Laboratory Investigation

Melanoma incidence continues to rise globally, with formalin-fixed paraffin-embedded (FFPE) tissue archives representing an invaluable resource for large-scale retrospective proteomic studies. However, inconsistent deparaffinization remains a critical pre-analytical bottleneck limiting protein yield, reproducibility, and downstream data quality. In this study, we developed and validated a fully automated FFPE deparaffinization workflow using the Fluent(R) 780 liquid handling workstation (Tecan (C)) and evaluated its performance against a conventional manual protocol in a cohort of 54 patients with primary cutaneous melanoma, predominantly at early AJCC 8th edition stage I-II. The automated workflow achieved superior protein identification (6,146 {+/-} 860 vs. 4,941 {+/-} 1,091 proteins; p < 0.0001) with lower technical variability, while maintaining highly comparable global proteomic profiles as confirmed by principal component analysis and hierarchical clustering. A total of 8,305 proteins (96.1%) were identified by both methods, supporting the reproducibility and equivalence of the automated approach. Patients were stratified by the presence (N=21) or absence (N=33) of histological regression in the primary tumor. Proteomic comparison revealed 97 upregulated and 226 downregulated proteins in regressing melanomas, with pathway enrichment analysis demonstrating elevated mitochondrial and translational activity alongside reduced innate immune and complement pathway activation in the regression group. No statistically significant differences in overall, disease-free, or progression-free survival were observed between groups, consistent with the early-stage composition of the cohort. Digital pathology validated tissue morphology preservation across processing conditions. These findings support the integration of automated FFPE processing with proteomic and digital pathology workflows as a scalable platform for precision melanoma research. TOC Figure O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=133 SRC="FIGDIR/small/744404v1_ufig1.gif" ALT="Figure 1"> View larger version (49K): org.highwire.dtl.DTLVardef@1d51629org.highwire.dtl.DTLVardef@a1f126org.highwire.dtl.DTLVardef@1df1b0aorg.highwire.dtl.DTLVardef@686f1c_HPS_FORMAT_FIGEXP M_FIG C_FIG

BackgroundTraditional immunohistochemistry (IHC) with chromogen detection has limited multiplex capacity, detecting at most 4 protein markers per tissue section simultaneously, thereby restricting comprehensive spatial analysis of valuable human biopsies. We developed and validated a robust serial IHC (sIHC) staining method to detect multiple antigens on a single kidney biopsy slide, maximizing data yield for diagnosing and studying complex kidney diseases. MethodsFormalin-fixed, paraffin-embedded kidney biopsy sections were subjected to repeated IHC/imaging cycles with antibody removal using an optimized sodium dodecyl sulfate-glycerol buffer stripping protocol. Images were then co-registered, and analysis was performed using a variety of methodologies, including color deconvolution, cell segmentation, and spatial clustering. ResultsThis optimized sIHC method successfully detected up to 20 antigens on a single slide. Combining image analysis and artificial intelligence software, for example with HALO (Indica Labs), the assay assembles high-dimensional images and enables quantitative histology and single-cell spatial analysis. Using this advanced method, we were able to identify rare cell populations, such as double-negative T cells, that are challenging to detect conventionally. ConclusionWe have developed a validated, high-capacity sIHC protocol that uses standard IHC procedures with commercially available, clinically validated off-the-shelf antibodies. This method is a valuable, cost-effective tool for obtaining extensive, high-dimensional single-cell-resolved spatial data from limited pathology samples, such as a human kidney biopsy.

Raman spectroscopy (RS) has been shown to differentiate various disease states in tissue samples, yet the impact of sample substrate on spectral quality and downstream machine-learning (ML) performance is under-explored. We collected 960 spectra from 36 formalin-fixed, paraffin-embedded colonic specimens mounted in parallel on calcium fluoride (CaF2) and mirrored stainless-steel (SS) slides. Raw SS spectra were more intense than CaF2, and routine dewaxing left prominent paraffin peaks on CaF2 that were largely absent on SS. Extending CaF2 xylene immersion to 48 h reduced key paraffin peaks by 11 to 23%, aligning its biochemical profile with SS. Nested cross-validated classifiers were built to distinguish true disease (T1) from epithelial misplacement (EM). The highest accuracies were observed on SS (0.65 - 0.66), while CaF2 improved from 0.41 - 0.57 to 0.65 - 0.69 after extended dewaxing, chiefly via gains in recall. Although optimal dewaxing renders CaF2 diagnostically non-inferior, SS still offers superior accuracy, faster preparation, at approximately 4% of the cost, supporting its integration into scalable, AI-enabled RS pathology workflows.

Genomic, transcriptomic and epigenomic characterization has accelerated the discovery of clinically-relevant alterations in cancer, predominantly using fresh frozen (FF) specimens. However, clinical molecular pathology laboratories prefer formalin-fixed paraffin-embedded (FFPE) methods, known to introduce artifacts at the nucleic acid level, over fresh frozen methods. Extending the multi-platform analysis to FFPE specimens for comprehensive clinical molecular diagnosis requires a thorough understanding of the consequence of formalin-fixation. We present a detailed multi-platform characterization of FFPE preservation using paired FF specimens as the 'gold standard'. DNA and RNA were obtained from 38 patients across 6 cancer types using a FFPE optimized co-isolation. The impact of FFPE on exome sequencing was dependent on filtering, where a minimum coverage or supporting read filter can mitigate FFPE-specific false positives. Copy number alterations, MSI assessment, mutational signatures, and DNA methylation were comparable between FFPE and FF. FFPE biases in RNA expression can be overcome when using biology-relevant genes and we describe a novel consequence of FFPE on miRNA species diversity. Collectively, this data provides a broad view of FFPE artifact and offers best practices for overcome these biases.

Context: Cytological specimens allow minimally invasive, repeated sampling for diagnosis and molecular testing. However, cytology biobanking protocols have primarily been optimized for morphology, and the effects of preanalytical handling and storage on intracellular proteins remain unclear. Objective: To assess how fixation, staining, storage temperature, and duration affect intracellular protein preservation in cytological specimens and to identify conditions suitable for in situ proximity ligation assay (isPLA). Design: Human cancer cell lines were processed under different preanalytical conditions. Intracellular proteins and protein protein interactions were assessed by isPLA, fluorescence microscopy, and quantitative image analysis. Slides were stored at room temperature or minus 20 Celsius, with or without MayGrunwaldGiemsa (MGG) staining. Results: Storage temperature had the greatest effect on protein signal. Unstained slides stored at minus 20 Celsius retained strong signals for up to 24 months, whereas room temperature storage reduced detectable protein interactions by approximately 95%. MGG staining further reduced signal intensity during prolonged storage. Nevertheless, stained slides remained suitable for isPLA when staining, microscopic evaluation, destaining, and antibody labeling were completed on the same day. Prolonged room temperature storage of stained slides caused loss of antigenicity, increased autofluorescence, and false negative findings. Conclusions: Low temperature storage and standardized preanalytical handling preserve intracellular protein signals in cytological specimens. These conditions support reliable single cell protein profiling of archived samples and their use in biomarker research and translational pathology.

SignificanceRare-cell identification in fluorescence microscopy remains challenging because targets are sparse and background varies between specimens. Combining specimen-specific fluorescence enrichment with image classification may enable efficient and more specific automated detection of rare cells. AimWe developed a two-stage framework to identify and quantify candidate rare circulating hybrid neoplastic cells (CHCs, ECAD+/CD45+) in peripheral blood mononuclear cell (PBMC) preparations from tumor-bearing and tumor-naive mice. ApproachPBMCs from 28 mice were imaged by multichannel fluorescence microscopy. Matched unstained samples established animal-specific ECAD and CD45 background distributions for candidate cell enrichment. Blinded multi-annotator consensus labels were used to train a convolutional neural network (CNN) from DAPI, ECAD, and CD45 image crops. Generalization was evaluated by leave-one-animal-out validation across 10 random initializations. Final classification used a 10-model ensemble, and rare-cell burden was compared between groups using negative-binomial regression with total segmented-cell count as an exposure. ResultsOf the 1,065,512 segmented cells, enrichment retained 10,176 candidates (0.96%), reducing the search space by >99%. Four of five evaluable tumor-bearing animals showed reproducible held-out discrimination, with median quantified area under the receiver operator characteristic curve (AUROCs) of 0.918-0.951; one animal was a reproducible outlier (median AUROC, 0.338). Ensemble deployment identified 157.94 positive-consensus cells per 50,000 segmented cells in tumor-bearing animals versus 49.55 in controls. The estimated rare-cell rate was 3.15-fold higher in tumor-bearing animals (95% CI, 0.91-10.99; two-sided p=0.071; prespecified one-sided p=0.036). ConclusionsSpecimen-specific fluorescence enrichment combined with supervised image classification reduced the cellular search space and enabled automated quantification of a rare CHC (ECAD+/CD45+) phenotypes. Cross-animal validation also identified specimen-specific generalization failure, highlighting the importance of biological-specimen-level validation.

Three-dimensional (3D) spheroid models have become essential in cancer biology, drug screening, and tissue engineering. However, their small size, fragile structure, and tendency to disintegrate during routine histoprocessing present persistent technical challenges. Conventional paraffin embedding often results in tissue fragmentation, loss of spatial orientation, and poor section quality, whereas cryosectioning often compromises cellular morphology. Here, we present a robust, cost-effective protocol for preserving and sectioning fragile 3D spheroids, resulting in high-quality histological sections with intact architecture and excellent cellular detail. The method involves optimized handling and embedding procedures that stabilize spheroids during standard formalin fixation, paraffin infiltration, and microtomy, eliminating mechanical distortion and preserving spherical integrity for consistent sectioning. We demonstrate successful application across different cell line spheroids, with subsequent compatibility with hematoxylin and eosin (H&E) staining protocols. Compared to conventional methods, our approach significantly reduces sample loss, improves inter-section reproducibility, and preserves fine structural features such as necrotic cores, proliferative zones, and extracellular matrix components. This protocol provides a reliable, accessible solution for routine histological analysis of fragile 3D spheroids, facilitating more accurate morphological and molecular assessment in translational research settings. Key featuresO_LIMaintains spheroid integrity: Prevents mechanical distortion, fragmentation, and loss of spatial orientation during processing. C_LIO_LISignificantly reduces sample loss: Decreases failure rate compared to traditional methods, conserving valuable samples. C_LIO_LIBroad spheroid compatibility: Works effectively with primary tumor-derived, stem cell-derived, and co-culture spheroid models. C_LIO_LIEnables high-quality sectioning and staining: Delivers consistent, reproducible sections that are fully compatible with H&E, IHC, and IF. C_LI Graphical overview O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=140 SRC="FIGDIR/small/743094v1_ufig1.gif" ALT="Figure 1"> View larger version (44K): org.highwire.dtl.DTLVardef@1670c4org.highwire.dtl.DTLVardef@145810aorg.highwire.dtl.DTLVardef@1accb1org.highwire.dtl.DTLVardef@17481c0_HPS_FORMAT_FIGEXP M_FIG C_FIG

Background: Prostate-specific membrane antigen (PSMA) positron emission tomography (PET) is increasingly used to characterize newly diagnosed prostate cancer on the assumption that uptake highlights aggressive disease. However, maximum standardized uptake value (SUVmax) varies widely across localized tumors. Recent genomic assessments have suggested SUVmax reflects tumor biology even when accounting for other tumor indices. Here, we examined highly annotated pathologic, imaging and clinical correlates of intraprostatic SUVmax at the level of individual tumor foci in a prospective multimodal cohort. Methods: Exploratory lesion-level analysis of the radical prostatectomy arm of a single-center prospective diagnostic-accuracy trial (NCT04461509). Patients underwent simultaneous 18F-DCFPyL PSMA PET, multiparametric magnetic resonance imaging (MRI) and high-resolution MRI before prostatectomy, with central blinded pathology and imaging review. Grade Group (GG) [&ge;]2 foci measuring [&ge;]10 mm were eligible. The primary outcome was focus-level SUVmax. Correlates spanning histology, tumor microenvironment, MRI and clinical indices were examined by Spearman correlation and rank-based group comparison, followed by separate linear regressions of log(1+SUVmax) each adjusted for GG and tumor size. A digital-pathology sensitivity analysis quantified immune, stromal and tumor pixel-area fractions on hematoxylin and eosin (H&E) sections from a subset of foci using a trained QuPath classifier. Results: Twenty-seven patients contributed 34 tumor foci, of which 21 (61.8%) were GG2. Median SUVmax was 5.7 (interquartile range [IQR] 3.9 - 12.1; range 0.0 - 57.9). SUVmax rose across the cribriform/intraductal carcinoma (Crib/IDC) gradient (median 3.9, 4.8 and 10.2 for absent, focal and extensive; p = 0.010), correlated with % Gleason pattern 4 ({rho} = +0.47, p = 0.005) and correlated inversely with stromal content ({rho} = - 0.40, p = 0.020). Grade Group (p = 0.115), Prostate Imaging Reporting and Data System (PI-RADS) 4 versus 5 (p = 0.705), standard apparent diffusion coefficient (ADC) ({rho} = - 0.27, p = 0.130), baseline prostate-specific antigen (PSA) ({rho} = +0.04, p = 0.816) and PSA density ({rho} = - 0.02, p = 0.918) were not associated with SUVmax. In separate models of log(1+SUVmax) adjusted for Grade Group and tumor size, % Gleason pattern 4 remained associated with uptake (standardized {beta} = +0.76, 95% CI +0.11 to +1.41), Crib/IDC ({beta} = +0.36, - 0.03 to +0.74), and stromal content ({beta} = - 0.30, - 0.62 to +0.02). On 18 slides re-quantified by pixel classifier, no pixel-based compartment fraction correlated with SUVmax (immune {rho} = - 0.06, stroma {rho} = +0.02, tumor {rho} = - 0.04; all p > 0.5). Conclusions: In this prospective lesion-level cohort, intraprostatic PSMA SUVmax was associated with increased Gleason pattern 4 and cribriform/intraductal burden but not with other common risk indices. SUVmax therefore appears to carry information additive to conventional risk factors. Further investigations examining the tumor biology that reflects SUVmax are warranted.

Background: Bone mechanical competence depends not only on bone mass but also on the spatial organization of mineral and organic matrix properties. Whereas quantitative backscattered electron imaging (qBEI) characterizes mineral heterogeneity using Bone Mineral Density Distributions (BMDDs), no equivalent framework currently exists for describing the biochemical heterogeneity of the bone matrix by Raman microspectroscopy. Methods: We developed a quantitative Raman analysis (qRA) workflow to generate Bone Material Properties Distributions (BMPDs). Raman spectra were acquired along full-thickness line scans crossing successive bone structural units in human trabecular and cortical bone. Pixel-wise Raman-derived biochemical parameters describing collagen organization, advanced glycation end-products, glycosaminoglycans, mineral-to-matrix ratios, carbonate substitution, and mineral crystallinity were converted into normalized frequency distributions and modeled using gaussian functions. BMPDs were generated from undecalcified pMMA-embedded iliac biopsies (n=38) and routinely processed decalcified paraffin-embedded osteomedullary biopsies (n=21). The analytical framework was validated against conventional two-dimensional Raman mapping, and reproducibility, interindividual variability, and demographic influences were evaluated. Results: BMPDs were consistently well described by gaussian functions (typically R2 > 0.95), allowing extraction of three descriptors for each biochemical parameters: Mean, Peak, and Width. One-dimensional line scans showed excellent agreement with conventional two-dimensional Raman mapping while reducing acquisition time approximately 100-fold. Technical variability remained substantially lower than biological variability for all parameters, and a single trabecular line scan provided reliable estimates of BMPD descriptors for nearly all Raman-derived indices. Cortical bone exhibited narrower BMPDs than trabecular bone, indicating lower biochemical heterogeneity. Comparable BMPDs were obtained from undecalcified and routinely decalcified specimens, demonstrating robustness to tissue processing. No significant associations with age or sex were observed after correction for multiple testing. Reference BMPDs further enabled visualization and standardized quantification of deviations in representative pathological bone biopsies. Conclusions: Quantitative Raman analysis introduces BMPDs as a novel framework for assessing the spatial heterogeneity of bone extracellular matrix composition. By extending Raman microspectroscopy beyond conventional mean measurements, BMPDs provide robust, reproducible descriptors of bone material organization and establish a methodological foundation for investigating alterations of bone quality in metabolic bone diseases.

Background Histological diagnosis of early-stage mycosis fungoides (MF) is hindered by profound overlap with benign inflammatory dermatoses (BIDs), leading to diagnostic delays and extensive ancillary testing. We developed MIMIC (Multiple Instance-learning for Identification of Mycosis fungoides In Cutaneous biopsies), a weakly supervised deep learning model designed as a triage tool at initial H&E whole slide image (WSI) review to distinguish classic patch and plaque stage MF from BIDs. We externally validated the model and evaluated its clinical utility. Methods In this retrospective multicentre study, we trained a base model using weakly supervised attention based multiple instance learning on 3,339 WSIs from two Dutch centres. Crucially, all MF training labels were derived from a deeply phenotyped national cohort featuring strict multidisciplinary expert panel consensus diagnoses (the clinical gold standard). Transportability was evaluated on 371 WSIs from four independent European centres. A blinded reader study on 171 WSIs compared morphology only performance of MIMIC with 11 (dermato-)pathologists. We then retrained an updated model on all retrospective multicentre data and assessed clinical utility in a strictly held out, consecutive Utrecht cohort (2022-2023; 486 accessions, 863 WSIs). Primary analysis focused on classic MF versus BIDs (453 accessions). Decision curve analysis, using Platt scaled probabilities to correct for spectrum bias, evaluated net benefit at a prespecified, safety oriented threshold of 0.04. Findings The base model showed good multicentre transportability (mean centre specific AUROC 0.91; pooled AUROC 0.84). In the reader study, MIMIC achieved an AUROC of 0.87, exceeding the mean pathologist AUROC (0.79) and the best individual reader (0.83). In the consecutive MF versus BID cohort, the updated model achieved an AUROC of 0.87 (95% CI 0.81-0.92). At the 0.04 threshold, sensitivity was 97.8% (44/45 MF cases) and specificity 50.2%, reducing unnecessary ancillary workups by 39.9 per 100 screening cases versus a test all strategy. Interpretation By identifying nearly half of BIDs as low risk while preserving near complete sensitivity for classic early stage MF in a European digital pathology workflow, this unimodal H&E approach offers a scalable digital solution to reduce defensive ancillary testing and accelerate the diagnostic journey for patients with MF. Further validation is needed in non European centres and in populations with darker skin phototypes.

Blood-based metabolomic profiling has been widely investigated for breast cancer (BC) detection; however, clinical implementation remains limited due to variability in sample handling, analytical reproducibility, and overfitting during statistical analysis. We established a plasma GC/MS metabolomics workflow for discriminating BC from healthy controls (HC) using conventional machine-learning algorithms. Plasma samples (n = 360; BC = 180, HC = 180) were collected prospectively under standardized preanalytical conditions before surgery and the initiation of systematic anticancer therapy and analyzed using a quantitative GC/MS platform with automated derivatization. Feature selection and model development were conducted using three machine-learning (ML) algorithms (Lasso logistic regression (LR), random forest classifier (RFC), and support vector machine (SVM)). A total of 45 metabolite candidate biomarkers were identified, and the optimal number of metabolite features for each algorithm was estimated by a recursive feature elimination (RFE)-based strategy. The best-performing models achieved area under the ROC curve values (AUC) of 0.910 (LR), 0.893 (RFC), and 0.843 (SVM). We selected prioritizing candidate biomarkers consistently expressed across the multi-algorithm pipeline. A bagging ensemble model improved stability (AUC = 0.911) and reduced false-positive predictions in the independent HC dataset. In addition, model stability with respect to false-positive predictions was assessed using an independent HC cohort (n = 15) that was collected at a separate institution. These results indicate that a plasma metabolomics workflow combined with conventional multi-algorithm ML, algorithm-specific feature selection, and independent assessment provides stable discrimination between BC and HC in a moderately sized cohort.

Current approaches for imaging fibrotic remodeling have sensitivity, specificity and cost drawbacks that limit both preclinical research and clinical diagnosis. Here, we show that fast green FCF, a small molecule that binds to fibrillar collagen, enables highly sensitive and specific imaging of fibrosis in lung samples from mice and humans using fluorescence microscopy. We report strategies for using fast green FCF staining to assess fibrotic remodeling using precision-cut lung slice and whole-biopsy preparations. Our findings demonstrate that fluorescence imaging of fast green FCF-stained collagen will be useful for fibrosis research and may help to improve detection of fibrosis in clinical pathology.

Mass spectrometry imaging (MSI) records rich molecular spectra at each pixel, but pathology-oriented interpretation requires visualizations analogous to complementary histopathological stains. We present an expert-aligned framework for constructing multi-view MSI panels. Soft Landmark Contrast Edges (SoLaCE) extracts molecular boundaries directly from high-dimensional spectra. Because standard visualization metrics correlated poorly with rankings from a single expert pathologist, we combine luminance contrast and chromatic diversity with SpecEdge-Dice, a boundary-aware measure of agreement between visualization edges and SoLaCE boundaries. Parametric MiCS+LMC (pMiCS) uses a neural network trained on subsampled data to distill multiple MSI segmentations into a reusable spectral-to-RGB mapping, enabling rapid full-image inference, out-of-sample projection, and more consistent color semantics across aligned images. A concept-based interpretation procedure explains pMiCS outputs through sparse mixtures of spectral concepts. In a blinded benchmark, pMiCS ranked highest among the compared methods. We integrate these components into Virtual Pathology Panels, which use hyperparameter optimization to select high-performing or spatially complementary views. This framework supports future workflows that combine morphology-oriented tissue assessment and molecular analysis within a single MSI acquisition. TeaserVirtual pathology panels transform MSI spectra into complementary views for scalable, interpretable tissue analysis.

Accurate, reproducible interpretation of kidney allograft biopsies is critical for diagnosis of graft injury to guide prognosis and management. The international Banff classification is a consensus diagnostic system based on semiquantitative histological lesion scoring on either extent or severity of kidney transplant biopsies. However, pathologist scoring is limited by substantial interobserver variability, constrained scalability, and the inherent nature of the scoring system itself. Here we present BanffNET, a weakly supervised, probabilistic deep learning framework that combines self-supervised feature extraction with a novel Bayesian multiple-instance learning framework to predict (continuously) the full spectrum of Banff lesion scores directly from whole-slide images (WSIs). Using lesion-specific aggregation functions tailored to localized (modeling lesion severity) and diffuse pathologies (modeling lesion extent), BanffNET generates interpretable, patch-level probability maps and calibrated slide-level scores. BanffNET's performance was assessed relative to consensus, biological correlates of rejection and clinical outcome, demonstrating superior consistency, transportability and generalization. Trained on 7,249 WSIs from three cohorts, BanffNET demonstrates consistent performance on 11,028 WSIs across five external test sets, performing on par or exceeding expert consensus across lesions. BanffNET scores align more closely than pathologist Banff scores with molecular profiles of rejection, offering a transparent, biologically grounded framework for computational pathology with relevance beyond transplantation.

Active surveillance (AS) delays or avoids unnecessary treatment-related side effects for early localised non-aggressive prostate cancer (PCa) through monitoring with regular prostate-specific antigen (PSA) testing, digital rectal examination (DRE), magnetic resonance imaging (MRI) and prostate biopsy. Whilst baseline MRI has prognostic value and is increasingly used for disease monitoring, molecular biomarkers could be an adjunct to refine risk stratification and disease prognostication [2]. Unlike tissue-based genomic classifiers such as Decipher (Veracyte) and Prolaris CCP (Myriad), blood-based molecular testing for example cell-free RNA (cfRNA) offers minimally invasive real-time monitoring. Moreover, cfRNA may yield genetic information shed from different cell populations within the tumour microenvironment, as well as the host circulation, expanding the opportunities for biomarker discovery. Here, we test the feasibility of whole transcriptome profiling of cfRNAs from the blood of PCa patients under AS, exploring differences between patients with stable disease (SD), and progression to radical prostatectomy (RP).

ObjectivesThe preferential phosphodiesterase 4B (PDE4B) inhibitor nerandomilast was recently approved for treatment of idiopathic pulmonary fibrosis (IPF) and progressive pulmonary fibrosis. Its proposed immunomodulatory, anti-fibrotic, and endothelial-stabilising actions target all three cardinal features of SSc, yet PDE4B expression has not been systematically characterised in SSc tissue. We aimed to define PDE4B expression across fibrotic organs and cellular compartments in SSc. MethodsPDE4B expression was profiled in SSc lung, peripheral blood mononuclear cells (PBMCs) and skin on the transcript level using single-cell RNA sequencing data and on the protein level using immunohistochemistry, immunofluorescence and multiplexed immunofluorescent stainings. ResultsPDE4B was consistently dysregulated in immune cells across SSc tissue and PBMCs, with compartment-specific direction and distribution. In SSc-ILD lung, expression was increased in CD8 and CD4 memory T-cells. In PBMCs, expression was increased in B cells, monocytes, and CD8 T-cells, and stratified patients into three endotypes (PDE4B//hi) not distinguishable by clinical variables. In skin, bulk RNA-seq showed a significant global increase, which localized to myeloid cells in scRNA-seq data. Approximately 90% of FAP activated fibroblasts co-expressed PDE4B at the protein level in SSc skin, identifying the activated fibroblast compartment as a candidate target for PDE4B inhibition. No PDE4B dysregulation was detected in vascular cell types. ConclusionsThis first cell-type-resolved characterisation of PDE4B in SSc demonstrates consistent immune-cell dysregulation across tissues and protein-level enrichment in activated fibroblasts. This provides a human-tissue rationale for the immunomodulatory and anti-fibrotic effects of PDE4B inhibition and supporting PDE4B as a disease-relevant therapeutic target in SSc. Key messagesO_ST_ABSWhat is already known on this topicC_ST_ABSO_LINerandomilast (BI 1015550), a PDE4B-preferential inhibitor, was approved for idiopathic pulmonary fibrosis and progressive pulmonary fibrosis. C_LIO_LIPre-clinical studies indicate that PDE4B inhibition may act on all cardinal features of SSc. C_LI What this study addsO_LIFirst cell-type-resolved characterization of PDE4B expression across SSc-affected lung, PBMCs, and skin. C_LIO_LIPBMC PDE4B expression is heterogeneous, stratifying patients into PDE4B// endotypes independent of standard clinical variables. C_LIO_LIscRNA-seq shows increased myeloid PDE4B expression in SSc skin, while [~]90% of FAP activated fibroblasts in SSc skin express PDE4B protein. C_LI How this study might affect research, practice or policyO_LIThe study strengthens the human-level evidence underpinning the target rationale for PDE4B inhibition in SSc. C_LI

Retinoblastoma (RB), the most common pediatric intraocular malignancy, is initiated by biallelic inactivation of the RB1 tumor suppressor gene. Although LINE-1 (L1) retrotransposon activation has been linked to genomic instability and tumor evolution in several adult cancers, its contribution to pediatric malignancies remains poorly understood. Here, we investigated L1 expression in retinoblastoma samples from an Indian pediatric cohort by integrating transcriptomic, locus-specific, and immunohistochemistry. Transcriptomic profiling revealed increased L1 expression in RB tumors compared with control retinal tissues, which was validated by ORF1p immunostaining in tumor sections. Locus-specific analysis identified a subset of transcriptionally active L1 loci, suggesting selective activation of young L1 elements. We also observed altered expression of genes associated with proliferation, chromatin regulation, and oncogenic signaling, including MYCN, MDM2, E2F3, SOX4 and CREBBP. Our findings identify previously underexplored L1 activation as a molecular feature associated with retinoblastoma and suggest that disruption of transposon silencing accompanies the RB transcriptional landscape. Validation in larger patient cohorts will be important; however, this study provides a foundation for exploring L1 activity as a potential molecular marker and future therapeutic vulnerability in retinoblastoma. HighlightsO_LITranscriptomic profiling reveals increased LINE-1 expression in retinoblastoma. C_LIO_LIORF1p protein is robustly expressed in retinoblastoma. C_LIO_LIA subset of LINE-1 loci exhibits transcriptional activation, suggesting locus-specific derepression. C_LI In BriefRetinoblastoma is initiated by RB1 loss, but the contribution of transposable elements to its molecular landscape remains poorly understood. Here, we show that retinoblastoma is associated with LINE-1 promoter hypomethylation, increased LINE-1 transcript expression, ORF1p accumulation, and preferential activation of selected LINE-1 loci. These changes occur together with altered expression of oncogenic signalling pathways and host factors involved in transposable element repression, identifying dysregulated LINE-1 control as a molecular feature of this paediatric cancer. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=115 SRC="FIGDIR/small/740688v1_ufig1.gif" ALT="Figure 1"> View larger version (24K): org.highwire.dtl.DTLVardef@10dcf8forg.highwire.dtl.DTLVardef@1b5d07forg.highwire.dtl.DTLVardef@18b5116org.highwire.dtl.DTLVardef@1788145_HPS_FORMAT_FIGEXP M_FIG C_FIG Retinoblastoma exhibits epigenetic activation of LINE-1 characterized by promoter hypomethylation, increased expression of young LINE-1 elements, and ORF1p accumulation. This is accompanied by coordinated transcriptomic remodeling and disruption of host mechanisms that normally restrain transposable elements, linking LINE-1 activation to the molecular landscape of retinoblastoma.

Muscle-invasive bladder cancer (MIBC) carries high risks of relapse and death despite contemporary care. In this study we aimed to evaluate whether the pretreatment immune microenvironment varied with HER2 status and if the differences influenced the response to neoadjuvant chemotherapy (NAC) and survival. We conducted a retrospective study of 62 treatment-naiive MIBC patients diagnosed on pretreatment transurethral resection of bladder tumor (TURBT) specimens collected between 2008 and 2024. All patients received NAC followed by radical cystectomy. HER2 immunohistochemistry (IHC) was scored 0/1+/2+/3+ using gastroesophageal criteria. PD-L1 was assessed by combined positive score (CPS). CD3, CD20, and CD68 cell densities were quantified by digital image analysis. Overall survival was measured from the date of TURBT. HER2 IHC scores were 2+/3+ in 28 of 62 tumors (45%) and 0/1+ in 34 (55%). All seven IHC 2+ tumors lacked ERBB2 amplification by fluorescence in situ hybridization (FISH). NAC response occurred in 11 of 34 tumors with HER2 IHC 0/1+ (32%) and 9 of 28 tumors with HER2 IHC 2+/3+ (32%) (P = 1.00). PD-L1 expression was not associated with HER2 status. Immune-cell analyses showed trends toward higher CD3+ T-cell density, CD20+ B-cell density, and TM ratio in NAC responders, but no significant differences by HER2 status or combined PD-L1/HER2 subgroup status. After Bonferroni correction for multiple comparisons, none of the immune-cell metrics remained statistically significant. Overall survival did not differ between the HER2 IHC groups (P = 0.71). These findings indicate that HER2 IHC 2+/3+ expression is present in a substantial subset of MIBC but is not associated with NAC response or overall survival in this cohort. Further study is warranted to define the clinical relevance of MIBC with HER2 IHC 2+/3+ and its potential role in HER2-directed therapeutic strategies.

Targeted gene panels (TGPs) dominate clinical sequencing, yet most copy-number (CN) signature studies rely on genome-wide assays. Whether signatures can be recovered from TGPs and retain clinical relevance remains unclear. We analyzed two real-world TGP cohorts comprising 1,726 patients across 62 tumor types, including 825 with clinical annotations, and made the underlying data publicly available. TGP-derived signatures recapitulated established biological associations, including links to homologous recombination deficiency and TP53 alterations, concordant with published genome-wide assay-derived CN signatures. Using our detailed clinical data, we found TGP-derived CN signatures associated with overall survival under standard therapies in ovarian, pancreatic, and colorectal cancer. In ovarian cancer, CN2 was associated with CCNE1 amplification and shorter survival under paclitaxel/carboplatin, with the survival association validated in an independent SNP-array cohort. These findings demonstrate that routine TGPs yield biologically and clinically relevant CN signatures with potential as biomarkers for therapy stratification.

Background: Deep-learning systems for Gleason grading are developed almost entirely on high-end clinical scanners and on cohorts from a small number of Western institutions, yet deployment increasingly involves other devices and other populations. These two distribution shifts, device and population, are rarely tested together on the same physical slides. The PAR dataset, from Erbil, Iraq, digitizes each biopsy on three scanners and provides three distinct pathologist grades, so it permits both tests at once on a Middle Eastern cohort. A concurrent study by the dataset originators validated a task-specific model and two foundation models on PAR; we complement it by testing an inde-pendently developed detect-then-grade pipeline and by separating scanner effects on detection from scanner effects on grading. Methods: We applied one fixed model de-veloped on North American and European material to all 1017 whole-slide images (339 slides from 185 patients, three scanners; 49.6% clinically significant cancer) with no scanner-specific or population-specific tuning. We measured cancer detection (area under the ROC curve of the predicted cancer-tissue fraction), all-slide ISUP agreement of the deployed detect-then-grade pipeline (quadratic-weighted kappa, QWK), and grading agreement on pathologist-confirmed cancers, at the slide level and, because a case carries up to two slides, at the patient level. The reference reader was S.A.; thresholds and operating points were cross-validated leave-one-out; scanners were compared by paired within-biopsy bootstrap and confidence intervals confirmed by patient-cluster bootstrap. Results: Detection was statistically equivalent across scanners (AUC 0.987 to 0.991; paired differences at most 0.003) and transferred to this non-Western cohort with no per-population tuning. At a 95% sensitivity operating point the deployed pipeline reached cross-validated all-slide QWK of 0.86, 0.81, and 0.86 (Grundium, Hamamatsu, Leica), matching the inter-pathologist ceiling of 0.81, against 0.23 to 0.62 for the ungated model. Grading of confirmed cancers was scanner dependent: the compact Grundium (0.63) did not differ from the clinical Leica (0.67; paired difference 0.04, 95% CI -0.03 to 0.11), while both exceeded Hamamatsu (0.44). Results held at the patient level, with grading somewhat lower for two scanners; the two slides of a case disagreed in grade in 43% of cases, and patient clustering did not widen the intervals. Conclusions: Can-cer-tissue fraction is a triage signal robust across scanner and transferable to an un-derrepresented population for detection, while grading is the scanner-sensitive step. Prostate grading models should be deployed as a detect-then-grade pipeline, with grading validated per device and confirmed on the local population.