Laboratory Investigation

Traditional clonogenic assays remain central to evaluating the self-renewal capacity of tumor cells. However, the assay relies on subjective endpoint measurements, is restricted to two-dimensional monolayer growth, and lacks the single cell resolution required to resolve heterogeneous expansion behaviors. We describe a high-density microwell array-based platform for quantitative assessment of single cell clonogenic growth outcomes, defined by cell count distributions spanning non-dividing, slow-dividing, and fast-dividing three-dimensional colony forming phenotypes. This approach links initial single-cell occupancy to defined growth outcomes across thousands of indexed microwells per well. The platform integrates high-density, low-adhesion microwell arrays within industry standard device plate formats and an automated image analysis pipeline incorporating machine learning, enabling parallel quantification of spatially indexed founder-derived microwells using widely accessible automated imaging systems. The assay was implemented in both 4-well and 96-well plate formats to evaluate reproducibility and scalability across different plate configurations. Using three glioblastoma cell lines as model systems, we demonstrate reproducible single founder occupancy and consistent clonal growth outcome distributions across replicate formats. This integrated microscale assay platform enables systematic quantitative characterization of clonogenic expansion capacity at single cell resolution and is compatible with applications in cancer biology, therapeutic testing, and functional single cell phenotyping. By resolving single-cell persistence, limited expansion and high expansion outcomes within a scalable high-density format, this approach expands the analytical resolution of single cell clonogenic profiling beyond traditional binary colony scoring.

BackgroundClassification of central nervous system (CNS) tumors has become increasingly complex over the past decade, raising concerns about the availability, feasibility and sustainability of comprehensive molecular diagnostics. We have evaluated nanopore whole genome sequencing (nWGS) as a single workflow to replace multiple diagnostic assays. MethodsWe 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. ResultsNanopore WGS enabled 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 standard-of-care testing, and 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. ConclusionsNanopore WGS 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. Its broad applicability supports its implementation in routine clinical practice and may be extended to other cancer types requiring complex genomic profiling.

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.

A wide variety of protocols have been proposed for optical clearing of tissues, whole-mount organs, and other bulky specimens to enable their volumetric fluorescence imaging. However, quantitative comparisons of tissue clearing protocols that take into account the fluorescence of the final specimens remain rare. Here, we propose a volumetric fluorescence image-based workflow for evaluating tissue clearing and fluorescence staining protocols. The workflow calculates depth-dependent fluorescence attenuation coefficients using data from entire 3D images, thereby avoiding spatial sampling bias and eliminating reliance on simple readouts, such as light transmittance, to predict fluorescence image quality. By combining autofluorescence signal with the signal from a specific fluorescence label, we independently evaluated transparency and the quality of fluorescence staining in cleared specimens. Using the proposed workflow, we systematically compared clearing and staining performance of three CUBIC-based protocols in murine liver, kidney, spleen, thymus, and intestine, and revealed differences in final fluorescence image quality across protocol-organ combinations.

BackgroundPatient-derived tumor organoids are widely used in cancer research, yet the biological impact of tissue processing during model generation remains unclear. Fragment-based and dissociation-based approaches are commonly assumed to trade fidelity for uniformity, but their molecular consequences remain incompletely defined. MethodsWe performed a proteome-wide comparison of fragment-based (CUT) and dissociation-based (DIS) glioblastoma organoid protocols using quantitative mass spectrometry. Organoids from multiple patient tumors were cultured under growth factor-free or growth factor-supplemented conditions and compared with matched primary tissue. ResultsBoth protocols produced technically robust glioblastoma organoids when maintained in their native media. However, CUT organoids matched the reproducibility of DIS cultures while preserving a broader extracellular matrix repertoire and networks linked to collagen assembly, vascular support, and cell-matrix signaling. DIS cultures were biased toward exogenous basement membrane components and proliferative, growth factor-responsive states. Across tumors, CUT organoids consistently showed greater proteomic similarity to matched primary tissue, retaining neural, glial, stromal, and extracellular features largely absent from DIS models. ConclusionsFragment-based glioblastoma organoids can be both reproducible and biologically faithful. Tissue dissociation acts as a major perturbation that reshapes extracellular matrix organization, cellular states, and tumor identity, making protocol choice a critical determinant of model fidelity and translational relevance.

Whole-genome sequencing (WGS) enables comprehensive analysis of tumour genomes, but its use in formalin-fixed paraffin-embedded (FFPE) samples is limited by DNA fragmentation and low yields. Whole-genome amplification (WGA) methods such as multiple displacement amplification (MDA) can boost DNA availability but distort copy-number alteration (CNA) profiles. DNA ligation-mediated MDA (DLMDA) mitigates this bias by reconstituting fragmented templates, yet its performance in FFPE-derived DNA remains uncertain. We compared paired DLMDA pre-amplified (2h, 8h) and non-pre-amplified FFPE prostate tumour samples from 22 archival blocks (5, 15 and 20 years old). DLMDA increased DNA yield by 42- to 86-fold, with global CNA patterns largely preserved. However, DLMDA significantly reduced the number of detected CNA deletions and amplifications. These effects were independent of both block age and reaction time. CNA dropouts were randomly distributed across the genome, indicating that DLMDA does not introduce regional bias. Our results show that DLMDA enables robust DNA yield recovery and avoids false-positive CNA artefacts, but at the cost of reduced CNA sensitivity. While suitable for CNA screening pipelines through WGS, further improvements are required to minimise the false-negative risk and improve the techniques sensitivity for FFPE-based genomics.

Different omics types such as genomics and proteomics all contribute to deciphering biology. Applying these omics approaches in a spatial context helps reveal biology in situ at a single cell level. Here we present a protocol for the combined multiplexed detection of targeted genes using DNA FISH, and proteins using multiplexed immunofluorescence. The protocol is integrated on the commercial PhenoCycler platform and generates one single dataset with gene and protein readout at a single cell level in large tissue sections, allowing for a throughput of thousands to millions of cells. The workflow can be used for characterising malignant cells in large tumor areas based on genetic aberrations, while deciphering the cellular landscape and microenvironment from multiplexed protein detection using immunofluorescence.

Osteocytes play a central role in bone remodeling, mineral metabolism, and skeletal homeostasis, but direct molecular analysis of human osteocytes remains technically challenging because they are embedded within the mineralized bone matrix. Surgically obtained human bone specimens provide valuable material for studying human bone biology; however, surface-associated cells, marrow-derived cells, and adherent soft tissues can confound downstream transcript analysis. Here, we describe a bone fragment-based protocol for preparing surgically obtained human bone specimens for molecular analysis of osteocyte-associated transcripts. The protocol consists of mechanical trimming, mincing into small bone fragments, repeated washing, and five sequential rounds of collagenase digestion to reduce non-osteocytic cellular components associated with the bone surface and marrow spaces. The remaining mineralized bone fragments are then frozen in liquid nitrogen, cryogenically pulverized, and lysed in TRIzol reagent for total RNA extraction. Histological validation using residual maxillary bone specimens showed that sequential collagenase digestion markedly reduced adherent soft tissue and extra-matrix nuclei while preserving osteocyte lacunar occupancy. This protocol provides a practical workflow for bone fragment-based RNA analysis focused on osteocyte-associated transcripts in human bone specimens. Specifications table O_TBL View this table: org.highwire.dtl.DTLVardef@1cec618org.highwire.dtl.DTLVardef@2f746forg.highwire.dtl.DTLVardef@1854247org.highwire.dtl.DTLVardef@1c26c1aorg.highwire.dtl.DTLVardef@1473a88_HPS_FORMAT_FIGEXP M_TBL C_TBL

Circulating tumor cells (CTCs), particularly multicellular clusters, are associated with poor prognosis and may provide insight into mechanisms of metastasis and therapy resistance. Unbiased approaches for functionally characterizing CTCs in liquid biopsies are therefore urgently needed. Here, we evaluate multiplex imaging mass cytometry (IMC) for CTC analysis in mice bearing human xenograft tumors. In a single-step workflow, IMC uses metal-conjugated antibodies to simultaneously detect numerous proteins and post-translational modifications in minimally processed, small-volume blood samples collected from the tail vein or heart. Using breast cancer cell lines and a patient-derived xenograft (PDX), we assessed a panel of antibodies, including human-specific markers such as Lamin B1 (LMNB1), to enable cross-species interpretation. Combined with manual review, HALO AI-based cell segmentation was used to identify CTCs and quantify marker expression. This approach enables studies of how genetic and pharmacologic interventions alter the properties of single CTCs and CTC clusters in tumor-bearing mice.

BackgroundTumor vasculature is a key driver of glioma progression, yet routine quantification depends on subjective histopathologic assessment or resource-intensive ancillary immunohistochemistry. A scalable, objective method for vascular phenotyping from routine histology remains an unmet need. MethodsWe leveraged 10x Genomics Xenium spatial transcriptomics data from a glioblastoma specimen to generate molecularly resolved annotations of GBM-associated endothelial cells and pericytes across 809,041 cells. These annotations were transferred to matched H&E-stained sections to train a DINO-DETR-based object detection model using a binary classification scheme (vascular vs. other). The model was validated on four independent Xenium patient slides and applied to a retrospective cohort of 119 diffuse gliomas spanning WHO grades 2-4 (oligodendroglioma, astrocytoma, and glioblastoma) with linked survival data. ResultsBinary vascular cell detection achieved a precision of 0.78, a recall of 0.63, and an F1 score of 0.70, with an overall accuracy of 98.6%. Orthogonal spatial validation confirmed that predicted vascular cells were preferentially localized within annotated blood vessel regions. In subtype-stratified survival analysis, high AI-derived vascular cell proportion was significantly associated with worse overall survival in astrocytoma patients (log-rank p < 0.019). ConclusionCross-modal AI training using spatial transcriptomics enables scalable, molecularly informed vascular quantification directly from routine H&E slides. Within the astrocytoma subtype, where tumor grade is most heterogeneous and vascular phenotype most variable, objective vascular quantification provides independent prognostic information demonstrating the potential of spatially supervised deep learning to extract clinically meaningful microenvironmental signals from universally available histologic material.

The tissue-resident immune system involves complex 3D assemblies that interact with extended structures such as blood vessels and nerves. These interactions are difficult to study using conventional 2D profiling because they span many tissue sections. In animal tissues, volumetric imaging approaches such as light-sheet fluorescence microscopy (LSFM) are widely used to study 3D tissue organization, with labelling often aided by genetically encoded reporters and vascular dyes. In contrast, LSFM of human specimens remains underdeveloped because most clinical samples are available only as formalin-fixed paraffin-embedded (FFPE) tissue, limiting labeling strategies primarily to dyes and antibodies. Here, we present a volumetric cyclic immunofluorescence (v-CyCIF) and virtual H&E toolbox that overcomes key barriers to multiplexed imaging of immune cells and nerves in human specimens up to 1 mm thick. We use v-CyCIF to study neuroimmune interactions in normal and cancer tissues and to immunoprofile intact secondary and tertiary lymphoid structures. Re-embedding and sectioning of specimens following volumetric imaging enables high-plex high-resolution analysis of subcellular structures and cell-cell interactions associated with immune cell activity. v-CyCIF therefore provides a flexible framework for multi-scale 3D profiling of clinical specimens across imaging formats and resolutions.

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.

Bile represents a clinically accessible biological fluid that can mitigates major limitations associated with tissue-based sampling for the generation of organoid models to study hepatobiliary disease, including biliary tract cancers where tissue availability is often limited. Importantly, bile can also enable the generation of non-malignant cholangiocyte organoids that are otherwise difficult to obtain. Here, we describe an operator-oriented, step-by-step protocol to generate organoids from fresh bile collected during endoscopic retrograde cholangiopancreatography (ERCP), together with two complementary workflows for siRNA delivery in 3D cultures. We detail critical control points that are often under-reported, yet considerably influence success and reproducibility. The protocol was optimized and applied in a real-world cohort of 21 patients undergoing ERCP, including benign biliary obstruction due to choledocholithiasis (n=5) and malignant strictures (n=16: cholangiocarcinoma n=13, gallbladder adenocarcinoma n=1, ampullary tumors n=2). Expandable organoids were established in 17/21 cases (81%), with establishment rates of 60% for choledocholithiasis and 85-100% across malignant entities. Anticipated results include organoid outgrowth within [~]2-3 weeks and morphological heterogeneity in cultures derived from malignant strictures, where normal-like and tumor-like populations may initially coexist and can drift toward a cystic phenotype under routine expansion, motivating optional manual handpicking when tumor-enriched lines are required. As downstream readouts, we show feasibility of DNA-based profiling in selected paired bile-organoid samples (targeted sequencing and ULP-WGS copy-number analysis) and demonstrate proof-of-concept gene silencing via siRNA in both dissociated cells prior to re-embedding, and intact fully formed organoids while preserving 3D architecture. Collectively, this workflow provides a practical and reproducible framework to establish, expand, characterize and functionally perturb bile-derived organoids from routine clinical procedures, facilitating standardized implementation across laboratories.

Pathologist-guided distinctions within histology and spatial omic images provide insights into health and disease, with digital pathology leveraging artificial intelligence to automate such assessments. To train computational models, current digital pathology methods rely on upfront manual annotations, which are time-consuming to generate. Pre-annotation is poorly suited to investigating novel spatial behaviors--a major need driven by advances in spatial profiling--for which annotation criteria and data needs will be uncertain. To address these challenges, we present DIANNE, a digital pathology approach for rapid training and inference of spatial differential attributes based on train-time Positive Class Mixup Augmentation. DIANNE can compute foundation model-derived segmentation-free localization of differential classifiers across whole slide H&E images within seconds on a workstation, enabling interactive investigation of spatial niches. Predictive models can be re-trained in real-time in response to patch or regional annotation changes, clarifying determinative biological attributes across slides from only a few dozen annotated patches. We demonstrate the effectiveness of DIANNE for tumor detection, artifact identification, and exploration of pancreatic, fetal membranes and kidney tissue structures. DIANNE also provides analogous capabilities for IHC, multiplex immunofluorescence, and registered spatial transcriptomic+H&E images. DIANNE is implemented in a Jupyter toolkit, enabling rapid development of high-resolution classifiers from weakly-supervised training. DIANNE provides a practical system to quantitatively understand known and novel spatial phenotypes.

Fluorescent in situ hybridization (FISH) enables highly sensitive, high-resolution detection of gene transcripts. Moreover, by employing multiple probes, this technique allows for multiplexed, simultaneous detection of distinct gene expression patterns spatiotemporally, making it a valuable spatial transcriptomics approach. Owing to these advantages, FISH techniques are rapidly being adopted across diverse areas of basic biology. However, conventional protocols often rely on volatile, toxic reagents such as formalin or methanol, posing potential health risks to researchers. Here, we present a safer protocol that replaces these chemicals with low-toxicity alternatives, without compromising the high detection sensitivity of FISH. We validated this protocol using both in situ hybridization chain reaction (HCR) and signal amplification by exchange reaction (SABER)-FISH in frozen sections of various model organisms, including mouse (Mus musculus), amphibians (Xenopus laevis and Pleurodeles waltl), and medaka (Oryzias latipes). Our results demonstrate successful multiplexed detection of morphogenetic and cell-type marker genes in these model animals using this safer protocol. The protocol has the additional advantage of requiring no proteolytic enzyme treatment, thus preserving tissue integrity. Furthermore, we show that this protocol is fully compatible with EGFP immunostaining, allowing for the simultaneous detection of mRNAs and reporter proteins in transgenic animals. This protocol retains the benefits of highly sensitive, multiplexed, and multimodal detection afforded by integrating in situ HCR and SABER-FISH with immunohistochemistry, while providing a safer option for researchers, thereby offering a valuable tool for basic biology.

Single-cell RNA-sequencing-based characterization of cells that belong to the neoplastic clone is a major challenge in hematologic neoplasms, where malignant and normal cells coexist. Confident molecular profiling requires simultaneous analysis of gene expression and genetic mutations in individual cells, an ability that is not supported by the standard 10X Genomics workflow. Here, we developed a post-hoc targeted genotyping method for samples processed with the 10X Genomics 3 workflow. To establish the approach, we mixed two types of leukemic cells harboring distinct mutations and subjected them to single-cell RNA-sequencing. Repurposing an intermediate product of the experimental process allowed us to enrich for transcripts containing mutation sites. Long-read PacBio sequencing genotyped the transcripts and captured the associated cellular and molecular barcodes, allowing us to bioinformatically integrate the mutation and transcriptomic data at single-cell resolution. Our method demonstrates the detection of mast cell leukemia-associated point mutations in the KIT gene and chronic myeloid leukemia-associated BCR::ABL1 fusion transcripts. Single-cell analysis of primary leukocytes from chronic myeloid leukemia detected mutated cells at diagnosis, but not during imatinib treatment. Taken together, the method constitutes a broadly applicable framework for post-hoc genotyping of cells analyzed with single-cell RNA-sequencing.

Gallbladder cancer (GBC) is a highly lethal malignancy with limited experimental models to study disease biology or evaluate therapeutic responses. Although canonical Wnt activation is commonly used for patient-derived organoid (PDO) development and expansion, gallbladder PDOs has also been generated under Wnt-inhibitory conditions. No comparative assessment has determined how Wnt pathway modulation influences gallbladder PDO development, phenotype or drug response. This study systematically compared the impact of canonical Wnt activation (WNTAct medium containing CHIR99021) versus inhibition (WNTInh medium containing DKK1) on the establishment, propagation, molecular features and therapeutic responses of PDOs generated from malignant or non-malignant gallbladder tissues derived from the same patient. Both media supported successful PDO generation with comparable efficiency, preserving biliary epithelial functions and marker expression. Transcriptomic profiling confirmed selective enrichment of canonical Wnt target genes in PDOs generated in WNTAct cultures. WNTAct conditions enabled markedly superior long-term propagation, whereas WNTInh cultures more consistently retained the dysplastic features in malignant samples. Gemcitabine response assays demonstrated significantly greater drug sensitivity in PDOs grown in WNTAct medium, a phenotype reversible upon media switching but requiring extended adaptation, indicating a dynamic and context-dependent influence of Wnt signaling on chemotherapeutic vulnerability. Collectively, the findings reveal a trade-off between long-term propagation and histological fidelity in gallbladder PDOs and show that Wnt signaling modulates gemcitabine sensitivity in a reversible manner. This comparative framework provides practical guidance for selecting culture conditions for gallbladder PDO based disease modelling and precision oncology applications.

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.

Chordoma, a rare sarcoma of notochordal origin, exhibits slow growth and local aggressiveness. While copy-number (CN) events are recognized as key chordoma drivers, no comprehensive classification, based on CN, has yet been developed. Here, we establish a robust, reproducible genomic subtyping of chordoma, based on CN events. Two independent skull base chordoma cohorts (N=32,N=71) were analyzed, utilizing distinct analytical platforms, DNA methylation microarrays and whole-genome sequencing, both controlled for B-allele frequencies. Samples were clustered using unsupervised hierarchical methods. The CN events defined four consistent molecular clusters across both cohorts: C1 (CN-stable), C9 (chromosomal losses, especially of chr9/CDKN2A), C7 (chr7 gain), and C2 (gains of chr2 and chr7). The findings were validated in fluorescence in situ hybridization (FISH) with concordance of 84-89%. The CN clusters explain 31-33% of the RNA-sequencing transcriptional variance. Moreover, the C2 cluster showed up-regulation of Sonic Hedgehog signaling and clusters C2 and C9 were enriched in cell-cycle-related genes. The proposed CN clusters correlate with existing chordoma classificators e.g. chromosomal instability (CIN), mutation burden, immune score, and methylation clusters. Furthermore, comparison with over 2,000 sarcomas highlighted CN patterns more common in chordoma (i.e. chr1q, chr2, chr7 gains and chr1p, chr3, chr9, chr10, chr13, chr14, chr18 losses) but also revealed shared aberrations, e.g. chr22 loss shared with Gastrointestinal Stromal Tumours (GISTs). This study provides a unifying classification for skull base chordoma, linking distinct genomic architectures to specific transcriptional programs and potential therapeutic vulnerabilities.

BackgroundFor pancreatic cancer, practical blood-based tests for early detection and postoperative surveillance remain elusive. We sought to develop a qPCR-measurable serum microRNA (miRNA) panel that robustly discriminates pancreatic cancer from non-cancer controls and other malignancies. MethodsWe profiled 255 serum miRNAs in batch 1 (n=72) and selected 27 candidates. Candidates were refined in batch 2 (n=552) and cross-batch evaluation was performed with batch 3 (n=391) to derive a miRNA model. Independent validation used batch 4 (n=616). Clinical relevance was assessed in an independent clinical cohort of resection patients with samples obtained preoperatively and at 1 and 12 months postoperatively. ResultsThe miRNA model trained on batches 2 and 3 achieved an area under the curve (AUC) of 0.91 and 0.83 for pancreatic cancer versus non-cancer controls and non-cancer plus other cancers, respectively, when independently validated in batch 4. Stage-wise AUCs in batch 4 were 0.91 (I), 0.94 (II), 0.86 (III) and 0.90 (IV). In the clinical batch, the score decreased postoperatively (preoperative vs month 1; p<0.01) and was higher in recurrence than non-recurrence (p<0.001). ConclusionsThe developed compact miRNA qPCR assay discriminated pancreatic cancer across independent assay batches and showed clinical relevance for postoperative surveillance. Clinical Trial RegistrationNot applicable.