Dominic Guanzon’s research while affiliated with The University of Queensland and other places

5582 Background: The high mortality of Ovarian cancer (OC) has been attributed to late-stage diagnosis and the lack of an effective early detection strategy, particularly for asymptomatic women. In this study, we developed and validated a high-throughput OC detection test based on plasma extracellular vesicle (EV)-associated biomarkers. Methods: A case-control study was conducted to evaluate blood-borne EV-associated ovarian cancer biomarkers, including miRNAs, proteins, lncRNAs, miscRNAs, MtrRNAs, MttRNAs, rRNAs, scaRNAs, snRNAs, and tRNAs. Protein and RNA biomarkers were identified by mass spectrometry and RNA sequencing, respectively. Training (n=453) and independent test (n=471) sample sets were used to develop and validate a multivariate index assay (MIA). The MIA was further validated using a high-throughput, pathology laboratory compatible, EV isolation platform (EXO-NET) and two independent sample cohorts (n=97 and n=532). The classification accuracy, sensitivity and specificity of the MIA was compared to that of CA125 levels. Results: Discovery and Training phases - more than 100,000 EV-associated biomarkers were identified from 453 EV samples. The classification performance of these biomarkers was assessed using machine learning algorithms. EV-associated protein and miRNA biomarkers delivered the highest performing classifiers and, therefore, were used in subsequent MIA development and training. During the training phase, multivariate classification algorithms were validated using a 10-fold cross-validation method. The highest performing classifiers for EV-associated protein and miRNA, at specificity of 98%, achieved sensitivities of 90% and 82%, respectively. Validation phase: Locked classification algorithms ( i.e. MIAs) were validated using two independent sample cohorts and reported classification accuracies of 92-98%, significantly outperforming CA-125 (CE = 62%, p<0.001). Automated high-throughput MIA – All stages OC: the best performing automated high-throughput MIA demonstrated an overall sensitivity of 92% (95% CI, 75–96%) and specificity of 93% (95% CI, 86–96%) for all stages of OC, Positive Predictive Value of 95% (CI, 93-96%) and Negative Predictive Value of 80% (CI, 76-89%) at 98% specificity (n=532). Stage I OC: Importantly, the MIA displayed a sensitivity of 90% (95% CI, 76–100%) and specificity of 96% (95% CI, 40%–99%) for stage I OC. While CA125 have an overall sensitivity for all stages of OC of 61% (95% CI, 53–69%), with a sensitivity of 44% for stage I (95% CI, 28–62%). Conclusions: In this study we report the development and validation of an accurate, automated high-throughput EV-based test for early detection of ovarian cancer. The test delivers significant improvements in sensitivity and specificity compared to CA-125, especially in detecting early-stage OC.

Background Gestational diabetes mellitus (GDM) affects 2–20% of pregnant women worldwide and is linked to fetal overgrowth, increased perinatal morbidity, and mortality, as well as a higher risk of developing cardiovascular disease later in life for mother and child. MicroRNAs (miRNAs), which regulate gene expression, can be transported within extracellular vesicles (EVs). Adipose tissue-derived EVs have been associated with changes in placental metabolism in GDM, potentially influencing cardiovascular health outcomes. This study aimed to evaluate the miRNA profile in EVs from omental adipose tissue in GDM and their effect on placental nutrient uptake and fetal growth. Methods This case–control study included patients with normal glucose tolerance (NGT) and GDM. We conducted a miRNA expression profiling on omental adipose tissue and its derived EVs from women with NGT (n = 20) and GDM (n = 36). Trophoblast cells were utilized to assess the effect of EVs on glucose and fatty acid uptake, pro-inflammatory cytokine, and chemokine release. Double-stranded miRNA mimics were used to investigate the effect of selected miRNAs on trophoblast cells. Subsequently, the impact of EVs from NGT and GDM, as well as miR-515-5p, on in vivo glucose tolerance and fetal growth was assessed in pregnant mice. Results Fifty-four miRNAs showed significant differences between EVs from the adipose tissue of NGT and GDM groups. EVs from GDM increased glucose uptake in trophoblast cells, whereas EVs from NGT increased the secretion of CXCL8, IL-6, CXCL1, CXCL4, and CXCL5 from trophoblasts compared to the effect without EVs. Specifically, miR-515-5p increased glucose uptake and abolished TNF-α-dependent increase in pro-inflammatory cytokines and chemokines from trophoblast cells. Injection of pregnant mice with EVs from NGT adipose tissue loaded with miR-515-5p resulted in increased fetal weight and glucose levels. Conclusion miR-515-5p, specifically encapsulated within EVs from omental adipose tissue in GDM, regulates placental nutrient uptake, glucose homeostasis, and fetal growth.

Herein, we developed a specific, rapid sensor to quantify placental extracellular vesicle (EV) protein biomarkers of early pregnancy complications. A distinct tetraspanin CD9 and placental alkaline phosphatase (PLAP) expression pattern was observed via targeted multiple reaction monitoring of EVs from maternal plasma collected before 18 weeks of gestation. A classification model was developed using training and validation patient sets, distinguishing between individuals at high risk of developing complications from those with normal pregnancies, achieving 80% sensitivity, 90% specificity, 89% positive predictive value (PPV), and 82% negative predictive value (NPV). Superparamagnetic nanoflowers that captured target EVs (CD9 ⁺ /PLAP ⁺ ) were used to construct a 4-flex glass strip nanozymatic readout system. The sensor analyzes plasma for EVs, identifying gestational diabetes mellitus risk with a 95% combined sensitivity, 100% specificity, 100% PPV, and 96% NPV. This nanoplatform identifies individuals at risk of developing pregnancy complications with a >90% classification accuracy, exhibiting potential for clinical applications.

Ovarian cancer is the most common gynaecological malignancy and the seventh most diagnosed cancer in females worldwide. Currently, it is the sixth leading cause of cancer related mortality among patients globally. The heterogenous origin of the disease and unambiguous nature of the clinical symptoms leading to delayed detection has been one of the key reasons for increasing mortality. Hence new approaches are required to understand the biology of ovarian cancer, where the use of cell culture models that mimic the physiology of the disease is fundamental. Cell culture serves as a crucial in vitro tool, contributing to our comprehension of various aspects of cell biology, tissue morphology, disease mechanisms, drug responses, protein production, and tissue engineering. A significant portion of in vitro studies rely on two-dimensional (2D) cell cultures, however, these cultures present notable limitations, for example disruptions in cellular and extracellular interactions, alterations in cell morphology, polarity, and division mechanisms. Recently, extracellular vesicles have been identified as crucial players in cell biology as part of the communication system that cancer cells use to metastasize. We optimized and compared three-dimensional (3D) culture of ovarian cancer cells lines (SKOV-3 and OVCAR-3) with two-dimensional models based on their protein and miRNA content. We further investigated whether extracellular vesicles from these models reflect changes in cancer cells, and aid in the identification of overall survival in women with ovarian cancer.

DNA methylation is an epigenetic mechanism that regulates gene expression, and for mammals typically occurs on cytosines within CpG dinucleotides. A significant challenge for methylation detection methods is accurately measuring methylation levels within GC-rich regions such as gene promoters, as inaccuracies compromise downstream biological interpretation of the data. To address this challenge, we compared methylation levels assayed using four different Methods Enzymatic Methyl-seq (EM-seq), whole genome bisulphite sequencing (WGBS), Infinium arrays (Illumina MethylationEPIC, “EPIC”), and Oxford Nanopore Technologies nanopore sequencing (ONT) applied to human DNA. Overall, all methods produced comparable and consistent methylation readouts across the human genome. The flexibility offered by current gold standard WGBS in interrogating genome-wide cytosines is surpassed technically by both EM-seq and ONT, as their coverages and methylation readouts are less prone to GC bias. These advantages are tempered by increased laboratory time (EM-seq) and higher complexity (ONT). We further assess the strengths and weaknesses of each method, and provide recommendations in choosing the most appropriate methylation method for specific scientific questions or translational needs.

Extracellular vesicles (EVs) function as natural mediators of intercellular communication, secreted by cells to facilitate cell–cell signaling. Due to their low toxicity, immunogenicity, biodegradability, and potential to encapsulate therapeutic drugs, EVs hold significant therapeutic promise. Nevertheless, their limited targeting ability often diminishes their therapeutic impact. Therefore, enhancing EVs by incorporating targeting units onto their membranes could bolster their targeting capabilities, enabling them to accumulate in specific cells and tissues. In this study, we engineered EVs to fuse ephrin‐B2 with the EV membrane protein LAMP2b. This modification aimed to direct the engineered EVs toward the ephrin‐B4 receptor expressed on the surface of ovarian cancer cells. The engineered EVs retained their inherent properties, including size, expression of EV membrane proteins, and morphology, upon isolation. In vitro experiments using real‐time imaging revealed that EVs engineered with the ephrin‐B2 ligand exhibited substantial internalization and uptake by ovarian cancer cells, in stark contrast to native EVs. In vivo, the engineered EVs carrying the ephrin‐B2 ligand effectively targeted ovarian cancer cells, surpassing the targeting efficiency of control EVs. This innovative approach establishes a novel targeting system, enhancing the uptake of EVs by ovarian cancer cells. Our findings underscore the potential of using EVs to target cancer cells, thereby enhancing the effectiveness of anti‐cancer therapies while minimizing off‐target effects and toxicity in normal cells and organs.

Extracellular vesicles (EVs), including exosomes, have significant potential for diagnostic and therapeutic applications. The lack of standardized methods for efficient and high-throughput isolation and analysis of EVs, however, has limited their widespread use in clinical practice. Surface epitope immunoaffinity (SEI) isolation utilizes affinity ligands, including antibodies, aptamers, or lectins, that target specific surface proteins present on EVs. Paramagnetic bead-SEI isolation represents a fit-for-purpose solution for the reproducible, high-throughput isolation of EVs from biofluids and downstream analysis of RNA, protein, and lipid biomarkers that is compatible with clinical laboratory workflows. This study evaluates a new SEI isolation method for enriching subpopulations of EVs. EVs were isolated from human plasma using a bead-based SEI method designed for on-bead and downstream analysis of EV-associated RNA and protein biomarkers. Western blot analysis confirmed the presence of EV markers in the captured nanoparticles. Mass spectrometry analysis of the SEI lysate identified over 1500 proteins, with the top 100 including known EV-associated proteins. microRNA (miRNA) sequencing followed by RT-qPCR analysis identified EV-associated miRNA transcripts. Using SEI, EVs were isolated using automated high-throughput particle moving instruments, demonstrating equal or higher protein and miRNA yield and recovery compared to manual processing. SEI is a rapid, efficient, and high-throughput method for isolating enriched populations of EVs; effectively reducing contamination and enabling the isolation of a specific subpopulation of EVs. In this study, high-throughput EV isolation and RNA extraction have been successfully implemented. This technology holds great promise for advancing the field of EV research and facilitating their application for biomarker discovery and clinical research.

Ovarian Cancer (OC) is the most common gynaecological malignancy and the eighth most diagnosed cancer in females worldwide. Currently, it is the fifth leading cause of cancer-related mortality among patients globally, largely due to delayed diagnosis, chemotherapy resistance, high metastasis rates, and subtype heterogeneity. Recent OC research highlights extracellular vesicles (EVs) as pivotal contributors to intercellular communication and disease progression, elucidating the intricacies of OC pathology. EVs, diverse membrane-derived vesicles released by most cells, carry molecular cargoes containing proteins and nucleic acids. Studies indicate that the biogenesis, packaging, and release of EVs are highly dependent and sensitive to the cellular microenvironment and depend on the in-vitro and in-vivo modelling systems. Labs use varied model systems—2D monolayers, animal models, and innovative 3D models—to investigate EVs' ovarian cancer roles. Therefore, in this study we aimed to compare the miRNA profiles associated with EVs in 3D ovarian cancer cell models, and to identify the pathophysiological relevance of the EVs isolated from these models to the patient derived EVs.In this study, two OC epithelial cell lines, SKOV-3 and OVCAR-, were initially cultured as 2D monolayers and embedded within Gelatin Methacryloyl hydrogels. Over nine days, multiple assays observed spheroid formation within the cell-laden hydrogels. EVs isolated from the cell-conditioned media were characterized per MISEV 2018 guidelines. Small RNA sequencing identified statistically significant miRNAs, subject to gene ontology and gene rank analyses. EVs from a cohort of 60 OC patients were used to identify survival-associated miRNA profiles.Our findings in the cell-laden hydrogels demonstrated OC cell growth, proliferation, and aggregation into spheroidal structures, establishing an ideal 3D model. The isolated EVs were characterized for size, concentration, morphology, and surface markers. Small RNA sequencing revealed 18 significantly different EV-associated miRNA species across 3D vs 2D models, influencing apoptosis, angiogenesis, migration, and proliferation in ovarian cancer. Notably, 3D model-derived EV-associated miRNAs mirrored patient-derived EV-associated miRNA profiles, indicating their pathophysiological relevance.This study establishes a robust 3D OC model in hydrogels, showcasing growth, proliferation, and aggregation capabilities. Differential miRNA profiles between 3D and 2D model EVs highlight the critical roles of these miRNAs in essential ovarian cancer processes. Moreover, similarities between 3D model and patient-derived EV-associated miRNA profiles emphasize the clinical relevance of our model. Citation Format: Nihar Godbole, Akhilandeshwari Ravichandran, Dominic Guanzon, Andrew Lai, Flavio Carrion, Priyakshi Kalita de Croft, Lewis Perrin, John Hooper, Laura Bray, Carlos Salomon. Changes in extracellular vesicle miRNAs from three-dimensional ovarian cancer cell models reflect physiological changes and cancer survival [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3386.

DNA methylation is an epigenetic mechanism that regulates gene expression, and for mammals typically occurs on cytosines within CpG dinucleotides. A significant challenge for methylation detection methods is accurately measuring methylation levels within GC-rich regions such as gene promoters, as inaccuracies compromise downstream biological interpretation of the data. To address this challenge, we compared methylation levels assayed using four different methods: Enzymatic Methyl-seq (EM-seq), whole genome bisulphite sequencing (WGBS), Infinium arrays (Illumina MethylationEPIC, "EPIC"), and Oxford Nanopore Technologies nanopore sequencing (ONT) applied to human DNA. Overall, all methods produced comparable and consistent methylation readouts across the human genome. The flexibility offered by current gold standard WGBS in interrogating genome-wide cytosines is surpassed technically by both EM-seq and ONT, as their coverages and methylation readouts are less prone to GC bias. These advantages are tempered by higher costs (EM-seq) and higher complexity (ONT). We further assess the strengths and weaknesses of each method, and provide recommendations in choosing the most appropriate methylation method for specific scientific questions or translational needs.