EGFRvIII uses intrinsic and extrinsic mechanisms to reduce glioma adhesion and increase migration.

A lack of biological markers has limited our ability to identify the invasive cells responsible for glioblastoma multiforme (GBM). To become migratory and invasive, cells must downregulate matrix adhesions, which could be a physical marker of invasive potential. We engineered murine astrocytes with common GBM mutations, e.g. Ink4a (Ink) or PTEN deletion and expressing a constitutively active EGF receptor truncation (EGFRvIII), to elucidate their effect on adhesion. While loss of Ink or PTEN did not affect adhesion, counterparts expressing EGFRvIII were significantly less adhesive. EGFRvIII reduced focal adhesion size and number, and these cells - with more labile adhesions - displayed enhanced migration. Regulation appears to depend not on physical receptor association to integrins but, rather, on the activity of the receptor kinase, resulting in transcriptional integrin repression. Interestingly, EGFRvIII intrinsic signals can be propagated by cytokine crosstalk to cells expressing wild-type EGFR, resulting in reduced adhesion and enhanced migration. These data identify potential intrinsic and extrinsic mechanisms that gliomas use to invade surrounding parenchyma.

The Hippo pathway mediates Semaphorin signaling.

Semaphorins were originally identified as axonal guidance molecules, but they also control processes such as vascular development and tumorigenesis. The downstream signaling cascades of Semaphorins in these biological processes remain unclear. Here, we show that the class 3 Semaphorins (SEMA3s) activate the Hippo pathway to attenuate tissue growth, angiogenesis, and tumorigenesis. SEMA3B restoration in lung cancer cells with SEMA3B loss of heterozygosity suppresses cancer cell growth via activating the core Hippo kinases LATS1/2 (large tumor suppressor kinase 1/2). Furthermore, SEMA3 also acts through LATS1/2 to inhibit angiogenesis. We identified p190RhoGAPs as essential partners of the SEMA3A receptor PlexinA in Hippo regulation. Upon SEMA3 treatment, PlexinA interacts with the pseudo-guanosine triphosphatase (GTPase) domain of p190RhoGAP and simultaneously recruits RND GTPases to activate p190RhoGAP, which then stimulates LATS1/2. Disease-associated etiological factors, such as genetic lesions and oscillatory shear, diminish Hippo pathway regulation by SEMA3. Our study thus discovers a critical role of Hippo signaling in mediating SEMA3 physiological function.

Adhesion strength and contractility enable metastatic cells to become adurotactic.

Significant changes in cell stiffness, contractility, and adhesion, i.e., mechanotype, are observed during a variety of biological processes. Whether cell mechanics merely change as a side effect of or driver for biological processes is still unclear. Here, we sort genotypically similar metastatic cancer cells into strongly adherent (SA) versus weakly adherent (WA) phenotypes to study how contractility and adhesion differences alter the ability of cells to sense and respond to gradients in material stiffness. We observe that SA cells migrate up a stiffness gradient, or durotax, while WA cells largely ignore the gradient, i.e., adurotax. Biophysical modeling and experimental validation suggest that differences in cell migration and durotaxis between weakly and strongly adherent cells are driven by differences in intra-cellular actomyosin activity. These results provide a direct relationship between cell phenotype and durotaxis and suggest how, unlike other senescent cells, metastatic cancer cells navigate against stiffness gradients.

High shear stress enhances endothelial permeability in the presence of the risk haplotype at 9p21.3.

Single nucleotide polymorphisms (SNPs) are exceedingly common in non-coding loci, and while they are significantly associated with a myriad of diseases, their specific impact on cellular dysfunction remains unclear. Here, we show that when exposed to external stressors, the presence of risk SNPs in the 9p21.3 coronary artery disease (CAD) risk locus increases endothelial monolayer and microvessel dysfunction. Endothelial cells (ECs) derived from induced pluripotent stem cells of patients carrying the risk haplotype (R/R WT) differentiated similarly to their non-risk and isogenic knockout (R/R KO) counterparts. Monolayers exhibited greater permeability and reactive oxygen species signaling when the risk haplotype was present. Addition of the inflammatory cytokine TNFα further enhanced EC monolayer permeability but independent of risk haplotype; TNFα also did not substantially alter haplotype transcriptomes. Conversely, when wall shear stress was applied to ECs in a microfluidic vessel, R/R WT vessels were more permeable at lower shear stresses than R/R KO vessels. Transcriptomes of sheared cells clustered more by risk haplotype than by patient or clone, resulting in significant differential regulation of EC adhesion and extracellular matrix genes vs static conditions. A subset of previously identified CAD risk genes invert expression patterns in the presence of high shear concomitant with altered cell adhesion genes, vessel permeability, and endothelial erosion in the presence of the risk haplotype, suggesting that shear stress could be a regulator of non-coding loci with a key impact on CAD.

To lead or to herd: optimal strategies for 3D collective migration of cell clusters.

Cells migrating in clusters play a significant role in a number of biological processes such as embryogenesis, wound healing, and tumor metastasis during cancer progression. A variety of environmental and biochemical factors can influence the collective migration of cells with differing degrees of cell autonomy and inter-cellular coupling strength. For example, weakly coupled cells can move collectively under the influence of contact guidance from neighboring cells or the environment. Alternatively strongly coupled cells might follow one or more leader cells to move as a single cohesive unit. Additionally, chemical and mechanical signaling between these cells may alter the degree of coupling and determine effective cluster sizes. Being able to understand this collective cell migration process is critical in the prediction and manipulation of outcomes of key biological processes. Here we focus on understanding how various environmental and cellular factors influence small clusters of cells migrating collectively within a 3D fibrous matrix. We combine existing knowledge of single-cell migration in 2D and 3D environments, prior experimental observations of cell-cell interactions and collective migration, and a newly developed stochastic model of cell migration in 3D matrices, to simulate the migration of cell clusters in different physiologically relevant environments. Our results show that based on the extracellular environment and the strength of cell-cell mechanical coupling, two distinct optimal approaches to driving collective cell migration emerge. The ability to effectively employ these two distinct migration strategies might be critical for cells to collectively migrate through the heterogeneous tissue environments within the body.

Computational models of migration modes improve our understanding of metastasis.

Tumor cells migrate through changing microenvironments of diseased and healthy tissue, making their migration particularly challenging to describe. To better understand this process, computational models have been developed for both the ameboid and mesenchymal modes of cell migration. Here, we review various approaches that have been used to account for the physical environment's effect on cell migration in computational models, with a focus on their application to understanding cancer metastasis and the related phenomenon of durotaxis. We then discuss how mesenchymal migration models typically simulate complex cell-extracellular matrix (ECM) interactions, while ameboid migration models use a cell-focused approach that largely ignores ECM when not acting as a physical barrier. This approach greatly simplifies or ignores the mechanosensing ability of ameboid migrating cells and should be reevaluated in future models. We conclude by describing future model elements that have not been included to date but would enhance model accuracy.

Cell Adhesiveness Serves as a Biophysical Marker for Metastatic Potential.

Tumors are heterogeneous and composed of cells with different dissemination abilities. Despite significant effort, there is no universal biological marker that serves as a metric for metastatic potential of solid tumors. Common to disseminating cells from such tumors, however, is the need to modulate their adhesion as they detach from the tumor and migrate through stroma to intravasate. Adhesion strength is heterogeneous even among cancer cells within a given population, and using a parallel plate flow chamber, we separated and sorted these populations into weakly and strongly adherent groups; when cultured under stromal conditions, this adhesion phenotype was stable over multiple days, sorting cycles, and common across all epithelial tumor lines investigated. Weakly adherent cells displayed increased migration in both two-dimensional and three-dimensional migration assays; this was maintained for several days in culture. Subpopulations did not show differences in expression of proteins involved in the focal adhesion complex but did exhibit intrinsic focal adhesion assembly as well as contractile differences that resulted from differential expression of genes involved in microtubules, cytoskeleton linkages, and motor activity. In human breast tumors, expression of genes associated with the weakly adherent population resulted in worse progression-free and disease-free intervals. These data suggest that adhesion strength could potentially serve as a stable marker for migration and metastatic potential within a given tumor population and that the fraction of weakly adherent cells present within a tumor could act as a physical marker for metastatic potential. SIGNIFICANCE: Cancer cells exhibit heterogeneity in adhesivity, which can be used to predict metastatic potential.

Correction: A stochastic algorithm for accurately predicting path persistence of cells migrating in 3D matrix environments.

[This corrects the article DOI: 10.1371/journal.pone.0207216.].

A stochastic algorithm for accurately predicting path persistence of cells migrating in 3D matrix environments.

Cell mobility plays a critical role in immune response, wound healing, and the rate of cancer metastasis and tumor progression. Mobility within a three-dimensional (3D) matrix environment can be characterized by the average velocity of cell migration and the persistence length of the path it follows. Computational models that aim to predict cell migration within such 3D environments need to be able predict both of these properties as a function of the various cellular and extra-cellular factors that influence the migration process. A large number of models have been developed to predict the velocity of cell migration driven by cellular protrusions in 3D environments. However, prediction of the persistence of a cell's path is a more tedious matter, as it requires simulating cells for a long time while they migrate through the model extra-cellular matrix (ECM). This can be a computationally expensive process, and only recently have there been attempts to quantify cell persistence as a function of key cellular or matrix properties. Here, we propose a new stochastic algorithm that can simulate and analyze 3D cell migration occurring over days with a computation time of minutes, opening new possibilities of testing and predicting long-term cell migration behavior as a function of a large variety of cell and matrix properties. In this model, the matrix elements are generated as needed and stochastically based on the biophysical and biochemical properties of the ECM the cell migrates through. This approach significantly reduces the computational resources required to track and calculate cell matrix interactions. Using this algorithm, we predict the effect of various cellular and matrix properties such as cell polarity, cell mechanoactivity, matrix fiber density, matrix stiffness, fiber alignment, and fiber binding site density on path persistence of cellular migration and the mean squared displacement of cells over long periods of time.