Browsing by Subject "heterogeneity"

Now showing 1 - 3 of 3
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    Cytokine Interaction With Cancer-Associated Fibroblasts in Esophageal Cancer
    (Sage, 2022) Hassan, Md Sazzad; Cwidak, Nicholas; Awasthi, Niranjan; von Holzen, Urs; Surgery, School of Medicine
    Esophageal cancer (EC) is a highly aggressive cancer with poor outcomes under current treatment regimens. More recent findings suggest stroma elements, specifically cancer-associated fibroblasts (CAFs), play a role in disease occurrence and progression. Cancer-associated fibroblasts are largely the product of converted fibroblasts, but a variety of other local cell types including epithelial cells, endothelial cells, and mesenchymal cells have also been shown to transform to CAFs under the correct conditions. Cancer-associated fibroblasts primarily function in the communication between the tumor microenvironment and cancer cells via cytokine and chemokine secretions that accentuate immunosuppression and cancer growth. Cancer-associated fibroblasts also pose issues for EC treatment by contributing to resistance of current chemotherapeutics like cisplatin. Targeting this cell type directly proves difficult given the heterogeneity between CAFs subpopulations, but emerging research provides hope that treatment is on the horizon. This review aims to unravel some of the complexities surrounding CAFs’ impact on EC growth and therapy.
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    The Price of Preserving Neighborhoods: The Unequal Impacts of Historic District Designation
    (Sage, 2020-11) Oba, Tetsuharu; Noonan, Douglas S.; School of Public and Environmental Affairs
    Policies affecting cultural assets are popular yet imperfectly understood tools to shape local economic development. Historic preservation policies, for example, can have markedly different implications for original residents, prospective residents, and developers, even in the same city. Therefore, merely identifying its average effect can obscure important heterogeneity in its impact. This study examines the property value impacts of local and national historic districts across the distribution of property prices and how those differential impacts vary with the restrictiveness of the policy. A quantile regression model identifies the heterogeneity of effects among higher and lower end properties. The results reveal large differences between national and local districts, interior and buffer properties, and for different property values. These findings highlight the importance of and complexity in how housing markets react to attempts to guide local economic development.
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    Targeting intra-tumoral heterogeneity of human brain tumors with in vivo imaging: A roadmap for imaging genomics from multiparametric MR signals
    (AAPM, 2023-04) Parker, Jason G.; Servati, Mahsa; Diller, Emily E.; Cao, Sha; Ho, Chang; Lober, Robert; Cohen-Gadol, Aaron; Biostatistics and Health Data Science, School of Medicine
    Resistance of high grade tumors to treatment involves cancer stem cell features, deregulated cell division, acceleration of genomic errors, and emergence of cellular variants that rely upon diverse signaling pathways. This heterogeneous tumor landscape limits the utility of the focal sampling provided by invasive biopsy when designing strategies for targeted therapies. In this roadmap review paper, we propose and develop methods for enabling mapping of cellular and molecular features in vivo to inform and optimize cancer treatment strategies in the brain. This approach leverages (1) the spatial and temporal advantages of in vivo imaging compared with surgical biopsy, (2) the rapid expansion of meaningful anatomical and functional magnetic resonance signals, (3) widespread access to cellular and molecular information enabled by next-generation sequencing, and (4) the enhanced accuracy and computational efficiency of deep learning techniques. As multiple cellular variants may be present within volumes below the resolution of imaging, we describe a mapping process to decode micro- and even nano-scale properties from the macro-scale data by simultaneously utilizing complimentary multiparametric image signals acquired in routine clinical practice. We outline design protocols for future research efforts that marry revolutionary bioinformation technologies, growing access to increased computational capability, and powerful statistical classification techniques to guide rational treatment selection.