The American Journal of Pathology

Claudin-18.2 is a promising therapeutic target for gastrointestinal cancer. However, its expression pattern in pancreatic ductal adenocarcinoma, especially the concordance between biopsy and resection specimens, is unknown. This study aimed to evaluate the consistency of claudin-18.2 positivity across different specimen types using the clinically validated antibody clone 43-14A employed in ongoing zolbetuximab trials. Immunohistochemical analysis for claudin-18 was conducted on 211 resected pancreatic cancer tissues, 133 matched preoperative biopsy samples, and 60 samples from recurrent lesions. Concordance rates were calculated based on a clinically relevant cutoff ([≥]75% of tumor cells with moderate-to-strong membranous staining). Receiver operating characteristic analysis was used to optimize the biopsy thresholds. Claudin-18.2 positivity was observed in 9.5% of the resection specimens. The concordance rates were 92.5% between biopsy and resection specimens and 83.3% between primary and recurrent lesions. Receiver operating characteristic analysis suggested that a lower cutoff (20%) in biopsy samples achieved 100% sensitivity for detecting positive cases. While overall claudin-18 expression levels were maintained in most recurrent lesions, decreased expression was frequently observed in cases of local recurrence and liver metastasis. Despite intrinsic heterogeneity and limited biopsy yield in pancreatic cancer, claudin-18 expression in small biopsy samples, assessed using the clinical trial-validated clone 43-14A, strongly correlated with that in resection specimens. These results suggest that claudin-18.2 is a clinically stable biomarker for zolbetuximab therapy in patients with pancreatic ductal adenocarcinoma. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=80 SRC="FIGDIR/small/648482v2_ufig1.gif" ALT="Figure 1"> View larger version (44K): org.highwire.dtl.DTLVardef@1b822e5org.highwire.dtl.DTLVardef@1fb03f0org.highwire.dtl.DTLVardef@bfa888org.highwire.dtl.DTLVardef@d1a015_HPS_FORMAT_FIGEXP M_FIG C_FIG Core TipsO_LIThis study demonstrated a high concordance (92.5%) of CLDN18.2 positivity between preoperative biopsy and resected pancreatic ductal adenocarcinoma specimens, using the clinical trial-validated antibody clone 43-14A. C_LIO_LICLDN18.2 expression in recurrent lesions generally remained consistent with that in the corresponding primary tumor, although a reduction in expression levels was observed in local recurrence and liver metastases. C_LIO_LIThese findings support the clinical utility of biopsy-based CLDN18.2 evaluation for selecting patients with pancreatic ductal adenocarcinoma who may benefit from zolbetuximab. C_LI

IntroductionAccurate intraoperative assessment of macrovesicular steatosis in donor liver biopsies is critical for transplantation decisions but is often limited by inter-observer variability and freezing artifacts that can obscure histological details. Artificial intelligence (AI) offers a potential solution for standardized and reproducible evaluation. To evaluate the diagnostic performance of two self-supervised learning (SSL)-based foundation models, Prov-GigaPath and UNI, for classifying macrovesicular steatosis in frozen liver biopsy sections, compared with assessments by surgical pathologists. MethodsWe retrospectively analyzed 131 frozen liver biopsy specimens from 68 donors collected between November 2022 and September 2024. Slides were digitized into whole-slide images, tiled into patches, and used to extract embeddings with Prov-GigaPath and UNI; slide-level classifiers were then trained and tested. Intraoperative diagnoses by on-call surgical pathologists were compared with ground truth determined from independent reviews of permanent sections by two liver pathologists. Accuracy was evaluated for both five-category classification and a clinically significant binary threshold (<30% vs. [&ge;]30%). ResultsFor binary classification, Prov-GigaPath achieved 96.4% accuracy, UNI 85.7%, and surgical pathologists 84.0% (P = .22). In five-category classification, accuracies were lower: Prov-GigaPath 57.1%, UNI 50.0%, and pathologists 58.7% (P = .70). Misclassification primarily occurred in intermediate categories (5%-<30% steatosis). ConclusionsSSL-based foundation models performed comparably to surgical pathologists in classifying macrovesicular steatosis, at the clinically relevant <30% vs. [&ge;]30% threshold. These findings support the potential role of AI in standardizing intraoperative evaluation of donor liver biopsies; however, the small sample size limits generalizability and requires validation in larger, balanced cohorts.

GPER (G protein-coupled estrogen receptor) has been reported to play roles in several areas of physiology including cancer, metabolic disorders, and cardiovascular disease. However, the understanding of where this receptor is expressed in human tissue is limited due to limited available tools and methodologies that can reliably detect GPER protein. Recently, a highly specific monoclonal antibody against GPER (20H15L21) was developed and is suitable for immunohistochemistry. Using this antibody, we show that GPER protein expression varies markedly between normal human tissue, and also among cancer tissue. As GPER is an emerging therapeutic target for cancer and other diseases, this new understanding of GPER distribution will likely be helpful in design and interpretation of ongoing and future GPER research.

Alpha-1 antitrypsin (AAT) deficiency is the most common genetic cause of emphysema. Chymotrypsin-like Elastase 1 (CELA1) is a serine protease neutralized by AAT and is important in emphysema progression. Cela1-deficiency is protective in a murine models of AAT-deficient emphysema. KF4 anti-CELA1 antibody prevented emphysema in PPE and cigarette smoke models in wild type mice. We evaluated potential toxicities of KF4 and its ability to prevent emphysema in AAT deficiency. We found Cela1 protein expression in mouse lung, pancreas, small intestine, and spleen. In toxicity studies, mice treated with KF4 25 mg/kg weekly for four weeks showed an elevation in blood urea nitrogen and slower weight gain compared to lower doses or equivalent dose IgG. In histologic grading of tissue injury of the lung, kidney, liver, and heart, there was some evidence of liver injury with KF4 25 mg/kg, but in all tissues, injury was less than in control mice subjected to cecal ligation and puncture. In efficacy studies, KF4 doses as low as 0.5 mg/kg reduced the lung elastase activity of AAT-/-mice treated with 0.2 units of PPE. In this injury model, AAT-/-mice treated with KF4 1 mg/kg weekly, human purified AAT 60 mg/kg weekly, and combined KF4 and AAT treatment had less emphysema than mice treated with IgG 1 mg/kg weekly. However, the efficacy of KF4, AAT, or KF4 & AAT was similar. While KF4 might be an alternative to AAT replacement, combined KF4 and AAT replacement does not confer additional benefit.

Lung adenocarcinoma is the most frequent subtype of thoracic malignancy, which is itself the largest contributor to cancer mortality. The lepidic subtype is a non-invasive tumor morphology, whereas the acinar subtype represents one of the invasive morphologies. This study investigates the transition from a non-invasive to an invasive subtype in the context of lung adenocarcinoma. Patients with pathologically confirmed mixed subtype tumors consented to analysis of RNA-seq data extracted from each subtype area separately. The study included 17 patients with tumors found to exhibit a lepidic-acinar transition. 87 genes were found to be differentially expressed between the lepidic and acinar subtypes, with 44 genes significantly upregulated in lepidic samples, and 43 genes significantly upregulated in acinar samples. Gene ontology analysis showed that many of the genes upregulated in the acinar subtype were related to immune response. Immune deconvolution analysis showed that there was a significantly higher proportion of M1 macrophages and total B cells in acinar areas. Immunohistochemistry showed that B cells were mainly localized to tertiary lymphoid structures in the tumor area. This is the first study to investigate the molecular features of mixed subtype lepidic-acinar transitional tumors. Immunological dynamics are presumed to be involved in this transition from lepidic to acinar subtype. Further research should be conducted to elucidate the progression of disease from non-invasive to invasive morphologies.

BackgroundT-cell responses influence recurrence and survival in colorectal cancer. T-cell subset distributions vary by molecular phenotype and location, shaping cytotoxic or immune-cold tumor immune microenvironments (TiMEs). However, the T-cell contexture and their spatial proximities within preinvasive lesions are not well characterized. MethodsWe analyzed sessile serrated lesions (SSLs), tubulovillous/villous adenomas (TVs), and tubular adenomas (TAs) from three completed studies (N=120). Whole-slide multiplex immunofluorescence was used to quantify eight T-cell subsets (CD4, CD8, Th1, Th17, Treg, Tc1, Tc17, TcTreg). Counts were compared by histology using a generalized linear mixed model with a negative binomial distribution, including an offset for total cell counts and adjusting for age, sex, anatomic location, and lesion size. Nearest-neighbor (NN) analyses assessed proximities of T-cell pairs across lesion types. ResultsTAs and SSLs had higher CD4 and CD8 T-cell counts compared with TVs (q<0.05). SSLs had lower Th17 counts than TAs (q<0.05) and compared with TVs leaned toward fewer Tregs (q=0.07). NN analysis showed that TVs, compared with SSLs and TAs, had increased Treg clustering. In contrast, TA versus SSL comparisons revealed predominant CD4 clustering with Th17, Th1, and CD8 subsets. ConclusionTVs are characterized by lower T-cell infiltration and a greater tendency for regulatory T-cell clustering, consistent with an immune-cold TiME relative to TAs. SSLs and TAs were both more immune-infiltrated than TVs, but SSLs appeared less inflamed and less dominated by regulatory subsets. In contrast, CD4dominant clustering in TAs suggested stronger helper coordination. Preinvasive lesions therefore demonstrate immune and spatial heterogeneity by lesion types.

The role of artificial intelligence (AI) in health care delivery is growing rapidly. Due to its visual nature, the specialty of anatomic pathology has great promise for applications in AI. We examine the potential of six different AI models for differentiating and diagnosing the three most common primary liver tumors: hepatocellular carcinoma (HCC), cholangiocarcinoma (CCA), and combined HCC and CCA (cHCC/CCA). Our results demonstrated that for all three diagnoses, the sensitivity, specificity, positive predictive value, and negative predictive value was [&ge;] 94% in the best model tested, with results [&ge;] 92% in all categories in three of the models. These values are comparable to interpretation by general pathologists alone and demonstrate AIs potential in interpreting patient specimens for primary liver carcinoma. Applications such as these have multiple implications for delivering quality patient care, including assisting with intraoperative consultations and providing a rapid "second opinion" for confirmation and increased accuracy of final diagnoses. These applications may be particularly useful in underserved areas with shortages of subspecialized pathologists or after hours in larger medical centers. In addition, AI models such as these can decrease turnaround times and the inter- and intra-observer variability well documented in pathologic diagnoses. AI offers great potential in assisting pathologists in their day-to-day practice.

The apical-basal polarity of pancreatic acinar cells is essential for maintaining tissue architecture. However, the mechanisms by which polarity proteins regulate acinar pancreas tissue homeostasis are poorly understood. Here, we evaluate the role of Par3 in acinar pancreas injury and homeostasis. While Par3 loss in the mouse pancreas disrupts tight junctions, Par3 loss is dispensable for pancreatogenesis. However, with aging, Par3 loss results in low-grade inflammation, acinar degeneration, and pancreatic lipomatosis. Par3 loss also exacerbates pancreatitis-induced acinar cell loss, resulting in pronounced pancreatic lipomatosis and failure to regenerate. Moreover, Par3 loss in mice harboring mutant Kras causes extensive pancreatic intraepithelial neoplastic (PanIN) lesions and large pancreatic cysts. We also show that Par3 loss restricts injury-induced primary ciliogenesis. Significantly, targeting BET proteins enhances primary ciliogenesis during pancreatitis-induced injury and, in mice with Par3 loss, limits pancreatitis-induced acinar loss and facilitates acinar cell regeneration. Combined, this study demonstrates how Par3 restrains pancreatitis- and Kras-induced changes in the pancreas and identifies a potential role for BET inhibitors to attenuate pancreas injury and facilitate pancreas tissue regeneration.

Although liver transplantation (LT) is an effective therapy for cirrhosis, the risk of post-LT NASH is alarmingly high and is associated with accelerated progression to fibrosis/cirrhosis, cardiovascular disease, and decreased survival. Lack of risk stratification strategies hamper liver undergoes significant remodeling during inflammatory injury. During such remodeling, degraded peptide fragments (i.e., degradome) of the ECM and other proteins increase in plasma, making it a useful diagnostic/prognostic tool in chronic liver disease. To investigate whether inflammatory liver injury caused by post-LT NASH would yield a unique degradome profile, predictive of severe post-LT NASH fibrosis, we performed a retrospective analysis of 22 biobanked samples from the Starzl Transplantation Institute (12 with post-LT NASH after 5 years and 10 without). Total plasma peptides were isolated and analyzed by 1D-LC-MS/MS analysis using a Proxeon EASY-nLC 1000 UHPLC and nanoelectrospray ionization into an Orbitrap Elite mass spectrometer. Qualitative and quantitative peptide features data were developed from MSn datasets using PEAKS Studio X (v10). LC-MS/MS yielded [~]2700 identifiable peptide features based on the results from Peaks Studio analysis. Several peptides were significantly altered in patients that later developed fibrosis and heatmap analysis of the top 25 most significantly-changed peptides, most of which were ECM-derived, clustered the 2 patient groups well. Supervised modeling of the dataset indicated that a fraction of the total peptide signal ([~]15%) could explain the differences between the groups, indicating a strong potential for representative biomarker selection. A similar degradome profile was observed when the plasma degradome patterns were compared being obesity sensitive (C57Bl6/J) and insensitive (AJ) mouse strains. Both The plasma degradome profile of post-LT patients yields stark difference based on later development of post-LT NASH fibrosis. This approach could yield new "fingerprints" that can serve as minimally-invasive biomarkers of negative outcomes post-LT.

The histologic and molecular heterogeneity of human muscle-invasive bladder cancer (MIBC) is a major contributor to poor treatment outcomes. While most cases of MIBC are diagnosed as conventional urothelial carcinoma (UC), there is growing recognition of histologic subtypes and divergent differentiation within conventional UC. This is clinically significant, as some require modification of therapy and some are associated with more aggressive behavior. Spontaneously occurring UC in dogs has been shown to exhibit histologic and molecular features that closely resemble those of human MIBC. In this study, we evaluated 31 canine UC tumor samples for histologic subtypes and divergent differentiation. Slides were reviewed by a human uropathologist and three board-certified veterinary pathologists and assessed for expression of uroplakin III and E-cadherin. All tumors were classified as high-grade UC. Fifteen cases were identified as conventional UC. Among the remainder, eight displayed glandular differentiation, four were classified as sarcomatoid UC, two showed squamous differentiation, and one case each was classified as large nested and tubular and microcystic subtypes. In summary, this study found a higher frequency of certain histologic subtypes and divergent differentiation in canine UC--particularly sarcomatoid UC and UC with glandular differentiation--compared to previous reports in both canine UC and human MIBC. ConclusionThe relatively high prevalence of the sarcomatoid UC subtype in dogs observed in this study suggests that canine UC may serve as a valuable translational model for evaluating novel therapeutic agents, particularly for this rare and aggressive variant in humans.

Mucinous colorectal carcinoma (CRC) is a distinct histomorphological subtype characterized by abundant extracellular mucin that may promote immune evasion and chemoresistance. We describe a metastatic mucinous CRC case integrating digital pathology and proteomics to investigate disease progression and therapy resistance. Formalin-fixed paraffin-embedded samples from the primary tumor, peritoneal metastasis, and hepatoduodenal ligament metastasis of a 56-year-old patient were analyzed. Whole-slide imaging with QuPath-based AI enabled detailed histological annotation, while data-independent acquisition mass spectrometry identified over 6,000 proteins. Digital pathology revealed extensive mucin pools, architectural evolution from heterogeneous glandular patterns in the primary tumor to cribriform morphology in advanced metastases, and immune cell exclusion from mucin-rich regions. Proteomics revealed metabolic reprogramming, suppressed antigen presentation, and stage-specific activation of inflammatory, angiogenic, EMT, and PI3K/AKT/mTOR-MYC signaling pathways, consistent with proliferative and therapy-resistant phenotypes.Integration of AI-assisted histopathology with spatial proteomics highlighted the mucin barrier as a key mediator of immune evasion and chemoresistance. These findings support a personalized therapeutic framework targeting mucin-associated mechanisms alongside pathway-directed inhibitors, suggesting that spatial multi-omics may guide precision management strategies for aggressive mucinous colorectal cancer.

AimsDetermine if pathologic assessment of disease activity in steatohepatitis, performed using Whole Slide Images (WSIs) on the AISight Clinical Trials platform, yields results that are comparable to those obtained from the analysis performed using glass slides. Methods and ResultsThe accuracy of scoring for steatohepatitis (NAS [&ge;]4 with [&ge;]1 for each feature and absence of atypical features suggestive of other liver disease) performed on the WSI viewing platform was evaluated against scoring conducted on glass slides. Both methods were assessed for overall percent agreement (OPA) with a consensus ground truth (GT) score, defined as the median score of a panel of 3 expert pathologists on glass slides. Each case was also read by 3 different pathologists, once on glass and once using WSIs with a minimum 2-week washout period between glass and WSI reads. It was demonstrated that the average OPA across 3 pathologists of WSI scoring with GT was non-inferior to the average OPA of glass scoring with GT (non-inferiority margin of -0.05, difference of -0.001, 95% CI of (-0.027,0.026), and p<0.0001). For each pathologist, there was a similar average OPA of WSI and glass reads with glass GT (pathologist A 0.843 and 0.849, pathologist B 0.633 and 0.605 and pathologist C 0.755 and 0.780), with intra-reader, inter-modality agreements per histologic feature being greater than published intra-reader agreements. ConclusionAccuracy of digital reads for steatohepatitis using WSIs is equivalent to glass reads in the context of a clinical trial for scoring using the Clinical Research Network scoring system.

Lung adenocarcinoma (LUAD), the most common subtype of non-small cell lung cancer, exhibits profound histological and molecular heterogeneity. While genomic profiling has identified key oncogenic drivers and immune signatures, its use is limited by cost, technical demands and tissue availability. In addition, spatial transcriptomics provides spatially resolved molecular insights but remains challenging and time-consuming. To address this gap, we developed XpressO-Lung, an explanatory deep learning model that predicts gene expression heterogeneity spatially in tumor and its microenvironment on hematoxylin and eosin based diagnostic (Dx) whole-slide images (WSIs) by learning associations between tissue morphology and the corresponding bulk-transcriptomic data. Utilizing 200 LUAD cases from The Cancer Genome Atlas, XpressO-Lung predicted spatial expression patterns of NAPSA, TP53I3, CD8A, TTF1, KRT7, CDKN2A, FOXO1, KEAP1, RB1 and TP53 on Dx-WSIs with AUCs ranging from 0.64 to 0.92. The predicted spatial gene expression patterns aligned with the known morphologic interactions of the tumor and its microenvironment, capturing biological events directly on Dx-WSIs. These spatio-morpho-molecular associations were further validated using immunohistochemistry on an external set of clinical samples at Dartmouth Health, demonstrating concordance between model-predicted spatial patterns and observed histomorphologic features. By coupling predictive performance with spatial interpretability of gene expression on Dx-WSIs, the XpressO-Lung model bridges histopathology and bulk-transcriptomics, enabling explainable spatio-morpho-genomic analyses to advance biomarker discovery, therapeutic stratification and precision oncology in LUAD.

Background and AimsIn severe cases of acetaminophen (APAP) overdose, acute liver injury rapidly progresses to acute liver failure (ALF), producing life-threatening complications including, hepatic encephalopathy (HE) and multi-organ failure (MOF). Systemic levels of interleukin-6 (IL-6) and IL-10 are highest in ALF patients with the most severe complications and the poorest prognosis. The mechanistic basis for dysregulation of these cytokines, and their association with outcome in ALF, remain poorly defined. MethodsTo investigate the impact of IL-6 and IL-10 in ALF, we used an experimental setting of failed liver repair after APAP overdose in which a high dose of APAP is administered (i.e., 500-600 mg/kg). Mice were treated with neutralizing antibodies to block IL-6 and IL-10. ResultsIn mice with APAP-induced ALF, high levels of IL-10 reduced monocyte recruitment and trafficking in the liver resulting in impaired clearance of dead cell debris. Kupffer cells in these mice, displayed features of myeloid-derived suppressor cells, including high level expression of IL-10 and PD-L1, which were increased in an IL-6-dependent manner. Similar to ALF patients with HE, cerebral blood flow was reduced in mice with APAP-induced ALF. Remarkably, although IL-6 is hepatoprotective in mice treated with low doses of APAP (i.e., 300 mg/kg), IL-6 neutralization in mice with APAP-induced ALF fully restored cerebral blood flow and reduced mortality. ConclusionCollectively, these studies demonstrate that exaggerated production of IL-6 in APAP-induced ALF triggers immune suppression (i.e., high levels of IL-10 and PD-L1), reduces cerebral blood flow (a feature of hepatic encephalopathy), disrupts liver repair (i.e., failed clearance of dead cells), and increases mortality.

The RGB trichrome staining method has been used to highlight two major components of the extracellular matrix, collagen and glycosaminoglycans. While the RGB trichrome efficiently stains extracellular matrix components, it lacks a nuclear stain, limiting its application in histopathology. To address this issue, a modification of the original stain, named HemRGB trichrome, has been developed. This modification incorporates iron hematoxylin for improving nuclear staining while retaining specificity for the staining of extracellular matrix. The application of HemRGB trichrome staining to samples from both normal colonic tissues and colorectal adenocarcinomas (CRC) provides a robust nuclear staining, together with a high-contrasted staining of tumor microenvironmental components, such as infiltrating immune cells, collagen and ground substance, extracellular mucins, as well as contrasted interfaces between CRC metastases and liver parenchyma. This study underscores the potential of HemRGB trichrome as a valuable tool for histopathological studies, especially for cancer evaluation, where nuclear characteristics are particularly relevant.

AimsHistological assessment is essential for the diagnosis and management of celiac disease. Current scoring systems, including modified Marsh (Marsh-Oberhuber) score, lack inter-pathologist agreement. To address this unmet need, we aimed to develop a fully automated, quantitative approach for histology characterisation of celiac disease. MethodsConvolutional neural network models were trained using pathologist annotations of haematoxylin and eosin-stained biopsies of celiac disease mucosa and normal duodenum to identify cells, tissue and artifact regions. Human interpretable features were extracted and the strength of their correlation with Marsh scores were calculated using Spearman rank correlations. ResultsOur model accurately identified cells, tissue regions and artifacts, including distinguishing intraepithelial lymphocytes and differentiating villous epithelium from crypt epithelium. Proportional area measurements representing villous atrophy negatively correlated with Marsh scores (r=-0.79), while measurements indicative of crypt hyperplasia and intraepithelial lymphocytosis positively correlated (r=0.71 and r=0.44, respectively). Furthermore, features distinguishing celiac disease from normal colon were identified. ConclusionsOur novel model provides an explainable and fully automated approach for histology characterisation of celiac disease that correlates with modified Marsh scores, facilitating diagnosis, prognosis, clinical trials and treatment response monitoring. KEY MESSAGESO_ST_ABSWhat is already known on this topicC_ST_ABS[tpltrtarr] Prior research has utilised machine learning (ML) techniques to detect celiac disease and evaluate disease severity based on Marsh scores. [tpltrtarr]However, existing approaches lack the capability to provide fully explainable tissue segmentation and cell classifications across whole slide images in celiac disease histology. [tpltrtarr]The need for a more comprehensive and interpretable ML-based method for celiac disease diagnosis and characterisation is evident from the limitations of currently available scoring systems as well as inter-pathologist variability. What this study adds[tpltrtarr] This study is the first to introduce an explainable ML-based approach that provides comprehensive, objective celiac disease histology characterisation, overcoming inter-observer variability and offering a scalable tool for assessing disease severity and monitoring treatment response. How this study might affect research, practice or policy[tpltrtarr] This studys fully automated and ML-based histological analysis, including the correlation of Marsh scores, has the potential to enable more precise disease severity measurement, risk assessment and clinical trial endpoint evaluation, ultimately improving patient care.

Non-destructive 3D pathology methods have emerged in recent years with the potential to enhance standard 2D histopathology by greatly increasing the amount of tissue sampled by imaging and by providing volumetric morphological context. Another key advantage is that tissues remain intact, allowing re-embedding after imaging for potential long-term storage and future histological or molecular analyses. However, the impact of 3D pathology protocols on biomolecules -- including DNA, RNA, and proteins -- and their compatibility with downstream assays, has not been systematically evaluated. Here, we applied a previously optimized 3D pathology protocol -- involving deparaffinization, fluorescent H&E-analog staining, optical clearing, and open-top light-sheet microscopy -- to formalin-fixed paraffin-embedded (FFPE) specimens of breast, prostate, and head and neck cancer. Following the protocol, tissues were re-embedded in paraffin and compared with paired FFPE controls that did not undergo 3D pathology processing. DNA and RNA were extracted and subjected to quality assessments. Amplifiability was tested by PCR and reverse transcription quantitative PCR (RT-qPCR) of housekeeping genes. Although the results showed a slight decrease in the average yield and increased fragmentation of both DNA and RNA, amplifiability was largely preserved. Sanger sequencing of the PCR products confirmed accurate sequence determinations, while total RNA sequencing indicated that the global transcriptomic profile was largely unchanged. IHC staining of common biomarkers produced comparable signals, suggesting those proteins are well preserved after the 3D pathology workflow. These results demonstrate the feasibility of combining 3D pathology with downstream molecular applications.

Myeloid-derived suppressor cells (MDSCs) are immunosuppressive cells that are increased in patients with numerous malignancies including viral-derived hepatocellular carcinoma (HCC). Here, we report an elevation of MDSC in other hepatobiliary malignancies including non-viral HCC, neuroendocrine tumors (NET), colorectal carcinoma with liver metastases (CRLM), but not cholangiocarcinoma (CCA). Investigation of myeloid cell infiltration in HCC, NET and intrahepatic CCA tumors further established that the frequency of antigen-presenting cells was limited compared to benign lesions suggesting that primary and metastatic hepatobiliary cancers have distinct peripheral and tumoral myeloid signatures. Bioinformatics analysis of the Cancer Genome Atlas demonstrated that a high MDSC score in HCC patients predicted poor disease outcome. Mechanistic studies indicated that the oncometabolite D-2-hydroxyglutarate resulting from isocitrate dehydrogenase 1 mutation could be a limiting factor of MDSC accumulation in CCA patients. Given our observation that MDSCs are increased in non-CCA malignant liver cancers, they may comprise suitable targets for effective immunotherapy approaches.

In clinical trials for ulcerative colitis (UC), pathologists assess disease severity through standardized histological indices, including the Geboes Score, Robarts Histopathology Index (RHI), and Nancy Histologic Index (NHI). Despite strong associations with clinical outcomes, histologic scoring suffers from inter- and intra-reader variability, and consensus criteria for histologic remission remain uncertain. Through a consortium approach, we developed an artificial intelligence-based measurement (AIM) tool for scoring histology in UC mucosal biopsies (AIM-HI UC). This model, trained on a large dataset of UC biopsies (N=10,230), utilizes additive multiple instance learning models leveraging PLUTO, a pathology foundation model, that predict each of the Geboes subgrades, from which the Geboes grade-level score, RHI, and NHI can be calculated. Evaluation of this model on a standalone verification set including clinical trial specimens established algorithm non-inferiority and/or superiority relative to standard qualified pathologists through comparison of algorithm-consensus and pathologist-consensus agreement metrics (non-inferior if difference >-0.1, superior if difference >0, inclusive of confidence intervals). AIM-HI UC was determined to be non-inferior to pathologists (N=3) for the prediction of all seven Geboes subgrades, grade-level Geboes, RHI, NHI, histologic improvement (GS<3.1), 2A histologic remission (GS<2A.0), and 2B histologic remission (GS<2B.0). AIM-HI UC was superior to pathologists for several Geboes subgrades (GS 0, GS 1, GS 2B, and GS 5), as well as grade-level Geboes, RHI, and positive percent agreement of 2A histologic remission. The model was shown to be greater than 99% repeatable for all histologic scoring metrics examined. Model-derived scores were shown to strongly correlate with canonical histologic features of inflammation, including the proportion of total epithelium that is inflamed (Spearman r=0.83; p<0.01), the proportion of neutrophils localized within crypt epithelium (Spearman r=0.83, p<0.01), and the amount of mucosal area classified as erosion or ulceration (Spearman r=0.80, p<0.01). Overall, these results suggest that AIM-HI UC has the potential to improve consistency of UC histology interpretation, providing a path toward standardization of UC histology scoring in clinical trials.

The liver lymphatic system plays a critical role in maintaining interstitial fluid balance and immune regulation. Efficient lymphatic drainage is essential for liver homeostasis, but its role in liver disease progression remains poorly understood. In cirrhosis, lymphangiogenesis initially compensates for increased lymph production, but impaired lymphatic drainage in advanced stages may lead to complications such as ascites and portal hypertension. This study aimed to evaluate how liver lymphatic dysfunction affects disease progression and to assess therapeutic strategies. Using a surgical model to block liver lymphatic outflow, we found that impaired drainage accelerates liver injury, fibrosis, and immune cell infiltration, even in healthy livers. Mechanistically, enhanced TGF-{beta} signaling in liver lymphatic endothelial cells (LyECs) contributed to reduced lymphatic vessel (LV) density and function in late-stage decompensated cirrhosis. This dysfunction was linked to the progression from compensated to decompensated cirrhosis, particularly in patients with primary sclerosing cholangitis (PSC). Conversely, liver-specific overexpression of VEGF-C via AAV8 improved lymphatic drainage, restored LV density, reduced fibrosis, mitigated liver injury, and alleviated portal hypertension in cirrhotic rats. These findings establish impaired liver lymphatic function as a pivotal driver of cirrhosis progression and identify VEGF-C as a promising therapeutic target to prevent decompensation.