1,337 research outputs found
Simulating psoriasis by altering transit amplifying cells
No full textComputational models of tissue homeostasis will facilitate a deeper understanding of many diseases. They link molecular networks, cellular differentiation and the spatial and temporal organization of tissues. Here we show an approach which is able to computationally turn a healthy in silico epidermis into one with four central properties of psoriatic epidermis. We achieve this by altering a single simulation parameter in the cellular differentiation program of the simulated epidermal keratinocytes: the fractional time period during which transit amplifying cells proliferate (tau). Prolonging tau results in the four main pathological characteristics of psoriatic skin: (1) an absolute increase of the germinative compartment, (2) an absolute increase of the differentiated compartment, (3) a higher proportion of germinative cells and (4) a marked reduction in turnover time. The prolongation of tau is able to increase the proliferation capacity of the epidermal tissue without altering the cell cycle frequency
From virtual microscopy to systems pathology: a meeting report of the 1st European workshop on tissue imaging and analysis, Heidelberg, Germany, 13-14 February 2009
No full textA multicellular systems biology model predicts epidermal morphology, kinetics and Ca2+ flow
No full textSystems biology is currently focused on integrating intracellular networks, although clinically, diseases are largely defined by their histological features. For example, no computational model can simulate today the formation of a horizontally layered epidermis. Since the epidermis is the most complex structured epithelial tissue, systems biology models could yield important insights in epithelial tissue, in which most of all human cancers arise
Highly specific prediction of phosphorylation sites in proteins
No full textSummary: The prediction of significant short functional protein sequences has inherent problems. In predicting phos-phorylation sites, problems came from the shortness of phos-phorylation sites, the difficulties in maintaining many different predefined models of binding sites, and the difficulties of obtaining highly sensitive predictions and of obtaining predic-tions with a constant sensitivity and specificity. The algorithm presented in this paper overcomes these problems. The proposed algorithm PHOSITE is based on the case-based sequence analysis. This enables the prediction of phosphorylation sites with constant specificity and sensitivity. Furthermore, this method leads not only to the prediction of phosphorylation sites in general but also predicts the most probable type of kinase involved. Availability: The tool PHOSITE implementing the presen-ted method can be evaluated under the websit
The local immunological microenvironment in colorectal cancer as a prognostic factor for treatment decisions in the clinic: The way ahead
No full textAnalysis of the local immunological microenvironment in colorectal cancer lesions yielded prognostic markers. Harnessing these insights for clinical application however requires the use of sophisticated technology and algorithms, especially the robust and reproducible quantification of immune cells. These technologies are available and will allow individualized treatment decisions beyond the current standard
Überblick zur Analyse von Immunzellinfiltraten mittels Whole Slide Imaging
No full textImmuno-oncology requires objective and standardized methods for measuring immune cell infiltrates for therapy selection and clinical trials
Abstract 1917: Immunological Tumor Maps: a Landscape of Infiltrating Immune Cells in Colorectal Cancer Based on Complete Tissue Section Analyses
No full textAbstract
In colorectal cancer (CRC) large scale tissue microarray (TMA) based quantitative immune cell counts using immune cell surface molecules (CD3, CD8, Granzyme B, and CD45RO) have identified the number of infiltrating immune cells to be potentially better predictors for patient survival than the classical TNM system. The spatial heterogeneity of immune cells may not be well reflected in the highly selected, and typically small (0,6-1 mm2) tissue cores of the TMA. This represents an obstacle in the individual prognosis prediction or classification of a single patient. To investigate this aspect, the localization and distribution of immune cell subpopulations based on the analysis of complete tissue sections by a dedicated novel staining and imaging system were performed. Using a specialized staining platform and whole slide imaging & analysis by virtual microscopy (VM), immunological “tumor maps” were generated. These tumor maps are based on cell densities in fields of 1mm2 size, visualizing intratumoral heterogeneity for the surface markers CD3, CD8, Granzyme B, and CD45RO. In total, an area of 867 mm2 was automatically evaluated with an average of 48 mm2 of evaluated tumor tissue per patient slide. Cell counts varied within a patient significantly, ranging from 0 to up to 2550 cells / mm2. Further analyses revealed, that sampling of single field counts within the tumor can only yield clear diagnostic decisions for a fraction of the analyzed patients, with ambiguous decisions for 11 out of 20 patients. Interestingly, the overall degree of heterogeneity also varied between patients, with lower heterogeneity found only in samples with lower cell counts. No samples with a homogeneous high cell density distribution were observed. The observed variability has implications for the individual prognosis prediction and represents the first spatial quantitative study of immune cells in a set of CRC primary tumors. The presented tumor maps therefore are a suitable tool to visualize heterogeneity. Furthermore, whole slide imaging & analysis by VM is essential in the identification of prognostic markers as well as in their subsequent application. In the future, spatial marker signatures could contribute to individual patient classification.
Note: This abstract was not presented at the AACR 101st Annual Meeting 2010 because the presenter was unable to attend.
Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 1917.</jats:p
Automatic tumor-stroma separation in fluorescence TMAs enables the quantitative high-throughput analysis of multiple cancer biomarkers.
No full textThe upcoming quantification and automation in biomarker based histological tumor evaluation will require computational methods capable of automatically identifying tumor areas and differentiating them from the stroma. As no single generally applicable tumor biomarker is available, pathology routinely uses morphological criteria as a spatial reference system. We here present and evaluate a method capable of performing the classification in immunofluorescence histological slides solely using a DAPI background stain. Due to the restriction to a single color channel this is inherently challenging. We formed cell graphs based on the topological distribution of the tissue cell nuclei and extracted the corresponding graph features. By using topological, morphological and intensity based features we could systematically quantify and compare the discrimination capability individual features contribute to the overall algorithm. We here show that when classifying fluorescence tissue slides in the DAPI channel, morphological and intensity based features clearly outpace topological ones which have been used exclusively in related previous approaches. We assembled the 15 best features to train a support vector machine based on Keratin stained tumor areas. On a test set of TMAs with 210 cores of triple negative breast cancers our classifier was able to distinguish between tumor and stroma tissue with a total overall accuracy of 88%. Our method yields first results on the discrimination capability of features groups which is essential for an automated tumor diagnostics. Also, it provides an objective spatial reference system for the multiplex analysis of biomarkers in fluorescence immunohistochemistry
Antiproliferative efficacies but minor drug transporter inducing effects of paclitaxel, cisplatin, or 5-fluorouracil in a murine xenograft model for head and neck squamous cell carcinoma
No full textDrug-induced multidrug resistance (MDR) has been linked to overexpression of drug transporting proteins in head and neck squamous cell carcinoma (HNSCC) in vitro. The aim of this work was to reassess these findings in a murine xenograft model. NOD-SCID mice xenotransplanted with 106 HNO97 cells were treated for four consecutive weeks with weekly paclitaxel, biweekly cisplatin (both intraperitoneal), or 5-fluorouracil (5-FU, administered by osmotic pump). Tumor volume and body weight were weekly documented. Expression of drug transporters and Ki-67 marker were examined using quantitative real-time polymerase chain reaction and/or immunohistochemistry. Both paclitaxel and cisplatin significantly reduced tumor volumes after 2–3 weeks. 5-FU-treated animals had significantly lower body weights after 2 or 4 weeks of chemotherapy. None of the drugs affected expression of drug transporters at the mRNA level. However, P-glycoprotein (Pgp) protein expression was increased by paclitaxel (P < 0.01). Ki-67 expression did not change during treatment irrespective of the drug applied. Paclitaxel and cisplatin are effectively tumor volume reducing drugs in a murine xenograft model of HNSCC. Paclitaxel enhanced Pgp expression at the protein level, but not at the mRNA level suggesting transcriptional induction to be of minor relevance. In contrast, posttranscriptional mechanisms or Darwinian selection of intrinsically drug transporter overexpressing MDR cells might lead to iatrogenic chemotherapy resistance in HNSCC.Fil: Theile, Dirk. Universität Heidelberg; AlemaniaFil: Gal, Zoltan. Universität Heidelberg; AlemaniaFil: Warta, Rolf. Universität Heidelberg; AlemaniaFil: Rigalli, Juan Pablo. Universität Heidelberg; Alemania. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Lahrmann, Bernd. Universität Heidelberg; AlemaniaFil: Grabe, Niels. Universität Heidelberg; AlemaniaFil: Herold Mende, Christel. Universität Heidelberg; AlemaniaFil: Dyckhoff, Gerhard. Universität Heidelberg; AlemaniaFil: Weiss, Johanna. Universität Heidelberg; Alemani
Abstract 396: Sequential metastases of colorectal cancer: Treatment, immunophenotypes and spatial distributions of infiltrating immune cells
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