Mohamed Abdouh's research while affiliated with McGill University Health Centre and other places

Background Although rare, uveal melanoma (UM) is a life-threatening malignancy. Understanding its biology is necessary to improve disease outcome. Three-dimensional (3D) in vitro culture methods have emerged as tools that incorporate physical and spatial cues that better mimic tumor biology and in turn deliver more predictive preclinical data. Herein, we comprehensively characterize UM cells under different 3D culture settings as a suitable model to study tumor cell behavior and therapeutic intervention. Methods Six UM cell lines were tested in two-dimensional (2D) and 3D-culture conditions. For 3D cultures, we used anchorage-dependent (AD) methods where cells were embedded or seeded on top of basement membrane extracts and anchorage-free (AF) methods where cells were seeded on agarose pre-coated plates, ultra-low attachment plates, and on hanging drops, with or without methylcellulose. Cultures were analyzed for multicellular tumor structures (MCTs) development by phase contrast and confocal imaging, and cell wellbeing was assessed based on viability, membrane integrity, vitality, apoptotic features, and DNA synthesis. Vascular endothelial growth factor (VEGF) production was evaluated under hypoxic conditions for cell function analysis. Results UM cells cultured following anchorage-free methods developed MCTs shaped as spheres. Regardless of their sizes and degree of compaction, these spheres displayed an outer ring of viable and proliferating cells, and a core with less proliferating and apoptotic cells. In contrast, UM cells maintained under anchorage-dependent conditions established several morphological adaptations. Some remained isolated and rounded, formed multi-size irregular aggregates, or adopted a 2D-like flat appearance. These cells invariably conserved their metabolic activity and conserved melanocytic markers (i.e., expression of Melan A/Mart-1 and HMB45). Notably, under hypoxia, cells maintained under 3D conditions secrete more VEGF compared to cells cultured under 2D conditions. Conclusions Under an anchorage-free environment, UM cells form sphere-like MCTs that acquire attributes reminiscent of abnormal vascularized solid tumors. UM cells behavior in anchorage-dependent manner exposed diverse cells populations in response to cues from an enriched extracellular matrix proteins (ECM) environment, highlighting the plasticity of UM cells. This study provides a 3D cell culture platform that is more predictive of the biology of UM. The integration of such platforms to explore mechanisms of ECM-mediated tumor resistance, metastatic abilities, and to test novel therapeutics (i.e., anti-angiogenics and immunomodulators) would benefit UM care.

Metastases are responsible for the vast majority of cancer deaths, yet most therapeutic efforts have focused on targeting and interrupting tumor growth rather than impairing the metastatic process. Traditionally, cancer metastasis is attributed to the dissemination of neoplastic cells from the primary tumor to distant organs through blood and lymphatic circulation. A thorough understanding of the metastatic process is essential to develop new therapeutic strategies that improve cancer survival. Since Paget’s original description of the “Seed and Soil” hypothesis over a hundred years ago, alternative theories and new players have been proposed. In particular, the role of extracellular vesicles (EVs) released by cancer cells and their uptake by neighboring cells or at distinct anatomical sites has been explored. Here, we will outline and discuss these alternative theories and emphasize the horizontal transfer of EV-associated biomolecules as a possibly major event leading to cell transformation and the induction of metastases. We will also highlight the recently discovered intracellular pathway used by EVs to deliver their cargoes into the nucleus of recipient cells, which is a potential target for novel anti-metastatic strategies.

Patient-derived extracellular vesicles (EVs) that contains a complex biological cargo is a valuable source of liquid-biopsy diagnostics to aid in early detection, cancer screening, and precision nanotherapeutics. In this study, we predicted that coupling cancer patient blood-derived EVs to time-resolved spectroscopy and artificial intelligence (AI) could provide a robust cancer screening and follow-up tools. In our pilot study, fluorescence correlation spectroscopy (FCS) measurements were taken on 24 blood samples-derived EVs. Blood samples were obtained from 15 cancer patients (presenting five different types of cancers), and nine healthy controls (including patients with benign lesions). EVs samples were labeled with PKH67 dye. The obtained FCS autocorrelation spectra were processed into power spectra using the fast Fourier transform algorithm. The processed power spectra were subjected to various machine learning algorithms to distinguish cancer spectra from healthy control spectra. The performance of AdaBoost Random Forest (RF) classifier, support vector machine, and multilayer perceptron were tested on selected frequencies in the N = 118 power spectra. The RF classifier exhibited the highest classification accuracy and performance metrics in distinguishing the FCS power spectra of cancer patients from those of healthy controls. Further, neural computing via an image convolutional neural network (CNN), ResNet network, and a quantum CNN were assessed on the power spectral images as additional validation tools. All image-based CNNs exhibited a nearly equal classification performance and reasonably high sensitivity and specificity scores. Our pilot study demonstrates that AI-algorithms coupled to time-resolved FCS power spectra can accurately and differentially classify the complex patient-derived EVs from different cancer samples of distinct tissue subtypes. As such, our findings hold promise in the diagnostic and prognostic screening in clinical medicine.

Extracellular vesicles (EVs) play a key role in cellular communication both in physiological conditions and in pathologies such as cancer. Emerging evidence has shown that EVs are active carriers of molecular cargo (e.g. protein and nucleic acids) and a powerful source of biomarkers and targets. While recent studies on EV-associated DNA (EV-DNA) in human biofluids have generated a large amount of data, there is currently no database that catalogues information on EV-DNA. To fill this gap, we have manually curated a database of EV-DNA data derived from human biofluids (liquid biopsy) and in-vitro studies, called the Extracellular Vesicle-Associated DNA Database (EV-ADD). This database contains validated experimental details and data extracted from peer-reviewed published literature. It can be easily queried to search for EV isolation methods and characterization, EV-DNA isolation techniques, quality validation, DNA fragment size, volume of starting material, gene names and disease context. Currently, our database contains samples representing 23 diseases, with 13 different types of EV isolation techniques applied on eight different human biofluids (e.g. blood, saliva). In addition, EV-ADD encompasses EV-DNA data both representing the whole genome and specifically including oncogenes, such as KRAS, EGFR, BRAF, MYC, and mitochondrial DNA (mtDNA). An EV-ADD data metric system was also integrated to assign a compliancy score to the MISEV guidelines based on experimental parameters reported in each study. While currently available databases document the presence of proteins, lipids, RNA and metabolites in EVs (e.g. Vesiclepedia, ExoCarta, ExoBCD, EVpedia, and EV-TRACK), to the best of our knowledge, EV-ADD is the first of its kind to compile all available EV-DNA datasets derived from human biofluid samples. We believe that this database provides an important reference resource on EV-DNA-based liquid biopsy research, serving as a learning tool and to showcase the latest developments in the EV-DNA field. EV-ADD will be updated yearly as newly published EV-DNA data becomes available and it is freely available at www.evdnadatabase.com.

Liquid biopsy-based detection of circulating free DNA (cfDNA) is a promising tool to monitor tumor progression and treatment response. cfDNA release is thought to result from a combination of cell death (apoptosis and necrosis), and active cellular secretion. As such, cytotoxic anti-cancer therapies can impact cfDNA kinetics. This makes the interpretation of cfDNA analyses pivotal for its applicability as a biomarker. In this study, we assessed the kinetics and fragmentation of cfDNA in cancer cells of various origins following standard anti-cancer treatments with different cytotoxic effects and mechanisms of action.Human colorectal carcinoma, lung adenocarcinoma, and uveal melanoma cancer cells were subjected to different forms of cytotoxic stress, including induction of apoptosis (tumor necrosis factor (TNF)-related apoptosis-inducing ligand (Apo2L/TRAIL)), cell cycle arrest (Roscovitine, Valproic acid), necrosis (radiotherapy), and senescence. Following treatments, cells were analyzed for their cell cycle progression, level of senescence, and mechanism of cell death (apoptosis vs. necrosis). Heat treatment was used as a control for necrosis-related cell death. Total cfDNA and mutant cfDNA (based on the mutations of each parental cell line) were isolated from cultured media and quantified using the Qubit assay and digital droplet PCR targeting, respectively. Fragment length of the isolated cfDNA was visualized using the Bioanalyzer 2100.Total and mutant cfDNA levels increased during all cytotoxic treatments in a concentration-dependent manner. Notably, cells undergoing apoptosis shed higher levels of cfDNA compared to necrotic and senescent cells. In addition, electropherogram images showed that cytotoxic conditions alter fragment size distribution, with smaller fragments of <200bp associated with apoptosis and >1000 bp with necrosis.The kinetics and fragmentation of released cfDNA are influenced by cytotoxic insults. Determining the characteristics of cfDNA can facilitate and improve its use as a clinical biomarker during anti-cancer therapy. Our data pave the way for the establishment of criteria to apply when monitoring cfDNA for cancer management based on the anticancer therapeutic strategy.

Purpose: Uveal melanoma (UM) is the most common intraocular malignant tumor in adults. Extracellular vesicles (EVs) have been extensively studied as a biomarker to monitor disease in patients. The study of new biomarkers in melanoma patients could prevent metastasis by earlier diagnosis. In this study, we determined the proteomic profile of EVs isolated from aqueous humor (AH), vitreous humor (VH), and plasma from UM patients in comparison with cancer-free control patients. Methods: AH, VH and plasma were collected from seven patients with UM after enucleation; AH and plasma were collected from seven cancer-free patients with cataract (CAT; control group). EVs were isolated using the membrane-based affinity binding column method. Nanoparticle tracking analysis (NTA) was performed to determine the size and concentration of EVs. EV markers, CD63 and TSG101, were assessed by immunoblotting, and the EV proteome was characterized by mass spectrometry. Results: Mean EV concentration was higher in all analytes of UM patients compared to those in the CAT group. In the UM cohort, the mean concentration of EVs was significantly lower in AH and plasma than in VH. In contrast, the mean size and size distribution of EVs was invariably identical in all analyzed analytes and in both studied groups (UM vs. CAT). Mass spectrometry analyses from the different analytes from UM patients showed the presence of EV markers. Conclusion: EVs isolated from AH, VH, and plasma from patients with UM showed consistent profiles and support the use of blood to monitor UM patients as a noninvasive liquid biopsy.

The early detection of cancer is a challenging problem in medicine. The blood sera of cancer patients are enriched with heterogeneous secretory lipid-bound extracellular vesicles (EVs), which present a complex repertoire of information and biomarkers, representing their cell of origin, that are being currently studied in the field of liquid biopsy and cancer screening. Vibrational spectroscopies provide non- invasive approaches for the assessment of structural and biophysical properties in complex biological samples. Methods In this pilot study, multiple Raman spectroscopy measurements were performed on the EVs extracted from the blood sera of n = 9 patients consisting of four different cancer subtypes (colorectal cancer, hepatocellular carcinoma, breast cancer and pancreatic cancer) and five healthy patients (controls). FTIR (Fourier Transform Infrared) spectroscopy measurements were performed as a complementary approach to Raman analysis, on two of the four cancer subtypes. The spectra were subjected to various machine learning classifiers with hyperparameter optimization to discriminate between healthy and cancer patients-derived EVs. The AdaBoost Random Forest Classifier, Decision Trees, and Support Vector Machines (SVM) distinguished the baseline corrected Raman spectra of cancer EVs from those of healthy controls (N = 18 spectra) with a classification accuracy of >90% when reduced to a spectral frequency range of 1800 − 1940 𝑐𝑚⁻¹ and subjected to a 50:50 training: testing split. FTIR classification accuracy on N = 14 spectra showed an 80% classification accuracy. Our findings demonstrate that basic machine learning algorithms are powerful applied intelligence tools to distinguish the complex vibrational spectra of cancer patient EVs from those of healthy patients. These experimental methods hold promise as valid and efficient liquid biopsy for artificial intelligence-assisted early cancer screening.

Simple Summary Uveal melanoma is a rare but deadly cancer that shows remarkable metastatic tropism to the liver. Extracellular vesicles (EVs) are nanometer-sized, lipid bilayer-membraned particles that are released from cells. In our study we used EVs derived from primary normal choroidal melanocytes and matched primary and metastatic uveal melanoma cell lines from a patient. Analysis of these EVs revealed important protein signatures that may be involved in tumorigenesis and metastatic dissemination. We have established a model to study EV functions and phenotypes which can be used in EV-based liquid biopsy. Abstract Extracellular vesicles (EVs) carry molecules derived from donor cells and are able to alter the properties of recipient cells. They are important players during the genesis and progression of tumors. Uveal melanoma (UM) is the most common primary intraocular tumor in adults and is associated with a high rate of metastasis, primarily to the liver. However, the mechanisms underlying this process are poorly understood. In the present study, we analyzed the oncogenic potential of UM-derived EVs and their protein signature. We isolated and characterized EVs from five UM cell lines and from normal choroidal melanocytes (NCMs). BRCA1-deficient fibroblasts (Fibro-BKO) were exposed to the EVs and analyzed for their growth in vitro and their reprograming potential in vivo following inoculation into NOD-SCID mice. Mass spectrometry of proteins from UM-EVs and NCM-EVs was performed to determine a protein signature that could elucidate potential key players in UM progression. In-depth analyses showed the presence of exosomal markers, and proteins involved in cell-cell and focal adhesion, endocytosis, and PI3K-Akt signaling pathway. Notably, we observed high expression levels of HSP90, HSP70 and integrin V in UM-EVs. Our data bring new evidence on the involvement of UM-EVs in cancer progression and metastasis.