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  • 1
    In: Journal for ImmunoTherapy of Cancer, BMJ, Vol. 4, No. S1 ( 2016-11)
    Type of Medium: Online Resource
    ISSN: 2051-1426
    Language: English
    Publisher: BMJ
    Publication Date: 2016
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  • 2
    In: Scientific Reports, Springer Science and Business Media LLC, Vol. 7, No. 1 ( 2017-05-23)
    Abstract: In recent years, long non-coding RNA (lncRNA) research has identified essential roles of these transcripts in virtually all physiological cellular processes including tumorigenesis, but their functions and molecular mechanisms are poorly understood. In this study, we performed a high-throughput siRNA screen targeting 638 lncRNAs deregulated in cancer entities to analyse their impact on cell division by using time-lapse microscopy. We identified 26 lncRNAs affecting cell morphology and cell cycle including LINC00152 . This transcript was ubiquitously expressed in many human cell lines and its RNA levels were significantly upregulated in lung, liver and breast cancer tissues. A comprehensive sequence analysis of LINC00152 revealed a highly similar paralog annotated as MIR4435-2HG and several splice variants of both transcripts. The shortest and most abundant isoform preferentially localized to the cytoplasm. Cells depleted of LINC00152 arrested in prometaphase of mitosis and showed reduced cell viability. In RNA affinity purification (RAP) studies, LINC00152 interacted with a network of proteins that were associated with M phase of the cell cycle. In summary, we provide new insights into the properties and biological function of LINC00152 suggesting that this transcript is crucial for cell cycle progression through mitosis and thus, could act as a non-coding oncogene.
    Type of Medium: Online Resource
    ISSN: 2045-2322
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2017
    detail.hit.zdb_id: 2615211-3
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  • 3
    In: Scientific Reports, Springer Science and Business Media LLC, Vol. 9, No. 1 ( 2019-05-15)
    Abstract: In the context of precision medicine with immunotherapies there is an increasing need for companion diagnostic tests to identify potential therapy responders and avoid treatment coming along with severe adverse events for non-responders. Here, we present a retrospective case study to discover image-based signatures for developing a potential companion diagnostic test for ipilimumab (IPI) in malignant melanoma. Signature discovery is based on digital pathology and fully automatic quantitative image analysis using virtual multiplexing as well as machine learning and deep learning on whole-slide images. We systematically correlated the patient outcome data with potentially relevant local image features using a Tissue Phenomics approach with a sound cross validation procedure for reliable performance evaluation. Besides uni-variate models we also studied combinations of signatures in several multi-variate models. The most robust and best performing model was a decision tree model based on relative densities of CD8+ tumor infiltrating lymphocytes in the intra-tumoral infiltration region. Our results are well in agreement with observations described in previously published studies regarding the predictive value of the immune contexture, and thus, provide predictive potential for future development of a companion diagnostic test.
    Type of Medium: Online Resource
    ISSN: 2045-2322
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2019
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  • 4
    In: Scientific Reports, Springer Science and Business Media LLC, Vol. 3, No. 1 ( 2013-12-20)
    Type of Medium: Online Resource
    ISSN: 2045-2322
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2013
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  • 5
    Online Resource
    Online Resource
    Cold Spring Harbor Laboratory ; 2010
    In:  Cold Spring Harbor Protocols Vol. 2010, No. 6 ( 2010-06), p. pdb.top80-
    In: Cold Spring Harbor Protocols, Cold Spring Harbor Laboratory, Vol. 2010, No. 6 ( 2010-06), p. pdb.top80-
    Abstract: Understanding complex cellular processes requires investigating the underlying mechanisms within a spatiotemporal context. Although cellular processes are dynamic in nature, most studies in molecular cell biology are based on fixed specimens, for example, using immunocytochemistry or fluorescence in situ hybridization (FISH). However, breakthroughs in fluorescence microscopy imaging techniques, in particular, the discovery of green fluorescent protein (GFP) and its spectral variants, have facilitated the study of a wide range of dynamic processes by allowing nondestructive labeling of target structures in living cells. In addition, the tremendous improvements in spatial and temporal resolution of light microscopes now allow cellular processes to be analyzed in unprecedented detail. These state-of-the-art imaging technologies, however, provide a huge amount of digital image data. To cope with the enormous amount of image data and to extract reproducible as well as quantitative information, computer-based image analysis is required. In this article, we describe methods for computer-based analysis of multidimensional live cell microscopy images and their application to study the dynamics of cells and particles. First, we sketch a general workflow for quantitative analysis of live cell images. Then, we detail computational methods for automatic image analysis comprising image preprocessing, segmentation, registration, tracking, and classification. We conclude with a discussion of quantitative analysis and systems biology.
    Type of Medium: Online Resource
    ISSN: 1940-3402 , 1559-6095 , 1559-6095
    Language: English
    Publisher: Cold Spring Harbor Laboratory
    Publication Date: 2010
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  • 6
    In: Cytometry Part A, Wiley, Vol. 87, No. 6 ( 2015-06), p. 524-540
    Abstract: Computational approaches for automatic analysis of image‐based high‐throughput and high‐content screens are gaining increased importance to cope with the large amounts of data generated by automated microscopy systems. Typically, automatic image analysis is used to extract phenotypic information once all images of a screen have been acquired. However, also in earlier stages of large‐scale experiments image analysis is important, in particular, to support and accelerate the tedious and time‐consuming optimization of the experimental conditions and technical settings. We here present a novel approach for automatic, large‐scale analysis and experimental optimization with application to a screen on neuroblastoma cell lines. Our approach consists of cell segmentation, tracking, feature extraction, classification, and model‐based error correction. The approach can be used for experimental optimization by extracting quantitative information which allows experimentalists to optimally choose and to verify the experimental parameters. This involves systematically studying the global cell movement and proliferation behavior. Moreover, we performed a comprehensive phenotypic analysis of a large‐scale neuroblastoma screen including the detection of rare division events such as multi‐polar divisions. Major challenges of the analyzed high‐throughput data are the relatively low spatio‐temporal resolution in conjunction with densely growing cells as well as the high variability of the data. To account for the data variability we optimized feature extraction and classification, and introduced a gray value normalization technique as well as a novel approach for automatic model‐based correction of classification errors. In total, we analyzed 4,400 real image sequences, covering observation periods of around 120 h each. We performed an extensive quantitative evaluation, which showed that our approach yields high accuracies of 92.2% for segmentation, 98.2% for tracking, and 86.5% for classification. © 2015 International Society for Advancement of Cytometry
    Type of Medium: Online Resource
    ISSN: 1552-4922 , 1552-4930
    URL: Issue
    Language: English
    Publisher: Wiley
    Publication Date: 2015
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    SSG: 12
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  • 7
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 77, No. 13_Supplement ( 2017-07-01), p. 3958-3958
    Abstract: Gastric cancer (GC) is the third leading cause of cancer-related death worldwide. With the advent of immunotherapies, there is a need to characterize the phenotype of tumor infiltrating immune cells and co-localized cancer cells. We have shown previously that high density of CD45R0+ T cells is related to better prognosis in Japanese GC. However, the variation of T-cell infiltration in GC is still not understood. We hypothesized that an increased CD8+ T-cell infiltration is related to T-cell activation (high T cell Ki67 proliferation index). Purpose: To establish the frequency of co-occurrence of Ki67+ and CD8+ in T cells and their co-localization with tumor cells and evaluate the relationship with clinicopathological variables including survival. Patients and Methods: Immunohistochemistry for T cells (CD8), proliferation (Ki67), and epithelial cells (CK) was performed on tissue microarrays (TMAs) from 213 GC from the Kanagawa Cancer Centre Hospital (Yokohama, Japan). Stained slides were scanned, quality controlled, and analyzed using Tissue Phenomics (Definiens, Munich, Germany) for cell/nuclei segmentation and automatic co-registration of consecutive sections. The TMA cores were subdivided into tiles of size 64 µm2 to count co-localized positive cells. Average ratio of CD8+ cells and Ki67+ cells per tile/patient was used for statistical analyses. The relationship with pT, pN and histological tumor type was assessed using the Kruskal-Wallis test. Prognostic features were determined by univariate stratification which optimizes Kaplan-Meier p-value using 50 independent pre-validations with 3 folds and ranked by the median pre-validation p-values. P-values & lt; 0.05 were considered significant. Results: 60887 tiles were analyzed in total. Median (range) number of tiles analyzed per patient was 291 (81-345). Median (range) CD8+/Ki67+ ratio was 0.39 (0.01-0.92). Manual inspection of selected image tiles showed that CD8+ cells are rarely Ki67+. Median (range) % of tiles/patient where CD8+Ki67- cells co-localized with Ki67+ tumor cells was 17% (0%-93%). Significant difference of ratio was observed between histological subtypes (p=0.0096). There was no significant relationship between CD8+/Ki67+ and pT or pN. A high CD8+/Ki67+ ratio was related to better survival (p=0.012). Conclusions: This is the first study to suggest that the majority of CD8+ T cells in GC appear to be resting (Ki67-) T cells rejecting our hypothesis that high numbers of intratumoral T cells are due to high intratumoral T cell proliferation. The co-localization of CD8+ T cells and Ki67+ tumor cells seems to be clinically relevant and characterize certain histological phenotypes in GC. However, the potential underlying biological mechanisms of interaction between T cells and tumor cells are currently unknown. Further studies are needed to validate our findings and characterize the interface between tumor and immune cells. Citation Format: Mehmet Yigitsoy, Sophie Earle, Armin Meier, Nathalie Harder, Matthew Hale, Aleksandra Zuraw, Takaki Yoshikawa, Günter Schmidt, Ralf Huss, Heike I. Grabsch. The importance of co-localized resting CD8+ T cells and proliferating tumor cells in gastric cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 3958. doi:10.1158/1538-7445.AM2017-3958
    Type of Medium: Online Resource
    ISSN: 0008-5472 , 1538-7445
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    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2017
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  • 8
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 78, No. 16_Supplement ( 2018-08-15), p. A069-A069
    Abstract: Introduction: The composition of different immune cell populations in the tumor microenvironment plays an important role for tumor progression in various cancer types. In particular, tumor-associated macrophages (TAMs) and tumor-infiltrating T cells (TILs) are relevant players. On the other hand, structural changes such as neoangiogenesis triggered by cancerous tumors to increase their nutrient supplies have also been associated with tumor progression. In this work we systematically quantified these factors in prostate cancer (PCa) and correlated them with clinical outcome data using a Tissue Phenomics approach. By investigating the prognostic relevance of TAMs (CD68/CD163), TILs (CD3/CD8), and microvessels (CD34) in the tumor, tumor microenvironment (TME), and stroma we identified strong prognostic markers for PCa recurrence prediction in patients after radical prostatectomy. Methods: In this study, we analyzed a cohort of 90 PCa patients, of whom 40 suffered from tumor progression measured by prostate cancer antigen (PSA) recurrence after prostatectomy. The cohort comprised low- and intermediate-risk PCa patients (Gleason-Score≤7b) since providing a reliable prognosis is particularly difficult for such grades. Tissue sections were immunohistochemically stained using the duplex stains CD68/CD163 for TAMs, CD3/CD8 for TILs, and CK18/p63 to identify and characterize glands as cancerous vs. noncancerous based on their expression level of p63 (in cancerous glands p63 is not expressed). To quantify tumor neoangiogenesis microvessels were stained by CD34. All sections were geometrically aligned per case (virtual multiplexing) to enable coanalysis of stains, and quantified within relevant regions-of-interest (tumor, TME, stroma) using fully automated computational methods (1, 2). In particular, we determined region-specific densities and average distances of TAMs, TILs, and microvessels, as well as ratios of all measures. We systematically analyzed the prognostic power of each measure by optimizing a cutoff with respect to the disease-free survival statistic (log-rank test) using cross-validation to avoid for overfitting. Results: The top-ranking prognostic markers regarding robustness and prediction performance were related to microvessel density combined with immune cell densities. In particular, we found that within the TME, a coverage of CD8(+) cytotoxic T cells larger than 10% of the coverage of CD34(+) microvessels is correlated with a good prognosis and long-term disease-free survival (cross-validated p & lt;3.1•10-7, accuracy=83%). This corresponds to high densities of CD8(+) cells and/or low microvessel densities, which both have been shown to be associated with good prognosis in prostate cancer. In addition, we found that a larger average distance of CD68(+) M1 macrophages to CD34(+) microvessels above 75.7µm in the tumor region is associated with good prognosis (cross-validated p & lt;2.9•10-8, accuracy=82%). Again, low tumor microvessel density seems to be beneficial as well as high densities of CD68(+) macrophages. The CD68(+) M1-polarized phenotype is associated with tumor-suppressing properties, indicating that a higher density of this population compared to the tumor-promoting M2-polarized phenotype fosters disease-free survival. Conclusion: Our results indicate a considerable prognostic potential of markers combining microvessel density with measures of TAMs and TILs to predict PSA recurrence in PCa. This application shows that systematic analysis as performed by Tissue Phenomics enables discovery of non-obvious combined prognostic markers characterizing the tumor landscape with high potential to improve patient treatment. In future work we aim to validate our findings on additional data from other clinical sites. References: 1. Yigitsoy M, et al. Hierarchical patch-based co-registration of differently stained histopathology slides. Proc SPIE 2017. doi:10.1117/12.2254266. 2. Brieu N, et al. Slide specific models for segmentation of differently stained digital histopathology whole slide images. Proc SPIE 2016. doi:10.1117/12.2208620. Citation Format: Nathalie Harder, Maria Athelogou, Harald Hessel, Alexander Buchner, Christian Stief, Thomas Kirchner, Günter Schmidt, Ralf Huss, Tze Heng Tan. Combination of immune status and tumor microvascularization provides strong prognostic markers for prostate cancer recurrence prediction [abstract]. In: Proceedings of the AACR Special Conference: Prostate Cancer: Advances in Basic, Translational, and Clinical Research; 2017 Dec 2-5; Orlando, Florida. Philadelphia (PA): AACR; Cancer Res 2018;78(16 Suppl):Abstract nr A069.
    Type of Medium: Online Resource
    ISSN: 0008-5472 , 1538-7445
    RVK:
    RVK:
    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2018
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  • 9
    Online Resource
    Online Resource
    Proceedings of the National Academy of Sciences ; 2013
    In:  Proceedings of the National Academy of Sciences Vol. 110, No. 37 ( 2013-09-10)
    In: Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, Vol. 110, No. 37 ( 2013-09-10)
    Abstract: Promiscuous expression of numerous tissue-restricted self-antigens (TRAs) in medullary thymic epithelial cells (mTECs) is essential to safeguard self-tolerance. A distinct feature of promiscuous gene expression is its mosaic pattern (i.e., at a given time, each self-antigen is expressed only in 1–3% of mTECs). How this mosaic pattern is generated at the single-cell level is currently not understood. Here, we show that subsets of human mTECs expressing a particular TRA coexpress distinct sets of genes. We identified three coexpression groups comprising overlapping and complementary gene sets, which preferentially mapped to certain chromosomes and intrachromosomal gene clusters. Coexpressed gene loci tended to colocalize to the same nuclear subdomain. The TRA subsets aligned along progressive differentiation stages within the mature mTEC subset and, in vitro, interconverted along this sequence. Our data suggest that single mTECs shift through distinct gene pools, thus scanning a sizeable fraction of the overall repertoire of promiscuously expressed self-antigens. These findings have implications for the temporal and spatial (re)presentation of self-antigens in the medulla in the context of tolerance induction.
    Type of Medium: Online Resource
    ISSN: 0027-8424 , 1091-6490
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    Language: English
    Publisher: Proceedings of the National Academy of Sciences
    Publication Date: 2013
    detail.hit.zdb_id: 209104-5
    detail.hit.zdb_id: 1461794-8
    SSG: 11
    SSG: 12
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  • 10
    In: Cancer Letters, Elsevier BV, Vol. 331, No. 1 ( 2013-4), p. 35-45
    Type of Medium: Online Resource
    ISSN: 0304-3835
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2013
    detail.hit.zdb_id: 195674-7
    detail.hit.zdb_id: 2004212-7
    SSG: 12
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