NAR Cancer

Long noncoding RNAs (lncRNAs) are important regulators of gene expression and are frequently dysregulated in cancer. The mitotically associated lncRNA MANCR is highly expressed in aggressive cancers and contributes to genomic instability in triple-negative breast cancer (TNBC), but the molecular mechanisms underlying its activity remain poorly defined. Here we integrate computational and experimental approaches to examine the structure and regulatory interactions of MANCR isoforms. Analysis of transcriptomic datasets revealed tumor-type-specific expression patterns for seven MANCR isoforms in breast cancer cell lines. Computational prediction of RNA secondary structures identified conserved structural features across isoforms, suggesting potential functional specialization. We identify p53 as a MANCR-interacting protein through computational docking and RNA immunoprecipitation sequencing (RIP-seq) and demonstrate that MANCR depletion reduces p53-dependent transcriptional activity. Chromatin isolation by RNA purification sequencing (ChIRP-seq) revealed 1, 250 genomic regions associated with MANCR, including enrichment of p53 consensus motifs and GC-rich sequence elements. Motif analysis further identified candidate sequence features associated with MANCR-occupied chromatin regions. Computational prediction of RNA-miRNA interactions identified multiple potential miRNA binding sites across MANCR isoforms, including miR-6756-5p, which targets the androgen receptor (AR). Consistent with this prediction, AR expression decreased following MANCR knockdown in TNBC cells. Together, these results suggest that MANCR isoforms may contribute to transcriptional regulation in TNBC through interactions with chromatin, p53 signaling pathways, and potential miRNA regulatory networks. One Sentence SummaryMitotically-associated lncRNA (MANCR) is prevalent in aggressive cancers interacting with DNA, P53, and miRNAs, to mediate multiple levels of epigenetic transcriptional control in triple negative breast cancer.

Anti-hormonal therapies such as selective estrogen receptor modulators like tamoxifen or aromatase inhibitors like letrozole represent a cornerstone for breast cancer prevention and therapy of estrogen receptor-positive breast cancer. Therapeutic monitoring can include blood tests and imaging; however, genetically-based approaches are not yet in practice. Ideally, a test would be able to detect a positive molecular response across different estrogen pathway-suppressive approaches. Circular RNAs are a species of non-coding RNAs detectable in plasma that have been proposed as non-invasive therapeutic biomarkers. To determine whether a set of specific circular RNAs is altered across estrogen-suppressive pathway approaches, we analyzed mammary gland-specific total RNA sequencing data from two individual genetically engineered mouse models (GEMMs) of estrogen pathway-induced breast cancer, with or without exposure to tamoxifen or letrozole. The nf-core/circrna pipeline was used to identify circRNAs that were differentially expressed in response to either tamoxifen or letrozole. We then screened for circRNAs that were differentially regulated by both anti-hormonals. Four up-regulated and 31 down-regulated circRNAs with host genes known to be expressed in human breast epithelial cells were identified as showing reproducible differential regulation in response to anti-hormonal treatment.

Y-box binding protein 1 (YB-1; YBX1) is a multifunctional DNA- and RNA-binding protein involved in cell cycle regulation, DNA repair, stress adaptation, and therapy resistance. Elevated YBX1 mRNA expression is associated with aggressive disease across multiple cancers, yet its pan-cancer genomic and clinical correlates remain unclear. Here, we performed a comprehensive pan-cancer analysis across 53 datasets spanning 33 tumour types, integrating RNA expression, somatic mutations, copy number, hypoxia, and clinical outcomes. YBX1 was rarely mutated or amplified, indicating that oncogenic relevance is primarily driven by its expression. Tumours with high YBX1 mRNA exhibited a conserved transcriptional program enriched for cell cycle, DNA repair, and chromatin regulation pathways, and were preferentially mutated in genes involved in maintaining genomic stability, including TP53. These tumours were associated with increased mutation burden, fraction of genome altered, homologous recombination deficiency, and elevated hypoxia. Clinically, high YBX1 mRNA associated with advanced stage, higher grade, shorter progression-free survival, and reduced overall survival. Collectively, high YBX1 mRNA expression defines a conserved, genomically unstable, and clinically aggressive tumour state across multiple cancer types.

TP53 mutations occur in 80-90% of triple-negative breast cancers (TNBCs) and drive genomic instability and metastatic progression. Poly (ADP-ribose) polymerase (PARP) is critical for DNA repair and replication fork stability. How oncogenic signaling influences PARP function to sustain proliferation during replication stress remains unclear. Mutant p53 (mtp53) R273H associates tightly with chromatin, forms complexes with PARP, and enhances PARP recruitment to replication forks [1-3]. The C-terminal region of mtp53 mediates mtp53-PARP and mtp53-Poly (ADP-ribose) (PAR) interactions that facilitate S phase progression [4, 5]. The PARP inhibitor talazoparib (TAL) combined with the alkylating agent temozolomide (TMZ) produces synergistic cytotoxicity selectively in mtp53, but not wild-type p53 (wtp53), breast cancer cells and organoids. Herein we evaluated the mechanism of mtp53-associated cell death and tested if this could translate to a preclinical xenograft model. We found that TMZ+TAL treatment induced elevated cleaved PARP and {gamma}H2AX and reduced the metastasis-promoting oncoprotein MDMX. In orthotopic xenografts expressing mtp53 R273H, but not wtp53, combination therapy significantly decreased circulating tumor cells (CTCs) and lung metastases. Transcriptomic profiling of tumors from combination treated animals demonstrated downregulation of MDMX, VEGF, and NF-{kappa}B, consistent with the observed suppression of CTCs and lung metastasis, and increased {gamma}H2AX, indicative of replication stress in mtp53 xenografts. Inhibition of metastasis was also observed in mtp53 R273H WHIM25 and p53-undetectable WHIM6 TNBC patient-derived xenografts (PDX). The mtp53 C-terminal domain (347-393) demonstrated a critical tumor promoting function, as CRISPR-mediated deletion impaired replication fork progression, tumor growth, and metastatic dissemination. DNA fiber combing showed that expression of full-length mtp53 R273H, but not C-terminal deleted {Delta}347-393, supported sustained single-stranded DNA gaps (ssGAPs) following Poly (ADP-ribose) glycohydrolase (PARG) inhibition. These findings support that mtp53 uses C-terminal amino acids to exploit PARP to enable replication stress adaptation and that mtp53 is a predictive biomarker for combined PARP inhibitor and DNA damaging therapies targeting TNBC. Significance statementTP53 mutations are the most common genetic alterations in TNBC and a major driver of replication stress and metastasis. This study shows that missense mutant p53 uses C-terminal amino acids to reprogram PARP activity to maintain tumor cell survival under replication stress. We demonstrate that p53 status governs the response to combined PARP inhibitor (PARPi) and DNA-damaging chemotherapy, establishing an additional molecular basis beyond BRCA1 mutations for treating TNBC with PARPi therapy. These findings reveal a previously unrecognized mechanism by which the mutant p53-PARP axis enables replication stress tolerance and drives cancer metastasis. We show mutation of p53 in TNBC provides an additional biomarker-guided framework to improve PARPi therapeutic outcomes.

Extrachromosomal DNA (ecDNA) has emerged as a critical mediator of oncogene amplification and transcriptional dynamics in aggressive cancers, yet its contribution to chemotherapy resistance in vivo remains incompletely understood. This study investigates the contribution of ecDNA-associated molecular features to predictive chemotherapy resistance in TNBC. We analyzed RNA-seq data from 4T1 TNBC cells and 4T1 bulk tumors at different growth stages (1-, 3-, and 6-week) to identify differentially expressed ecDNA alterations. We then utilized molecular docking tools to predict ecDNA protein-drug interactions and employed machine learning (ML) models to predict ecDNA-associated therapeutic resistance. Our results revealed changes in global gene expression, including expression of ecDNA-associated genes, that continued over time, with significant molecular remodeling observed at six weeks. Additionally, we found gradual accumulation of mutations in ecDNA genes, which may have contributed to reduced drug binding affinity, indicating potential resistance. ML models generated stable, high-confidence classifications of resistant phenotypes, consistently identifying ecDNA burden and prevalence as dominant predictive features of drug resistance. Drug specific predictions further highlighted elevated resistance probabilities for paclitaxel and doxorubicin, whereas hydroxyurea, which depletes ecDNA, showed reduced resistance probabilities, indicating potential roles of ecDNA in chemoresistance. This study provides new insights into temporal remodeling of ecDNA within TNBC tumors over time and their potential association with drug resistance.

Hypoxia is a defining feature of the solid tumour microenvironment and a major determinant of therapeutic response. Hypoxia-inducible factors (HIFs) are central regulators of transcriptional reprogramming under hypoxic stress. Hypoxia can paradoxically elicit both tumour-promoting and tumour-suppressive outcomes, suggesting regulatory mechanisms beyond canonical HIF-dependent pathways. Emerging evidence indicates that hypoxia-responsive RNAs (HRRs) may also be regulated independently of HIFs, with posttranscriptional stabilization playing a critical determinant of hypoxic adaptation. Cytoplasmic mRNA recapping mediated by the cytoplasmic capping enzyme (cCE) has recently emerged as an important post-transcriptional regulatory process, yet its role in hypoxia-driven RNA regulation remains poorly understood. Here, we aimed to identify novel HRRs that modulate cellular adaptability to hypoxia and to determine whether these transcripts are regulated by cCE. Using CoCl2-induced hypoxia, we observed a significant reduction in osteosarcoma cell aggressiveness, characterized by decreased proliferation, clonogenic survival, and migratory capacity. Transcriptomic profiling of hypoxic osteosarcoma cells identified RORA and KCTD16 as significantly upregulated and function as suppressors of tumour cell aggressiveness. Integrative in-silico CAGE tag analysis followed by cap-specific biochemical assays confirmed that both transcripts are post-transcriptionally stabilized by cCE. Mechanistically, hypoxia-induced stabilization of HIF1 transcriptionally elevated RORA and KCTD16 expression, while cCE further reinforced their stability post-transcriptionally. Stabilization of these cCE-targeted HRRs resulted in suppression of the oncogenic proliferation driver c-Myc, thereby attenuating the aggressive phenotype of hypoxic osteosarcoma cells. Collectively, our findings identify cCE as a previously unrecognized post-transcriptional regulator in hypoxia biology and reveal a RNA-centric mechanism by which hypoxia can restrain tumour aggressiveness.

BackgroundLong non-coding RNAs (lncRNAs) have emerged as key regulators of tumor biology, however, thus far none have translated to cancer therapies. The lncRNA MALAT1 is overexpressed in more than 20 cancers, including breast cancer and has been shown to function via various mechanisms in a context-dependent manner, in 2D cell lines and mouse models. However, its functional role and therapeutic potential have not been evaluated in clinically relevant patient-derived models. MethodsWe investigated the therapeutic potential of a MALAT1-targeting antisense oligonucleotide (ASO) for breast cancer, using clinically relevant 3D human patient-derived organoids (PDOs) and PDO-xenograft (PDO-X) models. We systematically evaluated the efficiency of MALAT1-targeting ASOs using a biobank of 28 PDO models. Using three independent PDO-X models of triple negative breast cancer (TNBC), we targeted MALAT1 in vivo to study its impact on transcription, alternative splicing, stromal remodeling and metastasis. ResultsAcross PDO-X models, MALAT1 depletion reproducibly drove widespread alternative splicing changes across all event types, particularly intron retention events, accompanied by modest gene expression alterations. Differentially spliced transcripts were enriched for targets of shared cancer-associated transcription factors, and MALAT1 knockdown altered the relative abundance of previously unannotated splicing isoforms. Beyond tumor-intrinsic effects, tumor-specific MALAT1 depletion induced a consistent reduction in macrophage-associated gene signatures and reduced lung metastatic burden. ConclusionsOur data define MALAT1s multifaceted role in TNBC, coordinating alternative splicing, transcriptional fine-tuning, tumor-stroma crosstalk, and metastatic progression. Our study provides strong preclinical evidence supporting MALAT1-targeted ASO therapy and establishes PDO-X models as a clinically relevant platform for functional interrogation of TNBC therapies.

PRMT5 is a type II arginine methyltransferase that forms an active complex with methylosome protein WDR77 (MEP50) to catalyze the symmetric dimethylation (SDMA) of arginine residues in proteins that regulate biological roles including apoptosis, DNA damage response and RNA processing. Some of the best characterized PRMT5 substrates are the small nuclear ribonucleoproteins SNRPB, SNRPD1 and SNRPD3, which are critical for spliceosome assembly and RNA splicing fidelity. MTAP-deleted cancers exhibit increased sensitivity to PRMT5 inhibition due to elevated levels of methylthioadenosine (MTA), a natural inhibitor of PRMT5. This vulnerability is exploited by MTA-cooperative PRMT5 inhibitors, exemplified by TNG908 and TNG462 which selectively target PRMT5 in MTAP-deleted cells while sparing MTAP-wildtype (WT) cells. Consistent with this mechanism, treatment with TNG908 in preclinical studies induces widespread splicing alterations in MTAP-deleted cancer models, with minimal effects in MTAP-WT cells. These splicing changes are consistent across diverse MTAP-deleted tumor types, including glioblastoma, pancreatic, and non-small cell lung cancer, indicating a histology-agnostic response to PRMT5 inhibition. Moreover, treatment of MTAP-WT cells with exogenous MTA mimics the splicing alterations observed with PRMT5 inhibition, as does pharmacologic inhibition of MTAP further supporting a mechanistic link between MTA accumulation, PRMT5 modulation, and aberrant splicing. Given that MTAP deletions occur in approximately 10-15% of human cancers, the identification of a robust RNA splicing signature offers a valuable pharmacodynamic biomarker for monitoring the activity of PRMT5 inhibitors. This splicing-based readout may also serve as a predictive biomarker of therapeutic response, offering greater specificity than global SDMA levels. Collectively these data suggest that a PRMT5-dependent RNA splicing signature can monitor the pharmacodynamic activity of MTA-cooperative PRMT5 inhibitors in MTAP-deleted cells.

Ewing sarcoma (EwS) is an aggressive, human-exclusive tumor typically driven by the EWS::FLI1 fusion protein. To assess whether the neomorphic functions of EWS::FLI1 are fundamentally dependent on evolutionarily recent cofactors such as ETS transcription factors (ETS-TFs), Plycomb group (PcG) proteins, CBP/p300, or specific subunits of the BAF complex, we expressed EWS::FLI1 in the model organism Saccharomyces cerevisiae. This minimal system was chosen because several key EWS::FLI 's cofactors possess greatly reduced sequence homology (e.g., BAF) or are lacking altogether (e.g., ETS-TFs, PcG, or CBP/p300). We used co-IP/MS to map the yeast interactome, Chip-Seq to identify gDNA binding sequences, RNA-Seq for global gene expression, and engineered reporters to test conversion of (GGAA) tandem repeats (GGAASat) into neoenhancers. We found that the yeast EWS::FLI1 interactome was more limited and qualitatively distinct from its human counterpart, sharing core machinery (e.g. RNA Polymerase II, FACT) but lacking the BAF/SWI-SNF and spliceosome complexes, and showing strong enrichment for the SAGA chromatin remodeling complex. We also found that EWS::FLI1 binds to hundreds of sites in the yeast genome with a clear preference for putative ETS-TF consensus sequences and (CA) dinucleotide repeats. Yet, EWS::FLI1 expressing cells presented only minimal transcriptional dysregulation, a stark contrast to the extensive changes observed in humans and Drosophila cells. Finally, we found that EWS::FLI1 successfully converted silent GGAASat sequences into active enhancers in yeast. This remarkable result occurs despite the absence of homologs for key human activators, such as CBP/p300, strongly suggesting that EWS::FLI1 can mobilize functionally related, non-homologous pathways to establish neoenhancers at GGAASat sites. Altogether, our results indicate that EWS::FLI1's core ability to drive GGAASat-dependent gene expression is a conserved, ancient property, while GGAASat-independent extensive transcriptome reprogramming is dependent on co-factors and pathways specific to animal cells.

La-related protein 1 (LARP1) is an RNA-binding protein that post-transcriptionally regulates mRNA with potential oncogenic role in multiple cancers; however, its function in endometrial cancer remains unknown. An analysis of the TCGA endometrial cancer cohort showed that overexpression of LARP1 is associated with shorter overall survival (OS) and progression-free interval (PFI) as indicated by Kaplan-Meier analysis. Functional in vitro studies revealed that LARP1 knockdown by two different siRNAs markedly suppressed cell viability and triggered apoptosis, as confirmed by increased protein levels of cleaved PARP1 and cleaved caspase-3. Mechanistically, LARP1 knockdown remarkably reduced E2F1 protein levels as confirmed by immunofluorescence and Western blotting. Clinically, co-overexpression of LARP1 and E2F1 further decreased OS and PFI, suggesting a co-operative oncogenic axis. Importantly, LARP1 knockdown enhanced the sensitivity of ISHI and HEC-1A endometrial cancer cell lines to carboplatin treatment. These findings suggest that LARP1 promotes endometrial cancer survival and resistance to chemotherapy, at least in part, through the regulation of E2F1 and suppression of apoptosis. Targeting LARP1 could represent a promising therapeutic strategy to suppress tumor growth and enhance sensitivity to platinum-based chemotherapy.

The PBAF is one of three biochemically distinct BAF chromatin remodelers in humans. We previously proposed the role of ARID2, a PBAF component, as a bonafide tumor suppressor in colorectal cancer (CRC). Here, we validated loss of tumor suppression under conditions of ARID2 deficiency emanating from a marked reduction in PBAF complex assembly resulting from destabilization of PBAF-specific components BRD7, PHF10, and PBRM1. Transcriptome profiling of ARID2 deficient CRC cells revealed perturbation of disease processes, including CRC and neurodegenerative disorders, as well as CRC relevant pathways including Wnt/{beta}-catenin signalling, but transcript levels of PBAF-specific components remained unchanged, confirmed by RT-qPCR and TCGA data analysis. Our study establishes ARID2 as a critical stabilizer of the PBAF complex of relevance to CRC.

MotivationDeep neural networks have proven effective in classifying tumour types using next-generation sequencing data. However, developing transferable models that work across heterogeneous operating environments remains challenging due to differences in cohort compositions and data generation protocols, privacy concerns, and limited computational capabilities. ResultsWe introduce muat, a transformer-based software for tumour classification using somatic variant data from whole-genome (WGS) and whole-exome sequencing (WES). Building on previously developed MuAt and MuAt2 models, we distribute the software via Docker containers and Bioconda for deployment in high-performance computing (HPC) systems and Secure Processing Environments (SPEs). Using a downloadable MuAt checkpoint, we reproduce the performance reported in the original study on whole genome (PCAWG; 89% accuracy in histological tumour typing) and exome sequencing data (TCGA; 64% accuracy). Cross-cohort evaluation in Genomics England SPE achieved 81% accuracy without retraining and 89% following fine-tuning. As a demonstration of the softwares adaptability, we also deployed muat within the iCAN Digital Precision Cancer Medicine Flagships SPE and integrated it into a Nextflow-managed workflow. Availability and implementationmuat is available through conda (www.anaconda.org/bioconda/muat) and GitHub (https://github.com/primasanjaya/muat), under the Apache 2.0 License. Contactprima.sanjaya@helsinki.fi, esa.pitkanen@helsinki.fi; website: mlbiomed.net

MotivationVariant calling for next-generation sequencing (NGS) data relies on a diverse ecosystem of tools and workflows. Large-scale collaborative studies increasingly adopt federated analysis, where each institution processes sensitive data locally using standardized pipelines. Deploying identical pipelines across multiple centers remains challenging because heterogeneous software environments and computing policies can cause workflow divergence and inconsistent results. ResultsWe developed CBIcall, a workflow-agnostic, configuration-driven framework that runs standardized variant-calling pipelines from raw FASTQ files to analysis-ready VCFs using a single YAML file. An execution driver validates user parameters, enforces compatibility across pipelines, analysis modes, work-flow backends, genome builds, and tool versions, and records structured provenance for each run, ensuring consistent and reproducible pipeline execution across computing environments. CBIcall dispatches validated workflows through Bash or Snakemake backends and provides production-ready pipelines for germline WES, WGS (single-sample or cohort joint genotyping following GATK Best Practices), and mitochondrial DNA analysis. We validated CBIcall on public datasets and deployed it in the EU HEREDITARY project, processing 1,111 samples with both WES and mtDNA pipelines on an institutional HPC system, demonstrating its suitability for reproducible cohort-scale genomic analyses. Availability and implementationCBIcall is open source (GPLv3) and distributed with ready-to-run pipelines; full dependency and installation documentation is available at https://github.com/CNAG-Biomedical-Informatics/cbicall.

Soft tissue leiomyosarcoma (STLMS) is an aggressive malignancy for which robust molecular subclassification and mechanism-based therapeutic strategies still remain limited. We performed integrative proteogenomic analyses of primary and metastatic STLMS to define subtype-associated molecular programs. Joint analysis of the proteome and phosphoproteome identified 3 biologically distinct subtypes. P1 was characterized by relative genomic stability, low proliferative activity, and enrichment of FGFR2- and PDK-associated signaling. In contrast, P2 and P3 showed greater chromosomal instability and more aggressive clinical behavior, but with distinct molecular features. Notably, P2 was associated with inflammatory and RTK-RAS pathway programs, activation of CDK-AURKA/B-mTOR-ERK kinase networks, IGF1R/PDGFRA alterations, and the poorest outcomes. On the other hand, P3 showed strong cell cycle and DNA repair programs, elevated NCOR1 expression, and increased expression of nonhomologous end joining components, including PARP1. Homologous recombination deficiency analyses distinguished HRD-low P1 from HRD-high P2/P3, and paired analyses suggested increased HRD-related features in metastatic lesions within P3. Immune profiling identified an immune-hot yet potentially suppressive state in P2, marked by higher LGALS9 expression and M2-like macrophage infiltration. To support clinical translation, we developed a tissue microarray-based immunohistochemical classifier that enabled surrogate assignment of proteome-defined subtypes in an independent cohort and showed recurrence-free survival differences across inferred subtypes. These findings together establish a proteogenomic framework for STLMS heterogeneity and nominate subtype-associated biological vulnerabilities for future translational and clinical investigation.

SummaryCentral nervous system (CNS) tumor diagnosis requires comprehensive genomic profiling including DNA-methylation classification, copy-number variants (CNV), gene fusion analysis, small variant detection and MGMT promoter methylation status. Long-read sequencing platforms such as nanopore sequencing by Oxford Nanopore Technologies and SMRTseq by PacBio can capture all these in a single assay, but integrating diverse analytical tools to leverage the advantages of long-read sequencing remains complex. We present DIANA (Diagnostic Integrated Analytics of Neoplastic Alterations), a pipeline providing fully automated end-to-end processing of long-read whole-genome sequencing data from aligned sequence reads. DIANA produces a human readable report that combines methylation classification with prioritized genetic variants to support CNS tumor diagnostics and clinical decision-making. Availability and implementationDIANA is an open-source Nextflow pipeline, freely available through Docker or Apptainer/Singularity technologies. The source code, comprehensive documentation, and installation protocols are available on GitHub: https://github.com/VilhelmMagnusLab/DIANA.git. Supplementary informationSupplementary data are available at Bioinformatics online.

Lifestyle, environmental and other exposures to exogenous mutagens generate somatic mutations in normal human cells in vivo and increase cancer risk. However, the global repertoire of exogenous mutagen exposures is uncertain. The mutational signatures of mutagens in normal tissues offer opportunities to detect such exposures and survey them at population level. Using single-molecule duplex sequencing of normal kidney (n=319) and blood (n=272) samples from 10 countries, we show that normal kidney cell genomes report an extensive repertoire of somatic mutational signatures. Microdissection of kidney structures revealed that proximal tubules exhibit higher mutation rates than other components of the nephron and most normal cell types despite low cell division rates. This is explained by marked enrichment of mutational signatures due to known exogenous carcinogenic mutagens including the plant-derived aristolochic acids, as well as several signatures of unknown causes including an unknown agent prevalent in Japan (SBS12), and signatures of uncertain origins (SBS40b and SBS40c). The results suggest the existence of multiple, common, systemically circulating mutagens affecting human populations and indicate that the genomes of kidney proximal tubule cells report such exposures with high sensitivity.

SummaryThe rapid accumulation of cancer genomic data across repositories such as ClinVar, cBioPortal, and the TCGA Pan-Cancer Atlas has created an urgent need for integrated tools that allow researchers to explore mutations in their structural and clinical context without requiring specialized bioinformatics expertise. Here we present OncoMORPHIA, a free, browser-based web platform that unifies 3D protein structure visualization, clinical variant annotation, drug-target interaction mapping, survival analysis, mutational signature decomposition, and AI-powered interpretation within a single interface. OncoMORPHIA automatically retrieves and integrates data from ten public databases, maps missense mutations onto experimentally determined or AlphaFold-predicted protein structures, computes mutation density heatmaps with Gaussian smoothing, and renders interactive visualizations including lollipop plots, Kaplan-Meier survival curves, protein-protein interaction networks, and pan-cancer tissue distribution charts. The platform supports 45 major cancer driver genes with extensibility to any human gene with available structural data. AvailabilityOncoMORPHIA is freely available at https://oncomorphia.com. Source code is available upon request. The platform requires no installation, no account registration, and no API keys for core functionality.

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

Hypoxia-inducible factors (HIFs) are key regulators of cellular responses to low oxygen (hypoxia), controlling the expression of genes required for survival and adaptation. KDM2B, a chromatin-modifying enzyme, is a direct target of HIF-1, but its precise role in regulating HIF and the hypoxia response remains unclear. Here, we investigated the role of KDM2B in the response to hypoxia in a variety of cell lines. Our analysis reveals that KDM2B depletion regulates HIF activity in a cell type dependent manner, with KDM2B depletion decreasing HIF activity in U2OS and MDA-MB-231 cells and increasing HIF activity in HeLa cells. We show that KDM2B depletion also reduces HIF-1 protein and RNA expression and reduces HIF-1 binding at hypoxia-response elements of its target genes in U2OS and MDA-MB-231 cells. Conversely, overexpression of KDM2B enhances HIF activity and HIF-1 levels in both U2OS and HEK293 cells. Mechanistically, we find that KDM2B requires its JmjC demethylase and CxxC DNA-binding domains for HIF regulation. Furthermore, we demonstrate that KDM2B is required for RNA Pol II recruitment to the promoter of HIF-1. At the cellular level, KDM2B supports cell proliferation, with its depletion impairing proliferation and reducing cell numbers under hypoxic conditions. Our work highlights a new function of KDM2B, as a key regulator of HIF-1 expression, acting through its demethylase and DNA-binding functions. Our data indicate that KDM2B is essential for cellular adaptation to hypoxia, impacting both HIF-dependent gene expression and cell survival, and has important implications for our understanding of HIF regulation.

Well- and dedifferentiated liposarcomas (WDLPS and DDLPS) are characterized by extensive copy- number alterations rather than recurrent gene-inactivating mutations, obscuring the molecular mechanisms that drive disease progression and, critically, the transition from well-differentiated to the more aggressive dedifferentiated tumor states. Despite marked differences in clinical behavior and prognosis, the regulatory events underlying adipocytic lineage destabilization in DDLPS remain poorly understood. Here, we establish an in vivo model of retroperitoneal liposarcoma in Xenopus tropicalis through early embryonic mosaic perturbation of p53 and Rb pathway components. Combined disruption reproducibly induced retroperitoneal WDLPS development, demonstrating that pathway-level deregulation of the MDM2-p53 and CDK4-Rb axes is sufficient to initiate liposarcoma development in vivo. Strikingly, additional perturbation of transcriptional co-activator ep300 in this context resulted in increased tumor dedifferentiation, yielding lesions composed of spatially coexisting well- and dedifferentiated adipocytic states. In contrast, direct targeted disruption of downstream adipogenic regulators recurrently lost in human DDLPS, including cebpa, g0s2, and dgat2, failed to induce dedifferentiation in the same genetic context in vivo. These findings indicate that dedifferentiation cannot be explained by loss of downstream adipocytic effectors alone but instead reflects destabilization of higher-order regulatory programs governing adipocytic identity. Together, these results establish an in vivo model that closely reflects the clinical situation on a pathway level and provides initial mechanistic insight into how adipocytic differentiation may become destabilized during disease progression. This framework offers a foundation for future studies leveraging higher-order and multi-omic approaches to dissect the molecular processes underlying the WDLPS-to-DDLPS transition.