Hsa-miR-21-3p associates with breast cancer patient survival and targets genes in tumor suppressive pathways.

To access publisher's full text version of this article, please click on the hyperlink in Additional Links field or click on the hyperlink at the top of the page marked DownloadBreast cancer is the cancer most often diagnosed in women. MicroRNAs (MIRs) are short RNA molecules that bind mRNA resulting in their downregulation. MIR21 has been shown to be an oncomiR in most cancer types, including breast cancer. Most of the effects of miR-21 have been attributed to hsa-miR-21-5p that is transcribed from the leading strand of MIR21, but hsa-miR-21-3p (miR-21-3p), transcribed from the lagging strand, is much less studied. The aim of the study is to analyze whether expression of miR-21-3p is prognostic for breast cancer. MiR-21-3p association with survival, clinical and pathological characteristics was analyzed in a large breast cancer cohort and validated in three separate cohorts, including TCGA and METABRIC. Analytical tools were also used to infer miR-21-3p function and to identify potential target genes and functional pathways. The results showed that in the exploration cohort, high miR-21-3p levels associated with shorter survival and lymph node positivity. In the three validation cohorts, high miR-21-3p levels associated with pathological characteristics that predict worse prognosis. Specifically, in the largest validation cohort, METABRIC (n = 1174), high miR-21-3p levels associated with large tumors, a high grade, lymph node and HER2 positivity, and shorter breast-cancer-specific survival (HR = 1.38, CI 1.13-1.68). This association remained significant after adjusting for confounding factors. The genes with expression levels that correlated with miR-21-3p were enriched in particular pathways, including the epithelial-to-mesenchymal transition and proliferation. Among the most significantly downregulated targets were MAT2A and the tumor suppressive genes STARD13 and ZNF132. The results from this study emphasize that both 3p- and 5p-arms from a MIR warrant independent study. The data show that miR-21-3p overexpression in breast tumors is a marker of worse breast cancer progression and it affects genes in pathways that drive breast cancer by down-regulating tumor suppressor genes. The results suggest miR-21-3p as a potential biomarker.This research was funded by grants to IR, RBB, BAA, OTJ and AdAr from the Icelandic Centre for Research (grant number 152530-051, www.rannis.is), The Scientific Fund of Landspitali – The National University Hospital in Iceland (grant numbers A-2016-033 and A-2019-042, www.landspitali.is), The Scientific Fund of The Icelandic Cancer Society (year 2017, www.krabb.is/english/), Gongum saman (https://gongumsaman.is/; years 2013 and 2017) and a grant to ArAm and IR from Gongum saman in 2018. The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript

Histopathology and levels of proteins in plasma associate with survival after colorectal cancer diagnosis

Funding Information: The authors thank the subjects who have donated their time and their samples that were used in this research. Publisher Copyright: © 2023, The Author(s).Background: The TNM system is used to assess prognosis after colorectal cancer (CRC) diagnosis. Other prognostic factors reported include histopathological assessments of the tumour, tumour mutations and proteins in the blood. As some of these factors are strongly correlated, it is important to evaluate the independent effects they may have on survival. Methods: Tumour samples from 2162 CRC patients were visually assessed for amount of tumour stroma, severity of lymphocytic infiltrate at the tumour margins and the presence of lymphoid follicles. Somatic mutations in the tumour were assessed for 2134 individuals. Pre-surgical levels of 4963 plasma proteins were measured in 128 individuals. The associations between these features and prognosis were inspected by a Cox Proportional Hazards Model (CPH). Results: Levels of stroma, lymphocytic infiltration and presence of lymphoid follicles all associate with prognosis, along with high tumour mutation burden, high microsatellite instability and TP53 and BRAF mutations. The somatic mutations are correlated with the histopathology and none of the somatic mutations associate with survival in a multivariate analysis. Amount of stroma and lymphocytic infiltration associate with local invasion of tumours. Elevated levels of two plasma proteins, CA-125 and PPP1R1A, associate with a worse prognosis. Conclusions: Tumour stroma and lymphocytic infiltration variables are strongly associated with prognosis of CRC and capture the prognostic effects of tumour mutation status. CA-125 and PPP1R1A may be useful prognostic biomarkers in CRC.Peer reviewe

Computational model of breast cancer cell invasion: exploring the EGF/CSF-1 paracrine signaling between macrophages and tumor cells

Macrophages have been shown, experimentally, to be directly involved in the invasion of breast tumor cells into surrounding tissues and blood vessels. Tumor cells interact with macrophages via a short-ranged signaling pathway involving the epidermal growth factor, EGF, and colony-stimulating factor 1, CSF-1. To study this signaling pathway and the observed motility behaviour of tumor cells I developed a 3D individual cell based computational model. Simulations with my model successfully reproduced results from in vitro and in vivo experiments. The model can help explain mechanisms responsible for the observed motility behaviour of tumor cells and the noted ratio of 3 invasive tumor cells per 1 invasive macrophage. A parametric sensitivity analysis showed that changing model parameters such as the degradation and secretion of EGF and CSF-1 could alter and even eliminate the invasion of tumor cells. These results yield insight into possible new targets for chemotherapy

The multi-levelled organization of cell migration : from individual cells to tissues

Cell migration is a complex interplay of biochemical and biophysical mechanisms. I investigate the link between individual and collective cell behaviour using mathematical and computational modelling. Specifically, I study: (1) cell-cell interactions in a discrete framework with a spatial sensing range, (2) migration of a cluster of cells during zebrafish (Danio rerio) development, and (3) collective migration of cancer cells and their interactions with the extracellular-matrix (ECM). My 1D model (1), is approximated by a continuum equation and investigated using asymptotic approximations, steady-state analysis, and linear stability analysis. Analysis and computations characterize regimes corresponding to cell clustering, and provide a link between micro and macro-scale parameters. Results suggest that drift (i.e. due to chemotaxis), can disrupt the formation of cellular aggregates. In (2), I investigate spontaneous polarization of a cell-cluster (the posterior lateral line primordium, PLLP) in zebrafish development. I use a cell-based computational framework (hybrid discrete cell model, HyDiCell3D) coupled with differential equation model to track the segregation and migration of the PLLP. My model includes mutual inhibition between the diffusible growth factors Wnt and FGF. I find that a non-uniform degradation of an extracellular chemokine (CXCL12a) and chemotaxis is essential for long-range cohesive migration. Results compare favourably with data from the Chitnis lab (NIH). I continue using HyDiCell3D in (3) to elucidate mechanisms that facilitate cancer invasion. I focus on: wound healing in a cell-sheet (2D epithelium), and cell-clusters (3D spheroids) embedded in ECM with internal signalling mediated by podocalyxin, a trans-membrane molecule. Experimental data from the Roskelley lab (UBC) motivates the model derivation. I use the models to investigate the role of cell-cell and cell-ECM adhesion in collective migration as well as the emergence of a distinct phenotype (leader-cells) that guide the migration. ECM induced disruption in the localization of podocalyxin on the cell membrane is captured in the model along with morphological changes of spheroids. The model predicts that cell polarity and cell division axis influence the invasive potential. Lastly, I developed quantitative methods for image analysis and automated tracking of cells in a densely packed environment to compare modelling results and biological data.Science, Faculty ofMathematics, Department ofGraduat

Illustration of the simulations using the 3D deformable ellipsoid cell-based model.

<p>View from above of a 1-cell layer representing the PLLP. The stripe of CXCL12a would be directly underneath the PLLP, not explicitly shown. For visualization purposes, the Wnt (blue) and FGF (yellow) ligands are shown as a pair of clouds in each split-image. Left panel: cells are colored based on receptor expression level (red for high Wnt and green for high FGF receptor levels, <i>W</i>_<i>R</i>, <i>F</i>_<i>R</i>). FGFR and WntR expressing cells are colored yellow. Right panel: cells colored by their <i>bound</i> receptor levels (pink for high Wnt and purple for high FGF bound-receptor levels, <i>W</i>_<i>B</i>, <i>F</i>_<i>B</i>). In our model we interpret the latter as the Wnt or FGF <i>signalling levels</i>. Cells in the back of the PLLP express FGF receptors but do not signal since FGF ligand is so low that most FGF receptors are unbound. Grey or yellow cells at the back of the PLLP are those that are not yet committed to being either WntR or FGFR active cells. Results from the full 3D model after 30 min of simulation time (before the onset of migration).</p

Polarization and migration in the zebrafish posterior lateral line system

<div><p>Collective cell migration plays an important role in development. Here, we study the posterior lateral line primordium (PLLP) a group of about 100 cells, destined to form sensory structures, that migrates from head to tail in the zebrafish embryo. We model mutually inhibitory FGF-Wnt signalling network in the PLLP and link tissue subdivision (Wnt receptor and FGF receptor activity domains) to receptor-ligand parameters. We then use a 3D cell-based simulation with realistic cell-cell adhesion, interaction forces, and chemotaxis. Our model is able to reproduce experimentally observed motility with leading cells migrating up a gradient of CXCL12a, and trailing (FGF receptor active) cells moving actively by chemotaxis towards FGF ligand secreted by the leading cells. The 3D simulation framework, combined with experiments, allows an investigation of the role of cell division, chemotaxis, adhesion, and other parameters on the shape and speed of the PLLP. The 3D model demonstrates reasonable behaviour of control as well as mutant phenotypes.</p></div

The Wnt-FGF mutual inhibition model predicts the formation of signalling domains in response to graded <i>W</i>₁ across the primordium.

<p>Simulation kymographs for Wnt and FGF signalling. Initially, we assumed a gradient <i>W</i>₁(<i>x</i>) = <i>bx</i> + 0.03 in the uncoupled Wnt steady state parameter, with <i>b</i> = 0.01. Time increases downwards; position across the PLLP is horizontal, with leading edge on the right. (a) Wnt ligand, <i>W</i>(<i>x</i>, <i>t</i>), (b) Wnt receptors, <i>W</i>_<i>R</i>(<i>x</i>, <i>t</i>), (c) bound Wnt receptors, <i>W</i>_<i>B</i>(<i>x</i>, <i>t</i>), (d) FGF ligand, <i>F</i>(<i>x</i>, <i>t</i>), (e) FGF receptors, <i>F</i>_<i>R</i>(<i>x</i>, <i>t</i>), and (f) bound FGF receptors, <i>F</i>_<i>B</i>(<i>x</i>, <i>t</i>). Signalling domains form after a few minutes, with a sharp boundary between zones. Bound ligand concentrations are calculated using <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005451#pcbi.1005451.e003" target="_blank">Eq (3)</a>. Parameters are as in Table A in Supporting Information <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005451#pcbi.1005451.s006" target="_blank">S6 Text</a>. Parameter Estimation and Values. Initial conditions: <i>W</i>(<i>x</i>, 0) = 0.01, <i>F</i>(<i>x</i>, 0) = 0.005, <i>W</i>_<i>R</i>(<i>x</i>, 0) = 0.1, <i>F</i>_<i>R</i>(<i>x</i>, 0) = 0.01.</p

Recovery after partial ablation.

<p>As in laser ablation from [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005451#pcbi.1005451.ref008" target="_blank">8</a>], part of the PLLP is removed in the simulation, and recovery observed. Top row: the front segment (WntR active cells) is removed, leaving only FGFR active cells; the PLLP stalls and cannot continue. Middle row: the rear portion is removed, leaving a few FGFR cells behind; the PLLP migration continues. Bottom row: the middle segment is removed. Motion is stalled while the trailing FGFR active cluster catches up with the front. Once the clusters have merged, migration resumes.</p

Experimentally observed Wnt and FGF expression and signalling levels.

<p>RNA in-situ hybridization showing expression at 32 hours post fertilization in the PLLP. From top to bottom these are: wnt10a (Wnt ligand), <i>lef1</i> (Wnt signalling), fgf10a (FGF ligand), fgfR1 (FGF receptors) and <i>pea3</i> (FGF signalling). The scale bar is 10<i>μ</i>m.</p