Biology subjects

Tran, N. L.

Publications and source records attributed to Tran, N. L..

10 recordsLinked to original sources

bioRxiv2026

Aquaporin-4 Regulates Edema, Sustains Astrocyte Differentiation, and Constrains Immunosuppressive Remodeling in Glioblastoma

Vasogenic cerebral edema is a severe complication that affects most patients with glioblastoma (GBM). It leads to an increase in intracranial pressure, neurological deficits, and increased mortality rates. While there is considerable research on edema formation, the molecular and cellular mechanisms of edema resolution in GBM, particularly in response to dexamethasone or other anti-edema therapies, remain largely undefined. Our findings demonstrate that the water channel aquaporin-4 (AQP4), a key regulator of brain water homeostasis, is upregulated in human GBM compared to the normal brain. AQP4 expression correlates with peritumoral edema in GBM patients, with the classical (CL) GBM subtype exhibiting the highest AQP4 levels but the lowest edema content relative to the proneural (PN) and mesenchymal (MES) subtypes. Using the RCAS/tv-a system, we generated de novo GBM with defined driver mutations in Aqp4-proficient and -deficient mice. Loss of Aqp4 shortened survival and exacerbated edema, accompanied by reduced astrocytic coverage within the tumor core, but not at the tumor margin. Single-cell RNA-sequencing and spatial transcriptomics revealed that Aqp4 loss reprograms astrocytes toward an immature, progenitor-like state and alters their crosstalk with other cell types in the GBM microenvironment. Additionally, loss of Aqp4 skewed myeloid populations toward immunosuppressive phenotypes while reducing lymphoid infiltration. Treatment with anti-VEGFA antibody, an established anti-edema therapy, increased expression of Aqp4 and reduced vessel leakage, underscoring the role of AQP4 in the therapeutic modulation of brain water balance. Overall, this study highlights the important role of AQP4 in resolving edema and its critical function in maintaining astrocyte identity and shaping the immune landscape of GBM.

cancer biology↗

bioRxiv2026

Graph-based modeling of multiparametric MRI deciphers molecular states of high-grade glioma invasion with prognostic implications

AbstractThe infiltrative, non-enhancing margin of IDH wildtype high grade glioma (IDHwt HGG) harbors distinct molecular programs that drive invasion and therapeutic resistance, yet remains largely unevaluable by conventional tissue sampling approaches and by conventional imaging. Here we show that this invasive architecture is encoded within multiparametric MRI (mpMRI) feature relationships and can be decoded using a graph-based framework trained on multiregional image-localized biopsies. Across 134 spatially matched biopsy-imaging pairs from 35 patients with primary IDHwt HGG (29 glioblastomas (GBM) and 6 non-glioblastoma HGGs), unsupervised graph community detection identifies two imaging-defined clusters that localize to invasive tumor regions without molecular supervision. Transcriptomic profiling associates these clusters with neuronal (NEU) and glycolytic-plurimetabolic (GPM) molecular programs. Building on this framework, a graph convolutional network (GCN) accurately predicts NEU and GPM transcriptional states in independent training and validation cohorts and significantly outperforms conventional convolutional neural networks. Applied to whole-tumor mpMRI volumes, the trained GCN generates spatially resolved probability maps that quantify the distribution and relative burden of NEU and GPM programs across both MRI contrast-enhancing and non-enhancing invasive regions. These imaging-derived molecular maps stratify patients by overall survival. Increased GPM burden is associated with poorer survival, consistent with the aggressive behavior associated with mesenchymal-like transcriptional programs in IDHwt HGG. In contrast, increased NEU burden is associated with improved survival, identifying a previously unrecognized imaging-derived prognostic biomarker that was not detected by biopsy-based molecular classification alone. Together, these findings establish a graph-based imaging framework for spatially resolved molecular classification of invasive IDHwt HGG and demonstrate that whole-tumor molecular state architecture carries prognostic information beyond conventional tissue sampling.

cancer biology↗

bioRxiv2026

Integrative Multi-Omics Analysis Identifies a Functional Enhancer Driving Tumorigenesis in Head and Neck Squamous Cell Carcinoma

Enhancer is a critical epigenetic feature in head and neck squamous cell carcinoma (HNSCC), yet the functional roles of individual enhancers remain poorly understood. Here, we conducted an integrative multi-omics analysis based on publicly available ATAC-seq, H3K27ac ChIP-seq, transcriptomic profiling, and genetic association datasets to systematically map HNSCC-associated enhancers and their target genes. Integrating ATAC-seq and H3K27ac ChIP-seq, we identified 20,362 enhancers and 18,040 enhancer-associated genes, highlighting widespread regulatory complexity. Functional characterization of the TERT-associated enhancer GH05J001312 (GRCh38/hg38: chr5:1312099-1317743) revealed strong transcriptional activity in HNSCC. CRISPR-mediated deletion of a core sequence significantly reduced TERT expression, impaired cellular proliferation in vitro, and suppressed tumor growth in vivo, confirming its role as a key cis-regulatory element. RNA-seq analysis of enhancer-edited cells uncovered 742 differentially expressed genes enriched in cancer-related pathways, including MAPK and IL-17 signaling, indicating a broad transcriptional impact. Collectively, our findings establish GH05J001312 as a functional enhancer driving oncogenic programs in HNSCC and suggest enhancer-targeted strategies as a potential therapeutic avenue.

cancer biology↗

bioRxiv2024

ZBTB11 Depletion Targets Metabolic Vulnerabilities in K-Ras Inhibitor Resistant PDAC

Over 95% of pancreatic ductal adenocarcinomas (PDAC) harbor oncogenic mutations in K-Ras. Upon treatment with K-Ras inhibitors, PDAC cancer cells undergo metabolic reprogramming towards an oxidative phosphorylation-dependent, drug-resistant state. However, direct inhibition of complex I is poorly tolerated in patients due to on-target induction of peripheral neuropathy. In this work, we develop molecular glue degraders against ZBTB11, a C2H2 zinc finger transcription factor that regulates the nuclear transcription of components of the mitoribosome and electron transport chain. Our ZBTB11 degraders leverage the differences in demand for biogenesis of mitochondrial components between human neurons and rapidly-dividing pancreatic cancer cells, to selectively target the K-Ras inhibitor resistant state in PDAC. Combination treatment of both K-Ras inhibitor-resistant cell lines and multidrug resistant patient-derived organoids resulted in superior anti-cancer activity compared to single agent treatment, while sparing hiPSC-derived neurons. Proteomic and stable isotope tracing studies revealed mitoribosome depletion and impairment of the TCA cycle as key events that mediate this response. Together, this work validates ZBTB11 as a vulnerability in K-Ras inhibitor-resistant PDAC and provides a suite of molecular glue degrader tool compounds to investigate its function.

cancer biology↗

bioRxiv2024

The CryoEM structure of human serum albumin in complex with ligands

Human serum albumin (HSA) is the most prevalent plasma protein in the human body, accounting for 60% of the total plasma protein. HSA plays a major pharmacokinetic function, serving as a facilitator in the distribution of endobiotics and xenobiotics within the organism. In this paper we report the cryoEM structures of HSA in the apo form and in complex with two ligands (salicylic acid and teniposide) at a resolution of 3.5, 3.7 and 3.4 [A], respectively. We expand upon previously published work and further demonstrate that sub-4 [A] maps of [~]60 kDa proteins can be routinely obtained using a 200 kV microscope, employing standard workflows. Most importantly, these maps allowed for the identification of small molecule ligands, emphasizing the practical applicability of this methodology and providing a starting point for subsequent computational modeling and in silico optimization.

biochemistry↗

bioRxiv2022

Continuous sensing of IFNα by hepatic endothelial cells shapes a vascular antimetastatic barrier

Hepatic metastases are a poor prognostic factor of colorectal carcinoma (CRC) and new strategies to reduce the risk of liver CRC colonization are highly needed. Herein, we used mouse models of hepatic metastatization to demonstrate that the continuous infusion of therapeutic doses of interferon-alpha (IFN) controls CRC invasion by acting on hepatic endothelial cells (HECs). Mechanistically, IFN promoted the development of a vascular antimetastatic niche characterized by liver sinusoidal endothelial cells (LSECs) defenestration extracellular matrix and glycocalyx deposition, thus strengthening the liver vascular barrier impairing CRC trans-sinusoidal migration, without requiring a direct action on tumor cells, hepatic stellate cells, hepatocytes, or liver dendritic cells (DCs), Kupffer cells (KCs) and liver capsular macrophages (LCMs). Moreover, IFN endowed LSECs with efficient cross-priming potential that, along with the early intravascular tumor burden reduction, supported the generation of antitumor CD8+ T cells and ultimately led to the establishment of a protective long-term memory T cell response. These findings provide a rationale for the use of continuous IFN therapy in perioperative settings to reduce CRC metastatic spreading to the liver.

cancer biology↗

bioRxiv2022

Glioblastoma states are defined by cohabitating cellular populations with progression-, imaging- and sex-distinct patterns

Background: Magnetic Resonance Imaging (MRI) is the mainstay for neurosurgical oncology but not for informing us about glioma biology. An obstacle to developing MR-based glioma biomarkers is the absence of rigorous correlation between MRI features and glioma biology as assessed in multi-regional biopsies, within and across patients. Methods: We directly addressed this obstacle by collating a unique cohort of 202 MRI-localized biopsies from 58 patients. We define a low-dimensional transcriptional pseudotime continuum along which heterogeneous high-grade glioma (HGG) samples organize both within and across patients. Results: We observe three polarized transcriptional tissue states: infiltrated brain, immune/inflammatory, and proliferative associated with patterns of cohabitation of cellular subpopulations. The states and deconvolved populations show correlation with enhancement status on T1Gd MRI. Moreover, discrete MRI habitats, regions sharing common imaging features, defined as combinations of high or low signal intensity across multiparametric MRI revealed 14 MRI habitats. We order the MRI habitats according to the average pseudotime on the transcriptional continuum. We find that MRI habitats with low pseudotime (associated with early tumor development and diffusely invaded brain tissue) localized at the periphery of the tumor whilst high pseudotime either proliferative or immune/inflammatory states were towards the core of the lesion. We find that composition of MRI habitats is impacted by MGMT status. Conclusion: This suggests that ongoing aggregation of MRI-localized biopsies may augment our projection of biology onto MRI habitats to support the noninvasive identification of cellular ecologies within and across each patient's tumor.

cancer biology↗

bioRxiv2020

Identifying the spatial and temporal dynamics of molecularly-distinct glioblastoma sub-populations

Glioblastomas (GBMs) are the most aggressive primary brain tumours and have no known cure. Each individual tumour comprises multiple sub-populations of genetically-distinct cells that may respond differently to targeted therapies and may contribute to disappointing clinical trial results. Image-localized biopsy techniques allow multiple biopsies to be taken during surgery and provide information that identifies regions where particular sub-populations occur within an individual GBM, thus providing insight into their regional genetic variability. These sub-populations may also interact with one another through a competitive or cooperative nature; it is important to ascertain the nature of these interactions, as they may have implications for responses to targeted therapies. We combine genetic information from biopsies with a mechanistic model of interacting GBM sub-populations to characterise the nature of interactions between two commonly occurring GBM sub-populations, those with EGFR and PDGFRA genes amplified. We study population levels found across image-localized biopsy data from a cohort of 25 patients and compare this to model outputs under competitive, cooperative and neutral interaction assumptions. We explore other factors affecting the observed simulated sub-populations, such as selection advantages and phylogenetic ordering of mutations, which may also contribute to the levels of EGFR and PDGFRA amplified populations observed in biopsy data.

cancer biology↗

bioRxiv2018

Joint analysis of matched tumor samples with varying tumor contents improves somatic variant calling in the absence of a germline sample

Archival tumor samples represent a potential rich resource of annotated specimens for translational genomics research. However, standard variant calling approaches require a matched normal sample from the same individual, which is often not available in the retrospective setting, making it difficult to distinguish between true somatic variants and germline variants that are private to the individual. Archival sections often contain adjacent normal tissue, but this normal tissue can include infiltrating tumor cells. Comparative somatic variant callers are designed to exclude variants present in the normal sample, so a novel approach is required to leverage sequencing of adjacent normal tissue for somatic variant calling. Here we present LumosVar 2.0, a software package designed to jointly analyze multiple samples from the same patient. The approach is based on the concept that the allelic fraction of somatic variants, but not germline variants, would be reduced in samples with low tumor content. LumosVar 2.0 estimates allele specific copy number and tumor sample fractions from the data, and uses the model to determine expected allelic fractions for somatic and germline variants and classify variants accordingly. To evaluate using LumosVar 2.0 to jointly call somatic variants with tumor and adjacent normal samples, we used a glioblastoma dataset with matched high tumor content, low tumor content, and germline exome sequencing data (to define true somatic variants) available for each patient. We show that both sensitivity and positive predictive value are improved by analyzing the high tumor and low tumor samples jointly compared to analyzing the samples individually or compared to in-silico pooling of the two samples. Finally, we applied this approach to a set of breast and prostate archival tumor samples for which normal samples were not available for germline sequencing, but tumor blocks containing adjacent normal tissue were available for sequencing. Joint analysis using LumosVar 2.0 detected several variants, including known cancer hotspot mutations that were not detected by standard somatic variant calling tools using the adjacent normal as a reference. Together, these results demonstrate the potential utility of leveraging paired tissue samples to improve somatic variant calling when a constitutional DNA sample is not available.

bioinformatics↗

bioRxiv2017

Evaluation of pre-analytical factors affecting plasma DNA analysis

Pre-analytical factors can significantly affect circulating cell-free DNA (cfDNA) analysis. However, there are few robust methods to rapidly assess sample quality and the impact of pre-analytical processing. To address this gap and to evaluate effects of DNA extraction methods and blood collection tubes on cfDNA yield and fragment size, we developed a multiplexed droplet digital PCR (ddPCR) assay with 5 short and 4 long amplicons targeting single copy genomic loci (mean amplicon size: 71 bp and 471 bp respectively). Using this assay, we compared performance of 7 cfDNA extraction kits and found cfDNA yield and fragment size varies significantly between them. We also compared 3 blood collection protocols used to collect plasma samples from 23 healthy volunteers (EDTA tubes processed within 1 hour and Cell-free DNA BCT tubes at ambient temperature processed within 24 hours and 72 hours of collection). To assess whether cell-stabilizing preservative in BCT tubes introduced noise in cfDNA, we performed digital targeted sequencing. We found no significant differences in cfDNA yield, fragment size and background sequencing noise between these protocols. In 219 clinical samples tested for quality using the ddPCR assay, cfDNA fragment size was significantly shorter in plasma samples immediately processed for ctDNA analysis compared to archived samples, suggesting background DNA contributed by lysed peripheral blood cells. In summary, we describe a multiplexed ddPCR approach that enables cfDNA quality assessment and could inform the design of future circulating tumor DNA studies.\n\nGene namesNone

genomics↗