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bioRxiv2025

Epigenomic Heterogeneity of Non-Functional Pancreatic Neuroendocrine Tumors Uncovered by Single nucleus and Spatial ATAC Profiling

Non-functional pancreatic neuroendocrine tumors (NF-PanNETs) account for the majority of neuroendocrine neoplasms arising in the pancreas and exhibit substantial clinical and biological heterogeneity, yet their epigenetic regulation and spatial architecture remain poorly understood. Here, we present an integrative study of NF-PanNETs across multiple tumor grades using single-nucleus ATAC-seq (snATAC-seq) and spatial ATAC-seq. snATAC-seq delineates the chromatin accessibility landscapes of distinct tumor subtypes, immune cells, and cancer-associated fibroblasts (CAFs), revealing key transcription factor (TF) programs that drive tumor progression and shape microenvironmental interactions. Spatial ATAC-seq further identifies two distinct tumor-stroma ecological niches: a proliferative niche marked by MYC and FOX family, and an invasive niche enriched for Snail family TFs and KRAS pathway activity. These findings demonstrate that cellular behavior in NF-PanNETs is governed not only by intrinsic epigenetic states but also by spatial context. Together, our study provides a spatially resolved epigenomic framework for dissecting NF-PanNET heterogeneity and evolution, offering new biomarkers and regulatory axes for molecular stratification and precision therapy.

cancer biology↗

bioRxiv2019

Skull base meningiomas have a distinct immune landscape.

Modulation of tumor microenvironment is an emerging frontier for new therapeutics. However in meningiomas, the most frequent adult brain tumor, the correlation of microenvironment with tumor phenotype is scarcely studied. We applied a variety of systems biology approaches to bulk tumor transcriptomics to explore the immune environments of both skull base and convexity (hemispheric) meningiomas. We hypothesized that the more benign biology of skull base meningiomas parallels the relative composition and activity of immune cells that oppose tumor growth and/or survival. We firstly applied gene co-expression networks to tumor bulk transcriptomics from 107 meningiomas (derived from 3 independent studies) and found immune processes to be the sole biological mechanism correlated with anatomical location while correcting for tumour grade. We then derived tumor immune cell fractions from bulk transcriptomics data and examined the immune cell-cytokine interactions using a network-based approach. We demonstrate that oncolytic M1 macrophages dominate skull base meningiomas while mast cells, known to play a role in oncogenesis, show greater activity in convexity tumors. Our results are the first to suggest the importance of tumor microenvironment in meningioma biology in the context of anatomic location and immune landscape. These findings may help better inform surgical decision making and yield location-specific therapies through modulation of immune microenvironment.

cancer biology↗

bioRxiv2021

RNF43 inhibits WNT5A driven signaling and suppresses melanoma invasion

RNF43 is a E3 ubiquitin ligase and known negative regulator of WNT/{beta}-catenin signaling. We demonstrate that RNF43 is also regulator of noncanonical WNT5A-induced signaling in human cells. Analysis of the RNF43 interactome using BioID and immunoprecipitation showed that RNF43 can interact with the core receptor complex components dedicated to the noncanonical Wnt pathway such as ROR1, ROR2, VANGL1 and VANGL2. RNF43 triggers VANGL2 ubiquitination and proteasomal degradation and clathrin-dependent internalization of ROR1 receptor. This activity of RNF43 is physiologically relevant and blocks pro-metastatic WNT5A signaling in melanoma. RNF43 inhibits responses to WNT5A, which results in the suppression of invasive properties of melanoma cells. Furthermore, RNF43 prevented WNT5A-assisted development of resistance to BRAF V600E inhibitor. In line with these findings, RNF43 expression decreases during melanoma progression and RNF43-low patients have worse prognosis. We conclude that RNF43 is a newly discovered negative regulator of WNT5A-mediated biological responses that desensitizes cells to WNT5A.

cancer biology↗

bioRxiv2022

Quinolinate promotes alternatively activated macrophage-induced immune tolerance in glioblastoma through the NMDA/PPARγ signaling axis

There has been considerable scientific effort dedicated to understanding the biologic consequence and therapeutic implications of aberrant tryptophan metabolism in brain tumors and neurodegenerative diseases. An overwhelming majority of this work has focused on the upstream metabolism of tryptophan; however, this has not resulted in clinical application. Using global metabolomic profiling of patient-derived brain tumors, we identify the downstream metabolism of tryptophan and accumulation of quinolinate (QA) as a metabolic node in glioblastoma and went on to demonstrate its critical role in promoting immune tolerance. QA acts as a "metabolic checkpoint" in glioblastoma by inducing NMDA receptor activation and Foxo1/PPAR{gamma} signaling, resulting in amplification of immune suppressive macrophages. Using a genetically-engineered mouse model designed to inhibit production of QA, we identify kynureninase as a promising therapeutic target to revert the potent immune suppressive microenvironment in glioblastoma. These findings offer the scientific community an opportunity to revisit the biologic consequence of this pathway as it relates to oncogenesis and neurodegenerative disease and a framework for developing new immune modulatory agents to further clinical gains in these otherwise incurable diseases.

cancer biology↗

bioRxiv2024

Spatial multi-omics defines a shared infiltrative signature in glioblastoma at the resection margin

Glioblastoma (GBM) remains an untreatable disease. Understanding GBMs infiltrative biology at the resection margin is limited, despite causing disease recurrence and progression. To address this, we generated a high-throughput single-nucleus (sn)RNA-seq and snATAC-seq multi-omic dataset from six tumors with distinct genomic drivers and combined it with spatial transcriptomics to characterize the unique molecular phenotype of GBM near the margin. By contrasting GBM-specific biology in matching "Core" vs. "Margin" dissections, we define unique, shared "GBM infiltration" and chromatin accessibility signatures near the margin. We prioritize EGFR as a top differentially expressed and accessible "Margin" marker across GBM subtypes, show its dynamic expression along a core-to-margin infiltration trajectory, and validate its role in migration through CRISPR/Cas9 deletion in two patient-derived models. ChIP-seq studies furthermore corroborate preferential TEAD1 binding at EGFRs accessible regulatory elements. This validated multi-omic dataset enables further studies into tumor and microenvironment biology in the context of residual GBM disease.

cancer biology↗

bioRxiv2019

Dual-matrix 3D culture system as a biomimetic model of epithelial tissues

Recent years have seen an unprecedented rise in the use of 3D culture systems, both in fundamental research and in more translational settings such as drug testing and disease modeling. However, 3D cultures often remain underused by cell biology labs, both due to technical difficulties in system setup and inherent drawbacks of many of the common systems. Here we describe an easy to use, inexpensive and rapidly assembled 3D culture system, suitable for generation of both normal polarized epithelial cysts and in-situ tumor spheroids. This system allows for exploration of many questions of normal and cancer cell biology, including morphogenesis, epithelial polarization, cell motility, intra- and intercellular communication, invasion, metastasis, and tumor-stoma interaction. The 3D cultures are made up of a stiffness tunable, dual-matrix model that can incorporate co-culture of multiple cell types. The model allows for increased physiological relevance by mimicking the organization, ligand composition and stiffness present in-vivo. The setup allows for a wide spectrum of manipulation, including removing cells from the system for DNA/protein expression, transfection and high-resolution imaging of live or fixed cells.

cell biology↗

bioRxiv2021

MFmap: A semi-supervised generative model matching cell lines to tumours and cancer subtypes

Translating in vitro results from experiments with cancer cell lines to clinical applications requires the selection of appropriate cell line models. Here we present MFmap (model fidelity map), a machine learning model to simultaneously predict the cancer subtype of a cell line and its similarity to an individual tumour sample. The MFmap is a semi-supervised generative model, which compresses high dimensional gene expression, copy number variation and mutation data into cancer subtype informed low dimensional latent representations. The accuracy (test set F1 score > 90%) of the MFmap subtype prediction is validated in ten different cancer datasets. We use breast cancer and glioblastoma cohorts as examples to show how subtype specific drug sensitivity can be translated to individual tumour samples. The low dimensional latent representations extracted by MFmap explain known and novel subtype specific features and enable the analysis of cell-state transformations between different subtypes. From a methodological perspective, we report that MFmap is a semi-supervised method which simultaneously achieves good generative and predictive performance and thus opens opportunities in other areas of computational biology. Author summaryCancer researchers perform experiments with cell lines to better understand the biology of cancer and to develop new anti-cancer treatments. A prerequisite to translate promising results from these in vitro experiments to clinical applications is to use the most appropriate cell line for a given tumour or cancer subtype. We present MFmap (model fidelity map), a deep learning technique to integrate cancer genomic data from patients with cell line data. The MFmap neural network compresses complex genomic features from thousands of genes into a small set of features called latent representations. This makes cell line and tumour data comparable and allows cancer researchers to select the best cell line which closely resembles a specific type of tumours or even an individual tumour. By classifying cancer cell lines into subtypes, MFmap offers a new possibility to predict the effect of therapeutic compounds in a particular tumour subtype. For the example of an aggressive brain tumour we demonstrate that MFmap can be used to study cell-state transformations during the disease course. In addition, MFmap is a promising machine learning method with potential applications in many other areas of biology and medicine.

bioinformatics↗

bioRxiv2020

A novel human fetal liver-derived model reveals that MLL-AF4 drives a distinct fetal gene expression program in infant ALL

Although 90% of children with acute lymphoblastic leukemia (ALL) are now cured1, the prognosis of infant-ALL (diagnosis within the first year of life) remains dismal2. Infant-ALL is usually caused by a single genetic hit that arises in utero: rearrangement of the MLL/KMT2A gene (MLL-r). This is sufficient to give rise to a uniquely aggressive and treatment-refractory leukemia compared to older children with the same MLL-r3-5. The reasons for disparate outcomes in patients of different ages with identical driver mutations are unknown. This paper addresses the hypothesis that fetal-specific gene expression programs co-operate with MLL-AF4 to initiate and maintain infant-ALL. Using direct comparison of fetal and adult HSC and progenitor transcriptomes we identify fetal-specific gene expression programs in primary human cells. We show that MLL-AF4-driven infant-ALL, but not MLL-AF4 childhood-ALL, displays expression of fetal-specific genes. In a direct test of this observation, we find that CRISPR-Cas9 gene editing of primary human fetal liver cells to produce a t(4;11)/MLL-AF4 translocation replicates the clinical features of infant-ALL and drives infant-ALL-specific and fetal-specific gene expression programs. These data strongly support the hypothesis that fetal-specific gene expression programs co-operate with MLL-AF4 to initiate and maintain the distinct biology of infant-ALL.

cancer biology↗

bioRxiv2017

Clear Cell Renal Cell Carcinoma With Biallelic Inactivation Of CDKN2A/B On 9p21 Have Distinct Gene Expression Signature And Are Associated With Poor Prognosis

PurposeThe clinical implications of biallelic inactivation of CDKN2A/B in clear cell renal cell carcinoma (ccRCC) and relevant dysregulated biological pathways and gene signatures were investigated.\n\nMaterials and MethodsData were obtained from the TCGA data set and validated using Project GENIE and previously published dataset. CDKN2A/B allelic status was classified into 3 groups, including biallelic CDKN2A/B inactivation (homozygous deletion or combined heterozygous deletion and mutation), monoallelic CDKN2A/B loss (heterozygous deletion or mutation) and absent CDKN2A/B allelic loss. Univariate and multivariate cancer-specific survival and disease-free survival analyses were performed. Integrated analyses of copy number, gene expression (mRNA and miRNA), protein expression and methylation changes were conducted.\n\nResultsOf 440 patients with ccRCC 17 (3.9%) had biallelic CDKN2A/B inactivation and 116 (26.4%) had monoallelic CDKN2A/B loss. CDKN2A/B allelic inactivation was associated with late tumor stage, high histological grade, presence of metastasis and greater tumor size. Patients with biallelic deletion of CDKN2A/B showed significantly worse cancer-specific survival and disease-free survival (p<0.0001). Significant co-occurrence of MTAP homozygous deletion was observed in CDKN2A/B biallic inactivated tumors (46.7%; p<0.001). Significant underexpression of CDKN2A/B was observed in biallelic inactivated tumors at the mRNA and protein levels. miR-21 was the most highly expressed miRNA in biallelic inactivated tumors. Biallelic inactivated tumors were significantly enriched for genes related to activation of ATR in response to replication stress and miR-21 target genes.\n\nConclusionsCDKN2A/B biallelic inactivation may be a prognostic marker for ccRCC and is associated with distinct dysregulation of gene expression signatures.

cancer biology↗

bioRxiv2023

Modulation of Viability, Proliferation, and Stemness by Rosmarinic Acid in Medulloblastoma Cells: Involvement of HDACs and EGFR

Medulloblastoma (MB) is a heterogeneous group of malignant pediatric brain tumors, divided into molecular groups with distinct biological features and prognosis. Currently available therapy often results in poor long-term quality of life for patients, which will be afflicted by neurological, neuropsychiatric, and emotional sequelae. Identifying novel therapeutic agents capable of targeting the tumors without jeopardizing patients quality of life is imperative. Rosmarinic acid (RA) is a plant-derived compound whose action against a series of diseases including cancer has been investigated, with no side effects reported so far. Previous studies have not examined whether RA has effects in MB. Here, we show RA is cytotoxic against human Daoy (IC50= 168 M) and D283 (IC50= 334 M) MB cells. Exposure to RA for 48 h reduced histone deacetylase 1 (HDAC1) expression while increasing H3K9 hyperacetylation, reduced epidermal growth factor (EGFR) expression, and inhibited EGFR downstream targets extracellular-regulated kinase (ERK)1/2 and AKT in Daoy cells. These modifications were accompanied by increased expression of CDKN1A/p21, reduced expression of SOX2, and a decrease in proliferative rate. Treatment with RA also reduced cancer stem cell markers expression and neurosphere size. Taken together, our findings indicate that RA can reduce cell proliferation and stemness and induce cell cycle arrest in MB cells. Mechanisms mediating these effects may include targeting HDAC1, EGFR, and ERK signaling, and promoting p21 expression, possibly through an increase in H3K9ac and AKT deactivation. RA should be further investigated as a potential anticancer agent in experimental MB.

cancer biology↗

bioRxiv2019

Integrated analyses of early responses to radiation in glioblastoma identify new alterations in RNA processing and candidate target genes to improve treatment outcomes

BackgroundHigh-dose radiation is the main component of glioblastoma therapy. Unfortunately, radio-resistance is a common problem and a major contributor to tumor relapse. Understanding the molecular mechanisms driving response to radiation is critical for identifying regulatory routes that could be targeted to improve treatment response. MethodsWe conducted an integrated analysis in the U251 and U343 glioblastoma cell lines to map early alterations in the expression of genes at three levels: transcription, splicing, and translation in response to ionizing radiation. ResultsChanges at the transcriptional level were the most prevalent response. Downregulated genes are strongly associated with cell cycle and DNA replication and linked to a coordinated module of expression. Alterations in this group are likely driven by decreased expression of the transcription factor FOXM1 and members of the E2F family. Genes involved in RNA regulatory mechanisms were affected at the mRNA, splicing, and translation levels, highlighting their importance in radiation-response. We identified a number of oncogenic factors, with an increased expression upon radiation exposure, including BCL6, RRM2B, IDO1, FTH1, APIP, and LRIG2 and lncRNAs NEAT1 and FTX. Several of these targets have been previously implicated in radio-resistance. Therefore, antagonizing their effects post-radiation could increase therapeutic efficacy. ConclusionsOur integrated analysis provides a comprehensive view of early response to radiation in glioblastoma. We identify new biological processes involved in altered expression of various oncogenic factors and suggest new target options to increase radiation sensitivity and prevent relapse.

cancer biology↗

bioRxiv2026

Integrated multi-omic analysis of pediatric metastatic osteosarcoma reveals endothelial cell plasticity and lineage infidelity.

While recent research has increasingly focused on the role of fibroblasts and macrophages in osteosarcoma, the tumor vasculature remains poorly understood, particularly in metastatic disease. To address this gap, we performed single-nuclei multi-ome (RNA+ATAC) sequencing on 24 human metastatic osteosarcoma specimens. We found that endothelial cells adopt a hybrid endothelial-mesenchymal state resembling endothelial-to-mesenchymal transition (EndMT) and that a subset of diploid endothelial cells expresses osteoblastic transcriptional profiles and gene regulatory networks (GRN). Joint copy-number analysis further identified osteosarcoma cells with endothelial transcriptional programs and GRNs, consistent with vascular mimicry. In vitro assays and syngeneic lineage-tracing experiments validated that tumor educated endothelial cells acquire osteoblast-like features. Together, these findings reveal substantial plasticity among endothelial and osteosarcoma cells in human and murine metastatic osteosarcoma, provide new insight into the how the metastatic microenvironment shapes the tumor vasculature, and challenge current models of osteosarcoma biology.

cancer biology↗

bioRxiv2017

High accuracy label-free classification of kinetic cell states from holographic cytometry

Digital holographic microscopy permits live and label-free visualization of adherent cells. Here we report the application of this approach for high accuracy kinetic quantitative cytometry. We identify twenty-six label-free optical and morphological features that are biologically independent. When used as a basis for machine learning, these features allow blind single cell classification with up to 95% accuracy. We present methods to control for inherent holographic noise, thereby establishing a set of reliable quantitative features. Together, these contributions permit continuous digital holographic cytometry for three or more days. Applying our approach to human melanoma cells treated with a panel of cancer therapeutics, we can track the response of each cell, simultaneously classifying multiple behaviors such as cell cycle length, motility, apoptosis, senescence, and heterogeneity of response to each therapeutic. Importantly, we demonstrate relationships between these phenotypes over time. This work thus provides an experimental and computational roadmap for low cost live-cell imaging and kinetic classification of heterogeneous adherent cell populations.

cancer biology↗

bioRxiv2018

PDX Finder: A Portal for Patient-Derived tumor Xenograft Model Discovery

Patient-derived tumor xenograft (PDX) mouse models are a versatile oncology research platform for studying tumor biology and for testing chemotherapeutic approaches tailored to genomic characteristics of individual patients tumors. PDX models are generated and distributed by a diverse group of academic labs, research organizations, multi-institution consortia, and contract research organizations. The distributed nature of PDX repositories and the use of different standards in the associated metadata presents a significant challenge to finding PDX models relevant to specific cancer research questions. The Jackson Laboratory and EMBL-EBI are addressing these challenges by co-developing PDX Finder, a comprehensive open global catalog of PDX models and their associated datasets. Within PDX Finder, model attributes are harmonized and integrated using a previously developed community minimal information standard to support consistent searching across the originating resources. Links to repositories are provided from the PDX Finder search results to facilitate model acquisition and/or collaboration. The PDX Finder resource currently contains information for more than 1900 PDX models of diverse cancers including those from large resources such as the Patient-Derived Models Repository, PDXNet, and EurOPDX. Individuals or organizations that generate and distribute PDXs are invited to increase the \"findability\" of their models by participating in the PDX Finder initiative at www.pdxfinder.org.

cancer biology↗

bioRxiv2020

NOTCH-YAP1/TEAD-DNMT1 axis regulates hepatocytereprogramming into intrahepatic cholangiocarcinoma

Intrahepatic cholangiocarcinoma (ICC), a disease of poor prognosis, has increased in incidence. It is challenging to treat due to intra- and inter-tumoral heterogeneity, which in part is attributed to diverse cellular origin. Indeed, co-expression of AKT and NICD in hepatocytes (HCs) yielded ICC, with similarity to proliferative, Notch-activated, and stem cell-like subclasses of clinical ICC. NICD regulated SOX9 and YAP1 during ICC development. Yap1 deletion or TEAD inhibition impaired HC-to-biliary epithelial cell (BEC) reprogramming and ICC proliferation; Sox9 loss repressed tumor growth; and Yap1-Sox9 combined loss abolished ICC development in AKT-NICD model. DNMT1 was discovered as a novel downstream effector of YAP1-TEAD complex that directed HC-to-BEC/ICC fate-switch. DNMT1 loss prevented Notch-dependent HC-to-ICC development, and DNMT1 re-expression restored ICC development following TEAD repression. Coexpression of DNMT1 with AKT was sufficient to induce hepatic tumor development including ICC. Thus, we have identified a novel NOTCH-YAP1/TEAD-DNMT1 axis essential for HC-driven ICC development. SIGNIFICANCEWe evaluated the clinical relevance of hepatocyte-driven ICC model and revealed critical but distinct roles of YAP1 and SOX9 in AKT-NICD-driven hepatocyte-derived ICC. We also identified NOTCH-YAP1/TEAD-DNMT1 axis as a critical driver for hepatocyte-to-ICC reprogramming, which might have biological and therapeutic implications in ICC subsets.

cancer biology↗

bioRxiv2026

Phosphorylation Protects Oncogenic RAS from LZTR1-Mediated Degradation

Oncogenic KRAS and NRAS mutations are common in hematologic malignancies, but how they signal is less well characterized than in carcinomas. To uncover novel RAS biology and potential therapeutic vulnerabilities, we employed a multi-omics screening approach in multiple myeloma to identify regulators of RAS activity. We report that the phosphatase PP1C dephosphorylates the conserved T148 residue on RAS, which in turn permits LZTR1-dependent proteasomal degradation. Notably, LZTR1 is ineffective against KRAS A146 gain-of-function mutations, which are adjacent to T148 and prevalent in hematologic cancers. Remarkably, we find that KRAS protein is four-fold less stable in hematologic versus carcinoma cells, offering a unique therapeutic opportunity targeting RAS protein stability mechanisms. The kinases PAK1 and PAK2 shield RAS from LZTR1-dependent degradation by phosphorylating T148, and targeting PAK1/2 activity improves RAS-directed therapy. Collectively, our findings reveal a novel regulatory circuit governing RAS stability that is preferentially active in blood cancers and potentially druggable.

cancer biology↗

bioRxiv2022

Pick-up Single-Cell Proteomic Analysis for Quantifying up to 3000 Proteins in a Tumor Cell

The shotgun proteomic analysis is currently the most promising single-cell protein sequencing technology, however its identification level of [~]1000 proteins per cell is still insufficient for practical applications. Here, we develop a pick-up single-cell proteomic analysis (PiSPA) workflow to achieve a deep identification capable of quantifying up to 3000 protein groups in a tumor cell using the label-free quantitative method. The PiSPA workflow is specially established for single-cell samples mainly based on a nanoliter-scale microfluidic liquid handling robot, capable of achieving single-cell capture, pretreatment and injection under the pick-up operation strategy. Using this customized workflow with remarkable improvement in protein identification, 1804-3349, 1778-3049 and 1074-2487 protein groups are quantified in single A549 cells (n = 37), HeLa cells (n = 44) and U2OS cells (n = 27), respectively. Benefiting from the flexible cell picking-up ability, we study tumor cell migration at the single cell proteome level, demonstrating the potential in practical biological research from single-cell insight.

cancer biology↗

bioRxiv2025

Methylation-Based Deconvolution Unveils Glioblastoma Heterogeneity and Cell-Type Composition Linked to Patient Survival

BackgroundIDH-wildtype glioblastoma (GBM) is an aggressive, heterogeneous brain tumor with limited treatment options. DNA methylation profiling allows detailed tumor characterization. This study applies methylation-based deconvolution to define GBMs cellular composition and its association with patient outcomes. MethodsWe generated oligodendroglial precursor cells at various developmental stages from enriched human neural progenitor cultures and used their DNA methylation signatures, along with published signatures of brain tumor and tumor microenvironment-relevant cell types, to deconvolve 263 adult GBMs (Heidelberg-cohort). Tumor purity was estimated using RF_Purify. An independent cohort of 199 GBMs from TCGA and GEO, all treated with standard-of-care therapy, was similarly deconvolved, followed by Kaplan-Meier survival analysis to assess the prognostic value of neoplastic component proportions. ResultsDeconvolution uncovered distinct cellular compositions, consistent with single-cell RNA sequencing findings. Tumor purity analysis showed neoplastic fractions averaged 70% of tumor bulk, predominantly oligodendrocyte-like (43%), oligodendrocyte precursor-like (27%), astrocyte-like (19%), and mesenchymal stem cell-like (11%) populations. Non-neoplastic fractions were enriched for macrophages, vascular cells, and immune populations. A higher oligodendrocyte-like signature was linked to poorer survival (median survival 14.3 vs. 15.3 months; p = 0.017), while a higher astrocyte-like signature correlated with improved outcomes (15.3 vs. 13.4 months; p = 0.044). The astrocyte-to-oligodendrocyte ratio emerged as a strong prognostic marker, with a higher ratio predicting significantly longer survival (15.8 vs. 11.9 months; p < 0.00011). ConclusionsMethylation-based deconvolution provides insight into GBM heterogeneity, highlighting the prognostic relevance of the astrocyte-to-oligodendrocyte ratio, which may guide personalized treatment strategies. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=84 SRC="FIGDIR/small/640603v2_ufig1.gif" ALT="Figure 1"> View larger version (22K): org.highwire.dtl.DTLVardef@942af0org.highwire.dtl.DTLVardef@18f2626org.highwire.dtl.DTLVardef@111a756org.highwire.dtl.DTLVardef@1194b_HPS_FORMAT_FIGEXP M_FIG A. We generated oligodendroglial precursor cells (OPs) at various developmental stages from enriched human neural progenitor cultures. A reference atlas of methylation signatures from 14 normal cell types, including the in vitro-generated cells, combined with published profiles of brain tumor and tumor microenvironment-relevant cell types, was applied to deconvolve 263 GBM samples based on their methylation profiles (Heidelberg cohort). Cell type proportions were analyzed with RF_Purify to estimate tumor purity and distinguish neoplastic from non-neoplastic components. B. An independent cohort of 199 GBMs from TCGA and GEO, with available clinical data and all treated with standard-of-care therapy, was similarly deconvolved based on methylation profiles. Kaplan-Meier survival analysis assessed the prognostic impact of cell type proportions, identifying the astrocyte-to-oligodendrocyte ratio as the most significant marker. (Created with BioRender.com) C_FIG Key pointsO_LIMethylation-based deconvolution incorporating in-vitro-derived oligodendrocyte precursor differentiation data reveals GBM heterogeneity C_LIO_LIGBMs neoplastic fraction is dominated by oligodendrocyte-lineage cells C_LIO_LIAstrocyte/oligodendrocyte ratio is a strong prognostic marker for patient survival C_LI Importance of the studyThis study advances neuro-oncology research by using methylation-based deconvolution to uncover the cellular heterogeneity of glioblastoma. By constructing a comprehensive reference atlas and identifying cell-type-specific methylation signatures, it provides prognostic insights into the distinct cellular compositions of glioblastoma. Combining deconvolution results with purity analysis enables the differentiation between neoplastic and non-neoplastic tumor components. This approach complements single-cell RNA sequencing while offering greater clinical applicability, as DNA methylation profiling can be performed on FFPE or fresh frozen samples, unlike the more costly and tissue-sensitive single-cell methods. Importantly, the identification of the astrocyte-to-oligodendrocyte ratio as a strong prognostic marker highlights key cellular determinants of glioblastoma survival. These findings deepen our understanding of glioblastoma biology and offer a practical tool for patient stratification.

cancer biology↗