Tumour-stromal interactions generate emergent persistence in collective cancer cell migration

Re-evaluation of publicly available gene-expression databases using machine-learning yields a maximum prognostic power in breast cancer

Gene expression signatures refer to patterns of gene activities and are used to classify different types of cancer, determine prognosis, and guide treatment decisions. Advancements in high-throughput technology and machine learning have led to improvements to predict a patient's prognosis for different cancer phenotypes. However, computational methods for analyzing signatures have not been used to evaluate their prognostic power. Contention remains on the utility of gene expression signatures for prognosis. The prevalent approaches include random signatures, expert knowledge, and machine learning to construct an improved signature. We unify these approaches to evaluate their prognostic power. Re-evaluation of publicly available gene-expression data from 8 databases with 9 machine-learning models revealed previously unreported results. Gene-expression signatures are confirmed to be useful in predicting a patient's prognosis. Convergent evidence from [Formula: see text] 10,000 signatures implicates a maximum prognostic power. By calculating the concordance index, which measures how well patients with different prognoses can be discriminated, we show that a signature can correctly discriminate patients' prognoses no more than 80% of the time. Additionally, we show that more than 50% of the potentially available information is still missing at this value. We surmise that an accurate prognosis must incorporate molecular, clinical, histological, and other complementary factors.

Numerical Study on Dynamics of Blood Cell Migration and Deformation in Atherosclerotic Vessels

A phase field model is used to study the effect of atherosclerotic plaque on hemodynamics. The migration of cells in blood flows is described by a set of multiple phase field equations, which incorporate elastic energies and the interacting effects of cells. Several simulations are carried out to reveal the influences of initial velocities of blood cells, cellular elasticity and block rates of hemodynamic vessels. The results show that the cell deformation increases with the growth of the initial active velocity and block rate but with the decrease of the cellular elasticity. The atherosclerotic plaque not only affects the deformation and migration of cells but also can promote the variation in hemodynamic properties. The atherosclerotic plaque causes a burst in cell velocity, and the greater the block rate and cellular elasticity, the more dramatic the variation of instantaneous velocity. The present work demonstrates that the phase field method could be extended to reveal formation atherosclerosis at the microscopic level from the perspective of hemodynamics.

Phase transitions in biology: from bird flocks to population dynamics

Phase transitions are an important and extensively studied concept in physics. The insights derived from understanding phase transitions in physics have recently and successfully been applied to a number of different phenomena in biological systems. Here, we provide a brief review of phase transitions and their role in explaining biological processes ranging from collective behaviour in animal flocks to neuronal firing. We also highlight a new and exciting area where phase transition theory is particularly applicable: population collapse and extinction. We discuss how phase transition theory can give insight into a range of extinction events such as population decline due to climate change or microbial responses to stressors such as antibiotic treatment.

Modelling collective cell motion: are on- and off-lattice models equivalent?

Biological processes, such as embryonic development, wound repair and cancer invasion, or bacterial swarming and fruiting body formation, involve collective motion of cells as a coordinated group. Collective cell motion of eukaryotic cells often includes interactions that result in polar alignment of cell velocities, while bacterial patterns typically show features of apolar velocity alignment. For analysing the population-scale effects of these different alignment mechanisms, various on- and off-lattice agent-based models have been introduced. However, discriminating model-specific artefacts from general features of collective cell motion is challenging. In this work, we focus on equivalence criteria at the population level to compare on- and off-lattice models. In particular, we define prototypic off- and on-lattice models of polar and apolar alignment, and show how to obtain an on-lattice from an off-lattice model of velocity alignment. By characterizing the behaviour and dynamical description of collective migration models at the macroscopic level, we suggest the type of phase transitions and possible patterns in the approximative macroscopic partial differential equation descriptions as informative equivalence criteria between on- and off-lattice models. This article is part of the theme issue 'Multi-scale analysis and modelling of collective migration in biological systems'.

What makes leader cells arise: Intrinsic properties and support from neighboring cells

Cancer cells collectively invading as a cohesive and polarized group is termed collective invasion, which is a fundamental property of many types of cancers. In this multicellular unit, cancer cells are heterogeneous, consisting of two morphologically and functionally distinct subpopulations, leader cells and follower cells. Leader cells at the invasive front are responsible for exploring the microenvironment, paving the way, and transmitting information to follower cells. Here, in this review, we will describe the important role of leader cells in collective invasion and the emerging underlying mechanisms of leader cell formation including intrinsic properties and the support from neighboring cells. It will help us to elucidate the essence of collective invasion and provide new anticancer therapeutic clues.

Upregulation of lncRNA AGAP2-AS1 is an independent predictor of poor survival in patients with clear cell renal carcinoma

Long non-coding RNA (lncRNA) AGAP2-AS1 has been reported to be a potential biomarker for a variety of cancer types, while its function in clear cell renal carcinoma (ccRCC) has not yet been fully determined. The current study aimed to determine the value of lncRNA AGAP2-AS1 in ccRCC based on The Cancer Genome Atlas (TCGA) database. The association between AGAP2-AS1 expression and associated clinical characters were analyzed using the Wilcoxon signed-rank test and logistic regression. The diagnostic value of AGAP2-AS1 expression in ccRCC tissue was assessed using receiver operating characteristic (ROC) curve analysis. Clinicopathological characteristics associated with overall survival in patients with TCGA were analyzed using Cox regression and the Kaplan-Meier method. Gene set enrichment analysis (GSEA) was also performed to assess the biological function of AGAP2-AS1. The results demonstrated that increased expression of AGAP2-AS1 in ccRCC was significantly associated with male, T3/T4, lymph node metastasis, distant metastasis and high tumor stage (III/IV; all, P<0.05). The area under the ROC curve (normal vs. all tumors) was revealed to be 0.891. Kaplan-Meier survival analysis indicated that ccRCC with high lncRNA AGAP2-AS1 exhibited a worse prognosis compared with low AGAP2-AS1 (P<0.001). The univariate analysis revealed that high expression of AGAP2-AS1 was significantly associated with poor overall survival [hazard ratio (HR). 1.85; 95% confidence interval (CI), 1.48-2.33; P<0.001). Multivariate analysis revealed that AGAP2-AS1 remained independently associated with overall survival, with a HR of 1.57 (CI, 1.21-2.03; P<0.01). GSEA outcome demonstrated that stromal stimulation, angiogenesis, epithelial to mesenchymal transition, basal cell carcinoma, ECM receptor interaction and the Notch signaling pathway were differentially enriched in the AGAP2-AS1 high expression phenotype. Therefore, the high expression of AGAP2-AS1 may be an independent predictor of poor survival in patients with ccRCC.

Biomechanics of Collective Cell Migration in Cancer Progression: Experimental and Computational Methods

Cell migration is essential for regulating many biological processes in physiological or pathological conditions, including embryonic development and cancer invasion. In vitro and in silico studies suggest that collective cell migration is associated with some biomechanical particularities such as restructuring of extracellular matrix (ECM), stress and force distribution profiles, and reorganization of the cytoskeleton. Therefore, the phenomenon could be understood by an in-depth study of cells' behavior determinants, including but not limited to mechanical cues from the environment and from fellow "travelers". This review article aims to cover the recent development of experimental and computational methods for studying the biomechanics of collective cell migration during cancer progression and invasion. We also summarized the tested hypotheses regarding the mechanism underlying collective cell migration enabled by these methods. Together, the paper enables a broad overview on the methods and tools currently available to unravel the biophysical mechanisms pertinent to cell collective migration as well as providing perspectives on future development toward eventually deciphering the key mechanisms behind the most lethal feature of cancer.

The Ric-8A/Gα13/FAK signalling cascade controls focal adhesion formation during neural crest cell migration in Xenopus

Ric-8A is a pleiotropic guanine nucleotide exchange factor involved in the activation of various heterotrimeric G-protein pathways during adulthood and early development. Here, we sought to determine the downstream effectors of Ric-8A during the migration of the vertebrate cranial neural crest (NC) cells. We show that the Gα13 knockdown phenocopies the Ric-8A morphant condition, causing actin cytoskeleton alteration, protrusion instability, and a strong reduction in the number and dynamics of focal adhesions. In addition, the overexpression of Gα13 is sufficient to rescue Ric-8A-depleted cells. Ric-8A and Gα13 physically interact and colocalize in protrusions of the cells leading edge. The focal adhesion kinase FAK colocalizes and interacts with the endogenous Gα13, and a constitutively active form of Src efficiently rescues the Gα13 morphant phenotype in NC cells. We propose that Ric-8A-mediated Gα13 signalling is required for proper cranial NC cell migration by regulating focal adhesion dynamics and protrusion formation.

Non-local Parabolic and Hyperbolic Models for Cell Polarisation in Heterogeneous Cancer Cell Populations

Tumours consist of heterogeneous populations of cells. The sub-populations can have different features, including cell motility, proliferation and metastatic potential. The interactions between clonal sub-populations are complex, from stable coexistence to dominant behaviours. The cell–cell interactions, i.e. attraction, repulsion and alignment, processes critical in cancer invasion and metastasis, can be influenced by the mutation of cancer cells. In this study, we develop a mathematical model describing cancer cell invasion and movement for two polarised cancer cell populations with different levels of mutation. We consider a system of non-local hyperbolic equations that incorporate cell–cell interactions in the speed and the turning behaviour of cancer cells, and take a formal parabolic limit to transform this model into a non-local parabolic model. We then investigate the possibility of aggregations to form, and perform numerical simulations for both hyperbolic and parabolic models, comparing the patterns obtained for these models.

Dysregulation in Actin Cytoskeletal Organization Drives Increased Stiffness and Migratory Persistence in Polyploidal Giant Cancer Cells

Polyploidal giant cancer cells (PGCCs) have been observed by pathologists in patient tumor samples and are especially prominent in late stage, high grade disease or after chemotherapy. However, they are often overlooked due to their apparent dormancy. Recent research has shown PGCCs to be chemoresistant and express stem-like features, traits associated with disease progression and relapse. Here, we show the preferential survival of PGCCs during Paclitaxel (PTX) treatment and used multiple particle tracking analysis to probe their unique biophysical phenotype. We show that PGCCs have higher inherent cytoplasmic and nuclear stiffness in order to withstand the mechanical stress associated with their increased size and the chemical stress from PTX treatment. Inhibitor studies show the involvement of a dysregulated RhoA-Rock1 pathway and overall actin cytoskeletal network as the underlying mechanism for the altered biophysical phenotype of PGCCs. Furthermore, PGCCs exhibit a slow but persistent migratory phenotype, a trait commonly associated with metastatic dissemination and invasiveness. This work demonstrates the clinical relevance and the need to study this subpopulation, in order to devise therapeutic strategies to combat disease relapse. By highlighting the unique biophysical phenotype of PGCCs, we hope to provide unique avenues for therapeutic targeting of these cells in disease treatment.

Ric-8A, a GEF for heterotrimeric G-proteins, controls cranial neural crest cell polarity during migration

The neural crest (NC) is a transient embryonic cell population that migrates extensively during development. Ric-8A, a guanine nucleotide exchange factor (GEF) for different Gα subunits regulates cranial NC (CNC) cell migration in Xenopus through a mechanism that still remains to be elucidated. To properly migrate, CNC cells establish an axis of polarization and undergo morphological changes to generate protrusions at the leading edge and retraction of the cell rear. Here, we aim to study the role of Ric-8A in cell polarity during CNC cell migration by examining whether its signaling affects the localization of GTPase activity in Xenopus CNC using GTPase-based probes in live cells and aPKC and Par3 as polarity markers. We show that the levels of Ric-8A are critical during migration and affect the localization of polarity markers and the subcellular localization of GTPase activity, suggesting that Ric-8A, probably through heterotrimeric G-protein signaling, regulates cell polarity during CNC migration.

Enhancing Cancer Cell Collective Motion and Speeding up Confluent Endothelial Dynamics through Cancer Cell Invasion and Aggregation

We report the experimental observation of speeded-up collective motion of the monolayer endothelia-cancer mixture on a collagen-coated substrate, after the invasion of a small fraction of motile cancer cells into the confluent endothelial monolayer, through disrupting cell-cell junctions. It is found that, with an increasing waiting time, the cancer-free confluent endothelial monolayer exhibits a dynamical slowing-down of liquidlike micromotion with a gradually decreasing degree of superdiffusion. After invasion, cancer cells aggregate and exhibit turbulentlike cooperative motion, which is enhanced with the increasing size of gradually aggregated cancer clusters, confined by the fluctuating boundaries of surrounding endothelial cells. It, in turn, enhances the surrounding endothelial cell motion and speeds up the originally slowed-down motion.

Modeling triple-negative breast cancer heterogeneity: Effects of stromal macrophages, fibroblasts and tumor vasculature

A hallmark of breast tumors is its spatial heterogeneity that includes its distribution of cancer stem cells and progenitor cells, but also heterogeneity in the tumor microenvironment. In this study we focus on the contributions of stromal cells, specifically macrophages, fibroblasts, and endothelial cells on tumor progression. We develop a computational model of triple-negative breast cancer based on our previous work and expand it to include macrophage infiltration, fibroblasts, and angiogenesis. In vitro studies have shown that the secretomes of tumor-educated macrophages and fibroblasts increase both the migration and proliferation rates of triple-negative breast cancer cells. In vivo studies also demonstrated that blocking signaling of selected secreted factors inhibits tumor growth and metastasis in mouse xenograft models. We investigate the influences of increased migration and proliferation rates on tumor growth, the effect of the presence on fibroblasts or macrophages on growth and morphology, and the contributions of macrophage infiltration on tumor growth. We find that while the presence of macrophages increases overall tumor growth, the increase in macrophage infiltration does not substantially increase tumor growth and can even stifle tumor growth at excessive rates.

Semblance of Heterogeneity in Collective Cell Migration

Cell population heterogeneity is increasingly a focus of inquiry in biological research. For example, cell migration studies have investigated the heterogeneity of invasiveness and taxis in development, wound healing, and cancer. However, relatively little effort has been devoted to exploring when heterogeneity is mechanistically relevant and how to reliably measure it. Statistical methods from the animal movement literature offer the potential to analyze heterogeneity in collections of cell tracking data. A popular measure of heterogeneity, which we use here as an example, is the distribution of delays in directional cross-correlation. Employing a suitably generic, yet minimal, model of collective cell movement in three dimensions, we show how using such measures to quantify heterogeneity in tracking data can result in the inference of heterogeneity where there is none. Our study highlights a potential pitfall in the statistical analysis of cell population heterogeneity, and we argue that this can be mitigated by the appropriate choice of null models.

Xenopus as a model organism to study heterotrimeric G-protein pathway during collective cell migration of neural crest

Collective cell migration is essential in many fundamental aspects of normal development, like morphogenesis, organ formation, wound healing, and immune responses, as well as in the etiology of severe pathologies, like cancer metastasis. In spite of the huge amount of data accumulated on cell migration, such a complex process involves many molecular actors, some of which still remain to be functionally characterized. One of these signals is the heterotrimeric G-protein pathway that has been studied mainly in gastrulation movements. Recently we have reported that Ric-8A, a GEF for Gα proteins, plays an important role in neural crest migration in Xenopus development. Xenopus neural crest cells, a highly migratory embryonic cell population induced at the border of the neural plate that migrates extensively in order to differentiate in other tissues during development, have become a good model to understand the dynamics that regulate cell migration. In this review, we aim to provide sufficient evidence supporting how useful Xenopus model with its different tools, such as explants and transplants, paired with improved in vivo imaging techniques, will allow us to tackle the multiple signaling mechanisms involved in neural crest cell migration.

The role of tumor microenvironment in collective tumor cell invasion

For many cancer types, cancer cells invade into surrounding tissues by collective movement of cell groups that remain connected via cell-cell junctions. This migration is completely distinguished from single-cell migration, in which cancer cells disrupt the tight intercellular junctions and gain a mesenchymal phenotype. Recently, emerging evidence has revealed that collective cell invasion depends on not only cell-intrinsic mechanisms but also on extracellular mechanisms by bidirectional interplay between the tumor cell and the tumor environment. Herein, in this review we discuss the role and underline mechanisms of tumor microenvironment in collective tumor cell invasion, particularly focusing on extracellular matrix remodeling and cross-talk between tumor and stromal cells.

Modelling collective cell migration of neural crest

Collective cell migration has emerged in the recent decade as an important phenomenon in cell and developmental biology and can be defined as the coordinated and cooperative movement of groups of cells. Most studies concentrate on tightly connected epithelial tissues, even though collective migration does not require a constant physical contact. Movement of mesenchymal cells is more independent, making their emergent collective behaviour less intuitive and therefore lending importance to computational modelling. Here we focus on such modelling efforts that aim to understand the collective migration of neural crest cells, a mesenchymal embryonic population that migrates large distances as a group during early vertebrate development. By comparing different models of neural crest migration, we emphasize the similarity and complementary nature of these approaches and suggest a future direction for the field. The principles derived from neural crest modelling could aid understanding the collective migration of other mesenchymal cell types.

Microglial migration and interactions with dendrimer nanoparticles are altered in the presence of neuroinflammation

Background

Microglial cells have been implicated in neuroinflammation-mediated injury in the brain, including neurodevelopmental disorders such as cerebral palsy (CP) and autism. Pro-inflammatory activation of microglial cells results in the impairment of their neuroprotective functions, leading to an exaggerated, ongoing immune dysregulation that can persist long after the initial insult. We have previously shown that dendrimer-mediated delivery of an anti-inflammatory agent can attenuate inflammation in a rabbit model of maternal inflammation-induced CP and significantly improve the motor phenotype, due to the ability of the dendrimer to selectively localize in activated microglia.

Methods

To elucidate the interactions between dendrimers and microglia, we created an organotypic whole-hemisphere brain slice culture model from newborn rabbits with and without exposure to inflammation in utero. We then used this model to analyze the dynamics of microglial migration and their interactions with dendrimers in the presence of neuroinflammation.

Results

Microglial cells in animals with CP had an amoeboid morphology and impaired cell migration, demonstrated by decreased migration distance and velocity when compared to cells in healthy, age-matched controls. However, this decreased migration was associated with a greater, more rapid dendrimer uptake compared to microglial cells from healthy controls.

Conclusions

This study demonstrates that maternal intrauterine inflammation is associated with impaired microglial function and movement in the newborn brain. This microglial impairment may play a role in the development of ongoing brain injury and CP in the offspring. Increased uptake of dendrimers by the “impaired” microglia can be exploited to deliver drugs specifically to these cells and modulate their functions. Host tissue and target cell characteristics are important aspects to be considered in the design and evaluation of targeted dendrimer-based nanotherapeutics for improved and sustained efficacy. This ex vivo model also provides a rapid screening tool for evaluation of the effects of various therapies on microglial function.

Electronic supplementary material

The online version of this article (doi:10.1186/s12974-016-0529-3) contains supplementary material, which is available to authorized users.

In vitro formation of neuroclusters in microfluidic devices and cell migration as a function of stromal-derived growth factor 1 gradients

Central nervous system (CNS) cells cultured in vitro as neuroclusters are useful models of tissue regeneration and disease progression. However, the role of cluster formation and collective migration of these neuroclusters to external stimuli has been largely unstudied in vitro. Here, 3 distinct CNS cell types, medulloblastoma (MB), medulloblastoma-derived glial progenitor cells (MGPC), and retinal progenitor cells (RPC), were examined with respect to cluster formation and migration in response to Stromal-Derived Growth Factor (SDF-1). A microfluidic platform was used to distinguish collective migration of neuroclusters from that of individual cells in response to controlled concentration profiles of SDF-1. Cell lines were also compared with respect to expression of CXCR4, the receptor for SDF-1, and the gap junction protein Connexin 43 (Cx43). All cell types spontaneously formed clusters and expressed both CXCR4 and Cx43. RPC clusters exhibited collective chemotactic migration (i.e. movement as clusters) along SDF-1 concentration gradients. MGPCs clusters did not exhibit adhesion-based migration, and migration of MB clusters was inconsistent. This study demonstrates how controlled microenvironments can be used to examine the formation and collective migration of CNS-derived neuroclusters in varied cell populations.

Hyperswarming adaptations in a bacterium improve collective motility without enhancing single cell motility

Pseudomonas aeruginosa is a monoflagellated bacterium that can use its single polar flagellum to swim through liquids and move collectively over semisolid surfaces, a behavior called swarming. Previous studies have shown that experimental evolution in swarming colonies leads to the selection of hyperswarming bacteria with multiple flagella. Here we show that the advantage of such hyperswarmer mutants cannot be explained simply by an increase in the raw swimming speed of individual bacteria in liquids. Cell tracking of time-lapse microscopy to quantify single-cell swimming patterns reveals that both wild-type and hyperswarmers alternate between forward and backward runs, rather than doing the run-and-tumble characteristic of enteric bacteria such as E. coli. High-throughput measurement of swimming speeds reveals that hyperswarmers do not swim faster than wild-type in liquid. Wild-type reverses swimming direction in sharp turns without a significant impact on its speed, whereas multiflagellated hyperswarmers tend to alternate fast and slow runs and have wider turning angles. Nonetheless, macroscopic measurement of swimming and swarming speed in colonies shows that hyperswarmers expand faster than wild-type on surfaces and through soft agar matrices. A mathematical model explains how wider turning angles lead to faster spreading when swimming through agar. Our study describes for the first time the swimming patterns in multiflagellated P. aeruginosa mutants and reveals that collective and individual motility in bacteria are not necessarily correlated. Understanding bacterial adaptations to surface motility, such as hyperswarming, requires a collective behavior approach.