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Inflammatory bowel disease (IBD) is a chronic immune-mediated disease of the gastrointestinal tract. While therapies exist, response can be limited within the patient population. As such, researchers have studied mouse models of colitis to further understand its pathogenesis and identify new treatment targets. Although bench methods like flow cytometry and RNA-sequencing can characterize immune responses with single-cell resolution, whole murine colon specimens are processed at once. Given the simultaneous presence of colonic regions that are involved or uninvolved with abnormal histology, processing whole colons may lead to a loss of spatial context. Detecting these regions in hematoxylin and eosin (H&E)-stained colonic tissues offers the downstream potential of quantifying immune populations in areas with and without disease involvement by immunohistochemistry on serially sectioned slides. This could provide a complementary, spatially-aware approach to further characterize populations identified by other methods. However, detection of such regions requires expert interpretation by pathologists and is a tedious process that may be difficult to perform consistently across experiments. To this end, we have trained a deep learning model to detect ‘Involved’ and ‘Uninvolved’ regions from H&E-stained colonic slides across controls and three mouse models of colitis – the dextran sodium sulfate (DSS) chemical induction model, the recently established intestinal epithelium-specific, inducible Klf5ΔIND (Villin-CreERT2;Klf5fl/fl) genetic model, and one that combines both induction methods. The trained classifier allows for extraction of ‘Involved’ colonic regions across mice to cluster and identify histological classes. Here, we show that quantification of ‘Involved’ and ‘Uninvolved’ image patch classes in swiss rolls of colonic specimens can be utilized to train a linear determinant analysis classifier to distinguish between mouse models. Such an approach has the potential for revealing histological links and improving synergy between various colitis mouse model studies to identify new therapeutic targets and pathophysiological mechanisms.

Tumor-infiltrating lymphocytes (TILs) have been established as a robust prognostic biomarker in breast cancer, with emerging utility in predicting treatment response in the adjuvant and neoadjuvant settings. In this study, the role of TILs in predicting overall survival and progression-free interval was evaluated in two independent cohorts of breast cancer from the Cancer Genome Atlas (TCGA BRCA) and the Carolina Breast Cancer Study (UNC CBCS). We utilized machine learning and computer vision algorithms to characterize TIL infiltrates in digital whole-slide images (WSIs) of breast cancer stained with hematoxylin and eosin (H&E). Multiple parameters were used to characterize the global abundance and spatial features of TIL infiltrates. Univariate and multivariate analyses show that large aggregates of peritumoral and intratumoral TILs (forests) were associated with longer survival, whereas the absence of intratumoral TILs (deserts) is associated with increased risk of recurrence. Patients with two or more high-risk spatial features were associated with significantly shorter progression-free interval (PFI). This study demonstrates the practical utility of Pathomics in evaluating the clinical significance of the abundance and spatial patterns of distribution of TIL infiltrates as important biomarkers in breast cancer.

Inflammatory bowel disease (IBD) is a chronic illness characterized by dysregulated immune cascades in the intestines, in which the Th17 immune response plays an important role. We demonstrated that mice with intestinal epithelium-specific deletion of Krüppel-like factor 5 (Klf5) developed Th17-dependent colonic inflammation. In the absence of KLF5, there was aberrant cellular localization of phosphorylated STAT3, an essential mediator of the Th17-associated cytokine, IL-22, which is required for epithelial tissue regeneration. In contrast, mitigation of IL-17A with anti-IL-17A neutralizing antibody attenuated colitis in Klf5-deficient mice. There was also a considerable shift in the colonic microbiota of Klf5-deficient mice that phenocopied human IBD. Notably, the inflammatory response due to Klf5 deletion was alleviated by antibiotic treatment, implicating the role of microbiota in pathogenesis. Finally, human colitic tissues had reduced KLF5 levels when compared with healthy tissues. Together, these findings demonstrated the importance of KLF5 in protecting the intestinal epithelium against Th17-mediated immune and inflammatory responses. The mice described herein may serve as a potential model for human IBD.

Reduced Paneth cell (PC) numbers are observed in inflammatory bowel diseases and impaired PC function contributes to the ileal pathogenesis of Crohn's disease (CD). PCs reside in proximity to Lgr5Ileal tissue samples from patients with CD, mouse models for mitochondrial dysfunction (Hsp60In patients with CD and TNFWe provide evidence that inflammation-associated mitochondrial dysfunction in the intestinal epithelium triggers a metabolic imbalance, causing reduced stemness and acquisition of a dysfunctional PC phenotype. Blocking glycolysis might be a novel drug target to antagonise PC dysfunction in the pathogenesis of CD.

Tissue-resident macrophages (TRMs) populate all tissues and play key roles in homeostasis, immunity and repair. TRMs express a molecular program that is mostly shaped by tissue cues. However, TRM identity and the mechanisms that maintain TRMs in tissues remain poorly understood. We recently found that serous-cavity TRMs (LPMs) are highly enriched in RXR transcripts and RXR-response elements. Here, we show that RXRs control mouse serous-macrophage identity by regulating chromatin accessibility and the transcriptional regulation of canonical macrophage genes. RXR deficiency impairs neonatal expansion of the LPM pool and reduces the survival of adult LPMs through excess lipid accumulation. We also find that peritoneal LPMs infiltrate early ovarian tumours and that RXR deletion diminishes LPM accumulation in tumours and strongly reduces ovarian tumour progression in mice. Our study reveals that RXR signalling controls the maintenance of the serous macrophage pool and that targeting peritoneal LPMs may improve ovarian cancer outcomes., Macrophages can differentiate to perform homeostatic tissue-specific functions. Here the authors show that RXR signalling is critical for large peritoneal macrophage (LPM) expansion during neonatal life and LPM lipid metabolism and survival during adult homeostasis, and that ovarian cancer growth relies on RXR-dependent LPMs.

Background: Mass cytometry leverages inductively coupled mass spectrometry to perform high dimensional single cell analyses using antibodies tagged with rare earth isotopes that are considered to be largely absent in biological samples. We have recently noted an unusual exception to this rule while analyzing tissue samples from patients undergoing surgical resection for early stage lung cancer, and here we present a detailed cytometric characterization of cerium in a clinical patient sample. Methods: We performed a CyTOF analysis on cell suspensions derived from matched blood, tumor lesion, and non-involved lung tissue from an active smoker undergoing surgical resection for early stage lung adenocarcinoma. The samples were stained with a 31-parameter antibody panel to allow a detailed characterization of the cellular heterogeneity of the samples. The data were visualized using viSNE, major immune subsets were identified based on canonical marker expression patterns, and single cell cerium levels were evaluated across each of these defined subsets. Results: High dimensional immune cell mapping revealed that high levels of cerium were specifically associated with a phenotypically-distinct subset of lung macrophages that were most prevalent in non-involved lung tissue, whereas tumor associated macrophages showed relatively lower levels of cerium. We hypothesize that these findings reflect alveolar macrophage phagocytosis of inhaled cerium derived from cigarette flint lighters. Conclusions: These results demonstrate the first high-dimensional single cell characterization of environmental metal exposure associated with smoking, and offer a demonstration of the unique potential for applying mass cytometry to the field of environmental toxicology. This article is protected by copyright. All rights reserved.

The interaction of tumor, stroma, and immune cells in pancreatic ductal adenocarcinoma (PDAC) is complex and difficult to quantify in patient samples. Recently, deep learning algorithms have shown successes in identifying tumor and lymphocytes regions on whole-slide images derived from routinely collected histopathologic specimens. The Cancer Genome Atlas (TCGA) in particular has generated whole-slide images as well as paired molecular data, thus allowing for combined spatial and molecular analyses of tumor-lymphocyte interactions. We have previously highlighted this resource by computationally mapping tumor-infiltrating lymphocytes (TILs) on digital images across 13 tumor types. To achieve this, convolutional neural networks were trained on lymphocyte images annotated by expert pathologists and then used to detect spatial TIL patterns. This led to identification of four qualitative TIL pattern categories, which varied depending on tumor type as well as molecular immune subtype, demonstrating the potential of these spatial structures to provide further insights into tumor microenvironments and their relationship to overall survival. We have now extended this deep learning pipeline to include identification of tumor regions in PDAC, allowing study of TIL patterns in the context of their relative spatial localization to tumors. Using the deep learning algorithm to define the tumor region, we applied erosion and dilation operations to further capture the peritumoral region, the outer and inner regions of the tumor, as well as desmoplasia far from the tumor cells. We thus defined lymphocytes by their spatial localization as being internal, tumoral, peritumoral, or outer with these masks. We then used nearest-neighbor and density-based approaches to quantify TIL infiltration patterns with respect to tumor. These features vary significantly across the previously identified TIL patterns and may serve as additional parameters to define the microenvironment conditions in patient samples. Here we demonstrate that features extracted using our pipeline recapitulate canonical histologic properties. Using immune cell abundance estimates from gene expression generated by CIBERSORT, we find that samples with tumor TIL densities above median have more M1 macrophages, while those below median have more M2 macrophages. We also observe that slides with a higher peritumoral TIL density relative to tumoral TIL density have higher Treg fractions. Ongoing work on improving the resolution and cell specificity of our pipeline will allow us to ask more specific questions and permit higher granularity in linking clinical outcomes to spatial immune phenotypes. Citation Format: Soma Kobayashi, Han Le, Lucie Chrastecka, Rajarsi Gupta, Le Hou, Shahira Abousamra, Danielle Fassler, Kenneth R. Shroyer, Dimitris Samaras, Tahsin Kurc, Richard A. Moffitt, Joel H. Saltz. Deep learning for analysis of tumor-lymphocyte interactions in pancreatic ductal adenocarcinoma [abstract]. In: Proceedings of the AACR Special Conference on Pancreatic Cancer: Advances in Science and Clinical Care; 2019 Sept 6-9; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2019;79(24 Suppl):Abstract nr A27.

Summary To guide the design of immunotherapy strategies for patients with early stage lung tumors, we developed a multiscale immune profiling strategy to map the immune landscape of early lung adenocarcinoma lesions to search for tumor-driven immune changes. Utilizing a barcoding method that allows a simultaneous single-cell analysis of the tumor, non-involved lung, and blood cells, we provide a detailed immune cell atlas of early lung tumors. We show that stage I lung adenocarcinoma lesions already harbor significantly altered T cell and NK cell compartments. Moreover, we identified changes in tumor-infiltrating myeloid cell (TIM) subsets that likely compromise anti-tumor T cell immunity. Paired single-cell analyses thus offer valuable knowledge of tumor-driven immune changes, providing a powerful tool for the rational design of immune therapies. Video Abstract

Tumors form complex ecosystems that include normal epithelial cells, fibroblasts, blood and lymphatic vessels as well as structural components, and importantly, immune cells. The density, location, and organization of various tumor-infiltrating hematopoietic cells of myeloid and lymphoid origin can impact tumor growth, spreading, and clinical outcome of various cancers, including non-small cell lung cancer (NSCLC). Here, we propose to characterize the microenvironment of lung tumors using a unique set of matched fresh tissue single cell suspensions of tumor and normal lung, paraffin-embedded tissues, and peripheral blood plasma and cells obtained at time of surgery in the same patient. We aim to query mechanisms for local immunogenicity and immunosuppression of NSCLC, by defining interactions between tumor cells and various immune cell subsets in the local microenvironment, and by correlating these findings with presence of tumor-associated antigens (TAAs) and their capacity to elicit spontaneous local and systemic immune responses, as well as immune checkpoints known to impair immune responses. We applied multiplexed immunohistochemistry for formalin-fixed paraffin embedded cancer tissues (MICSSS method; n = 40), serological assays for immunogenicity of tumor antigens from plasma samples and supernatants of B cell cultures from the same patients (ELISA; n = 40), as well as mass cytometry on cell suspensions obtained from matched fresh specimens (CYTOF, n = 20) to (1) characterize the composition and organization of the immune microenvironment, (2) identify drivers of immunosuppression in NSCLC and (3) identify the expression profile of TAAs and B cell specificity in NSCLC. We found that TAAs, such as p53, NY-ESO-1, or ERG, were expressed by lung cancer cells in a subset of patients and that protein expression at the tumor site correlated with the antibody (Ab) response found in plasma of matched patients. While circulating plasma Ab titers did not appear to be correlated with the density of infiltrating B cells, potential associations with presence of tertiary-lymphoid structure, plasma cells (CD138+ cytokeratin− cells) or T follicular helper cells (CXCR5+ CD3+ CD8−) are currently being assessed. Association between molecular alterations and immune cell composition and location will also be discussed. We will also quantify expression of checkpoint molecules (PD-L1, VISTA and HHLA-2) on immune and tumor cells. The correlation between checkpoint inhibitor expression and immune cell densities and presence of immune responses will be presented, to assess whether immune cells contribute to the modulation of T cell effector functions. In conclusion, this study is the stepping-stone to a comprehensive understanding of the complex interactions between lung tumors and their immune microenvironment. By using novel multiplexed approaches, we intend to map the immune and antigenic landscape of human NSCLC, to identify mechanisms of early disease immunogenicity and immunosuppression, and to find new prognostic and predictive markers for the development of novel therapeutic strategies and immunotherapy trials in NSCLC. Citation Format: Romain Remark, Yonit Lavin, Adeeb Rahman, Camille Bigenwald, Soma Kobayashi, Christian Becker, Raja Flores, Sacha Gnjatic, Miriam Merad. Comprehensive multidimensional analysis of the immune contexture in non-small cell lung cancer [abstract]. In: Proceedings of the Second CRI-CIMT-EATI-AACR International Cancer Immunotherapy Conference: Translating Science into Survival; 2016 Sept 25-28; New York, NY. Philadelphia (PA): AACR; Cancer Immunol Res 2016;4(11 Suppl):Abstract nr A092.