Estimation of Immune Cell Densities in Immune Cell Conglomerates: An Approach for High-Throughput Quantification

Determining the correct number of positive immune cells in immunohistological sections of colorectal cancer and other tumor entities is emerging as an important clinical predictor and therapy selector for an individual patient. This task is usually obstructed by cell conglomerates of various sizes. We here show that at least in colorectal cancer the inclusion of immune cell conglomerates is indispensable for estimating reliable patient cell counts. Integrating virtual microscopy and image processing principally allows the high-throughput evaluation of complete tissue slides.For such large-scale systems we demonstrate a robust quantitative image processing algorithm for the reproducible quantification of cell conglomerates on CD3 positive T cells in colorectal cancer. While isolated cells (28 to 80 microm(2)) are counted directly, the number of cells contained in a conglomerate is estimated by dividing the area of the conglomerate in thin tissues sections (< or =6 microm) by the median area covered by an isolated T cell which we determined as 58 microm(2). We applied our algorithm to large numbers of CD3 positive T cell conglomerates and compared the results to cell counts obtained manually by two independent observers. While especially for high cell counts, the manual counting showed a deviation of up to 400 cells/mm(2) (41% variation), algorithm-determined T cell numbers generally lay in between the manually observed cell numbers but with perfect reproducibility.In summary, we recommend our approach as an objective and robust strategy for quantifying immune cell densities in immunohistological sections which can be directly implemented into automated full slide image processing systems

The EDRN knowledge environment: an open source, scalable informatics platform for biological sciences research

We describe here the Early Detection Research Network (EDRN) for Cancer’s knowledge environment. It is an open source platform built by NASA’s Jet Propulsion Laboratory with contributions from the California Institute of Technology, and Giesel School of Medicine at Dartmouth. It uses tools like Apache OODT, Plone, and Solr, and borrows heavily from JPL’s Planetary Data System’s ontological infrastructure. It has accumulated data on hundreds of thousands of biospecemens and serves over 1300 registered users across the National Cancer Institute (NCI). The scalable computing infrastructure is built such that we are being able to reach out to other agencies, provide homogeneous access, and provide seamless analytics support and bioinformatics tools through community engagement

Automatic Tumor-Stroma Separation in Fluorescence TMAs Enables the Quantitative High-Throughput Analysis of Multiple Cancer Biomarkers

The 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

Computational pathology analysis of tissue microarrays predicts survival of renal clear cell carcinoma patients

Renal cell carcinoma (RCC) can be diagnosed by histological tissue analysis where exact counts of cancerous cell nuclei are required. We propose a completely automated image analysis pipeline to predict the survival of RCC patients based on the analysis of immunohistochemical staining of MIB-1 on tissue microarrays. A random forest classifier detects cell nuclei of cancerous cells and predicts their staining. The classifier training is achieved by expert annotations of 2300 nuclei gathered from tissues of 9 different RCC patients. The application to a test set of 133 patients clearly demonstrates that our computational pathology analysis matches the prognostic performance of expert pathologists

Targeted computational analysis of the C3HEB/FEJ mouse model for drug efficacy testing

2020 Spring.Includes bibliographical references.Efforts to develop effective and safe drugs for the treatment of tuberculosis (TB) require preclinical evaluation in animal models. Alongside efficacy testing of novel therapies, effects on pulmonary pathology and disease progression are monitored by using histopathology images from these infected animals. To compare the severity of disease across treatment cohorts, pathologists have historically assigned a semi-quantitative histopathology score that may be subjective in terms of their training, experience, and personal bias. Manual histopathology, therefore, has limitations regarding reproducibility between studies and pathologists, potentially masking successful treatments. This report describes a pathologist-assistive software tool that reduces these user limitations while providing a rapid, quantitative scoring system for digital histopathology image analysis. The software, called 'Lesion Image Recognition and Analysis' (LIRA), employs convolutional neural networks to classify seven different pathology features, including three different lesion types from pulmonary tissues of the C3HeB/FeJ tuberculosis mouse model. LIRA was developed to improve the efficiency of histopathology analysis for mouse tuberculosis infection models. The model approach also has broader applications to other diseases and tissues. This also includes animals that are undergoing anti-mycobacterial treatment and host immune system modulation. A complimentary software package called 'Mycobacterial Image Analysis' (MIA) had also been developed that characterizes the varying bacilli characteristics such as density, aggregate/planktonic bacilli size, fluorescent intensity, and total counts. This further groups the bacilli characteristic data depending on the seven different classifications that are selected by the user. Using this approach allows for an even more targeted analysis approach that can determine how therapy and microenvironments influence the Mtb response

Interaction of the host immune system with tumor cells in human papillomavirus associated diseases

Human papillomaviruses (HPV) are very common in the sexually active population and contribute to 610,000 cancers per year occurring at different locations. The initial step of HPV-related carcinogenesis is the induction of transforming processes in the host cells mediated by the viral oncoproteins E6 and E7 that interfere with critical host cell pathways. The transforming infection is highlighted by overexpression of the tumor suppressor protein p16INK4a. Only a small number of precancerous lesions progress while the majority can be controlled by the host’s immune system and undergo regression. Progressing lesions under the immunoselective pressure seem to acquire characteristics that enable them to circumvent the host’s immune attack and promote disease progression. Immune evasion might be mediated by the immune microenvironment of the tumor as well as by tumor cell intrinsic features. The here presented thesis addressed different questions and strategies with regard to the role of the immune system in HPV-associated diseases and can be subdivided in two main parts: In the first part immunologic characteristics of precancerous lesions and cancers are investigated to gain insight into possible immune evasion mechanisms developed during disease progression. In the second part treatment options to positively influence the balance between immune evasion and anti-tumoral immune responses are evaluated. In the first part a) the immunohistochemical characterization of cervical precancers and cancers for infiltration with different T cell phenotypes revealed that generally increasing T cell densities occur late in carcinogenesis – and not yet with the onset of early transforming infection - and are accompanied by immunosuppressive regulatory T cells (Tregs). Mean cell densities for Tregs in the stroma significantly increased from 121.6 cells/mm2 (range: 24-286.8 cells/mm2) in low-grade lesions to 308.8 cells/mm2 (24-724.8 cells/mm2) in high-grade lesions and 673.6 cells/mm2 (52.8-1564.8 cells/mm2) in cancer which points to their immunosuppressive role during carcinogenesis. The demonstrated large variances in T cell densities within one diagnostic category, however, point to a remarkable heterogeneity of the immune control with potential interesting prognostic implications. On keratinocytes themselves b) a selective loss for human leukocyte antigen (HLA) class I heavy chain A expression was observed in about 55% high-grade cervical intra-epithelial neoplasia (CIN) and 65% of cervical cancers. HLA class II de novo expression was found in 50% of low-grade CIN and in about 85% of high-grade CIN and cervical cancers. These alterations could represent another fundamental mechanism contributing to immune evasion. A c) longitudinal analysis of immune infiltrates in patients treated with imiquimod, an immuno-modulatory Toll-like receptor (TLR) agonist, revealed that the patient’s local immune constitution might be decisive for a possible response to immune-enhancing treatment strategies. Importantly, in patients responding to imiquimod immune cell densities increased during the treatment as epithelial CD3+ T cell counts (from 160.8 to 371.1 cells/mm2) and CD8+ T cell counts (from 113.8 to 174.1 cells/mm2) demonstrated. The d) development and establishment of an automated cell quantification tool for high-throughput analysis allows the search for immune evasion markers and strategies to be continued in an objective, standardized and faster way. In consideration of the clinical efficacy of imiquimod and the observed stimulatory effects on the immune infiltrate density in part one of this thesis e) a new second generation TLR-agonist (TMX-202) potentially having less side-effects than imiquimod was tested for the first time in an in vitro T cell stimulation model in part two of this thesis. Its potential to stimulate innate and adaptive immunity was demonstrated by an enhanced killing capacity of T cells that were stimulated with HPV-related antigens loaded on dendritic cells and then co-incubated with HPV16-positive CaSki cells. Based on the dense infiltration with Tregs observed in part one of the presented thesis the f) immune stimulating effects of Treg depletion was tested in an autologous in vitro model. In this regard, one major aim of the thesis was the generation of a new HPV-positive tumor cell line derived from an oropharyngeal squamous cell carcinoma that serves as model system for HPV-associated tumors. In combination with peripheral blood lymphocytes obtained from the same patient this autologous system allowed to address Treg depletion as an immunotherapeutic approach. The results demonstrated that this strategy might enhance the cell-mediated immune response against tumor cells and emphasize the role that this particular T cell phenotype is obviously playing in the carcinogenesis of HPV-associated tumors. Based on the results obtained in the first part of the thesis it is well conceivable that the combination of different immunologic markers contributes to the definition of a prognostic biomarker tool for progression and regression of precancerous lesions. Such a prognostic “immune score” has a high clinical relevance and allows risk-adapted treatment decisions minimizing the costs and long-term sequelae of surgical interventions. In particular the newly developed microscopy based method in this work allowing for the automated histological high-throughput quantification of infiltrating immune cells in cervical intraepithelial neoplasia provides an important methodical tool to realize this long term goal. The immuno-stimulating effects of the novel TLR7-agonist TMX-202 and Treg depletion demonstrated in the second part of this thesis by in vitro models indicate that immunomodulatory approaches could play an important role for the treatment of HPV-associated cancers in the future. In this regard, the established novel tumor cell line in combination with autologous immune cells provides a valuable in vitro model system for HPV-associated cancers that can be used to investigate further immunotherapeutic intervention and treatment strategies