Browsing by Author "Wang, Edwin"

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    DVA: predicting the functional impact of single nucleotide missense variants
    (2024-03-06) Wang, Dong; Li, Jie; Wang, Edwin; Wang, Yadong
    Abstract Background In the past decade, single nucleotide variants (SNVs) have been identified as having a significant relationship with the development and treatment of diseases. Among them, prioritizing missense variants for further functional impact investigation is an essential challenge in the study of common disease and cancer. Although several computational methods have been developed to predict the functional impacts of variants, the predictive ability of these methods is still insufficient in the Mendelian and cancer missense variants. Results We present a novel prediction method called the disease-related variant annotation (DVA) method that predicts the effect of missense variants based on a comprehensive feature set of variants, notably, the allele frequency and protein–protein interaction network feature based on graph embedding. Benchmarked against datasets of single nucleotide missense variants, the DVA method outperforms the state-of-the-art methods by up to 0.473 in the area under receiver operating characteristic curve. The results demonstrate that the proposed method can accurately predict the functional impact of single nucleotide missense variants and substantially outperforms existing methods. Conclusions DVA is an effective framework for identifying the functional impact of disease missense variants based on a comprehensive feature set. Based on different datasets, DVA shows its generalization ability and robustness, and it also provides innovative ideas for the study of the functional mechanism and impact of SNVs.
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    Fluid Force Alterations in Cultured Mammary Epithelial and Breast Cancer Cells: Applications in Breast Cancer Diagnosis
    (2016) Fuh, Kenneth Fuh; Rinker, Kristina; Shemanko, Carrie; Kallos, Michael; Ungrin, Mark; Wang, Edwin; Leask, Richard
    Metastatic progression of breast cancer is characterized by mechanical interactions between tumor cells and various microenvironments, including exposure to fluid flow. Complementing genomic and molecular signaling studies with fluid mechanics holds the promise of providing in-depth knowledge into how these interactions affect the ability of tumor cells to undergo metastasis, and identification of novel biomarkers that can potentially facilitate breast cancer diagnosis and treatment. In this thesis, a bioreactor system was used to expose cultured mammary epithelial and breast cancer cells to fluid shear stress in the physiological range of those experienced in the vascular microenvironment. Genome-wide expression analysis revealed an effect of fluid flow on gene expression patterns and cellular processes involved in metastasis such as EMT, cell migration and adhesion. In addition, TGF-β signaling activity was significantly enriched and several genes belonging to this pathway were overexpressed upon flow exposure. Subsequently, we sought to identify novel flow-responsive biomarkers for breast cancer. For this purpose, bioinformatics and network biology approaches were used to reveal significant enrichment of biological processes involved in metastatic progression. Expression levels of differentially expressed genes were evaluated in clinical expression datasets, and 14 genes were identified as potential biomarkers. Relative expression levels of seven of these biomarkers were quantified in breast cancer patients and healthy volunteers. Five biomarkers passed the threshold for statistical significance and were overexpressed in more than 80% of patients presenting with basal and HER2-enriched breast cancers, which are the most aggressive subtypes of breast cancer. To our knowledge, the studies presented herein are the first of their kind to demonstrate that using an in vitro model to simulate exposure of cells to fluid shear stresses allows for identification of biomarkers for breast cancer. Using this system to study cellular events involved in other types of cancers may lead to new diagnostic and therapeutic approaches for metastatic cancer progression.
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    Identification and Characterization of Different Metabolic Subtypes in Cancer
    (2020-01-07) Pervin, Jannat; Bathe, Oliver F.; Tang, Patricia A.; Wang, Edwin
    Cancer is a leading cause of death worldwide. Genomics based approaches represent a dominant approach in oncological research. However, multiple processes can modify genetic information and impact cancer’s phenotype in a non-coding manner such as epigenetic events, transcription of various splice variants, expression of non-coding RNA and miRNA, and post-translational modifications of proteins. Therefore, molecular events that are further downstream of the genome (perhaps reflected by the proteome or the metabolome) may better reflect the tumour phenotype. One feature of cancer is perturbed metabolism. Some of the aberrant metabolic pathways may enhance tumour viability and growth, and these perturbed pathways may be susceptible to pharmacologic inhibition. Thus, our overall goal is to categorize tumours by their metabolic features; to understand the biological implications of these metabolic features, and to identify pathways that could be potentially targeted with drugs. This project involves the development of a workflow to define the metabolic features of a tumour. The workflow will involve the categorization of tumours based on their metabolic features (at the transcriptome level), exploration of associated biological features of each metabolic subtype, and integration of multiple levels of molecular control (including mutation status, copy number variation, methylation, and metabolome). Our work began with breast cancer, which is already well characterized by a large cohort in The Cancer Genome Atlas (TCGA) project. Then we used the same principles to investigate a more complex tumour type, pancreatic cancer, which is characterized by a highly variable degree of stroma infiltration.
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    Mutational landscape differences between young-onset and older-onset breast cancer patients
    (2020-03-12) Mealey, Nicole E; O’Sullivan, Dylan E; Pader, Joy; Ruan, Yibing; Wang, Edwin; Quan, May L; Brenner, Darren R
    Abstract Background The incidence of breast cancer among young women (aged ≤40 years) has increased in North America and Europe. Fewer than 10% of cases among young women are attributable to inherited BRCA1 or BRCA2 mutations, suggesting an important role for somatic mutations. This study investigated genomic differences between young- and older-onset breast tumours. Methods In this study we characterized the mutational landscape of 89 young-onset breast tumours (≤40 years) and examined differences with 949 older-onset tumours (> 40 years) using data from The Cancer Genome Atlas. We examined mutated genes, mutational load, and types of mutations. We used complementary R packages “deconstructSigs” and “SomaticSignatures” to extract mutational signatures. A recursively partitioned mixture model was used to identify whether combinations of mutational signatures were related to age of onset. Results Older patients had a higher proportion of mutations in PIK3CA, CDH1, and MAP3K1 genes, while young-onset patients had a higher proportion of mutations in GATA3 and CTNNB1. Mutational load was lower for young-onset tumours, and a higher proportion of these mutations were C > A mutations, but a lower proportion were C > T mutations compared to older-onset tumours. The most common mutational signatures identified in both age groups were signatures 1 and 3 from the COSMIC database. Signatures resembling COSMIC signatures 2 and 13 were observed among both age groups. We identified a class of tumours with a unique combination of signatures that may be associated with young age of onset. Conclusions The results of this exploratory study provide some evidence that the mutational landscape and mutational signatures among young-onset breast cancer are different from those of older-onset patients. The characterization of young-onset tumours could provide clues to their etiology which may inform future prevention. Further studies are required to confirm our findings.
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    The Identification of Target Gene to Increase Immunotherapy Response in Patients with Solid Tumors using Experimental and Computational Approaches
    (2023-07) Nasr, Sahar; Wang, Edwin; Mahoney, Douglas; Bathe, Oliver; Bose, Pinaki
    Conventional cancer therapies have limitations which can lead to high recurrence rates and reduced quality of life. Immune checkpoint inhibitors (ICI) have been shown to have more durable responses and fewer side effects. This makes them an alternative treatment for solid tumors like bladder cancer and MSI-high colorectal carcinoma. However, many patients do not respond to ICI or develop resistance due to factors such as the absence of CD8+ T cells in the tumor microenvironment, dysfunctional CD8+ T cells, and impaired tumor-specific memory T cells generation. This study shows that inhibiting Sun1 enhances tumor-infiltrating lymphocyte infiltration, inhibits tumor growth in mice, and improves the response to anti-PD-1 treatment. Although the role of Sun1 in chromatin organization and gene expression regulation is not fully clear, its inhibition can upregulate the immune-related genes within the knockout cell lines. This approach suggests a potential strategy for enhancing ICI effectiveness in cancer treatment.