Immune cell infiltration pattern in non-small cell lung cancer PDX models is a model immanent feature and correlates with a distinct molecular and phenotypic make-up.

BACKGROUND: The field of cancer immunology is rapidly moving towards innovative therapeutic strategies, resulting in the need for robust and predictive preclinical platforms reflecting the immunological response to cancer. Well characterized preclinical models are essential for the development of predictive biomarkers in the oncology as well as the immune-oncology space. In the current study, gold standard preclinical models are being refined and combined with novel image analysis tools to meet those requirements. METHODS: A panel of 14 non-small cell lung cancer patient-derived xenograft models (NSCLC PDX) was propagated in humanized NOD/Shi-scid/IL-2Rnull mice. The models were comprehensively characterized for relevant phenotypic and molecular features, including flow cytometry, immunohistochemistry, histology, whole exome sequencing and cytokine secretion. RESULTS: Models reflecting hot (>5% tumor-infiltrating lymphocytes/TILs) as opposed to cold tumors (<5% TILs) significantly differed regarding their cytokine profiles, molecular genetic aberrations, stroma content, and programmed cell death ligand-1 status. Treatment experiments including anti cytotoxic T-lymphocyte-associated protein 4, anti-programmed cell death 1 or the combination thereof across all 14 models in the single mouse trial format showed distinctive tumor growth response and spatial immune cell patterns as monitored by computerized analysis of digitized whole-slide images. Image analysis provided for the first time qualitative evaluation of the extent to which PDX models retain the histological features from their original human donors. CONCLUSIONS: Deep phenotyping of PDX models in a humanized setting by combinations of computational pathology, immunohistochemistry, flow cytometry and proteomics enables the exhaustive analysis of innovative preclinical models and paves the way towards the development of translational biomarkers for immuno-oncology drugs.

TGF-ß activates pericytes via induction of the epithelial-to-mesenchymal transition protein SLUG in glioblastoma.

AIMS: In primary central nervous system tumours, epithelial-to-mesenchymal transition (EMT) gene expression is associated with increased malignancy. However, it has also been shown that EMT factors in gliomas are almost exclusively expressed by glioma vessel-associated pericytes (GA-Peris). In this study, we aimed to identify the mechanism of EMT in GA-Peris and its impact on angiogenic processes. METHODS: In glioma patients, vascular density and the expression of the pericytic markers platelet derived growth factor receptor (PDGFR)-ß and smooth muscle actin (αSMA) were examined in relation to the expression of the EMT transcription factor SLUG and were correlated with survival of patients with glioblastoma (GBM). Functional mechanisms of SLUG regulation and the effects on primary human brain vascular pericytes (HBVP) were studied in vitro by measuring proliferation, cell motility and growth characteristics. RESULTS: The number of PDGFR-ß- and αSMA-positive pericytes did not change with increased malignancy nor showed an association with the survival of GBM patients. However, SLUG-expressing pericytes displayed considerable morphological changes in GBM-associated vessels, and TGF-ß induced SLUG upregulation led to enhanced proliferation, motility and altered growth patterns in HBVP. Downregulation of SLUG or addition of a TGF-ß antagonising antibody abolished these effects. CONCLUSIONS: We provide evidence that in GA-Peris, elevated SLUG expression is mediated by TGF-ß, a cytokine secreted by most glioma cells, indicating that the latter actively modulate neovascularisation not only by modulating endothelial cells, but also by influencing pericytes. This process might be responsible for the formation of an unstructured tumour vasculature as well as for the breakdown of the blood-brain barrier in GBM.

Crowdsourcing of Histological Image Labeling and Object Delineation by Medical Students.

Crowdsourcing in pathology has been performed on tasks that are assumed to be manageable by nonexperts. Demand remains high for annotations of more complex elements in digital microscopic images, such as anatomical structures. Therefore, this paper investigates conditions to enable crowdsourced annotations of high-level image objects, a complex task considered to require expert knowledge. Seventy six medical students without specific domain knowledge who voluntarily participated in three experiments solved two relevant annotation tasks on histopathological images: 1) labeling of images showing tissue regions and 2) delineation of morphologically defined image objects. We focus on methods to ensure sufficient annotation quality including several tests on the required number of participants and on the correlation of participants' performance between tasks. In a set up simulating annotation of images with limited ground truth, we validated the feasibility of a confidence score using full ground truth. For this, we computed a majority vote using weighting factors based on individual assessment of contributors against scattered gold standard annotated by pathologists. In conclusion, we provide guidance for task design and quality control to enable a crowdsourced approach to obtain accurate annotations required in the era of digital pathology.

Distribution and prognostic impact of microglia/macrophage subpopulations in gliomas.

While the central nervous system is considered an immunoprivileged site and brain tumors display immunosuppressive features, both innate and adaptive immune responses affect glioblastoma (GBM) growth and treatment resistance. However, the impact of the major immune cell population in gliomas, represented by glioma-associated microglia/macrophages (GAMs), on patients' clinical course is still unclear. Thus, we aimed at assessing the immunohistochemical expression of selected microglia and macrophage markers in 344 gliomas (including gliomas from WHO grade I-IV). Furthermore, we analyzed a cohort of 241 IDH1R132H-non-mutant GBM patients for association of GAM subtypes and patient overall survival. Phenotypical properties of GAMs, isolated from high-grade astrocytomas by CD11b-based magnetic cell sorting, were analyzed by immunocytochemistry, mRNA microarray, qRT-PCR and bioinformatic analyses. A higher amount of CD68-, CD163- and CD206-positive GAMs in the vital tumor core was associated with beneficial patient survival. The mRNA expression profile of GAMs displayed an upregulation of factors that are considered as pro-inflammatory M1 (eg, CCL2, CCL3L3, CCL4, PTGS2) and anti-inflammatory M2 polarization markers (eg, MRC1, LGMN, CD163, IL10, MSR1), the latter rather being associated with phagocytic functions in the GBM microenvironment. In summary, we present evidence that human GBMs contain mixed M1/M2-like polarized GAMs and that the levels of different GAM subpopulations in the tumor core are positively associated with overall survival of patients with IDH1R132H-non-mutant GBMs.

Image analysis of immune cell patterns in the human mammary gland during the menstrual cycle refines lymphocytic lobulitis.

PURPOSE: To improve microscopic evaluation of immune cells relevant in breast cancer oncoimmunology, we aim at distinguishing normal infiltration patterns from lymphocytic lobulitis by advanced image analysis. We consider potential immune cell variations due to the menstrual cycle and oral contraceptives in non-neoplastic mammary gland tissue. METHODS: Lymphocyte and macrophage distributions were analyzed in the anatomical context of the resting mammary gland in immunohistochemically stained digital whole slide images obtained from 53 reduction mammoplasty specimens. Our image analysis workflow included automated regions of interest detection, immune cell recognition, and co-registration of regions of interest. RESULTS: In normal lobular epithelium, seven CD8[Formula: see text] lymphocytes per 100 epithelial cells were present on average and about 70% of this T-lymphocyte population was lined up along the basal cell layer in close proximity to the epithelium. The density of CD8[Formula: see text] T-cell was 1.6 fold higher in the luteal than in the follicular phase in spontaneous menstrual cycles and 1.4 fold increased under the influence of oral contraceptives, and not co-localized with epithelial proliferation. CD4[Formula: see text] T-cells were infrequent. Abundant CD163[Formula: see text] macrophages were widely spread, including the interstitial compartment, with minor variation during the menstrual cycle. CONCLUSIONS: Spatial patterns of different immune cell subtypes determine the range of normal, as opposed to inflammatory conditions of the breast tissue microenvironment. Advanced image analysis enables quantification of hormonal effects, refines lymphocytic lobulitis, and shows potential for comprehensive biopsy evaluation in oncoimmunology.

Detection of lobular structures in normal breast tissue.

BACKGROUND: Ongoing research into inflammatory conditions raises an increasing need to evaluate immune cells in histological sections in biologically relevant regions of interest (ROIs). Herein, we compare different approaches to automatically detect lobular structures in human normal breast tissue in digitized whole slide images (WSIs). This automation is required to perform objective and consistent quantitative studies on large data sets. METHODS: In normal breast tissue from nine healthy patients immunohistochemically stained for different markers, we evaluated and compared three different image analysis methods to automatically detect lobular structures in WSIs: (1) a bottom-up approach using the cell-based data for subsequent tissue level classification, (2) a top-down method starting with texture classification at tissue level analysis of cell densities in specific ROIs, and (3) a direct texture classification using deep learning technology. RESULTS: All three methods result in comparable overall quality allowing automated detection of lobular structures with minor advantage in sensitivity (approach 3), specificity (approach 2), or processing time (approach 1). Combining the outputs of the approaches further improved the precision. CONCLUSIONS: Different approaches of automated ROI detection are feasible and should be selected according to the individual needs of biomarker research. Additionally, detected ROIs could be used as a basis for quantification of immune infiltration in lobular structures.

Assessment of tumour viability in human lung cancer xenografts with texture-based image analysis.

AIMS: To build and evaluate an automated method for assessing tumour viability in histological tissue samples using texture features and supervised learning. METHODS: H&E-stained sections (n=56) of human non-small cell lung adenocarcinoma xenografts were digitised with a whole-slide scanner. A novel image analysis method based on local binary patterns and a support vector machine classifier was trained with a set of sample regions (n=177) extracted from the whole-slide images and tested with another set of images (n=494). The extracted regions, or single-tissue entity images, were chosen to represent as pure as possible examples of three morphological tissue entities: viable tumour tissue, non-viable tumour tissue and mouse host tissue. RESULTS: An agreement of 94.5% (area under the curve=0.995, kappa=0.90) was achieved to classify the single-tissue entity images in the test set (n=494) into the viable tumour and non-viable tumour tissue categories. The algorithm assigned 250 of the 252 non-viable and 219 of the 242 of viable sample regions to the correct categories, respectively. This corresponds to a sensitivity of 90.5% and specificity of 99.2%. CONCLUSIONS: The proposed image analysis-based tumour viability assessment resulted in a high agreement with expert annotations. By providing extraction of detailed information of the tumour microenvironment, the automated method can be used in preclinical research settings. The method could also have implications in cancer diagnostics, cancer outcome prognostics and prediction.

Exploring the spatial dimension of estrogen and progesterone signaling: detection of nuclear labeling in lobular epithelial cells in normal mammary glands adjacent to breast cancer.

BACKGROUND: Comprehensive spatial assessment of hormone receptor immunohistochemistry staining in digital whole slide images of breast cancer requires accurate detection of positive nuclei within biologically relevant regions of interest. Herein, we propose a combination of automated region labeling at low resolution and subsequent detailed tissue evaluation of subcellular structures in lobular structures adjacent to breast cancer, as a proof of concept for the approach to analyze estrogen and progesterone receptor expression in the spatial context of surrounding tissue. METHODS: Routinely processed paraffin sections of hormone receptor-negative ductal invasive breast cancer were stained for estrogen and progesterone receptor by immunohistochemistry. Digital whole slides were analyzed using commercially available image analysis software for advanced object-based analysis, applying textural, relational, and geometrical features. Mammary gland lobules were targeted as regions of interest for analysis at subcellular level in relation to their distance from coherent tumor as neighboring relevant tissue compartment. Lobule detection quality was evaluated visually by a pathologist. RESULTS: After rule set optimization in an estrogen receptor-stained training set, independent test sets (progesterone and estrogen receptor) showed acceptable detection quality in 33% of cases. Presence of disrupted lobular structures, either by brisk inflammatory infiltrate, or diffuse tumor infiltration, was common in cases with lower detection accuracy. Hormone receptor detection tended towards higher percentage of positively stained nuclei in lobules distant from the tumor border as compared to areas adjacent to the tumor. After adaptations of image analysis, corresponding evaluations were also feasible in hormone receptor positive breast cancer, with some limitations of automated separation of mammary epithelial cells from hormone receptor-positive tumor cells. CONCLUSIONS: As a proof of concept for object-oriented detection of steroid hormone receptors in their spatial context, we show that lobular structures can be classified based on texture-based image features, unless brisk inflammatory infiltration disrupts the normal morphological structure of the tubular gland epithelium. We consider this approach as prototypic for detection and spatial analysis of nuclear markers in defined regions of interest. We conclude that advanced image analysis at this level of complexity requires adaptation to the individual tumor phenotypes and morphological characteristics of the tumor environment.

On-chip imaging of Schistosoma haematobium eggs in urine for diagnosis by computer vision.

BACKGROUND: Microscopy, being relatively easy to perform at low cost, is the universal diagnostic method for detection of most globally important parasitic infections. As quality control is hard to maintain, misdiagnosis is common, which affects both estimates of parasite burdens and patient care. Novel techniques for high-resolution imaging and image transfer over data networks may offer solutions to these problems through provision of education, quality assurance and diagnostics. Imaging can be done directly on image sensor chips, a technique possible to exploit commercially for the development of inexpensive "mini-microscopes". Images can be transferred for analysis both visually and by computer vision both at point-of-care and at remote locations. METHODS/PRINCIPAL FINDINGS: Here we describe imaging of helminth eggs using mini-microscopes constructed from webcams and mobile phone cameras. The results show that an inexpensive webcam, stripped off its optics to allow direct application of the test sample on the exposed surface of the sensor, yields images of Schistosoma haematobium eggs, which can be identified visually. Using a highly specific image pattern recognition algorithm, 4 out of 5 eggs observed visually could be identified. CONCLUSIONS/SIGNIFICANCE: As proof of concept we show that an inexpensive imaging device, such as a webcam, may be easily modified into a microscope, for the detection of helminth eggs based on on-chip imaging. Furthermore, algorithms for helminth egg detection by machine vision can be generated for automated diagnostics. The results can be exploited for constructing simple imaging devices for low-cost diagnostics of urogenital schistosomiasis and other neglected tropical infectious diseases.