The Journal of Pathology

BackgroundFibrosis and tumor innervation are two features of the tumor microenvironment (TME) that contribute directly to the lethality of pancreatic ductal adenocarcinoma (PDAC), but their potential interactions have not been explored. Moreover, although it is known that activated Schwann cells (SCs) stimulate cancer cell invasion, it remains unclear how SCs are activated. ObjectiveWe determined how SCs are activated in the pancreatic fibrotic microenvironment. DesignThe correlation between physical features of the microenvironment and SC activation was assessed in human patient samples and in mice by SC c-Jun phosphorylation monitoring, atomic force microscopy and multiphoton live imaging. Several in vitro models in which forces were applied to SCs expressing a reporter for c-Jun phosphorylation and RNA-Seq analysis were used to decipher the cellular and molecular mechanisms of SC activation. ResultsNerves surrounded by stiff stroma present higher SC activation. Intravital imaging shows a matrix dependent SC activation. Mechanical forces on SCs induce c-Jun phosphorylation in SCs in a non-canonical manner that involves a nuclear sensing machinery with the proinflammatory enzyme Phospholipase A2. ConclusionFibrosis enhances the protumorigenic impact of innervation by activating SCs via a mechanism in which nuclear compression triggers non-canonical activation of the AP-1 transcription factor complex. Pancreatic fibrosis alone, without cancer cells, is sufficient to activate SCs, suggesting this mechanism may be common across non-malignant pancreatic diseases. Notably, SCs are more sensitive to mechanical activation than PDAC cells. These findings reveal TME interactions that may guide future microenvironment-targeted PDAC therapies. What is already known on this topicThe pancreatic cancer tumor microenvironment is highly innervated and fibrotic, two components of the tumor microenvironment that regulate tumorigenesis. How they impact each other is unknown. Schwann cells have emerged as a significant protumorigenic player, but the triggers of Schwann cell activation remain undefined. What this study addsWe establish that fibrosis induces Schwann cell activation and characterize the mechanism by which it occurs. We uncovered a mechanical mode of action that deforms nuclear membrane and activates c-Jun in Schwann cells, which contradicts the traditional view of c-Jun activation through a stimulus detected at the plasma membrane. How this study might affect research, practice or policyThis study provides a better understanding of the biology of pancreatic ductal adenocarcinoma and supports the development of novel precision therapies that target the fibrotic microenvironment to impact the protumorigenic effect of tumor innervation.

BackgroundHistomorphology is a strong prognostic biomarker correlated with basal-like and classical programs in surgically resected pancreatic ductal adenocarcinoma (PDAC). However, the spectrum of morphology and its biological associations remain poorly defined in advanced disease. ObjectivesWe explored the transcriptomic and genomic underpinnings and clinical relevance of morphological classes across localized and metastatic PDAC. DesignWe unified morphological classifications into four classes: glandular, cribriform, solid, and squamous. We integrated transcriptome and whole-genome sequencing following laser-capture microdissection with morphological classifications in 348 PDAC patients, where half of the cohort included locally advance and metastatic stages to uncover molecular associations. ResultsNon-glandular morphologies comprised three distinct classes that were enriched in metastatic disease. Transcriptomic profiling exhibited that glandular tumours predominantly expressed classical epithelial programs, although a subset displayed partial or full epithelial- mesenchymal transition signatures. In contrast, non-glandular morphologies showed basal-like transcriptional programs with subtype-specific pathways, including ciliogenesis in cribriform tumours, extracellular matrix remodelling and immune evasion in solid tumours, and keratinisation programs in squamous tumours. The solid class was significantly enriched in liver metastatic lesions and was associated with increased intra-tumoural morphological heterogeneity, whole-genome doubling, KRAS major allelic imbalance, and elevated KRAS-ERK signalling. ConclusionNon-glandular morphologies identify biologically distinct PDAC tumour states that are enriched in liver metastases and associated with subtype-specific transcriptional programs and KRAS-driven genomic alterations.

Collagen organisation within the tumour microenvironment plays a critical role in tumour progression and has emerged as an important structural biomarker in cancer. Second Harmonic Generation (SHG) microscopy enables label-free visualisation and quantitative assessment of fibrillar collagen architecture; however, its high cost, specialised instrumentation, and limited field-of-view restrict routine clinical application. In this study, we evaluated whether collagen features quantified from digitally scanned Masson-Goldners Trichrome-stained histopathological sections can approximate measurements obtained from SHG microscopy. Formalin-fixed paraffin-embedded breast tumour tissues, including benign and invasive ductal carcinoma (IDC) samples with varying collagen content, were analysed using SHG microscopy and whole-slide brightfield imaging. Matched regions of interest were analysed using two independent digital image analysis approaches: a conventional ImageJ-based workflow (TWOMBLI) and a machine learning-based computational pipeline. Collagen structural parameters including collagen deposition area, fibre number, and alignment metrics were quantified and compared across imaging modalities using correlation analysis. SHG signals were consistently detected from trichrome-stained sections, confirming compatibility of SHG imaging. Quantitative comparison demonstrated significant concordance between SHG-derived collagen metrics and those obtained from digital image analysis pipelines, particularly for collagen area and fibre alignment. These findings demonstrate that computational analysis of routine histopathological images can capture key spatial features of collagen organisation comparable to SHG microscopy. Digital pathology-based collagen quantification therefore, represents a scalable and clinically accessible approach for assessing extracellular matrix architecture in tumour tissues.

Digital pathology, coupled with advanced image recognition algorithms, represents a transformative frontier in histopathological diagnosis. This sub-Saharan African laboratorys exploratory study investigates the application of a Convolutional Neural Network (CNN) model, specifically leveraging the VGG16 architecture with transfer learning, for automated analysis and classification of selected gastrointestinal (GIT) and liver tissue samples, incorporating both routine and specialized staining protocols. The study utilized a dataset comprising 114 samples (18 liver, 96 GIT images) derived from archival formalin-fixed paraffin-embedded tissue blocks at University College Hospital, Ibadan, Nigeria. Specialized staining techniques included Alcian Yellow for GIT mucin visualization and Massons Trichrome for liver fibrosis assessment, alongside conventional H&E staining. Model performance was evaluated using statistical methodologies including Wilson Score confidence intervals (CI), Bayesian probability assessment, and effect size analysis. Results reveal a striking dichotomy in model performance. The GIT tissue model achieved perfect classification accuracy (100% test accuracy) with exceptional statistical significance (Z=10.0, p<0.0001), Wilson CI [96.29%, 99.99%], Cohens h=1.571, and Bayesian probability >99.99%. Conversely, the liver tissue model demonstrated diagnostic failure (42.86% test accuracy), with Z=-1.428, p=0.9236, Wilson CI [33.59%, 52.65%], Cohens h=-0.144, and Bayesian probability of 7.64%. This performance divergence correlates with training data availability, as the liver dataset fell far below empirically established thresholds (>100-200 samples) for reliable classification. The liver models failure reveals limitations in transfer learning with insufficient data. These findings underscore critical implications for AI-enhanced digital pathology, demonstrating potential deployment of the GIT model as a promising one that supports tissue-specific model development.

BackgroundWilms tumour (WT) relapse occurs more frequently in patients with blastemal-type WTs. The presence of cancer stem cells (CSCs) is linked to tumour survival and relapse, and CSCs may be found in greater numbers in blastemal cell foci. CSC-associated phenotypes have been described in untreated WT, but their persistence, organisation and relevance after neoadjuvant chemotherapy is unknown. MethodsWe analysed 23 formalin-fixed paraffin-embedded blocks from 18 chemotherapy-treated patients where WTs were enriched for viable blastema, using human fetal kidney as developmental control. Immunohistochemistry and -fluorescence analysis determined progenitor (PAX2, SIX2, CITED1) and CSC-associated (NCAM, ALDH1, CD133) marker expression. We qualitatively and semi-quantitatively evaluated spatial expression patterns and co-localisation across tumour compartments. ResultsPAX2 and SIX2 were co-expressed in blastema in most cases (15/18), with PAX2 expression higher at the periphery of blastemal foci and SIX2 expression found uniformly in central aspects. CITED1 expression was also associated with SIX2 in blastema tissues (14/18). NCAM was blastema-enriched (15/18) with higher central intensity, frequently adjacent to PAX2-expressing peripheral zones. ALDH1 expression was present across blastema and epithelium while NCAM-, ALDH1-double-positive cells were rarely observed (4/18). CD133 expression was less commonly seen (2/18), localising near epithelial/nephrogenic structures. ConclusionsAfter neoadjuvant chemotherapy, WT blastema retained overlapping but non-identical progenitor/CSC-associated marker landscapes with reproducible peripheral-centre gradients. These spatial arrangements suggest a blastemal niche for CSCs that may sustain a therapy-resistant state. Our analysis provides the foundation for future functional validation and molecular profiling to define key lineage relationships and therapeutic vulnerabilities in post-chemotherapy WT. [250/250 words]

Keratin-positive giant cell-rich tumor (KPGCT) is a newly described bone and soft tissue tumor. The tumor is characterized by scattered keratin-positive cells and the presence of HMGA2::NCOR2 fusions. It is not known if the HMGA2::NCOR2 fusion is located in the keratin-positive cells, and little is known about how KPGCT develops. KPGCT shares some histologic features with tenosynovial giant cell tumor (TGCT), a soft tissue tumor with CSF1 rearrangements. Single-nuclei RNA sequencing (snRNA-seq) and Xenium spatial transcriptomics were used to elucidate the mechanisms driving KPGCT and compare KPGCT to TGCT. We show that the neoplastic cells in KPGCT constitute only a minority of cells in the tumor, and that they co-express keratin, HMGA2 and CSF1. The neoplastic cells in KPGCT express no synovial markers, confirming KPGCT as a distinct entity, separate from TGCT. The bulk of the tumor consists of CSF1R-expressing macrophages and osteoclast-like giant cells, suggesting an important role for CSF1-CSF1R signaling. In addition, we find that the cells with the HMGA2 translocation show activation of the hippo signaling pathway, which is known to regulate CSF1 expression. We show that the CSF1-CSF1R axis, possibly regulated through the hippo signaling pathway, plays an important role in KPGCT. This axis likely stimulates the migration and proliferation of macrophages, which form the majority of cells in the tumor, as well as their differentiation into osteoclasts-like giant cells. These results provide a rationale for the use of CSF1R inhibitors, which have already shown efficacy in TGCT, as a therapy for KPGCT. SignificanceKeratin-positive giant cell-rich tumor (KPGCT) is a rare, newly described soft tissue and bone tumor. By examining this tumour on a single-cell level, we confirm the identity of the neoplastic cells on a molecular level, showing these form a minority of cells in the tumor. We show that activation of the hippo pathway in the neoplastic cells is a likely driver of tumorigenesis. Additionally, we show the neoplastic cells produce large amounts of CSF1, attracting the macrophages that form the majority of cells in the tumor. This finding gives supporting evidence for anecdotal reports of response to CSF1 inhibitor therapy. Finally, we identify key differences between KPGCT and tenosynovial giant cell tumor, a tumor that shares histological features with KPGCT.

Myeloproliferative neoplasms (MPNs) are characterized by progressive myelofibrosis that drives morbidity and mortality. Liquid biopsy approaches to noninvasively monitor fibrotic progression remain limited. We performed comparative transcriptomic profiling of CD45-depleted platelet-enriched and CD45+ leukocyte-enriched fractions from matched peripheral blood samples of 76 individuals (27 primary myelofibrosis, 17 polycythemia vera, 14 essential thrombocythemia, 18 healthy controls). Platelet RNA sequencing was performed in 2018-2020 on Illumina HiSeq 4000, while WBC RNA sequencing was conducted in 2023 on Illumina NovaSeq 6000 from cryopreserved CD45+ enriched fractions of specimens obtained at the identical time and from the same blood sample as the platelet RNA. Despite comparable library preparation protocols and higher sequencing depth in WBC samples, platelet transcriptomes exhibited 5.1-fold more differential expression in myelofibrosis (3,453 versus 681 genes, adjusted p<0.05, |log2FC|>1). Platelet signatures were enriched for proteostasis pathways including endoplasmic reticulum stress and unfolded protein response, reflecting megakaryocyte dysfunction in the fibrotic bone marrow niche. WBC signatures predominantly featured immune activation and proliferative pathways, indicating systemic inflammatory responses. Multinomial LASSO classification demonstrated superior performance of platelet-based models for myelofibrosis diagnosis (AUROC 0.85) compared to WBC-based (AUROC 0.77) or clinical models (AUROC 0.59). Combined platelet+WBC models did not improve performance (AUROC 0.80), indicating complementary but non-additive information. These findings establish platelet transcriptomic profiling as a superior noninvasive biomarker platform for monitoring myelofibrosis in MPNs, capturing megakaryocyte-driven fibrogenesis with greater sensitivity than peripheral leukocyte-based approaches. HighlightsUsing matched WBC and platelet RNA-seq from MPN patients, we identify myelofibrosis-associated transcriptomic signatures specifically enriched in platelets. Multinomial LASSO modeling highlights platelet-derived gene expression as a dominant and predictive biomarker of myelofibrosis, outperforming clinical parameters and WBC signatures. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=75 SRC="FIGDIR/small/714941v1_ufig1.gif" ALT="Figure 1"> View larger version (21K): org.highwire.dtl.DTLVardef@1d695aborg.highwire.dtl.DTLVardef@fc250forg.highwire.dtl.DTLVardef@1e52e8eorg.highwire.dtl.DTLVardef@15378e3_HPS_FORMAT_FIGEXP M_FIG C_FIG

PurposeTriple-negative breast cancer (TNBC) exhibits substantial clinical heterogeneity, with some patients experiencing early recurrence and poor survival despite similar clinicopathologic features. We sought to determine whether quantitative measures of intratumoral collagen architecture and composition derived from standard histopathologic specimens can identify patients at risk of recurrence and adverse survival outcomes. Experimental DesignWe analyzed a retrospective cohort of 79 TNBC tumors assembled into a tissue microarray using a multimodal computational pathology framework integrating Massons Trichrome staining with COL1 and COL3 immunohistochemistry. Collagen architecture was quantified using fiber-based image analysis and unsupervised clustering, while collagen composition was assessed using a normalized COL3:COL1 ratio. Associations with recurrence-free interval (RFI) and overall survival (OS) were evaluated using Kaplan-Meier analysis, restricted mean survival time (RMST), and Cox proportional hazards modeling. ResultsUnsupervised analysis identified four distinct collagen architectural states, which were consolidated into low-risk and high-risk groups based on recurrence patterns. High-risk collagen architecture was associated with significantly worse long-term RFI (log-rank p=0.025; RMST difference 10.1 months). Independently, a higher COL3:COL1 ratio was associated with improved OS (log-rank p=0.042; RMST difference 9.4 months). Integration of architectural and compositional biomarkers further refined risk stratification, identifying a subgroup with high-risk architecture and low COL3:COL1 ratio that exhibited the poorest survival outcomes. Notably, collagen-based stratification identified patients with divergent outcomes not readily predicted from tumor stage alone. ConclusionsQuantitative assessment of intratumoral collagen architecture and composition provides clinically meaningful prognostic information in TNBC and enables stratification of recurrence and survival risk. These findings support extracellular matrix phenotyping as a practical and scalable computational pathology approach for refining risk assessment in TNBC. Translational RelevanceTriple-negative breast cancer (TNBC) remains clinically challenging due to heterogeneous outcomes that are not fully captured by standard clinicopathologic variables. In this study, we demonstrate that quantitative features of intratumoral collagen architecture and composition, derived from routine pathology specimens, provide clinically meaningful prognostic information. Collagen-based biomarkers, including distinct collagen architectural phenotypes and the COL3:COL1 ratio, identify patient subgroups with distinct recurrence and survival outcomes, particularly among individuals whose risk is not adequately predicted by conventional staging. Importantly, these features can be extracted from widely available histological stains and immunohistochemistry, supporting the potential integration into existing pathology workflows. These findings support the tumor microenvironment as an underutilized source of biomarkers and suggest that extracellular matrix-based phenotyping may improve risk stratification and inform clinical decision-making in TNBC.

Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal cancer characterized by a high abundance of cancer-associated fibroblasts (CAFs), which influence therapy response, tumor biology and tumor aggressiveness. CAFs are a heterogeneous cell type and previous single-cell RNA sequencing (scRNAseq) of PDAC tumors identified three main CAF subtypes: myofibroblastic, inflammatory and antigen-presenting CAFs (myCAF, iCAF, apCAF, respectively). However, scRNAseq on large patient cohorts is often not feasible due to costs and technical constraints. Therefore, bulk RNAseq deconvolution can be used to identify cell types within the heterogeneous tumor microenvironment. Here, Statescope deconvolution was used to identify different cell types of the tumor microenvironment within an early onset PDAC cohort, comprising 74 patients aged under 60. Three CAF populations were identified (iCAFs, myCAFs and desmoplastic CAFs), and their correlations with tumor microenvironment components, mutational signatures and survival were examined. iCAFs were associated with classical-like tumor cells, whereas myCAFs and desmoplastic CAFs correlated with basal-like tumor cells. Desmoplastic CAFs are associated with inflammatory granulocytes/neutrophils, while negatively associating with monocyte-derived macrophages and immature/transitional B cells. No associations were observed between mutational signatures and the abundance of CAF and epithelial tumor subtypes. Interestingly, a high abundance of CAFs, and specifically increased iCAF abundance, was associated with improved survival. This iCAF-mediated survival effect was predominantly apparent in female patients. All in all, deconvolution of bulk RNA sequencing data, followed by its integration with clinical and biological parameters, reveals the heterogeneity and prognostic implications of CAF subpopulations in the tumor microenvironment of early onset PDAC patients.

Introduction: With recent approvals of multiple targeted therapies for triple-negative breast cancer (TNBC), including antibody-drug conjugates and immunotherapy in biomarker-selected populations, it is critical to define the temporal evolution of cell-surface target expression from early-stage to metastatic disease, the co-expression patterns across these markers, and optimal quantification methodologies. Here we report biomarker expression profiles measured by multi-omics and pathology-based platforms in patients with TNBC using a large cohort of matched longitudinal tumor samples. Methods: Patients who underwent neoadjuvant chemotherapy (NAC) for stage I-III TNBC or were diagnosed with any stage TNBC and developed metastatic recurrence were retrospectively identified from an institutional database and prospective research metastatic biopsy protocol. Tumor samples from diagnosis (DX), residual disease (RD) post-NAC (if applicable), and metastasis/recurrence (MR) were collected. Quantification of HER2, TROP2, and PD-L1 expression was performed by immunohistochemistry (IHC), whole-exome sequencing, transcriptome sequencing, and targeted mass spectrometry (MS). For HER2, TROP2, and stromal tumor-infiltrating lymphocytes (sTILs), both manual pathologist assessment and computational pathology quantification were obtained. HER2 status was categorized as HER2-0 or HER2-low by local (L-IHC) and central (C-IHC) review, TROP2 status was defined as low (H-score <100), medium (H-score 100-200) or high (H-score >200), and PD-L1 as low (tumor area positivity, TAP <5%) or high (TAP [&ge;]5%). Pathologist-assessed sTILs were classified as low (<10%), medium ([&ge;]10% and <40%) or high ([&ge;]40%). Biomarkers were compared between primary (DX/RD) and MR, and between pre- vs post-NAC (DX-RD) samples. Correlations between markers, quantification methods, inferred PAM50 subtype, and clinical variables of interest were evaluated. Results: A total of 359 samples from 110 patients with TNBC with data available from at least one platform were included in the analysis. HER2-low prevalence at DX, RD, and MR was: 51% (50/98), 40% (21/53), and 27% (16/60); TROP2 high/medium was 90% (47/52), 91% (42/46), and 88% (28/32); PD-L1-high was 51%, 50%, and 38% (9/24); and sTILs-high/medium was 88% (59/67), 80% (40/50), and 49% (17/35), respectively. While TROP2-high/medium vs low remained stable over time, HER2 IHC and sTILs significantly decreased from DX/RD to MR samples, both at the cohort-level (HER2, p=0.0081; sTILs, p=4.6x10e-5) and longitudinal patient-level (HER2, p=0.030; sTILs, p=0.0077), with a similar decreasing trend for PD-L1 that did not reach statistical significance. HER2 concordance (0 vs low) between L-IHC and C-IHC was 78% (91/116). ERBB2, TACSTD2 and CD274 mRNA expression were significantly correlated with IHC protein levels, though only TACSTD2 had limited overlap in distribution of gene expression between high/medium vs low groups. Strong correlation between protein membrane staining intensity from computational pathology, protein expression measured by MS, and pathologist-assed IHC was observed across all biomarkers tested by each method. In comparisons between biomarkers, pathologist-assessed PD-L1 IHC and sTILs were significantly correlated (p=0.0001); 94% (51/54) of PD-L1-high tumors were classified as sTILs high/medium. PAM50 subtype was not significantly correlated with time point or biomarker status, although there was a trend toward more HER2-enriched tumors in HER2-low (20%, 5/25) vs HER2-0 (6%, 3/52) (p=0.086). Across biomarkers and clinical variables, an association between age and sTILs was observed (p=0.038, FDR=0.42) due to a decrease in sTILs high/medium tumors with age, primarily driven by post-treatment (RD/MR) but not DX samples. Conclusions: Multi-platform and multi-omics profiling in this large unique cohort of longitudinal TNBC samples revealed distinct patterns of expression and dynamic changes of key biomarkers of interest for targeted therapies. Given variability with manual IHC scoring, improved methods for quantification of expression may help optimize treatment selection in an individualized manner.

Abstract Background Tumour hypoxia is a major determinant of treatment resistance and poor prognosis in cervical cancer but remains difficult to assess in clinical practice. Gene expression signatures offer a potential means to characterise hypoxia-related biology. This study aimed to develop and validate a hypoxia-associated gene expression signature for cervical cancer. Methods RNA sequencing was performed on five cervical cancer cell lines exposed to normoxia (21% O?) and hypoxia (1% O?). Differentially expressed genes were mapped to The Cancer Genome Atlas cervical cancer cohort (TCGA-CESC) to train a 55-gene hypoxia classifier using k-means clustering and Prediction Analysis for Microarrays. The model was validated in an institutional Manchester cohort (n=153) and two public datasets from Seoul (n=300) and Oslo (n=283). Results The Manchester 55-gene signature was enriched for canonical hypoxia pathways. In the Manchester cohort, hypoxia classification correlated with advanced FIGO stage, nodal involvement, tumour size ? 4 cm, and hydronephrosis (adjusted p<0.05). Hypoxic tumours showed reduced overall survival (OS) and progression-free survival (PFS) in all cohorts. In multivariable models, the signature remained independently prognostic for OS in both TCGA (HR 1.70, 95% CI 1.10-2.60, p=0.012) and Manchester (HR 1.95, 95% CI 1.08-3.51, p=0.026). A direct comparison with a published 6-gene hypoxia signature in the Oslo cohort demonstrated 71% concordance in classification. Conclusions Our 55-gene signature should be tested prospectively in trials to assess its ability to stratify patients for hypoxia-targeted therapies.

Secretory breast carcinoma (SBC) is typically indolent, yet mechanisms underlying aggressiveness and therapeutic resistance to tropomyosin receptor kinase inhibitors (TRKi) remain unclear. Autopsy-based longitudinal multi-organ high-dimensional profiling of metastatic TRKi-resistant SBC demonstrated histopathological heterogeneity, including secretory and squamous components, arising from a shared clonal origin. Integrated genomic and transcriptomic analyses revealed hierarchical transcriptional rewiring consistent with a lineage-plastic state, suggesting a potential link to tumor aggressiveness and therapeutic resistance.

BackgroundPancreatic adenocarcinoma (PDAC) is an abysmal disease, with a poor clinical outcome, largely due to limited life-extending treatments for patients. Notoriously, PDAC displays a T cell-suppressive tumor microenvironment where underlying molecular mechanisms that lead to this phenotype remain poorly understood. To unravel specific mechanisms, we utilized bioinformatic analyses with functional studies and revealed the cytolinker protein plectin (PLEC) as a novel player in regulating the T cell-suppressive tumor microenvironment of PDAC. MethodsUtilizing the TCGA-PAAD dataset, tumor samples were separated by PLEC expression to evaluate patient survival, and pathway analyses associated with increased tumorigenesis. Evaluation of immune infiltration and subsequent immune deconvolution was performed using tidyestimate and CIBERSORTx R packages. Single-cell RNA-seq (scRNA-seq) analysis from 229 PDAC patients was analyzed to investigate signaling dynamics and immune cell infiltration in PLECHigh patients. Functional validation was provided using a monoclonal antibody (mAb) against cell surface plectin (CSP) in two murine PDAC models to examine changes in tumor growth and immune cell subset abundance. ResultsOur studies revealed that high plectin expression results in an overall worse survival associated with activation of pro-tumorigenic pathways and decreased anti-tumor immune signature in PDAC patients. Analysis via GSEA indicates PLECHigh patients display an aggressive phenotype and suppressed pro-inflammatory signaling pathways. Immune ESTIMATE scores were significantly decreased in PLECHigh patients, and scRNA-seq analysis revealed that PLECHigh tumors display a decrease in anti-tumor CD8+ T cells. In vivo analyses using an anti-CSP mAb revealed a reduction in tumor growth kinetics compared to IgG control corresponding with a significant increase in proliferating and activated cytotoxic CD8+ T cells. Anti-CSP-mediated tumor suppression was inhibited when CD8+ T cells were depleted, indicating that anti-CSP treatment is contingent on cytotoxic T cell functionality. ConclusionOur findings identify plectin as a biomarker of aggressive disease in PDAC, with high plectin expression associated with decreased T cell infiltration, and that treatment with anti-CSP mAb reinstates anti-tumor immunity and decreases tumor volume in vivo. These findings position plectin as a high-priority therapeutic target, with the potential to fundamentally reshape immune responses in PDAC and improve outcomes for patients with few remaining options.

Pancreatic ductal adenocarcinoma (PDAC) is the most common form of pancreatic cancer and carries the poorest prognosis among all cancers, largely because it is frequently diagnosed at metastatic stages. It is therefore critical to identify reliable markers of preinvasive stages and to decipher the network driving preinvasive lesions to invasive carcinoma. Here, we generated a zebrafish model in which KRASG12D is specifically expressed in pancreatic acinar cells, inducing acinar-to-ductal metaplasia that faithfully mirrors mammalian tumorigenesis. Single cell RNA-seq allowed us to capture transcriptional changes occurring at early stages of the disease. Cross-species comparison with mouse and human scRNAseq transcriptomes revealed a striking conservation of the genes upregulated during metaplasia, triggering common signalling pathways and regulatory programs. Notably, metaplastic cells reactivate a broad set of developmental genes expressed in multipotent pancreatic progenitors. Mapping the acinar-to-cancer trajectories revealed a set of cytoskeletal and migration-related genes specifically upregulated during the late phase of metaplasia, immediately prior to malignant transformation, likely conferring invasive potential to these cells. SCENIC analysis further identified regulatory networks that become progressively activated as cells transition toward cancer, suggesting their involvement in the acquisition of malignant traits. In conclusion, our cross-species comparison demonstrates a high degree of conservation in the molecular mechanisms driving pancreatic cancer progression from early to late stages across evolutionarily distant species, including zebrafish, mouse, and human, highlighting critical pathways that should be targeted to prevent cancer progression. To allow researchers to easily explore gene expression profiles during pancreatic cancer progression across all three species, the datasets are publicly accessible via a user-friendly web platform (https://www.zddm.page.gd/)

Background & AimsHepatocellular carcinoma (HCC) frequently exhibits resistance to immune checkpoint inhibitors (ICIs), particularly in {beta} -catenin-driven tumors characterized by immune exclusion. While the Unfolded Protein Response (UPR) and the Integrated Stress Responses (ISR) enable tumor adaptation to metabolic stress their role in shaping tumor immunogenicity remains incompletely understood. We investigated whether ATF4, a central effector of the integrated stress response, couples metabolic reprogramming to suppression of anti-tumor immunity in HCC. MethodsWe combined transcriptomic analyses across three independent human HCC cohorts with mechanistic studies using an immunotherapy-resistant MYC/{beta}-catenin-driven murine HCC model. We integrated CRISPR/Cas9-mediated deletion of Atf4 with RNA-sequencing and targeted metabolomics. The impact of tumor-derived metabolites on macrophage differentiation and polarization was evaluated using primary bone marrow-derived cells. Therapeutic responses were evaluated in orthotopic and subcutaneous models treated with anti-PD-1 and anti-VEGFA. ResultsATF4 and XBP1 transcriptional signatures are selectively enriched in human HCC and associate with poor prognosis, vascular invasion, and an immunosuppressive myeloid-enriched tumor microenvironment. Genetic ablation of Atf4 markedly suppressed tumor growth in immunocompetent but not immunodeficient hosts, establishing a requirement for immune-mediated tumor control. Mechanistically, Atf4 loss downregulated Aldh18a1 and disrupted proline biosynthesis, resulting in extracellular proline depletion. This proline-deficient environment abrogated monocyte-to-macrophage differentiation and decreased M2 polarization, thereby reshaping the tumor microenvironment toward enhanced T cell infiltration and activation. Functionally, Atf4-deficient tumors exhibited restored sensitivity to anti-PD-1 monotherapy and showed pronounced responses to combined anti-PD-1/anti-VEGFA treatment in aggressive orthotopic models. ConclusionATF4 programs a proline-dependent metabolic axis that sustains macrophage-mediated immunosuppression and immune evasion in {beta}-catenin-driven HCC. Disruption of this pathway converts immune-excluded tumors into T cell-inflamed states and restores responsiveness to immunotherapy. By governing proline homeostasis and macrophage-mediated immunosuppression, ATF4 is a key metabolic checkpoint for immune evasion, linking stress adaptation to immune escape and a candidate therapeutic target in HCC. Impact and implicationsWe identify ATF4 as a crucial metabolic-immune orchestrator that sustains myeloid-driven immune evasion in {beta}-catenin-dependent HCC through proline-dependent circuitry. Disrupting the ATF4-proline axis converts immune-desert tumors into T cell-inflamed lesions by blocking macrophage differentiation, thereby sensitizing tumors to immune checkpoint therapy. This work positions ATF4 as a tractable therapeutic target to overcome immunotherapy resistance in HCC. Graphical abstract Highlights- ATF4 orchestrates an immunosuppressive tumor microenvironment in HCC by coupling metabolic stress adaptation to immune evasion. - Ablation of ATF4 disrupts proline biosynthesis, leading to a marked depletion of extracellular proline. - Cancer cell-derived proline availability contributes to macrophage differentiation and M2 polarization; its loss restores T cell-mediated anti-tumor surveillance and sensitizes beta-catenin-driven HCC to immune checkpoint blockade.

BackgroundThis study investigates the role of the pioneer transcription factor FOXA1 as a master gene in sustaining epithelial cell polarization in early-stage lung adenocarcinoma. The partial loss of FOXA1 is explored to determine if it will affect plasticity and progression of lung adenocarcinoma. The study also addresses the transcriptional circuitry that links polarity defects to lysosome homeostasis. MethodsA multiomics approach was used to define the status of the chromatin in epithelial and mesenchymal states of A549 adenocarcinoma cells obtained with a newly synthetized TGF-{beta} receptor inhibitor or TGF-{beta} respectively. The study leveraged ATAC-seq, RNA sequencing, Cut&Tag sequencing of FOXA1 and histone marks profiling. The functional impact of FOXA1 was examined by partial silencing in vitro and by heterozygous FOXA1 deletion in a KrasG12D mouse model. Three-dimensional organoid culture, high-resolution electron microscopy, spatial transcriptomics and multiplex immunohistochemistry assessed carcinoma cell polarity, proliferation, the tumor microenvironment and organelle content. Group differences were evaluated with two-tailed t tests or one-way analysis of variance. ResultsFOXA1 binding and expression were highest in cells harboring an epithelial phenotype. In mouse KrasG12D LUAD tumors FOXA1 marked polarized, CDH1-positive cells; heterozygous loss diminished CDH1, disrupted apical-basal architecture, lowered organoid-forming efficiency and remodeled the immune microenvironment. Spatial transcriptomics and ultrastructural analyses showed that FOXA1-deficient carcinoma cells accumulated lysosomes, down-regulated vesicle fusion genes of the SNARE family and activated the lysosomal CLEAR gene network. FOXA1 occupied enhancers of lysosome-associated genes and competed with the transcription factor TFE3, thereby suppressing transcription of cathepsin B and cathepsin C and restricting lysosome biogenesis. ConclusionsFOXA1 is a central regulator that preserves epithelial cell polarity and limits lysosome formation in lung adenocarcinoma. Targeting the FOXA1-TFE3-lysosome axis may affect tumor plasticity and provide new therapeutic opportunities.

BO_SCPLOWACKGROUNDC_SCPLOWThere is increasing momentum behind the clinical implementation of AI-based software for image analysis in digital pathology. As regulations, standards, and national approaches to the clinical use of AI continue to develop, the marketplace of AI products is expanding and evolving - presenting pathologists with a multitude of devices that offer the potential to improve pathology services. MO_SCPLOWETHODSC_SCPLOWTo maintain pace with this changing AI device landscape, we conducted a comprehensive search for, and analysis of, commercial AI products for image analysis in digital pathology. This included CE-marked and Research Use Only (RUO) products using images with histological stains (e.g., H&E) or immunohistochemical (IHC) labelling. Product information and published clinical validation studies were assessed, to understand the quality of supporting evidence on available products, and product details were compiled into a public register: https://osf.io/gb84r/overview. RO_SCPLOWESULTSC_SCPLOWIn total, we identified and assessed 90 CE-marked and 227 RUO AI products. We found that AI products for cancer detection in prostate and breast pathology comprised a substantial portion of the marketplace for H&E image analysis, while IHC products were almost exclusively for use in breast cancer. Clinical validation studies on these products have steadily increased; however, we found that published studies were only available for just over half of H&E products and just over a quarter of IHC products. For CE-marked products, the dataset quality and diversity for AI model performance validation was highly variable, and particularly limited for IHC products. Furthermore, only a limited number of products included studies that assessed measures of clinical utility. CO_SCPLOWONCLUSIONC_SCPLOWAs clinical deployment of AI products for image analysis in histopathology grows, there is a need for transparency, rigorous validation, and clear evidence supporting clinical utility and cost-effectiveness. Independent scrutiny of the expanding offering of AI products provides insight into the opportunities and shortcomings in this domain.

Barretts esophagus (BE) is the precursor lesion of esophageal adenocarcinoma (EAC). It affects approximately 5% of adults in the United States and significantly increases the risk of developing EAC. However, current surveillance strategies cannot reliably distinguish patients who will progress from those who will remain stable. Direct studies of progressor BE are extremely limited due to availability of tissue with known progression outcomes, and have largely been restricted to genomic profiling approaches. The premalignant cellular landscape of progressor BE remains poorly understood. Here, we used complementary spatial transcriptomic and proteomic imaging to profile 34 non-dysplastic BE patients under endoscopic surveillance, including those who subsequently progressed to dysplasia or EAC, termed "Progressors" and those who remained stable, termed "Non-progressors". Transcriptomics based Xenium analysis captured 974,604 cells across 70 whole-biopsy regions, while protein based imaging mass cytometry profiled 372,242 cells across 119 selected regions. FUME-TCRseq further quantified T cell clonotypes from matched tissues scrolls. Cellular composition was generally similar between Progressors and Non-progressors. However, Progressors showed increased intestinal Barretts columnar cells, B cells and gastric progenitor-like cells, together with enhanced immune-epithelial interactions, whereas Non-progressors retained coordinated stromal organization. Spatial interaction features strongly outperformed cell composition and density for progression prediction. Combined spatial interaction model achieved an area under the curve (AUC) of 0.97, compared with 0.62 and 0.68 for comparison and density alone. Complementary imaging mass cytometry further resolved the underlying immune programs, identifying cytotoxic and antigen presenting myeloid features enriched in progressors, and CD56 associated memory T cell interactions enriched in non progressors. Together, these findings support a model that BE progression is driven by progressive remodeling of epithelial-immune-stromal architecture rather than emergence of distinct dysplasia-like cell subsets. Increased T cell clonal diversity and recruitment of cytotoxic and antigen-presenting immune niches may also reflect an evolving response to genomic alteration prior to dysplasia. These results establish spatial tissue architecture, rather than specific cell types, captures progression associated microenvironmental states in BE and provides a framework for spatially informed patient stratification and early cancer risk assessment.

Invasive lobular breast carcinoma (ILC), the most common special histological subtype of breast cancer, is characterized by nearly universal expression of estrogen receptor alpha (ER) and unique sites of metastases, neither of which is fully recapitulated by genetically engineered mouse models. Using reporter-labeled ILC mouse xenografts, herein we used mammary fat pad, tail vein and intracardiac orthotopic growth to analyze spontaneous and experimental metastasis and gene expression. We observed ER-positive primary tumors with single-file histology and collagen deposition, and spontaneous metastasis from the mammary fat pad to bones, ovaries, and brain including the leptomeninges, thereby closely mirroring the growth and metastatic spread of human ILC. Brain metastases showed strong ER staining, confirmed by sequencing analyses which identified estrogen signaling as top activated pathway, and the lesions exhibited robust response to endocrine therapy. In summary, we report endocrine responsive mammary fat pad, tail vein and intracardiac xenografts that faithfully demonstrate unique ILC features and can serve as invaluable pre-clinical translational platforms for validating candidate ILC genetic drivers and testing novel therapeutics.

Background: CD8+ tumor-infiltrating lymphocytes (TILs) are established prognostic markers in colorectal cancer, yet the clinical significance of CD103+CD8+ tissue-resident memory-like (TRM-like) T cells in locally advanced rectal cancer (LARC) after neoadjuvant chemoradiotherapy (NACRT) remains unknown. Methods: We quantified CD8+ and CD103+CD8+ T-cell densities in stromal and intratumoral compartments of post-NACRT resection specimens from 40 LARC patients using Cu-Cyto, a deep learning-based imaging cytometry platform. Associations with survival, pathological response, and adjuvant chemotherapy (AC) were examined. Treatment-induced T-cell dynamics were assessed in paired pretreatment biopsies and post-NACRT resections (n = 9). Results: High stromal CD103+CD8+ density independently predicted better 5-year RFS (67.4% vs. 12.1%, p < 0.001) and OS (80.0% vs. 26.6%, p = 0.016); intratumoral density showed no prognostic significance. Pathological response correlated with stromal CD8+ but not CD103+CD8+ density. Paired analysis revealed a selective non-expansion of the CD103+ subset: stromal CD8+ T cells increased significantly after NACRT while CD103+CD8+ density remained unchanged. AC may preferentially benefit patients with low stromal CD103+CD8+ density. Conclusions: Stromal CD103+CD8+ T-cell density is a robust independent prognostic biomarker in rectal cancer after NACRT that appears to reflect pre-existing rather than treatment-induced immunity. Given its stability across NACRT, pretreatment biopsy assessment may provide equivalent prognostic information, with potential implications for patient stratification before treatment initiation.