Browsing by Author "Yu, Tao"

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    MAL2 drives immune evasion in breast cancer by suppressing tumor antigen presentation
    (The American Society for Clinical Investigation, 2021-01-07) Fang, Yuanzhang; Wang, Lifei; Wan, Changlin; Sun, Yifan; Van der Jeught, Kevin; Zhou, Zhuolong; Dong, Tianhan; So, Ka Man; Yu, Tao; Li, Yujing; Eyvani, Haniyeh; Colter, Austyn B.; Dong, Edward; Cao, Sha; Wang, Jin; Schneider, Bryan P.; Sandusky, George E.; Liu, Yunlong; Zhang, Chi; Lu, Xiongbin; Zhang, Xinna; Medical and Molecular Genetics, School of Medicine
    Immune evasion is a pivotal event in tumor progression. To eliminate human cancer cells, current immune checkpoint therapy is set to boost CD8+ T cell-mediated cytotoxicity. However, this action is eventually dependent on the efficient recognition of tumor-specific antigens via T cell receptors. One primary mechanism by which tumor cells evade immune surveillance is to downregulate their antigen presentation. Little progress has been made toward harnessing potential therapeutic targets for enhancing antigen presentation on the tumor cell. Here, we identified MAL2 as a key player that determines the turnover of the antigen-loaded MHC-I complex and reduces the antigen presentation on tumor cells. MAL2 promotes the endocytosis of tumor antigens via direct interaction with the MHC-I complex and endosome-associated RAB proteins. In preclinical models, depletion of MAL2 in breast tumor cells profoundly enhanced the cytotoxicity of tumor-infiltrating CD8+ T cells and suppressed breast tumor growth, suggesting that MAL2 is a potential therapeutic target for breast cancer immunotherapy.
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    ST2 as checkpoint target for colorectal cancer immunotherapy
    (American Society for Clinical Investigation, 2020-05-07) Jeught, Kevin Van der; Sun, Yifan; Fang, Yuanzhang; Zhou, Zhuolong; Jiang, Hua; Yu, Tao; Yang, Jinfeng; Kamocka, Malgorzata M.; So, Ka Man; Li, Yujing; Eyvani, Haniyeh; Sandusky, George E.; Frieden, Michael; Braun, Harald; Beyaert, Rudi; He, Xiaoming; Zhang, Xinna; Zhang, Chi; Paczesny, Sophie; Lu, Xiongbin; Pediatrics, School of Medicine
    Immune checkpoint blockade immunotherapy delivers promising clinical results in colorectal cancer (CRC). However, only a fraction of cancer patients develop durable responses. The tumor microenvironment (TME) negatively impacts tumor immunity and subsequently clinical outcomes. Therefore, there is a need to identify other checkpoint targets associated with the TME. Early-onset factors secreted by stromal cells as well as tumor cells often help recruit immune cells to the TME, among which are alarmins such as IL-33. The only known receptor for IL-33 is stimulation 2 (ST2). Here we demonstrated that high ST2 expression is associated with poor survival and is correlated with low CD8+ T cell cytotoxicity in CRC patients. ST2 is particularly expressed in tumor-associated macrophages (TAMs). In preclinical models of CRC, we demonstrated that ST2-expressing TAMs (ST2+ TAMs) were recruited into the tumor via CXCR3 expression and exacerbated the immunosuppressive TME; and that combination of ST2 depletion using ST2-KO mice with anti–programmed death 1 treatment resulted in profound growth inhibition of CRC. Finally, using the IL-33trap fusion protein, we suppressed CRC tumor growth and decreased tumor-infiltrating ST2+ TAMs. Together, our findings suggest that ST2 could serve as a potential checkpoint target for CRC immunotherapy.
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    Using a monotone single‐index model to stabilize the propensity score in missing data problems and causal inference
    (Wiley, 2019-04) Qin, Jing; Yu, Tao; Li, Pengfei; Liu, Hao; Chen, Baojiang; Biostatistics, School of Public Health
    The augmented inverse weighting method is one of the most popular methods for estimating the mean of the response in causal inference and missing data problems. An important component of this method is the propensity score. Popular parametric models for the propensity score include the logistic, probit, and complementary log‐log models. A common feature of these models is that the propensity score is a monotonic function of a linear combination of the explanatory variables. To avoid the need to choose a model, we model the propensity score via a semiparametric single‐index model, in which the score is an unknown monotonic nondecreasing function of the given single index. Under this new model, the augmented inverse weighting estimator (AIWE) of the mean of the response is asymptotically linear, semiparametrically efficient, and more robust than existing estimators. Moreover, we have made a surprising observation. The inverse probability weighting and AIWEs based on a correctly specified parametric model may have worse performance than their counterparts based on a nonparametric model. A heuristic explanation of this phenomenon is provided. A real‐data example is used to illustrate the proposed methods.