Modern Pathology

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

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.

Background: Classification of central nervous system (CNS) tumors has become increasingly complex, raising concerns about the sustainability of comprehensive molecular diagnostics. We have evaluated nanopore whole genome sequencing (nWGS) as a single workflow to replace multiple diagnostic assays. Methods: We performed nWGS on DNA extracted from 90 adult CNS tumor samples (58 retrospective, 32 prospective) and compared the results to findings from standard of care (SoC) diagnostic work-up. Analysis was done through an automated workflow that consolidated diagnostically and therapeutically relevant genomic alterations, including copy-number variation, structural, and single-nucleotide variants, chromosomal aberrations, gene fusions, and methylation-based classification. Results: nWGS supported final diagnostic classification in all samples with >15% tumor cell content, requiring ~3 hours of hands-on library preparation, parallel sample processing, and sequencing times within 72 hours. Methylation-based classification was available within 1 hour and was concordant with the integrated final diagnosis in 89% of cases (80/90). All diagnostically relevant copy-number variations, single-nucleotide variants, and gene fusions were concordant with SoC testing. MGMT promoter methylation status matched in 94% of cases. In addition, nWGS identified prognostic and potentially actionable variants that were not reported or covered by SoC. Conclusions: nWGS delivers comprehensive genetic and epigenetic results with a fast turn-around compared to standard methods. This enables efficient, accurate, and scalable molecular diagnostics of CNS tumors using a single platform. This data supports its implementation in routine clinical practice and may be extended to other cancer types requiring complex genomic profiling.

Although several ancillary tests are available in limited laboratories, diagnosis of microphthalmia (MiT)/TFE family translocation renal cell carcinoma (tRCC) could be challenging due to diverse and overlapping tumor morphology and the lack of reliable biomarkers. GPNMB has been recently identified as a diagnostic marker for various renal neoplasms with FLCN/TSC/mTOR-TFE alterations. However, the sensitivity and specificity of GPNMB immunostain are suboptimal and the result interpretation in ambiguous cases could be difficult. To search additional biomarkers that could improve the screening sensitivity and predict genetic aberrations in FLCN/TSC/mTOR-TFE pathway in renal tumors, we performed bioinformatic analysis of publicly available cancer databases and found GPR143, a transmembrane protein regulated by MiT transcription factors, was highly expressed in a subset of renal cell carcinomas (RCCs). In two the Cancer Genome Atlas (TCGA) kidney cancer cohorts, RCCs with high levels of GPR143 expression were enriched for renal neoplasms with FLCN/TSC/mTOR-TFE alterations. Similar to GPNMB labeling, GPR143 immunostain was positive in the majority of tRCC cases and renal tumors with FLCN/TSC/mTOR alterations, suggesting that GPR143 could function as another surrogate marker for FLCN/TSC/mTOR-TFE alterations in certain renal tumors. Interestingly, despite the concordant GPR143 and GPNMB immunoreactivity in most renal neoplasms with FLCN/TSC/mTOR-TFE alterations, diffuse GPR143 immunostain was observed in some cases with negative or focal GPNMB labeling. Taken together, our results indicate GPR143 could serve as a useful adjunct marker to improve the sensitivity for screening renal tumors with FLCN/TSC/mTOR-TFE alterations.

The immunohistochemistry (IHC) methods widely used in diagnostic medicine and biomedical research are kinetically complex reaction-diffusion processes that, ideally, produce stain intensities correlated with the local antigen concentration. Yet after 75 years of use, practical theoretical tools to rigorously plan and interpret IHC experiments are still lacking. Because modeling the reactions requires time-consuming computer simulation, impractical for regular use, most protocols are optimized empirically, without detailed knowledge of the reaction rates and antigen-antibody equilibria. The resulting stain intensities can be calibrated against standards with known antigen abundance, but they are typically not interpretable in terms of chemical antigen concentrations. To address these limitations, we developed a fast interpolation method to model reaction-diffusion behavior, and experimental methods to characterize IHC kinetic parameters in formalin-fixed paraffin-embedded (FFPE) samples. Used together, these allow experimental measurement of both the chemical concentration of antigen in the sample and the reaction-diffusion parameters consistent with the assay results. Results show 1) direct immunofluorescent detection has low nanomolar sensitivity with >1000-fold dynamic range, and 2) antibody diffusion rates in FFPE samples can be >1000-fold slower than in aqueous solutions, producing diffusion-limited conditions in which the IHC reaction time course may depend on the sample antigen concentration. Awareness of these details is necessary to avoid potential underestimation of both the absolute and relative antigen concentrations in different samples that may occur if staining is stopped before reaching equilibrium. Software tools are provided to allow users to rapidly model IHC reaction time courses and to fit experimental time course data with candidate reaction parameters. The principles described here apply equally to other tissue-based "spatial omics" analyses and should be considered when designing and interpreting experiments requiring any macromolecule to diffuse into and react in a tissue section. SIGNIFICANCEThe theoretical and experimental framework described here advances IHC staining from a qualitative or semi-quantitative method towards a more rigorously quantitative assay. The practical ability to predict IHC reaction kinetics and fit reaction parameters to experimental data has the potential to advance IHC applications in diagnostic medicine and biomedical research in three ways: 1) interpretation of experimental and diagnostic samples stained under different conditions can be more objective, facilitating comparison of results from different protocols and different laboratories; 2) IHC staining can be interpreted as molar chemical antigen-antibody concentrations calculated from the reaction parameters measured in the studied sample; 3) the correlation between antigen concentration and biological behavior can be examined more reliably. Practical software tools are provided.

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.

Digital Pathology (DP) is a fast-emerging branch of pathology focused on digitizing pathology data. A key challenge of DP usage for pathology laboratories, especially mid- to small-sized clinical labs, are the upfront costs associated with instrumentation and the logistical challenges of implementation. In the current project, we built an end-to-end DP solution using low-cost, open-source components that is user-friendly at a small scale. We repurposed readily available microscopy components in a pathology lab to assemble a fully functional DP pipeline for translational research applications. We tested multiple low-cost complementary metal-oxide semiconductor (CMOS) cameras in this project and chose a user-friendly Canon camera for image acquisition. An open-source DP server solution, OMERO v.5.6.4, was used as the image management system (IMS) to host and serve the WSIs on an Ubuntu 22.04 operating system. The server-hosted WSI images were evaluated remotely and asynchronously by multiple pathologists physically situated in Albuquerque, NM; Salt Lake City, UT; and Palo Alto, CA. Each pathologist assessed the quality of the WSI pipeline, image quality, and WSI interaction experience using a 23-question survey. Overall, the custom, low-cost WSI pipeline was noted to be a robust and user-friendly experience by the pathologists. The current DP setup is unlikely to be useful as a commercial, scalable DP pipeline for large-scale clinical applications. However, it demonstrates the feasibility of creating customized, small-scale DP solutions (at a low price point) for asynchronous translational pathology research applications. Additionally, building customized DP pipelines provides excellent educational opportunities for pathology residents to gain in-depth knowledge of the various technical elements of a DP workflow. In summary, we have established a low-cost, end-to-end WSI DP pipeline useful for spatiotemporally asynchronous translational pathology research, in an academic setting.

Diffuse large B-cell lymphoma (DLBCL), the most common aggressive lymphoma, encompasses histologically similar but genetically distinct cancers. Recent genetic studies have defined at least six molecular subtypes, yet none account for Epstein-Barr virus (EBV), despite 5-15% of DLBCLs being EBV-associated. By reanalyzing published whole-exome and RNA-sequencing data from 481 tumors, we identified 19 EBV-positive cases. These were significantly enriched in the BN2 subtype (6/19), while most (11/19) remained unclassified. In BN2 tumors, several subtype-defining mutations were reduced in frequency among EBV-positive cases, supporting the hypothesis that EBV oncogenes substitute for specific cellular alterations and may confound DLBCL classification algorithms. Extending our analysis to cell lines, we found that the widely used Val cell line harbors the B95-8 laboratory EBV strain; other EBV-positive lines appeared authentic but modeled only non-BN2 subtypes and expressed an atypical viral latency III program, whereas some DLBCL tumors expressed the atypical latency III program and others latency I or II. Together, these findings demonstrate that EBV-positive DLBCL, like DLBCL itself, is not a single disease, and that current in vitro models only partially capture its biological heterogeneity. Key pointsO_LIEBV-positive DLBCL is not a single disease and EBV status can impact genetic-based classifications. C_LIO_LICurrent EBV-positive DLBCL cell lines do not adequately capture tumor complexity; we determined that Val is a problematic cell line. C_LI

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.

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.

Objective: To demonstrate the proof of principle that machine learning (ML) can be used to quantify Gleason Pattern (GP) 4 on digitized biopsy slides using multiple measurement approaches, allowing direct comparison of their prognostic performance. Methods: We assembled a convenience sample of 726 patients with grade group 2-4 prostate cancer on systematic biopsy who underwent radical prostatectomy between 2014 and 2023. Digitized biopsy slides were analyzed using a machine-learning algorithm (PAIGE-AI) to quantify GP4 using multiple measurement approaches, particularly with respect to how gaps between cancer foci (interfocal stroma) were handled. GP4 extent was quantified using linear measurements or a pixel-based area metric. Discrimination of each GP4 quantification approach, along with Grade Group (GG), was assessed for adverse radical prostatectomy pathology and biochemical recurrence. Results: We identified 15 different quantification approaches and observed differences between their discrimination. The highest discrimination was in the pixel-counting method (AUC 0.648). GP4 quantification outperformed GG for predicting adverse pathology (AUC 0.627 vs 0.608). Amount of GP3 was non-predictive once GP4 was known. These findings were consistent for BCR. Conclusions: We were able to measure slides using 15 distinct measurement approaches and replicated prior findings using ML to quantify GP4. Our findings support the use of ML as a research tool to compare different GP4 quantification approaches. We intend to use our method on larger cohorts to determine with which measurement approach best predicts oncologic outcome.

High-grade serous ovarian carcinoma (HGSC) is an aggressive malignancy for which bulk transcriptomic subtypes are used to stratify tumors, interpret biology, and guide biomarker development. The four TCGA-derived subtypes, mesenchymal (C1.MES), immunoreactive (C2.IMM), proliferative (C5.PRO), and differentiated (C4.DIF), are consistently observed across cohorts. However, despite their prominence, these subtypes have not translated into therapeutic utility, and their biological basis remains unresolved. Here, we show that HGSC transcriptomic subtypes are largely determined by tumor cellular composition rather than intrinsic malignant transcriptional programs. By integrating controlled single-cell-derived pseudobulk simulations with deconvolution-based analysis of 1,834 primary HGSC tumors across RNA-seq and microarray cohorts, we demonstrate that subtype probabilities align along a composition-driven axis of stromal and immune variation. Cellular composition alone predicted subtype labels with high accuracy (ROC-AUC = 0.81-0.95) and explained a substantial fraction of subtype-associated transcriptomic variation, with the mesenchymal (C1.MES) subtype representing the most robust and reproducible example of composition-driven signal. Although a secondary, composition-independent expression signal is detectable, it does not define the dominant structure of subtype classification. These findings redefine HGSC transcriptomic subtypes as features of the tumor ecosystem rather than discrete malignant states. This reinterpretation has immediate implications for studies that use subtype labels to infer tumor-intrinsic biology and provides a generalizable framework for separating composition-driven and intrinsic signals in bulk tumor data. Significance StatementHGSC transcriptomic subtypes lack consistent clinical utility and remain biologically ambiguous. We show subtype assignments are largely driven by tumor cellular composition, and less so by distinct intrinsic tumor states.

AO_SCPLOWBSTRACTC_SCPLOWThe classification of pediatric brain tumors is investigated using deep learning on hematoxylin and eosin (H&E) and antigen Ki-67 (Ki-67) whole slide images (WSIs) from the Childrens Brain Tumor Network (CBTN) dataset. A total of 1,662 unregistered WSIs (1,047 H&E and 615 Ki-67 images) were analyzed, including low-grade glioma/astrocytoma (grades 1, 2) (LGG), high-grade glioma/astrocytoma (grades 3, 4) (HGG), medulloblastoma (MB), ependymoma (EP) and ganglioglioma. The The aim of this study was to effectively classify pediatric brain tumors using H&E and Ki-67 WSIs individually, and to investigate whether early, intermediate, and late fusion could improve the predictive performance. From each WSI, 224x 224 pixel patches were extracted, and the instance (patch)-level features were obtained using the histology foundation model CONCHv1_5. The instances were aggregated using clustering-constrained attention multiple instance learning (CLAM) for patient-level classification. Model interpretability and explainability was assessed through attention heatmaps, cell density and Ki-67 labelling index (LI) maps. In the binary grade classification between LGG and HGG, the intermediate concatenation fusion achieved the best performance with a balanced accuracy of 0.88 {+/-} 0.05, (p < 0.005) compared to the single-stain models (H&E: 0.84 {+/-} 0.05, Ki-67: 0.86 {+/-} 0.05). For the 5-class tumor type classification, the one-hidden layer late fusion learning model achieved the highest balanced accuracy of 0.83 {+/-} 0.04 (p < 0.005), outperforming the single-stain models (H&E: 0.77 {+/-} 0.05, Ki-67: 0.74 {+/-} 0.05). Overall, most of the fusion approaches outperformed the single-stain models in both classification tasks (p < 0.005). The Ki-67 attention maps demonstrated moderate to strong Spearman correlation ({rho} = 0.576 - 0.823) with the cell density and Ki-67 LI maps, suggesting that these features are associated with the models predictions, although additional features may contribute. The results show that H&E and Ki-67 images provide complementary information, and most of the multi-stain fusion approaches using deep learning improve pediatric brain tumor diagnosis.

Mucinous Ovarian Carcinoma (MOC) is a rare ovarian cancer histological subtype with distinct pathology, genomics and clinical outcomes compared to other epithelial ovarian cancers. Accordingly, there is little evidence to guide clinical care, particularly in the use of systemic therapies, and the field has lacked informative and diverse pre-clinical models. We developed MOC-specific methods for generating tumour organoids with a success rate of 70% for long-term cultured lines (n=19). Organoid lines were developed from localised, advanced and recurrent tumours, including from biopsy tissue, and represent diverse genomic features not previously captured by existing cell lines. The organoid lines were highly similar to the tumours of origin for genomic and immunohistochemical markers. Screening using a panel of 11 chemotherapy agents highlighted resistance to standard-of-care agents such as carboplatin. Gastrointestinal cancer chemotherapy agents and their combination regimens lacked activity. Paclitaxel was often highly potent at low doses but failed to kill all cells. However, less frequently used drugs such as gemcitabine, topotecan and doxorubicin inhibited many of the lines more effectively than paclitaxel. Available, but non-standard of care, chemotherapy agents should be considered for clinical management of MOC. This is the largest (by [~]10 fold) cohort of fully characterised patient-derived MOC organoid lines described and the first with extensive drug screening data affording an opportunity for drug discovery and screening for personalised treatment.

Cardiac transthyretin amyloidosis (ATTR-CA) is caused by myocardial deposition of misfolded transthyretin, leading to progressive heart failure. Disease pathology, however, extends beyond passive amyloid deposition and also involves active processes such as extracellular matrix (ECM) remodeling and immune activation. Mass spectrometry (MS) is the gold standard for amyloid typing in diagnostics. Here, we applied quantitative MS-driven proteomics on formalin-fixed paraffin-embedded whole cardiac tissue sections from six ATTR-CA cases, ten unaffected controls and four AL-CA controls to investigate protein expression changes. In addition to transthyretin, over 500 proteins were upregulated in ATTR-CA biopsies, including complement and coagulation factors as well as extracellular matrix (ECM) remodeling proteins. Among these, members of the A Disintegrin and Metalloproteinase with Thrombospondin Motifs (ADAMTS) family, metalloproteinases (MMPs), and Tissue Inhibitor of Metalloproteinases (TIMP3) showed significant upregulation. These proteins are key regulators of ECM turnover and structural integrity. Immunohistochemistry confirmed ADAMTS4 enrichment in amyloid deposits, while TIMP3 showed strong expression in cardiomyocytes and weaker staining within amyloid deposits. Together, these findings indicate that ECM remodeling, alongside complement and coagulation activation, represents a reproducible feature of cardiac ATTR amyloidosis. Whole-tissue proteomics provides biological insights that extend beyond amyloid typing, with potential implications for biomarker discovery and therapeutic targeting in ATTR-CA.

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.

BackgroundExcessive accumulation of fibrillar collagen causes pathological scarring and fibrosis. A promising anti-fibrotic strategy targets the extracellular assembly of collagen fibrils rather than intracellular synthesis pathways. We previously developed a chimeric monoclonal antibody targeting the C-terminal telopeptide of the 2(I) chain of human collagen I that effectively disrupts fibrillogenesis. This study details the engineering of a humanized antibody variant optimized for therapeutic application, augmented with a collagen-binding peptide (CBP) to enhance targeted retention in fibrotic tissues. MethodsA humanized ACA was engineered by in silico homology modeling, complementarity-determining region grafting, and sequence optimization to eliminate chemical liabilities. Variants were expressed in mammalian cells and evaluated for binding kinetics and specificity. To improve spatial localization, the CBP was fused to the antibody. The lead variant was assessed for in vitro cytotoxicity, matrix retention, and in vivo efficacy using a rabbit model of post-traumatic knee arthrofibrosis. ResultsThe humanized ACA variants maintained high specificity and affinity for the 2Ct target domain. Fusing the CBP to the C-terminus of the light chain (C-cbpACA) successfully enhanced matrix retention without compromising target engagement or causing cellular toxicity. In the rabbit arthrofibrosis model, intra-articular C-cbpACA delivery significantly reduced flexion contracture and decreased total collagen deposition in the joint capsule compared to untreated controls. ConclusionWe successfully engineered a clinically viable, humanized, and matrix-targeted anti-fibrotic antibody that specifically inhibited extracellular collagen assembly and exhibited enhanced localization within fibrotic tissues. This construct represents a promising therapeutic strategy for mitigating pathological scarring and improving post-traumatic functional outcomes.

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.

SignificanceQuantifying lipid droplet (LD) remodeling in 3D hepatic organoids is often limited to endpoint staining or phototoxic live fluorescence imaging, thereby obscuring droplet-level kinetics. AimWe aimed to develop a label-free method to track LD dynamics in living hepatic organoids under different fatty-acid loads. ApproachTime-lapse 3D refractive-index tomograms were acquired using holotomography and analyzed with a depth-adaptive, multi-threshold segmentation pipeline to quantify LD number, volume, sphericity, and refractive-index-derived concentration and dry mass at single-droplet resolution. ResultsOleic acid and linoleic acid induced LD accumulation while preserving organoid integrity, whereas palmitic acid triggered rapid structural collapse. Despite increases in total LD burden under both oleic acid and linoleic acid, droplet-level dynamics diverged: oleic acid produced volume-dominated accumulation via enlargement of fewer LDs and increased size heterogeneity, whereas linoleic acid produced number-dominated accumulation via sustained increases in LD number, yielding a more uniform population of small droplets. ConclusionsLabel-free holotomography with depth-adaptive analysis enables non-invasive, longitudinal, and multi-scale quantification of LD dynamics in intact organoids and reveals fatty-acid- dependent temporal modes of lipid storage. Statement of DiscoveryWe developed a label-free, longitudinal 3D holotomography framework with depth-adaptive lipid droplet segmentation that quantifies single-droplet dynamics in living mouse hepatic organoids. Using this platform, we found that oleic acid and linoleic acid induce LD accumulation via distinct strategies--oleic acid via droplet enlargement and linoleic acid via sustained increases in droplet number--while palmitic acid rapidly compromises organoid integrity.

Cetacean pathology is a cornerstone for population and marine ecosystem health monitoring, allowing clear differentiation among natural and anthropogenic threats. Previous studies in the Canary Islands reported natural causes of death in 59.4% (1999-2005) and 81% (2006-2012) of stranded cetaceans, versus anthropogenic causes in 33.3% and 19%, respectively. This study aimed to determine the causes of death (CD), pathologic findings, and epidemiological patterns of 316 cetaceans stranded in the Canary Islands between 2013 and 2018. The CDs were classified in pathologic entities (PEs) emphasizing natural versus anthropic origins. Of 316 animals, 224 (70.9%) from 18 species were suitable for pathological investigations. Among natural PEE, natural pathology associated with good nutritional status (NP-GNS) and natural pathology associated with significant loss of nutritional status (NP-LNS) represented 43/224 (19.2%) and 36/224 (16%) cases, respectively. Natural pathology with undetermined nutritional status (NP-UNS) occurred in 19/224 (8.5%) animals. Intra- and interspecific traumatic interactions (ITI) represented 30/224 (13.4%) cases, followed by neonatal/perinatal pathology (NPN) 19/224 (8.5%) and live-stranding stress and/or capture myopathy (LS-CM) 18/224 (8%). Infectious and parasitic diseases predominated in natural PEs. Anthropogenic PEs included interaction with fishing activities (IFA) in 17/224 (7.6%) cases, vessel collisions (VC) in 9/22 (4%) cases, and foreign body-associated pathology (FBAP) in 3/224 (1.3%) animals. Overall, anthropogenic causes accounted for 12.9% of deaths, natural causes for 73.6%, and the CD could not be established in 30/194 (13.4%) cases. This study reaffirms the trends concerning recognized PEs (NP-GNS, NP-LNS, NP-UNS, ITI, NPN, LS-CM, IFA, VC, and FBAP), expands the body of knowledge on cetacean pathology in the Canary Islands, and reports novel findings including mixed infections, clostridiosis in uncommon species, uremic syndrome secondary to urethral nematodiasis, gas embolism in unusual species, epibiont stomatitis, congenital musculo-skeletal malformations, or neoplastic processes. These findings advance understanding of cetacean mortality patterns and support conservation and management strategies.