IntroductionMultiple gene expression based prognostic biomarkers have been repeatedly identified in gastric carcinoma. However, without confirmation in an independent validation study, their clinical utility is limited. Our goal was to establish a robust database enabling the swift validation of previous and future gastric cancer survival biomarker candidates.ResultsThe entire database incorporates 1,065 gastric carcinoma samples, gene expression data. Out of 29 established markers, higher expression of BECN1 (HR = 0.68, p = 1.5E-05), CASP3 (HR = 0.5, p = 6E-14), COX2 (HR = 0.72, p = 0.0013), CTGF (HR = 0.72, p = 0.00051), CTNNB1 (HR = 0.47, p = 4.3E-15), MET (HR = 0.63, p = 1.3E-05), and SIRT1 (HR = 0.64, p = 2.2E-07) correlated to longer OS. Higher expression of BIRC5 (HR = 1.45, p = 1E-04), CNTN1 (HR = 1.44, p = 3.5E- 05), EGFR (HR = 1.86, p = 8.5E-11), ERCC1 (HR = 1.36, p = 0.0012), HER2 (HR = 1.41, p = 0.00011), MMP2 (HR = 1.78, p = 2.6E-09), PFKB4 (HR = 1.56, p = 3.2E-07), SPHK1 (HR = 1.61, p = 3.1E-06), SP1 (HR = 1.45, p = 1.6E-05), TIMP1 (HR = 1.92, p = 2.2E- 10) and VEGF (HR = 1.53, p = 5.7E-06) were predictive for poor OS.MATERIALS AND METHODSWe integrated samples of three major cancer research centers (Berlin, Bethesda and Melbourne datasets) and publicly available datasets with available follow-up data to form a single integrated database. Subsequently, we performed a literature search for prognostic markers in gastric carcinomas (PubMed, 2012–2015) and re-validated their findings predicting first progression (FP) and overall survival (OS) using uni- and multivariate Cox proportional hazards regression analysis.ConclusionsThe major advantage of our analysis is that we evaluated all genes in the same set of patients thereby making direct comparison of the markers feasible. The best performing genes include BIRC5, CASP3, CTNNB1, TIMP-1, MMP-2, SIRT, and VEGF.

In summary, we established an integrated platform capable to mine all available miRNA data to perform a survival analysis for the identification and validation of prognostic miRNA markers in breast cancer.

SummaryContact inhibition of locomotion (CIL) is the process through which cells move away from each other after cell-cell contact, and it contributes to malignant invasion and developmental migration. Various cell types exhibit CIL, whereas others remain in contact after collision and may form stable junctions. To investigate what determines this differential behavior, we study neural crest cells, a migratory stem cell population whose invasiveness has been likened to cancer metastasis. By comparing pre-migratory and migratory neural crest cells, we show that the switch from E- to N-cadherin during EMT is essential for acquisition of CIL behavior. Loss of E-cadherin leads to repolarization of protrusions, via p120 and Rac1, resulting in a redistribution of forces from intercellular tension to cell-matrix adhesions, which break down the cadherin junction. These data provide insight into the balance of physical forces that contributes to CIL in cells in vivo.

Collective cell motility is an important aspect of several developmental and pathophysiological processes. Despite its importance, the mechanisms that allow cells to be both motile and adhere to one another are poorly understood. In this study we establish statistical properties of the random streaming behavior of endothelial monolayer cultures. To understand the reported empirical findings, we expand the widely used cellular Potts model to include active cell motility. For spontaneous directed motility we assume a positive feedback between cell displacements and cell polarity. The resulting model is studied with computer simulations, and is shown to exhibit behavior compatible with experimental findings. In particular, in monolayer cultures both the speed and persistence of cell motion decreases, transient cell chains move together as groups, and velocity correlations extend over several cell diameters. As active cell motility is ubiquitous both in vitro and in vivo, our model is expected to be a generally applicable representation of cellular behavior.

Collective cell migration is fundamental throughout development and in many diseases. Spatial confinement using micropatterns has been shown to promote collective cell migration in vitro, but its effect in vivo remains unclear. Combining computational and experimental approaches, we show that the in vivo collective migration of neural crest cells (NCCs) depends on such confinement. We demonstrate that confinement may be imposed by the spatiotemporal distribution of a nonpermissive substrate provided by versican, an extracellular matrix molecule previously proposed to have contrasting roles: barrier or promoter of NCC migration. We resolve the controversy by demonstrating that versican works as an inhibitor of NCC migration and also acts as a guiding cue by forming exclusionary boundaries. Our model predicts an optimal number of cells in a given confinement width to allow for directional migration. This optimum coincides with the width of neural crest migratory streams analyzed across different species, proposing an explanation for the highly conserved nature of NCC streams during development.

Collective cell chemotaxis, the directed migration of cell groups along gradients of soluble chemical cues, underlies various developmental and pathological processes. Here we use neural crest cells, a migratory embryonic stem cell population whose behavior has been likened to malignant invasion, to study collective chemotaxis in vivo. Studying Xenopus and zebrafish, we show that the neural crest exhibits a tensile actomyosin ring at the edge of the migratory cell group that contracts in a supracellular fashion. This contractility is polarized during collective cell chemotaxis: it is inhibited at the front but persists at the rear of the cell cluster. The differential contractility drives directed collective cell migration ex vivo and in vivo through intercalation of rear cells. Thus, in neural crest cells, collective chemotaxis works by rear wheel drive.

High dietary phosphorus and hyperphosphatemia have significant effects on cardiac fibrosis and arterial wall thickening. Such abnormalities of cardiac architecture may be relevant for the increased cardiac risk in hyperphosphatemic uremic patients.

Despite a growing wealth of available molecular data, the growth of tumors, invasion of tumors into healthy tissue, and response of tumors to therapies are still poorly understood. Although genetic mutations are in general the first step in the development of a cancer, for the mutated cell to persist in a tissue, it must compete against the other, healthy or diseased cells, for example by becoming more motile, adhesive, or multiplying faster. Thus, the cellular phenotype determines the success of a cancer cell in competition with its neighbors, irrespective of the genetic mutations or physiological alterations that gave rise to the altered phenotype. What phenotypes can make a cell “successful” in an environment of healthy and cancerous cells, and how? A widely used tool for getting more insight into that question is cell-based modeling. Cell-based models constitute a class of computational, agent-based models that mimic biophysical and molecular interactions between cells. One of the most widely used cell-based modeling formalisms is the cellular Potts model (CPM), a lattice-based, multi particle cell-based modeling approach. The CPM has become a popular and accessible method for modeling mechanisms of multicellular processes including cell sorting, gastrulation, or angiogenesis. The CPM accounts for biophysical cellular properties, including cell proliferation, cell motility, and cell adhesion, which play a key role in cancer. Multiscale models are constructed by extending the agents with intracellular processes including metabolism, growth, and signaling. Here we review the use of the CPM for modeling tumor growth, tumor invasion, and tumor progression. We argue that the accessibility and flexibility of the CPM, and its accurate, yet coarse-grained and computationally efficient representation of cell and tissue biophysics, make the CPM the method of choice for modeling cellular processes in tumor development.