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bioRxiv2023

Spatial transcriptomics analysis identifies a unique tumor-promoting function of the meningeal stroma in melanoma leptomeningeal disease

Leptomeningeal disease (LMD) remains a rapidly lethal complication for late-stage melanoma patients. The inaccessible nature of the disease site and lack of understanding of the biology of this unique metastatic site are major barriers to developing efficacious therapies for patients with melanoma LMD. Here, we characterize the tumor microenvironment of the leptomeningeal tissues and patient-matched extra-cranial metastatic sites using spatial transcriptomic analyses with in vitro and in vivo validation. We show the spatial landscape of melanoma LMD to be characterized by a lack of immune infiltration and instead exhibit a higher level of stromal involvement. We show that the tumor-stroma interactions at the leptomeninges activate pathways implicated in tumor-promoting signaling, mediated through upregulation of SERPINA3 at the tumor-stroma interface. Our functional experiments establish that the meningeal stroma is required for melanoma cells to survive in the CSF environment and that these interactions lead to a lack of MAPK inhibitor sensitivity in the tumor. We show that knocking down SERPINA3 or inhibiting the downstream IGR1R/PI3K/AKT axis results in re-sensitization of the tumor to MAPK-targeting therapy and tumor cell death in the leptomeningeal environment. Our data provides a spatial atlas of melanoma LMD, identifies the tumor-promoting role of meningeal stroma, and demonstrates a mechanism for overcoming microenvironment-mediated drug resistance unique to this metastatic site.

cancer biology↗

bioRxiv2026

Deep learning enables feature extraction of 3D collagen architecture in cleared fibrotic tissues

Light-sheet fluorescence microscopy enables deep optical sectioning of large, cleared biological tissues. However, effective clearing of collagen-rich tissues remains a persistent technical challenge. Moreover, standardized workflows integrating three-dimensional imaging with computational analysis of collagen architecture are currently unavailable. Here, we present an integrated pipeline combining optimized tissue clearing, volumetric light-sheet imaging, and deep learning-based feature extraction of collagen architecture. Using experimental desmoid tumors as a proxy for collagen-dense tissues, we optimised DISCO-based clearing incorporating Fast Green FCF for collagen labelling. We achieved optical transparency and full 3D visualization of collagen architectures in desmoid tumors, human skin biopsies, and fibrotic mouse lung & liver tissues, including FFPE samples. Using the Benchtop mesoSPIM platform, we acquired high-resolution volumetric datasets and validated multimodal collagen imaging through two-photon microscopy with the Schmidt-Voigt objective. To enable automated feature extraction from these large volumetric datasets, we developed ColNet, a U-Net model for automated collagen fiber segmentation. ColNet demonstrated robust generalization across diverse human and mouse tissues without retraining or hyperparameter adjustment. This integrated workflow provides a foundation for future quantitative assessment of cell-extracellular matrix dynamics in a fibrotic context.

cancer biology↗

bioRxiv2019

JAM-A functions as a female microglial tumor suppressor in glioblastoma

Glioblastoma (GBM) remains refractory to treatment. In addition to its cellular and molecular heterogeneity, epidemiological studies indicate the presence of additional complexity associated with biological sex. GBM is more prevalent and aggressive in male compared to female patients, suggesting the existence of sex-specific growth, invasion, and therapeutic resistance mechanisms. While sex-specific molecular mechanisms have been reported at a tumor cell-intrinsic level, sex-specific differences in the tumor microenvironment have not been investigated. Using transgenic mouse models, we demonstrate that deficiency of junctional adhesion molecule-A (JAM-A) in female mice enhances microglia activation, GBM cell proliferation, and tumor growth. Mechanistically, JAM-A suppresses anti-inflammatory/pro-tumorigenic gene activation via interferon-activated gene 202b (Ifi202b) and found in inflammatory zone (Fizz1) in female microglia. Our findings suggest that cell adhesion mechanisms function to suppress pathogenic microglial activation in the female tumor microenvironment, which highlights an emerging role for sex differences in the GBM microenvironment and suggests that sex differences extend beyond previously reported tumor cell intrinsic differences.\n\nSummaryTuraga et al. demonstrate that female microglia drive a more aggressive glioblastoma phenotype in the context of JAM-A deficiency. These findings highlight a sex-specific role for JAM-A and represent the first evidence of sexual dimorphism in the glioblastoma microenvironment.

cancer biology↗

bioRxiv2023

Novel diagnosis and progression microRNA signatures in melanoma

Melanoma incidence is rising, and accurate risk stratification remains challenging because of the molecular heterogeneity underlying disease progression. Specifically, the transition from benign nevi to malignant melanoma and the acquisition of the aggressive ulcerated phenotype represent critical barriers in clinical management that require novel biomarkers. In addition, there is a critical need for novel biomarkers to identify patients with primary melanoma who, despite complete surgical resection, remain at high risk of recurrence and would benefit from adjuvant therapy. We applied an integrative systems biology approach to decipher the miRNA-dependent regulatory architecture of melanoma. Following PRISMA guidelines, we conducted a robust meta-analysis of six independent transcriptomic studies, overcoming inter-study heterogeneity. This was coupled with network inference algorithms to construct validated miRNA-mRNA interactomes and identify dynamic functional modules. In the diagnostic scenario, we identified a consensus signature of 24 miRNAs. Network topology analysis revealed hsa-miR-142-5p as a master regulator that orchestrates the dismantling of the Oncogene-Induced Senescence (OIS) barriers by targeting CDK6, SIRT1, and TGFBR2. In the prognostic scenario (ulceration), we identified a specific "stress-adaptive" signature of 23 miRNAs. Notably, the upregulation of hsa-miR-223-3p emerged as a key driver of invasiveness by suppressing the motility-limiting tumor suppressor RHOB, while the concurrent loss of hsa-miR-200c and hsa-miR-489-3p may unleash Epithelial-Mesenchymal Transition (EMT) and EGFR-driven survival pathways. Our study supports a double-switch mechanism where specific miRNA alterations first drive senescence escape and subsequently promote survival in the hypoxic ulcerated niche. These signatures offer robust biomarkers for diagnosis and highlight the miR-142-5p/CDK6 and miR-223-3p/RHOB axes as potential therapeutic targets for precise intervention.

cancer biology↗

bioRxiv2020

Integrative Molecular Characterization of Sarcomatoid and Rhabdoid Renal Cell Carcinoma Reveals Determinants of Poor Prognosis and Response to Immune Checkpoint Inhibitors

Sarcomatoid and rhabdoid (S/R) renal cell carcinoma (RCC) are highly aggressive tumors with limited molecular and clinical characterization. Emerging evidence suggests immune checkpoint inhibitors (ICI) are particularly effective for these tumors1-3, although the biological basis for this property is largely unknown. Here, we evaluate multiple clinical trial and real-world cohorts of S/R RCC to characterize their molecular features, clinical outcomes, and immunologic characteristics. We find that S/R RCC tumors harbor distinctive molecular features that may account for their aggressive behavior, including BAP1 mutations, CDKN2A deletions, and increased expression of MYC transcriptional programs. We show that these tumors are highly responsive to ICI and that they exhibit an immune-inflamed phenotype characterized by immune activation, increased cytotoxic immune infiltration, upregulation of antigen presentation machinery genes, and PD-L1 expression. Our findings shed light on the molecular drivers of aggressivity and responsiveness to immune checkpoint inhibitors of S/R RCC tumors.

cancer biology↗

bioRxiv2021

A potent tumour-suppressor TCF11, than its prototypic Nrf1α, is defined by similar yet different regulatory profiles of both isoforms, with a striking disparity from Nrf2

Nrf1 and Nrf2, as two principal CNC-bZIP transcription factors, regulate similar but different targets involved in a variety of biological functions for maintaining cell homeostasis and organ integrity. Of note, the unique topobiological behavior of Nrf1 makes its functions more complicated than Nrf2, because it is allowed for alternatively transcribing and selectively splicing to yield multiple isoforms (e.g., TCF11, Nrf1). In order to gain a better understanding of their similarities and differences in distinct regulatory profiles, all four distinct cell models for stably expressing TCF11, TCF11{Delta}N, Nrf1 or Nrf2 have been herein established by an Flp-In T-REx-293 system and then identified by transcriptomic sequencing. Further analysis revealed that Nrf1 and TCF11 have similar yet different regulatory profiles, although both contribute basically to positive regulation of their co-targets, which are disparate from those regulated by Nrf2. Such disparity in those gene regulation by Nrf1 and Nrf2 was further corroborated by scrutinizing comprehensive functional annotation of their specific and/or common target genes. Conversely, the mutant TCF11{Delta}N, resulting from a deletion of the N-terminal amino acids 2-156 from TCF11, resembles Nrf2 with the largely consistent structure and function. Interestingly, our further experimental evidence demonstrates that TCF11 acts as a potent tumor-repressor relative to Nrf1, albeit both isoforms possess a congruous capability to prevent malignant growth of tumor and upregulate those genes critical for improving the survival of patients with hepatocellular carcinoma.

cancer biology↗

bioRxiv2023

Computational modeling of methylation impact of AML drivers reveals new pathways and refines AML risk-stratification

Decades before its clinical onset, epigenetic changes start to accumulate in the progenitor cells of Acute Myelogenous Leukemia (AML). Delineating these changes can improve risk-stratification for patients and shed insights into AML etiology, dynamics and mechanisms. Towards this goal, we extracted "epigenetic signatures" through two parallel machine learning approaches: a supervised regression model using frequently mutated genes as labels and an unsupervised topic modeling approach to factorize covarying epigenetic changes into a small number of "topics". First, we created regression models for DNMT3A and TET2, the two most frequently mutated epigenetic drivers in AML. Our model differentiated wild-type vs. mutant genotypes based on their downstream epigenetic impacts with very high accuracy: AUROC 0.9 and 0.8, respectively. Methylation loci frequently selected by the models recapitulated known downstream pathways and identified several novel recurrent targets. Second, we used topic modeling to systematically factorize the high dimensional methylation profiles to a latent space of 15 topics. We annotated identified topics with biological and clinical features such as mutation status, prior malignancy and ELN criteria. Topic modeling successfully deconvoluted the combined effects of multiple upstream epigenetic drivers into individual topics including relatively infrequent cytogenetic events, improving the methylation-based subtyping of AML. Furthermore, they revealed complimentary and synergistic interactions between drivers, grouped them based on the similarity of their downstream methylation impact and linked them to prognostic criteria. Our models identify new signatures and methylation pathways, refine risk-stratification and inform detection and drug response studies for AML patients. KEY POINTSO_LISupervised and unsupervised models reveal new methylation pathways of AML driver events and validate previously known associations. C_LIO_LIIndividual DNMT3A and TET2 signatures are accurate and robust, despite the complex genetic and epigenetic make-up of samples at diagnosis. C_LIO_LIUnsupervised topic modeling factorizes covarying methylation changes and isolates methylation signatures caused by rare mutations. C_LIO_LITopic modeling reveals a group of mutations with similar downstream methylation impacts and mapped to adverse-risk class by ELN. C_LIO_LITopic modeling uncovers methylation signatures of infrequent cytogenetic events, significantly improving methylation-based subtyping. C_LIO_LIOur models can be leveraged to build predictive models for AML-risk. C_LIO_LIOur models show that cytogenetic events, such as t(15;17) have widespread trans downstream methylation impacts. C_LI

cancer biology↗

bioRxiv2022

A spatial statistical framework for the parametric study of fiber networks: application to fibronectin deposition by normal and activated fibroblasts

Changes in the spatial landscape of the extracellular matrix (ECM) in health and disease significantly impact the surrounding tissues. Quantifying the spatial variations in the fibrillar architecture of major ECM proteins could enable a profound understanding of the link between tissue structure and function. We propose a method to capture relevant ECM features using graph networks for fiber representation in normal and tumor-like states of 4 alternatively spliced isoforms of fibronectin (FN) associated with embryonic development and disease. Then, we construct graph-derived statistical parametric maps, to study the differences across variants in normal and tumor-like architectures. This novel statistical analysis approach, inspired from the analysis of functional magnetic resonance imaging (fMRI) images, provides an appropriate framework for measuring and detecting local variations of meaningful matrix parameters. We show that parametric maps representing fiber length and pore orientation isotropy can be studied within the proposed framework to differentiate among various tissue states. Such tools can potentially lead to a better understanding of dynamic matrix networks within the tumor microenvironment and contribute to the development of better imaging modalities for monitoring their remodeling and normalization following therapeutic intervention. Author SummaryDue to the complex architectural diversity of biological networks, there is an increasing need to complement statistical analyses with a qualitative and local description. The extracellular matrix (ECM) is one such network for which fiber arrangement has a major impact on tissue structure and function. Thus, a flexible numerical representation of fibrillar networks is needed for accurate analysis and meaningful statistical comparison of ECM in healthy and diseased tissue. First, we propose a versatile computational pipeline to study fiber-specific features of the ECM with graph networks. Then, we introduce a novel framework for the statistical analysis of graph-derived parametric maps, inspired from the statistical analysis of fMRI parametric maps. This analysis is useful for the quantitative/qualitative comparison of ECM fiber networks observed in normal and tumor-like, or fibrotic states. These methods are applied to study networks of fibronectin (FN), a provisional ECM component that dictates the organization of matrix structure. From 2D confocal images we analyzed architectural variations among 4 alternatively spliced isoforms of FN, termed oncofetal FN, that are prevalent in diseased tissue. We show how our approach can be used for the computation and statistical comparison of heterogeneous parametric maps representing FN variant-specific topological/geometrical features. These methods may be further developed and implemented into tumor tissue ECM profiling to decipher the specific roles of ECM landscapes and their remodeling in disease.

cancer biology↗

bioRxiv2023

HCCDB v2.0: Decompose the Expression Variations by Single-cell RNA-seq and Spatial Transcriptomics in HCC

Large-scale transcriptomic data are crucial for understanding the molecular features of hepatocellular carcinoma (HCC). By integrating 15 transcriptomic datasets of HCC clinical samples, the first version of HCCDB was released in 2018. The meta-analysis of differentially expressed genes and prognosis-related genes across multiple datasets provides a systematic view of the altered biological processes and the inter-patient heterogeneities of HCC with high reproducibility and robustness. After four years, the database needs to integrate recently published datasets. Furthermore, the latest single-cell and spatial transcriptomics provided a great opportunity to decipher the complex gene expression variations at the cellular level with spatial architecture. Here, we present HCCDB v2.0, an updated version that combines bulk, single-cell, and spatial transcriptomic data of HCC clinical samples. It dramatically expands the bulk sample size, adding 1656 new samples of 11 datasets to the existing 3917 samples, thereby enhancing the reliability of transcriptomic meta-analysis. A total of 182,832 cells and 69,352 spatial spots are added to the single-cell and spatial transcriptomics sections, respectively. A novel single-cell level and 2-dimension (sc-2D) metric was proposed to summarize the cell type-specific and dysregulated gene expression patterns. Results are all graphically visualized in our online portal, allowing users to easily retrieve data through a user-friendly interface and navigate between different views. With extensive clinical phenotypes and transcriptomic data in the database, we show two applications for identifying prognosis-associated cells and tumor microenvironment. HCCDB v2.0 is available at http://lifeome.net/database/hccdb2.

cancer biology↗

bioRxiv2026

Plasma proteome screen of individuals with elevated blood counts to identify diagnostic signatures and biomarkers inherent to patients with myeloproliferative neoplasms

BackgroundThe Philadelphia negative myeloproliferative neoplasms (MPN), including essential thrombocythemia (ET), polycythemia vera (PV) and primary myelofibrosis (PMF), are characterized by myeloid cell proliferation, thrombosis and inflammation. Suspicion of MPN arises from increased blood count in one or more lineages; however, knowledge on the MPN plasma proteome including biomarkers measurable in blood, are lacking. Comparing the plasma proteome of MPN patients to subjects with elevated blood counts but without MPN diagnosis, may provide an increased understanding of the MPN disease biology as well as diagnostic biomarkers measurable in blood. Patients and methodsWe performed plasma proteome profiling in 87 patients referred to the Department of Hematology due to elevated blood counts. Of these, 55 were diagnosed with MPN and 32 did not fulfill MPN diagnostic criteria and thus constituted the non-MPN control group. ResultsThe frequency of thrombosis was equal between the groups. We found 189 differentially expressed proteins between MPN and non-MPN, enriched for Hemostasis and Platelet activation proteins. Using Lasso multiple regression, we identified SORT1, GP1BA, PSPN, MMP1 and BAG6 separating MPN from non-MPN individuals, and TFRC and SEMA7A specific for MPN subtype PV. Interestingly, TFRC alone had a diagnostic accuracy for identifying PV of 89.3%, and when combined with serum erythropoietin it increased to 99.3%. Only two proteins, IL-6 and GH1, were increased in JAK2 mutant MPN compared to JAK2 wildtype MPN. The same trend was seen for JAK2 mutant ET compared to JAK2 wildtype ET, indicating that IL-6 is induced by JAK STAT activation. However, IL-6 levels did not differ between MPN and non-MPN patients. Discussion/conclusionIn conclusion, hemostasis and platelet activation are inherent to MPN disease whereas little difference was found in proinflammatory cytokines between MPN and non-MPN groups. We demonstrate novel potential blood biomarkers for MPN and MPN subtypes, in particular TFRC for identifying PV patients.

cancer biology↗

bioRxiv2023

Identification of glioblastoma stem cell-associated lncRNAs using single-cell RNA-sequencing datasets

Glioblastoma multiforme (GBM) is an aggressive, heterogeneous grade IV brain tumor. Glioblastoma stem cells (GSCs) initiate the tumor and are known culprits of therapy resistance. Mounting evidence has demonstrated a regulatory role of long non-coding RNAs (lncRNAs) in various biological processes, including pluripotency, differentiation, and tumorigenesis. A few studies have suggested that aberrant expression of lncRNAs is associated with GSCs. However, a comprehensive single-cell analysis of the GSC-associated lncRNA transcriptome has not been carried out. Here, we analyzed recently published single-cell RNA-sequencing datasets of adult human GBM tumors, GBM organoids, GSC-enriched GBM tumors, and developing human brains to identify lncRNAs highly expressed in GBM. To categorize GSC populations in the GBM tumors, we used the GSC marker genes SOX2, PROM1, FUT4, and L1CAM. We found three major GSC population clusters: radial glia, oligodendrocyte progenitor cells, and neurons. We found 10-100 lncRNAs significantly enriched in different GSC populations. We also validated the level of expression and localization of several GSC-enriched lncRNAs using qRT-PCR, single-molecule RNA FISH, and sub-cellular fractionation. We found that the radial glia GSC-enriched lncRNA PANTR1 is highly expressed in GSC lines and is localized to both the cytoplasmic and nuclear fractions. In contrast, the neuronal GSC-enriched lncRNAs LINC01563 and MALAT1 are highly enriched in the nuclear fraction of GSCs. Together, this study identified a panel of uncharacterized GSC-specific lncRNAs. These findings set the stage for future in-depth studies to examine their role in GBM pathology and their potential as biomarkers and/or therapeutic targets in GBM.

cancer biology↗

bioRxiv2021

Pin Electrode Reactor: A novel cold atmospheric plasma device and its potential in glioblastoma treatment

Glioblastoma multiforme (GBM) is the most common and biologically aggressive brain tumour. The current standard therapy for GBM consists in surgical resection, followed by radiotherapy and chemotherapy. Yet, the treatment is limited due to the area for the surgical resection and for the inability of some drugs to cross the brain blood barrier, leading to a general prognostic of no more than a year. Cold atmospheric plasma (CAP) is a new approach in the treatment of this challenging disease. CAP interaction with cells is dependent on physical and chemical factors, with different plasma discharges, cell type, and culture conditions leading to different CAP activity. Considering the plasma self-adaptation that different plasma discharge modes can undergo, which leads to different interaction plasma/cells, the characterization of a new device is essential. In this study we analysed the effect of a novel large pin-to-plate non-thermal atmospheric plasma on U-251 MG cells under different conditions. The analysis of reactive oxygen and nitrogen species (RONS) on plasma, media and cells were also assessed. We were able to demonstrate that the pin-to-plate device is cytotoxic to GBM cells in a dose, time and ROS dependent manner. The measurements of RONS on plasma/media also give us an insight on the chemical effect of this novelty device, and the possibility to better understand the use of this device as a promising GBM therapy.

cancer biology↗

bioRxiv2023

A syngeneic spontaneous zebrafish model of tp53-deficient, EGFRviii, and PI3KCA H1047R-driven glioblastoma reveals inhibitory roles for inflammation during tumor initiation and relapse in vivo

High-throughput vertebrate animal model systems for the study of patient-specific biology and new therapeutic approaches for aggressive brain tumors are currently lacking, and new approaches are urgently needed. Therefore, to build a patient-relevant in vivo model of human glioblastoma, we expressed common oncogenic variants including activated human EGFRvIII and PI3KCAH1047R under the control of the radial glial-specific promoter her4.1 in syngeneic tp53 loss-of-function mutant zebrafish. Robust tumor formation was observed prior to 45 days of life, and tumors had a gene expression signature similar to human glioblastoma of the mesenchymal subtype, with a strong inflammatory component. Within early stage tumor lesions, and in an in vivo and endogenous tumor microenvironment, we visualized infiltration of phagocytic cells, as well as internalization of tumor cells by mpeg1.1:EGFP+ microglia/macrophages, suggesting negative regulatory pressure by pro-inflammatory cell types on tumor growth at early stages of glioblastoma initiation. Furthermore, CRISPR/Cas9-mediated gene targeting of master inflammatory transcription factors irf7 or irf8 led to increased tumor formation in the primary context, while suppression of phagocyte activity led to enhanced tumor cell engraftment following transplantation into otherwise immune-competent zebrafish hosts. Altogether, we developed a genetically relevant model of aggressive human glioblastoma and harnessed the unique advantages of zebrafish including live imaging, high-throughput genetic and chemical manipulations to highlight important tumor suppressive roles for the innate immune system on glioblastoma initiation, with important future opportunities for therapeutic discovery and optimizations.

cancer biology↗

bioRxiv2024

Genomic and single-cell characterization of patient-derived tumor organoid models of head and neck squamous cell carcinoma

Head and Neck Squamous Cell Carcinoma (HNSCC) remains a significant health burden due to tumor heterogeneity and treatment resistance, emphasizing the need for improved biological understanding and tailored therapies. This study enrolled 31 HNSCC patients for the establishment of patient-derived tumor organoids (PDOs), which faithfully maintained genomic features and histopathological traits of primary tumors. Long-term culture preserved key characteristics, affirming PDOs as robust representative models. PDOs demonstrated predictive capability for cisplatin treatment responses, correlating ex vivo drug sensitivity with patient outcomes. Bulk and single-cell RNA sequencing unveiled molecular subtypes and intratumor heterogeneity (ITH) in PDOs, paralleling patient tumors. Notably, a hybrid epithelial-mesenchymal transition (hEMT)-like ITH program is associated with cisplatin resistance and poor patient survival. Functional analyses identified amphiregulin (AREG) as a potential regulator of the hybrid epithelial/mesenchymal state. Moreover, AREG contributes to cisplatin resistance via EGFR pathway activation, corroborated by clinical samples. In summary, HNSCC PDOs serve as reliable and versatile models, offer predictive insights into ITH programs and treatment responses, and uncover potential therapeutic targets for personalized medicine. One Sentence SummaryThis study establishes patient-derived tumor organoids (PDOs) from 31 Head and Neck Squamous Cell Carcinoma (HNSCC) patients, faithfully recapitulating characteristics of primary tumors and accurately predicting clinical responses to cisplatin treatment. We reveal intertumoral heterogeneity within PDOs and a hybrid epithelial-mesenchymal transition (hEMT) program conferring cisplatin resistance, highlighting amphiregulin (AREG) as a regulator of cellular plasticity and potential therapeutic target for HNSCC treatment.

cancer biology↗

bioRxiv2025

An immunocompetent mouse model of liposarcoma

Liposarcoma (LPS) is the most prevalent soft tissue sarcoma. The most common biological subtypes are well-differentiated (WDLPS), a low-grade disease that can evolve to high-grade dedifferentiated liposarcoma (DDLPS), with increased rates of recurrence and metastasis and low response rates to systemic therapies. Preclinical testing of immunotherapeutics for LPS has been held back by the lack of an immunocompetent mouse model. Here, we present an autochthonous immunocompetent LPS mouse model, ACPP, with targeted deletion of Trp53 and Pten in adipocytes to mimic signaling alterations observed in human LPS. Similar to humans, ACPP mice produce WDLPS, DDLPS, and tumors that exhibit both WD and DD components. Murine and human DDLPS tumors possess transcriptional similarities, including increased expression of oncogenes Cdk4 and Hmga2 and reduced expression of the tumor suppressor Cebpa; furthermore, both mouse and human DDLPS exhibit heterogenous T cell infiltration. Syngeneic cell lines derived from ACPP DDLPS reliably produce tumors following orthotopic implantation, each with distinct growth patterns, aggressiveness, and immune profiles. These unique models provide much needed tools to understand the complex immunobiology of LPS and greatly accelerate the pace of preclinical studies aimed at uncovering more effective new therapies for patients with this aggressive malignancy.

cancer biology↗

bioRxiv2025

Multiomic characterization, early detection, and therapeutic targeting of myeloid sarcoma

Myeloid sarcoma, an aggressive extramedullary subtype of acute myeloid leukemia (AML), occurs in [~]10% of patients, and has not yet been included in large-scale genomic studies. The critical biological changes that drive tumor evolution are unknown, its detection in asymptomatic patients remains a clinical challenge, and treatment options are limited as patients are often excluded from clinical trials, rendering it a neglected disease entity. Based on comprehensive multi-omic profiling, we demonstrate that myeloid sarcoma evolves from medullary AML with distinct sitespecific clonal evolution patterns. Additionally, we show that circulating tumor DNA sequencing can serve as a non-invasive method for molecular profiling of myeloid sarcoma, offering a novel avenue in molecular diagnostics. We characterize unique transcriptional profiles of myeloid sarcoma, reflecting immune evasion and adaptation to an extramedullary microenvironment. We provide evidence for a key role of RAS pathway activation and demonstrate in murine models of myeloid sarcoma that RAS inhibition effectively reduces tumor burden. Overall, our data highlight key differences between medullary AML and myeloid sarcoma including universal molecular evolution and RAS pathway activation as hallmarks of the disease and nominate RAS inhibition as a promising therapeutic strategy for patients with myeloid sarcoma.

cancer biology↗

bioRxiv2017

Glioma CpG Island Methylator Phenotype (G-CIMP): Biological and Clinical Implications

Gliomas are a heterogeneous group of brain tumors with distinct biological and clinical properties. Despite advances in surgical techniques and clinical regimens, treatment of high-grade glioma remains challenging and carries dismal rates of therapeutic success and overall survival. Challenges include the molecular complexity of gliomas, as well as inconsistencies in histopathological grading, resulting in an inaccurate prediction of disease progression and failure in the use of standard therapy. The updated 2016 World Health Organization (WHO) classification of tumors of the central nervous system reflects a refinement of tumor diagnostics by integrating the genotypic and phenotypic features, thereby narrowing the defined subgroups. The new classification recommends the molecular diagnosis of the IDH mutational status in gliomas. IDH-mutant gliomas manifest the CpG Island Methylator Phenotype (G-CIMP). Notably, the recent identification of clinically relevant subsets of G-CIMP tumors (G-CIMP-high and G-CIMP-low) provide a further refinement in glioma classification that is independent of grade and histology. This scheme may be useful for predicting patient outcome and may be translated into effective therapeutic strategies tailored to each patient. In this review, we highlight the evolution of our understanding of the G-CIMP subsets and how recent advances in characterizing the genome and epigenome of gliomas may influence future basic and translational research.\n\nConflict of interestThe authors declare no conflict of interest

cancer biology↗

bioRxiv2024

A single cell framework identifies functionally and molecularly distinct multipotent progenitors in adult human hematopoiesis

Hematopoietic multipotent progenitors (MPPs) regulate blood cell production to appropriately meet the biological demands of the human body. Human MPPs remain ill-defined whereas mouse MPPs have been well characterized with distinct immunophenotypes and lineage potencies. Using multiomic single cell analyses and complementary functional assays, we identified new human MPPs and oligopotent progenitor populations within Lin-CD34+CD38dim/lo adult bone marrow with distinct biomolecular and functional properties. These populations were prospectively isolated based on expression of CD69, CLL1, and CD2 in addition to classical markers like CD90 and CD45RA. We show that within the canonical Lin-CD34+CD38dim/loCD90CD45RA-MPP population, there is a CD69+ MPP with long-term engraftment and multilineage differentiation potential, a CLL1+ myeloid-biased MPP, and a CLL1-CD69-erythroid-biased MPP. We also show that the canonical Lin-CD34+CD38dim/loCD90-CD45RA+ LMPP population can be separated into a CD2+ LMPP with lymphoid and myeloid potential, a CD2-LMPP with high lymphoid potential, and a CLL1+ GMP with minimal lymphoid potential. We used these new HSPC profiles to study human and mouse bone marrow cells and observe limited cell type specific homology between humans and mice and cell type specific changes associated with aging. By identifying and functionally characterizing new adult MPP sub-populations, we provide an updated reference and framework for future studies in human hematopoiesis.

cancer biology↗