Browsing by Author "Xu, Xiaowei"

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    ALDH1 Bio-activates Nifuroxazide to Eradicate ALDHHigh Melanoma-Initiating Cells
    (Elsevier, 2018-12-20) Sarvi, Sana; Crispin, Richard; Lu, Yuting; Zeng, Lifan; Hurley, Thomas D.; Houston, Douglas R.; von Kriegsheim, Alex; Chen, Che-Hong; Mochly-Rosen, Daria; Ranzani, Marco; Mathers, Marie E.; Xu, Xiaowei; Xu, Wei; Adams, David J.; Carragher, Neil O.; Fujita, Mayumi; Schuchter, Lynn; Unciti-Broceta, Asier; Brunton, Valerie G.; Patton, E. Elizabeth; Biochemistry and Molecular Biology, School of Medicine
    5-Nitrofurans are antibiotic pro-drugs that have potential as cancer therapeutics. Here, we show that 5-nitrofurans can be bio-activated by aldehyde dehydrogenase (ALDH) 1A1/1A3 enzymes that are highly expressed in a subpopulation of cancer-initiating (stem) cells. We discover that the 5-nitrofuran, nifuroxazide, is selective for bio-activation by ALDH1 isoforms over ALDH2, whereby it both oxidizes ALDH1 and is converted to cytotoxic metabolites in a two-hit pro-drug mechanism. We show that ALDH1High melanoma cells are sensitive to nifuroxazide, while ALDH1A3 loss-of-function mutations confer drug resistance. In tumors, nifuroxazide targets ALDH1High melanoma subpopulations with the subsequent loss of melanoma-initiating cell potential. BRAF and MEK inhibitor therapy increases ALDH1 expression in patient melanomas, and effectively combines with nifuroxazide in melanoma cell models. The selective eradication of ALDH1High cells by nifuroxazide-ALDH1 activation goes beyond current strategies based on inhibiting ALDH1 and provides a rational basis for the nifuroxazide mechanism of action in cancer.
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    CD45 Phosphatase Inhibits STAT3 Transcription Factor Activity in Myeloid Cells and Promotes Tumor-Associated Macrophage Differentiation
    (Elsevier, 2016-02-16) Kumar, Vinit; Cheng, Pingyan; Condamine, Thomas; Mony, Sridevi; Languino, Lucia R.; McCaffrey, Judith C.; Hockstein, Neil; Guarino, Michael; Masters, Gregory; Penman, Emily; Denstman, Fred; Xu, Xiaowei; Altieri, Dario C.; Du, Hong; Yan, Cong; Gabrilovich, Dmitry I.; Department of Pathology and Laboratory Medicine, IU School of Medicine
    Recruitment of monocytic myeloid-derived suppressor cells (MDSCs) and differentiation of tumor-associated macrophages (TAMs) are the major factors contributing to tumor progression and metastasis. We demonstrated that differentiation of TAMs in tumor site from monocytic precursors was controlled by downregulation of the activity of the transcription factor STAT3. Decreased STAT3 activity was caused by hypoxia and affected all myeloid cells but was not observed in tumor cells. Upregulation of CD45 tyrosine phosphatase activity in MDSCs exposed to hypoxia in tumor site was responsible for downregulation of STAT3. This effect was mediated by the disruption of CD45 protein dimerization regulated by sialic acid. Thus, STAT3 has a unique function in the tumor environment in controlling the differentiation of MDSC into TAM, and its regulatory pathway could be a potential target for therapy.
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    Weakly-Supervised Cross-Domain Adaptation for Endoscopic Lesions Segmentation
    (IEEE, 2021) Dong, Jiahua; Cong, Yang; Sun, Gan; Yang, Yunsheng; Xu, Xiaowei; Ding, Zhengming; Computer Information and Graphics Technology, Purdue School of Engineering and Technology
    Weakly-supervised learning has attracted growing research attention on medical lesions segmentation due to significant saving in pixel-level annotation cost. However, 1) most existing methods require effective prior and constraints to explore the intrinsic lesions characterization, which only generates incorrect and rough prediction; 2) they neglect the underlying semantic dependencies among weakly-labeled target enteroscopy diseases and fully-annotated source gastroscope lesions, while forcefully utilizing untransferable dependencies leads to the negative performance. To tackle above issues, we propose a new weakly-supervised lesions transfer framework, which can not only explore transferable domain-invariant knowledge across different datasets, but also prevent the negative transfer of untransferable representations. Specifically, a Wasserstein quantified transferability framework is developed to highlight wide-range transferable contextual dependencies, while neglecting the irrelevant semantic characterizations. Moreover, a novel self-supervised pseudo label generator is designed to equally provide confident pseudo pixel labels for both hard-to-transfer and easy-to-transfer target samples. It inhibits the enormous deviation of false pseudo pixel labels under the self-supervision manner. Afterwards, dynamically-searched feature centroids are aligned to narrow category-wise distribution shift. Comprehensive theoretical analysis and experiments show the superiority of our model on the endoscopic dataset and several public datasets.