GPR56 Regulates VEGF Production and Angiogenesis during Melanoma Progression

2012 February 15Angiogenesis is a critical step during cancer progression. The VEGF is a major stimulator for angiogenesis and is predominantly contributed by cancer cells in tumors. Inhibition of the VEGF signaling pathway has shown promising therapeutic benefits for cancer patients, but adaptive tumor responses are often observed, indicating the need for further understanding of VEGF regulation. We report that a novel G protein–coupled receptor, GPR56, inhibits VEGF production from the melanoma cell lines and impedes melanoma angiogenesis and growth, through the serine threonine proline-rich segment in its N-terminus and a signaling pathway involving protein kinase Cα. We also present evidence that the two fragments of GPR56, which are generated by autocatalyzed cleavage, played distinct roles in regulating VEGF production and melanoma progression. Finally, consistent with its suppressive roles in melanoma progression, the expression levels of GPR56 are inversely correlated with the malignancy of melanomas in human subjects. We propose that components of the GPR56-mediated signaling pathway may serve as new targets for antiangiogenic treatment of melanoma. Cancer Res; 71(16); 5558–68.National Institutes of Health (U.S.) (U54CA126515)Howard Hughes Medical Institut

Synthesizing tabular data using conditional GAN

Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, February, 2020Cataloged from PDF version of thesis.Includes bibliographical references (pages 89-93).In data science, the ability to model the distribution of rows in tabular data and generate realistic synthetic data enables various important applications including data compression, data disclosure, and privacy-preserving machine learning. However, because tabular data usually contains a mix of discrete and continuous columns, building such a model is a non-trivial task. Continuous columns may have multiple modes, while discrete columns are sometimes imbalanced, making modeling difficult. To address this problem, I took two major steps. (1) I designed SDGym, a thorough benchmark, to compare existing models, identify different properties of tabular data and analyze how these properties challenge different models. Our experimental results show that statistical models, such as Bayesian networks, that are constrained to a fixed family of available distributions cannot model tabular data effectively, especially when both continuous and discrete columns are included. Recently proposed deep generative models are capable of modeling more sophisticated distributions, but cannot outperform Bayesian network models in practice, because the network structure and learning procedure are not optimized for tabular data which may contain non-Gaussian continuous columns and imbalanced discrete columns. (2) To address these problems, I designed CTGAN, which uses a conditional generative adversarial network to address the challenges in modeling tabular data. Because CTGAN uses reversible data transformations and is trained by re-sampling the data, it can address common challenges in synthetic data generation. I evaluated CTGAN on the benchmark and showed that it consistently and significantly outperforms existing statistical and deep learning models.by Lei Xu.S.M.S.M. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Scienc