imaging

Three-dimensional (3D) in vitro tumour spheroid experiments are an important tool for studying cancer progression and potential cancer drug therapies. Standard experiments involve growing and imaging spheroids to explore how different conditions lead to different rates of spheroid growth. These kinds of experiments, however, do not reveal any information about the spatial distribution of the cell cycle within the expanding spheroid. Since 2008, a new experimental technology called fluorescent ubiquitination-based cell cycle indicator (FUCCI) has enabled real-time in situ visualisation of the cell cycle progression. Observations of 3D tumour spheroids with FUCCI labelling reveal significant intratumoural structure, as the cell cycle status can vary with location. Although many mathematical models of tumour spheroid growth have been developed, none of the existing mathematical models are designed to interpret experimental observations with FUCCI labelling. In this work, we adapt the mathematical framework originally proposed by Ward and King (Math Med Biol 14:39-69, 1997. https://doi.org/10.1093/imammb/14.1.39 ) to produce a new mathematical model of FUCCI-labelled tumour spheroid growth. The mathematical model treats the spheroid as being composed of three subpopulations: (i) living cells in G1 phase that fluoresce red; (ii) living cells in S/G2/M phase that fluoresce green; and (iii) dead cells that are not fluorescent. We assume that the rates at which cells pass through different phases of the cell cycle, and the rate of cell death, depend upon the local oxygen concentration. Parameterising the new mathematical model using experimental measurements of cell cycle transition times, we show that the model can qualitatively capture important experimental observations that cannot be addressed using previous mathematical models. Further, we show that the mathematical model can be used to qualitatively mimic the action of anti-mitotic drugs applied to the spheroid. All software programs required to solve the nonlinear moving boundary problem associated with the new mathematical model are available on GitHub. at https://github.com/wang-jin-mathbio/Jin2021.

Synchronized oscillations in growing cell populations are explained by demographic noise

Understanding synchrony in growing populations is important for applications as diverse as epidemiology and cancer treatment. Recent experiments employing fluorescent reporters in melanoma cell lines have uncovered growing subpopulations exhibiting sustained oscillations, with nearby cells appearing to synchronize their cycles. In this study, we demonstrate that the behavior observed is consistent with long-lasting transient phenomenon initiated and amplified by the finite-sample effects and demographic noise. We present a novel mathematical analysis of a multistage model of cell growth, which accurately reproduces the synchronized oscillations. As part of the analysis, we elucidate the transient and asymptotic phases of the dynamics and derive an analytical formula to quantify the effect of demographic noise in the appearance of the oscillations. The implications of these findings are broad, such as providing insight into experimental protocols that are used to study the growth of asynchronous populations and, in particular, those investigations relating to anticancer drug discovery.

Oncogenic BRAF, endoplasmic reticulum stress, and autophagy: Crosstalk and therapeutic targets in cutaneous melanoma

BRAF is a member of the RAF family of serine/threonine-specific protein kinases. Oncogenic BRAF, in particular, BRAF V600E, can disturb the normal protein folding machinery in the endoplasmic reticulum (ER) leading to accumulation of unfolded/misfolded proteins in the ER lumen, a condition known as endoplasmic reticulum (ER) stress. To alleviate such conditions, ER-stressed cells have developed a highly robust and adaptable signaling network known as unfolded protein response (UPR). UPR is ordinarily a cytoprotective response and usually operates through the induction of autophagy, an intracellular lysosomal degradation pathway that directs damaged proteins, protein aggregates, and damaged organelles for bulk degradation and recycling. Both ER stress and autophagy are involved in the progression and chemoresistance of melanoma. Melanoma, which arises as a result of malignant transformation of melanocytes, exhibits exceptionally high therapeutic resistance. Many mechanisms of therapeutic resistance have been identified in individual melanoma patients and in preclinical BRAF-driven melanoma models. Recently, it has been recognized that oncogenic BRAF interacts with GRP78 and removes its inhibitory influence on the three fundamental ER stress sensors of UPR, PERK, IRE1α, and ATF6. Dissociation of GRP78 from these ER stress sensors prompts UPR that subsequently activates cytoprotective autophagy. Thus, pharmacological inhibition of BRAF-induced ER stress-mediated autophagy can potentially resensitize BRAF mutant melanoma tumors to apoptosis. However, the underlying molecular mechanism of how oncogenic BRAF elevates the basal level of ER stress-mediated autophagy in melanoma tumors is not well characterized. A better understanding of the crosstalk between oncogenic BRAF, ER stress and autophagy may provide a rationale for improving existing cancer therapies and identify novel targets for therapeutic intervention of melanoma.

Examining Go-or-Grow Using Fluorescent Cell-Cycle Indicators and Cell-Cycle-Inhibiting Drugs

The go-or-grow hypothesis states that adherent cells undergo reversible phenotype switching between migratory and proliferative states, with cells in the migratory state being more motile than cells in the proliferative state. Here, we examine go-or-grow in two-dimensional in vitro assays using melanoma cells with fluorescent cell-cycle indicators and cell-cycle-inhibiting drugs. We analyze the experimental data using single-cell tracking to calculate mean diffusivities and compare motility between cells in different cell-cycle phases and in cell-cycle arrest. Unequivocally, our analysis does not support the go-or-grow hypothesis. We present clear evidence that cell motility is independent of the cell-cycle phase and that nonproliferative arrested cells have the same motility as cycling cells.

Mathematical models incorporating a multi-stage cell cycle replicate normally-hidden inherent synchronization in cell proliferation

We present a suite of experimental data showing that cell proliferation assays, prepared using standard methods thought to produce asynchronous cell populations, persistently exhibit inherent synchronization. Our experiments use fluorescent cell cycle indicators to reveal the normally hidden cell synchronization, by highlighting oscillatory subpopulations within the total cell population. These oscillatory subpopulations would never be observed without these cell cycle indicators. On the other hand, our experimental data show that the total cell population appears to grow exponentially, as in an asynchronous population. We reconcile these seemingly inconsistent observations by employing a multi-stage mathematical model of cell proliferation that can replicate the oscillatory subpopulations. Our study has important implications for understanding and improving experimental reproducibility. In particular, inherent synchronization may affect the experimental reproducibility of studies aiming to investigate cell cycle-dependent mechanisms, including changes in migration and drug response.

The invasion speed of cell migration models with realistic cell cycle time distributions

Cell proliferation is typically incorporated into stochastic mathematical models of cell migration by assuming that cell divisions occur after an exponentially distributed waiting time. Experimental observations, however, show that this assumption is often far from the real cell cycle time distribution (CCTD). Recent studies have suggested an alternative approach to modelling cell proliferation based on a multi-stage representation of the CCTD. In this paper we investigate the connection between the CCTD and the speed of the collective invasion. We first state a result for a general CCTD, which allows the computation of the invasion speed using the Laplace transform of the CCTD. We use this to deduce the range of speeds for the general case. We then focus on the more realistic case of multi-stage models, using both a stochastic agent-based model and a set of reaction-diffusion equations for the cells' average density. By studying the corresponding travelling wave solutions, we obtain an analytical expression for the speed of invasion for a general N-stage model with identical transition rates, in which case the resulting cell cycle times are Erlang distributed. We show that, for a general N-stage model, the Erlang distribution and the exponential distribution lead to the minimum and maximum invasion speed, respectively. This result allows us to determine the range of possible invasion speeds in terms of the average proliferation time for any multi-stage model.

Microenvironment-Driven Dynamic Heterogeneity and Phenotypic Plasticity as a Mechanism of Melanoma Therapy Resistance

Drug resistance constitutes a major challenge in designing melanoma therapies. Microenvironment-driven tumor heterogeneity and plasticity play a key role in this phenomenon. Melanoma is highly heterogeneous with diverse genomic alterations and expression of different biological markers. In addition, melanoma cells are highly plastic and capable of adapting quickly to changing microenvironmental conditions. These contribute to variations in therapy response and durability between individual melanoma patients. In response to changing microenvironmental conditions, like hypoxia and nutrient starvation, proliferative melanoma cells can switch to an invasive slow-cycling state. Cells in this state are more aggressive and metastatic, and show increased intrinsic drug resistance. During continuous treatment, slow-cycling cells are enriched within the tumor and give rise to a new proliferative subpopulation with increased drug resistance, by exerting their stem cell-like behavior and phenotypic plasticity. In melanoma, the proliferative and invasive states are defined by high and low microphthalmia-associated transcription factor (MITF) expression, respectively. It has been observed that in MITFhigh melanomas, inhibition of MITF increases the efficacy of targeted therapies and delays the acquisition of drug resistance. Contrarily, MITF is downregulated in melanomas with acquired drug resistance. According to the phenotype switching theory, the gene expression profile of the MITFlow state is predominantly regulated by WNT5A, AXL, and NF-κB signaling. Thus, different combinations of therapies should be effective in treating different phases of melanoma, such as the combination of targeted therapies with inhibitors of MITF expression during the initial treatment phase, but with inhibitors of WNT5A/AXL/NF-κB signaling during relapse.

Endogenous Replication Stress Marks Melanomas Sensitive to CHEK1 Inhibitors In Vivo

Purpose: Checkpoint kinase 1 inhibitors (CHEK1i) have single-agent activity in vitro and in vivo Here, we have investigated the molecular basis of this activity.Experimental Design: We have assessed a panel of melanoma cell lines for their sensitivity to the CHEK1i GNE-323 and GDC-0575 in vitro and in vivo The effects of these compounds on responses to DNA replication stress were analyzed in the hypersensitive cell lines.Results: A subset of melanoma cell lines is hypersensitive to CHEK1i-induced cell death in vitro, and the drug effectively inhibits tumor growth in vivo In the hypersensitive cell lines, GNE-323 triggers cell death without cells entering mitosis. CHEK1i treatment triggers strong RPA2 hyperphosphorylation and increased DNA damage in only hypersensitive cells. The increased replication stress was associated with a defective S-phase cell-cycle checkpoint. The number and intensity of pRPA2 Ser4/8 foci in untreated tumors appeared to be a marker of elevated replication stress correlated with sensitivity to CHEK1i.Conclusions: CHEK1i have single-agent activity in a subset of melanomas with elevated endogenous replication stress. CHEK1i treatment strongly increased this replication stress and DNA damage, and this correlated with increased cell death. The level of endogenous replication is marked by the pRPA2Ser4/8 foci in the untreated tumors, and may be a useful marker of replication stress in vivoClin Cancer Res; 24(12); 2901-12. ©2018 AACR.