Cross-scale multi-instance learning for pathological image diagnosis.

Analyzing high resolution whole slide images (WSIs) with regard to information across multiple scales poses a significant challenge in digital pathology. Multi-instance learning (MIL) is a common solution for working with high resolution images by classifying bags of objects (i.e. sets of smaller image patches). However, such processing is typically performed at a single scale (e.g., 20× magnification) of WSIs, disregarding the vital inter-scale information that is key to diagnoses by human pathologists. In this study, we propose a novel cross-scale MIL algorithm to explicitly aggregate inter-scale relationships into a single MIL network for pathological image diagnosis. The contribution of this paper is three-fold: (1) A novel cross-scale MIL (CS-MIL) algorithm that integrates the multi-scale information and the inter-scale relationships is proposed; (2) A toy dataset with scale-specific morphological features is created and released to examine and visualize differential cross-scale attention; (3) Superior performance on both in-house and public datasets is demonstrated by our simple cross-scale MIL strategy. The official implementation is publicly available at https://github.com/hrlblab/CS-MIL.

Topological-Preserving Membrane Skeleton Segmentation in Multiplex Immunofluorescence Imaging.

Multiplex immunofluorescence (MxIF) is an emerging imaging technology whose downstream molecular analytics highly rely upon the effectiveness of cell segmentation. In practice, multiple membrane markers (e.g., NaKATPase, PanCK and ß-catenin) are employed to stain membranes for different cell types, so as to achieve a more comprehensive cell segmentation since no single marker fits all cell types. However, prevalent watershed-based image processing might yield inferior capability for modeling complicated relationships between markers. For example, some markers can be misleading due to questionable stain quality. In this paper, we propose a deep learning based membrane segmentation method to aggregate complementary information that is uniquely provided by large scale MxIF markers. We aim to segment tubular membrane structure in MxIF data using global (membrane markers z-stack projection image) and local (separate individual markers) information to maximize topology preservation with deep learning. Specifically, we investigate the feasibility of four SOTA 2D deep networks and four volumetric-based loss functions. We conducted a comprehensive ablation study to assess the sensitivity of the proposed method with various combinations of input channels. Beyond using adjusted rand index (ARI) as the evaluation metric, which was inspired by the clDice, we propose a novel volumetric metric that is specific for skeletal structure, denoted as clDiceSKEL. In total, 80 membrane MxIF images were manually traced for 5-fold cross-validation. Our model outperforms the baseline with a 20.2% and 41.3% increase in clDiceSKEL and ARI performance, which is significant (p<0.05) using the Wilcoxon signed rank test. Our work explores a promising direction for advancing MxIF imaging cell segmentation with deep learning membrane segmentation. Tools are available at https://github.com/MASILab/MxIF_Membrane_Segmentation.

A Specialized Epithelial Cell Type Regulating Mucosal Immunity and Driving Human Crohn's Disease.

Crohn's disease (CD) is a complex chronic inflammatory disorder that may affect any part of gastrointestinal tract with extra-intestinal manifestations and associated immune dysregulation. To characterize heterogeneity in CD, we profiled single-cell transcriptomics of 170 samples from 65 CD patients and 18 non-inflammatory bowel disease (IBD) controls in both the terminal ileum (TI) and ascending colon (AC). Analysis of 202,359 cells identified a novel epithelial cell type in both TI and AC, featuring high expression of LCN2, NOS2, and DUOX2, and thus is named LND. LND cells, confirmed by high-resolution in-situ RNA imaging, were rarely found in non-IBD controls, but expanded significantly in active CD. Compared to other epithelial cells, genes defining LND cells were enriched in antimicrobial response and immunoregulation. Moreover, multiplexed protein imaging demonstrated that LND cell abundance was associated with immune infiltration. Cross-talk between LND and immune cells was explored by ligand-receptor interactions and further evidenced by their spatial colocalization. LND cells showed significant enrichment of expression specificity of IBD/CD susceptibility genes, revealing its role in immunopathogenesis of CD. Investigating lineage relationships of epithelial cells detected two LND cell subpopulations with different origins and developmental potential, early and late LND. The ratio of the late to early LND cells was related to anti-TNF response. These findings emphasize the pathogenic role of the specialized LND cell type in both Crohn's ileitis and Crohn's colitis and identify novel biomarkers associated with disease activity and treatment response.

Eosinophils Exert Antitumorigenic Effects in the Development of Esophageal Squamous Cell Carcinoma.

BACKGROUND AND AIMS: Eosinophils are present in several solid tumors and have context-dependent function. Our aim is to define the contribution of eosinophils in esophageal squamous cell carcinoma (ESCC), as their role in ESCC is unknown. METHODS: Eosinophils were enumerated in tissues from 2 ESCC cohorts. Mice were treated with 4-NQO for 8 weeks to induce precancer or 16 weeks to induce carcinoma. The eosinophil number was modified by a monoclonal antibody to interleukin-5 (IL5mAb), recombinant IL-5 (rIL-5), or genetically with eosinophil-deficient (ΔdblGATA) mice or mice deficient in eosinophil chemoattractant eotaxin-1 (Ccl11-/-). Esophageal tissue and eosinophil-specific RNA sequencing was performed to understand eosinophil function. Three-dimensional coculturing of eosinophils with precancer or cancer cells was done to ascertain direct effects of eosinophils. RESULTS: Activated eosinophils are present in higher numbers in early-stage vs late-stage ESCC. Mice treated with 4-NQO exhibit more esophageal eosinophils in precancer vs cancer. Correspondingly, epithelial cell Ccl11 expression is higher in mice with precancer. Eosinophil depletion using 3 mouse models (Ccl11-/- mice, ΔdblGATA mice, IL5mAb treatment) all display exacerbated 4-NQO tumorigenesis. Conversely, treatment with rIL-5 increases esophageal eosinophilia and protects against precancer and carcinoma. Tissue and eosinophil RNA sequencing revealed eosinophils drive oxidative stress in precancer. In vitro coculturing of eosinophils with precancer or cancer cells resulted in increased apoptosis in the presence of a degranulating agent, which is reversed with NAC, a reactive oxygen species scavenger. ΔdblGATA mice exhibited increased CD4 T cell infiltration, IL-17, and enrichment of IL-17 protumorigenic pathways. CONCLUSION: Eosinophils likely protect against ESCC through reactive oxygen species release during degranulation and suppression of IL-17.

Predicting Crohn's disease severity in the colon using mixed cell nucleus density from pseudo labels.

Crohn's disease (CD) is a debilitating inflammatory bowel disease with no known cure. Computational analysis of hematoxylin and eosin (H&E) stained colon biopsy whole slide images (WSIs) from CD patients provides the opportunity to discover unknown and complex relationships between tissue cellular features and disease severity. While there have been works using cell nuclei-derived features for predicting slide-level traits, this has not been performed on CD H&E WSIs for classifying normal tissue from CD patients vs active CD and assessing slide label-predictive performance while using both separate and combined information from pseudo-segmentation labels of nuclei from neutrophils, eosinophils, epithelial cells, lymphocytes, plasma cells, and connective cells. We used 413 WSIs of CD patient biopsies and calculated normalized histograms of nucleus density for the six cell classes for each WSI. We used a support vector machine to classify the truncated singular value decomposition representations of the normalized histograms as normal or active CD with four-fold cross-validation in rounds where nucleus types were first compared individually, the best was selected, and further types were added each round. We found that neutrophils were the most predictive individual nucleus type, with an AUC of 0.92 ± 0.0003 on the withheld test set. Adding information improved cross-validation performance for the first two rounds and on the withheld test set for the first three rounds, though performance metrics did not increase substantially beyond when neutrophils were used alone.

Hypusination Maintains Intestinal Homeostasis and Prevents Colitis and Carcinogenesis by Enhancing Aldehyde Detoxification.

BACKGROUND & AIMS: The amino acid hypusine, synthesized from the polyamine spermidine by the enzyme deoxyhypusine synthase (DHPS), is essential for the activity of eukaryotic translation initiation factor 5A (EIF5A). The role of hypusinated EIF5A (EIF5AHyp) remains unknown in intestinal homeostasis. Our aim was to investigate EIF5AHyp in the gut epithelium in inflammation and carcinogenesis. METHODS: We used human colon tissue messenger RNA samples and publicly available transcriptomic datasets, tissue microarrays, and patient-derived colon organoids. Mice with intestinal epithelial-specific deletion of Dhps were investigated at baseline and in models of colitis and colon carcinogenesis. RESULTS: We found that patients with ulcerative colitis and Crohn's disease exhibit reduced colon levels of DHPS messenger RNA and DHPS protein and reduced levels of EIF5AHyp. Similarly, colonic organoids from colitis patients also show down-regulated DHPS expression. Mice with intestinal epithelial-specific deletion of Dhps develop spontaneous colon hyperplasia, epithelial proliferation, crypt distortion, and inflammation. Furthermore, these mice are highly susceptible to experimental colitis and show exacerbated colon tumorigenesis when treated with a carcinogen. Transcriptomic and proteomic analysis on colonic epithelial cells demonstrated that loss of hypusination induces multiple pathways related to cancer and immune response. Moreover, we found that hypusination enhances translation of numerous enzymes involved in aldehyde detoxification, including glutathione S-transferases and aldehyde dehydrogenases. Accordingly, hypusination-deficient mice exhibit increased levels of aldehyde adducts in the colon, and their treatment with a scavenger of electrophiles reduces colitis. CONCLUSIONS: Hypusination in intestinal epithelial cells has a key role in the prevention of colitis and colorectal cancer, and enhancement of this pathway via supplementation of spermidine could have a therapeutic impact.

Deep multimodal fusion of image and non-image data in disease diagnosis and prognosis: a review.

The rapid development of diagnostic technologies in healthcare is leading to higher requirements for physicians to handle and integrate the heterogeneous, yet complementary data that are produced during routine practice. For instance, the personalized diagnosis and treatment planning for a single cancer patient relies on various images (e.g. radiology, pathology and camera images) and non-image data (e.g. clinical data and genomic data). However, such decision-making procedures can be subjective, qualitative, and have large inter-subject variabilities. With the recent advances in multimodal deep learning technologies, an increasingly large number of efforts have been devoted to a key question: how do we extract and aggregate multimodal information to ultimately provide more objective, quantitative computer-aided clinical decision making? This paper reviews the recent studies on dealing with such a question. Briefly, this review will include the (a) overview of current multimodal learning workflows, (b) summarization of multimodal fusion methods, (c) discussion of the performance, (d) applications in disease diagnosis and prognosis, and (e) challenges and future directions.

Influence of Cell-type Ratio on Spatially Resolved Single-cell Transcriptomes using the Tangram Algorithm: Based on Implementation on Single-Cell and MxIF Data.

The Tangram algorithm is a benchmarking method of aligning single-cell (sc/snRNA-seq) data to various forms of spatial data collected from the same region. With this data alignment, the annotation of the single-cell data can be projected to spatial data. However, the cell composition (cell-type ratio) of the single-cell data and spatial data might be different because of heterogeneous cell distribution. Whether the Tangram algorithm can be adapted when the two data have different cell-type ratios has not been discussed in previous works. In our practical application that maps the cell-type classification results of single-cell data to the Multiplex immunofluorescence (MxIF) spatial data, cell-type ratios were different, though they were sampled from adjacent areas. In this work, both simulation and empirical validation were conducted to quantitatively explore the impact of the mismatched cell-type ratio on the Tangram mapping in different situations. Results show that the cell-type difference has a negative influence on classification accuracy.

A Roadmap for the Human Gut Cell Atlas.

The number of studies investigating the human gastrointestinal tract using various single-cell profiling methods has increased substantially in the past few years. Although this increase provides a unique opportunity for the generation of the first comprehensive Human Gut Cell Atlas (HGCA), there remains a range of major challenges ahead. Above all, the ultimate success will largely depend on a structured and coordinated approach that aligns global efforts undertaken by a large number of research groups. In this Roadmap, we discuss a comprehensive forward-thinking direction for the generation of the HGCA on behalf of the Gut Biological Network of the Human Cell Atlas. Based on the consensus opinion of experts from across the globe, we outline the main requirements for the first complete HGCA by summarizing existing data sets and highlighting anatomical regions and/or tissues with limited coverage. We provide recommendations for future studies and discuss key methodologies and the importance of integrating the healthy gut atlas with related diseases and gut organoids. Importantly, we critically overview the computational tools available and provide recommendations to overcome key challenges.

Electrophilic reactive aldehydes as a therapeutic target in colorectal cancer prevention and treatment.

Colorectal cancer (CRC) is a major health problem worldwide. Dicarbonyl electrophiles, such as isolevuglandins (isoLGs), are generated from lipid peroxidation and form covalent adducts with amine-containing macromolecules. We have shown high levels of adducts of isoLGs in colonic epithelial cells of patients with CRC. We thus investigated the role of these reactive aldehydes in colorectal cancer development. We found that 2-hydroxybenzylamine (2-HOBA), a natural compound derived from buckwheat seeds that acts as a potent scavenger of electrophiles, is bioavailable in the colon of mice after supplementation in the drinking water and does not affect the colonic microbiome. 2-HOBA reduced the level of isoLG adducts to lysine as well as tumorigenesis in models of colitis-associated carcinogenesis and of sporadic CRC driven by specific deletion of the adenomatous polyposis coli gene in colonic epithelial cells. In parallel, we found that oncogenic NRF2 activation and signaling were decreased in the colon of 2-HOBA-treated mice. Additionally, the growth of xenografted human HCT116 CRC cells in nude mice was significantly attenuated by 2-HOBA supplementation. In conclusion, 2-HOBA represents a promising natural compound for the prevention and treatment of CRC.

Feasibility of Universal Anomaly Detection without Knowing the Abnormality in Medical Images.

Many anomaly detection approaches, especially deep learning methods, have been recently developed to identify abnormal image morphology by only employing normal images during training. Unfortunately, many prior anomaly detection methods were optimized for a specific "known" abnormality (e.g., brain tumor, bone fraction, cell types). Moreover, even though only the normal images were used in the training process, the abnormal images were often employed during the validation process (e.g., epoch selection, hyper-parameter tuning), which might leak the supposed "unknown" abnormality unintentionally. In this study, we investigated these two essential aspects regarding universal anomaly detection in medical images by (1) comparing various anomaly detection methods across four medical datasets, (2) investigating the inevitable but often neglected issues on how to unbiasedly select the optimal anomaly detection model during the validation phase using only normal images, and (3) proposing a simple decision-level ensemble method to leverage the advantage of different kinds of anomaly detection without knowing the abnormality. The results of our experiments indicate that none of the evaluated methods consistently achieved the best performance across all datasets. Our proposed method enhanced the robustness of performance in general (average AUC 0.956).

Cross-scale Attention Guided Multi-instance Learning for Crohn's Disease Diagnosis with Pathological Images.

Multi-instance learning (MIL) is widely used in the computer-aided interpretation of pathological Whole Slide Images (WSIs) to solve the lack of pixel-wise or patch-wise annotations. Often, this approach directly applies "natural image driven" MIL algorithms which overlook the multi-scale (i.e. pyramidal) nature of WSIs. Off-the-shelf MIL algorithms are typically deployed on a single-scale of WSIs (e.g., 20× magnification), while human pathologists usually aggregate the global and local patterns in a multi-scale manner (e.g., by zooming in and out between different magnifications). In this study, we propose a novel cross-scale attention mechanism to explicitly aggregate inter-scale interactions into a single MIL network for Crohn's Disease (CD), which is a form of inflammatory bowel disease. The contribution of this paper is two-fold: (1) a cross-scale attention mechanism is proposed to aggregate features from different resolutions with multi-scale interaction; and (2) differential multi-scale attention visualizations are generated to localize explainable lesion patterns. By training ~250,000 H&E-stained Ascending Colon (AC) patches from 20 CD patient and 30 healthy control samples at different scales, our approach achieved a superior Area under the Curve (AUC) score of 0.8924 compared with baseline models. The official implementation is publicly available at https://github.com/hrlblab/CS-MIL.

Multi-modal Learning with Missing Data for Cancer Diagnosis Using Histopathological and Genomic Data.

Multi-modal learning (e.g., integrating pathological images with genomic features) tends to improve the accuracy of cancer diagnosis and prognosis as compared to learning with a single modality. However, missing data is a common problem in clinical practice, i.e., not every patient has all modalities available. Most of the previous works directly discarded samples with missing modalities, which might lose information in these data and increase the likelihood of overfitting. In this work, we generalize the multi-modal learning in cancer diagnosis with the capacity of dealing with missing data using histological images and genomic data. Our integrated model can utilize all available data from patients with both complete and partial modalities. The experiments on the public TCGA-GBM and TCGA-LGG datasets show that the data with missing modalities can contribute to multi-modal learning, which improves the model performance in grade classification of glioma cancer.

Inpainting Missing Tissue in Multiplexed Immunofluorescence Imaging.

Multiplex immunofluorescence (MxIF) is an emerging technique that allows for staining multiple cellular and histological markers to stain simultaneously on a single tissue section. However, with multiple rounds of staining and bleaching, it is inevitable that the scarce tissue may be physically depleted. Thus, a digital way of synthesizing such missing tissue would be appealing since it would increase the useable areas for the downstream single-cell analysis. In this work, we investigate the feasibility of employing generative adversarial network (GAN) approaches to synthesize missing tissues using 11 MxIF structural molecular markers (i.e., epithelial and stromal). Briefly, we integrate a multi-channel high-resolution image synthesis approach to synthesize the missing tissue from the remaining markers. The performance of different methods is quantitatively evaluated via the downstream cell membrane segmentation task. Our contribution is that we, for the first time, assess the feasibility of synthesizing missing tissues in MxIF via quantitative segmentation. The proposed synthesis method has comparable reproducibility with the baseline method on performance for the missing tissue region reconstruction only, but it improves 40% on whole tissue synthesis that is crucial for practical application. We conclude that GANs are a promising direction of advancing MxIF imaging with deep image synthesis.

Protective Role of Spermidine in Colitis and Colon Carcinogenesis.

BACKGROUND & AIMS: Because inflammatory bowel disease is increasing worldwide and can lead to colitis-associated carcinoma (CAC), new interventions are needed. We have shown that spermine oxidase (SMOX), which generates spermidine (Spd), regulates colitis. Here we determined whether Spd treatment reduces colitis and carcinogenesis. METHODS: SMOX was quantified in human colitis and associated dysplasia using quantitative reverse-transcription polymerase chain reaction and immunohistochemistry. We used wild-type (WT) and Smox-/- C57BL/6 mice treated with dextran sulfate sodium (DSS) or azoxymethane (AOM)-DSS as models of colitis and CAC, respectively. Mice with epithelial-specific deletion of Apc were used as a model of sporadic colon cancer. Animals were supplemented or not with Spd in the drinking water. Colonic polyamines, inflammation, tumorigenesis, transcriptomes, and microbiomes were assessed. RESULTS: SMOX messenger RNA levels were decreased in human ulcerative colitis tissues and inversely correlated with disease activity, and SMOX protein was reduced in colitis-associated dysplasia. DSS colitis and AOM-DSS-induced dysplasia and tumorigenesis were worsened in Smox-/- vs WT mice and improved in both genotypes with Spd. Tumor development caused by Apc deletion was also reduced by Spd. Smox deletion and AOM-DSS treatment were both strongly associated with increased expression of α-defensins, which was reduced by Spd. A shift in the microbiome, with reduced abundance of Prevotella and increased Proteobacteria and Deferribacteres, occurred in Smox-/- mice and was reversed with Spd. CONCLUSIONS: Loss of SMOX is associated with exacerbated colitis and CAC, increased α-defensin expression, and dysbiosis of the microbiome. Spd supplementation reverses these phenotypes, indicating that it has potential as an adjunctive treatment for colitis and chemopreventive for colon carcinogenesis.

CCL11 exacerbates colitis and inflammation-associated colon tumorigenesis.

CCL11, also known as eotaxin-1, is described as an eosinophil chemoattractant, which has been implicated in allergic and Th2 inflammatory diseases. We have reported that CCL11 is significantly increased in the serum of inflammatory bowel disease (IBD) patients, colonic eosinophils are increased and correlate with tissue CCL11 levels in ulcerative colitis patients, and CCL11 is increased in dextran sulfate sodium (DSS)-induced murine colitis. Here, we show that CCL11 is involved in the pathogenesis of DSS-induced colitis and in colon tumorigenesis in the azoxymethane (AOM)-DSS model of colitis-associated carcinogenesis (CAC). Ccl11-/- mice exposed to DSS then allowed to recover had significantly less body weight loss and a decrease in histologic injury versus wild-type (WT) mice. In the AOM-DSS model, Ccl11-/- mice exhibited decreased colonic tumor number and burden, histologic injury, and colonic eosinophil infiltration versus WT mice. Ccl11 is expressed by both colonic epithelial and lamina propria immune cells. Studies in bone marrow chimera mice revealed that hematopoietic- and epithelial-cell-derived CCL11 were both important for tumorigenesis in the AOM-DSS model. These findings indicate that CCL11 is important in the regulation of colitis and associated carcinogenesis and thus anti-CCL11 antibodies may be useful for treatment and cancer chemoprevention in IBD.

A cross-platform informatics system for the Gut Cell Atlas: integrating clinical, anatomical and histological data.

The Gut Cell Atlas (GCA), an initiative funded by the Helmsley Charitable Trust, seeks to create a reference platform to understand the human gut, with a specific focus on Crohn's disease. Although a primary focus of the GCA is on focusing on single-cell profiling, we seek to provide a framework to integrate other analyses on multi-modality data such as electronic health record data, radiological images, and histology tissues/images. Herein, we use the research electronic data capture (REDCap) system as the central tool for a secure web application that supports protected health information (PHI) restricted access. Our innovations focus on addressing the challenges with tracking all specimens and biopsies, validating manual data entry at scale, and sharing organizational data across the group. We present a scalable, cross-platform barcode printing/record system that integrates with REDCap. The central informatics infrastructure to support our design is a tuple table to track longitudinal data entry and sample tracking. The current data collection (by December 2020) is illustrated with types and formats of the data that the system collects. We estimate that one terabyte is needed for data storage per patient study. Our proposed data sharing informatics system addresses the challenges with integrating physical sample tracking, large files, and manual data entry with REDCap.

Random Multi-Channel Image Synthesis for Multiplexed Immunofluorescence Imaging.

Multiplex immunofluorescence (MxIF) is an emerging imaging technique that produces the high sensitivity and specificity of single-cell mapping. With a tenet of "seeing is believing", MxIF enables iterative staining and imaging extensive antibodies, which provides comprehensive biomarkers to segment and group different cells on a single tissue section. However, considerable depletion of the scarce tissue is inevitable from extensive rounds of staining and bleaching ("missing tissue"). Moreover, the immunofluorescence (IF) imaging can globally fail for particular rounds ("missing stain"). In this work, we focus on the "missing stain" issue. It would be appealing to develop digital image synthesis approaches to restore missing stain images without losing more tissue physically. Herein, we aim to develop image synthesis approaches for eleven MxIF structural molecular markers (i.e., epithelial and stromal) on real samples. We propose a novel multi-channel high-resolution image synthesis approach, called pixN2N-HD, to tackle possible missing stain scenarios via a high-resolution generative adversarial network (GAN). Our contribution is three-fold: (1) a single deep network framework is proposed to tackle missing stain in MxIF; (2) the proposed "N-to-N" strategy reduces theoretical four years of computational time to 20 hours when covering all possible missing stains scenarios, with up to five missing stains (e.g., "(N-1)-to-1", "(N-2)-to-2"); and (3) this work is the first comprehensive experimental study of investigating cross-stain synthesis in MxIF. Our results elucidate a promising direction of advancing MxIF imaging with deep image synthesis.

Dicarbonyl Electrophiles Mediate Inflammation-Induced Gastrointestinal Carcinogenesis.

BACKGROUND & AIMS: Inflammation in the gastrointestinal tract may lead to the development of cancer. Dicarbonyl electrophiles, such as isolevuglandins (isoLGs), are generated from lipid peroxidation during the inflammatory response and form covalent adducts with amine-containing macromolecules. Thus, we sought to determine the role of dicarbonyl electrophiles in inflammation-associated carcinogenesis. METHODS: The formation of isoLG adducts was analyzed in the gastric tissues of patients infected with Helicobacter pylori from gastritis to precancerous intestinal metaplasia, in human gastric organoids, and in patients with colitis and colitis-associated carcinoma (CAC). The effect on cancer development of a potent scavenger of dicarbonyl electrophiles, 5-ethyl-2-hydroxybenzylamine (EtHOBA), was determined in transgenic FVB/N insulin-gastrin (INS-GAS) mice and Mongolian gerbils as models of H pylori-induced carcinogenesis and in C57BL/6 mice treated with azoxymethane-dextran sulfate sodium as a model of CAC. The effect of EtHOBA on mutations in gastric epithelial cells of H pylori-infected INS-GAS mice was assessed by whole-exome sequencing. RESULTS: We show increased isoLG adducts in gastric epithelial cell nuclei in patients with gastritis and intestinal metaplasia and in human gastric organoids infected with H pylori. EtHOBA inhibited gastric carcinoma in infected INS-GAS mice and gerbils and attenuated isoLG adducts, DNA damage, and somatic mutation frequency. Additionally, isoLG adducts were elevated in tissues from patients with colitis, colitis-associated dysplasia, and CAC as well as in dysplastic tumors of C57BL/6 mice treated with azoxymethane-dextran sulfate sodium. In this model, EtHOBA significantly reduced adduct formation, tumorigenesis, and dysplasia severity. CONCLUSIONS: Dicarbonyl electrophiles represent a link between inflammation and somatic genomic alterations and are thus key targets for cancer chemoprevention.

An interspecies translation model implicates integrin signaling in infliximab-resistant inflammatory bowel disease.

Anti-tumor necrosis factor (anti-TNF) therapy resistance is a major clinical challenge in inflammatory bowel disease (IBD), due, in part, to insufficient understanding of disease-site, protein-level mechanisms. Although proteomics data from IBD mouse models exist, data and phenotype discrepancies contribute to confounding translation from preclinical animal models of disease to clinical cohorts. We developed an approach called translatable components regression (TransComp-R) to overcome interspecies and trans-omic discrepancies between mouse models and human subjects. TransComp-R combines mouse proteomic data with patient pretreatment transcriptomic data to identify molecular features discernable in the mouse data that are predictive of patient response to therapy. Interrogating the TransComp-R models revealed activated integrin pathway signaling in patients with anti-TNF-resistant colonic Crohn's disease (cCD) and ulcerative colitis (UC). As a step toward validation, we performed single-cell RNA sequencing (scRNA-seq) on biopsies from a patient with cCD and analyzed publicly available immune cell proteomics data to characterize the immune and intestinal cell types contributing to anti-TNF resistance. We found that ITGA1 was expressed in T cells and that interactions between these cells and intestinal cell types were associated with resistance to anti-TNF therapy. We experimentally showed that the α1 integrin subunit mediated the effectiveness of anti-TNF therapy in human immune cells. Thus, TransComp-R identified an integrin signaling mechanism with potential therapeutic implications for overcoming anti-TNF therapy resistance. We suggest that TransComp-R is a generalizable framework for addressing species, molecular, and phenotypic discrepancies between model systems and patients to translationally deliver relevant biological insights.