Browsing by Author "Chen, Hong"

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    Quantitative trait loci identification for brain endophenotypes via new additive model with random networks
    (Oxford University Press, 2018-09) Wang, Xiaoqian; Chen, Hong; Yan, Jingwen; Nho, Kwangsik; Risacher, Shannon L.; Saykin, Andrew J.; Shen, Li; Huang, Heng; Radiology and Imaging Sciences, School of Medicine
    Motivation: The identification of quantitative trait loci (QTL) is critical to the study of causal relationships between genetic variations and disease abnormalities. We focus on identifying the QTLs associated to the brain endophenotypes in imaging genomics study for Alzheimer's Disease (AD). Existing research works mainly depict the association between single nucleotide polymorphisms (SNPs) and the brain endophenotypes via the linear methods, which may introduce high bias due to the simplicity of the models. Since the influence of QTLs on brain endophenotypes is quite complex, it is desired to design the appropriate non-linear models to investigate the associations of genotypes and endophenotypes. Results: In this paper, we propose a new additive model to learn the non-linear associations between SNPs and brain endophenotypes in Alzheimer's disease. Our model can be flexibly employed to explain the non-linear influence of QTLs, thus is more adaptive for the complex distribution of the high-throughput biological data. Meanwhile, as an important computational learning theory contribution, we provide the generalization error analysis for the proposed approach. Unlike most previous theoretical analysis under independent and identically distributed samples assumption, our error bound is based on m-dependent observations, which is more appropriate for the high-throughput and noisy biological data. Experiments on the data from Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort demonstrate the promising performance of our approach for identifying biological meaningful SNPs.
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    Targeted inhibition of Wnt signaling with a Clostridioides difficile toxin B fragment suppresses breast cancer tumor growth
    (Public Library of Science, 2023-11-09) He, Aina; Tian, Songhai; Kopper, Oded; Horan, Daniel J.; Chen, Peng; Bronson, Roderick T.; Sheng, Ren; Wu, Hao; Sui, Lufei; Zhou, Kun; Tao, Liang; Wu, Quan; Huang, Yujing; Shen, Zan; Han, Sen; Chen, Xueqing; Chen, Hong; He, Xi; Robling, Alexander G.; Jin, Rongsheng; Clevers, Hans; Xiang, Dongxi; Li, Zhe; Dong, Min; Anatomy, Cell Biology and Physiology, School of Medicine
    Wnt signaling pathways are transmitted via 10 homologous frizzled receptors (FZD1-10) in humans. Reagents broadly inhibiting Wnt signaling pathways reduce growth and metastasis of many tumors, but their therapeutic development has been hampered by the side effect. Inhibitors targeting specific Wnt-FZD pair(s) enriched in cancer cells may reduce side effect, but the therapeutic effect of narrow-spectrum Wnt-FZD inhibitors remains to be established in vivo. Here, we developed a fragment of C. difficile toxin B (TcdBFBD), which recognizes and inhibits a subclass of FZDs, FZD1/2/7, and examined whether targeting this FZD subgroup may offer therapeutic benefits for treating breast cancer models in mice. Utilizing 2 basal-like and 1 luminal-like breast cancer models, we found that TcdBFBD reduces tumor-initiating cells and attenuates growth of basal-like mammary tumor organoids and xenografted tumors, without damaging Wnt-sensitive tissues such as bones in vivo. Furthermore, FZD1/2/7-positive cells are enriched in chemotherapy-resistant cells in both basal-like and luminal mammary tumors treated with cisplatin, and TcdBFBD synergizes strongly with cisplatin in inhibiting both tumor types. These data demonstrate the therapeutic value of narrow-spectrum Wnt signaling inhibitor in treating breast cancers.