Browsing by Author "Cheng, Michael"

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    Combinatorial analyses reveal cellular composition changes have different impacts on transcriptomic changes of cell type specific genes in Alzheimer’s Disease
    (Springer Nature, 2021-01-11) Johnson, Travis S.; Xiang, Shunian; Dong, Tianhan; Huang, Zhi; Cheng, Michael; Wang, Tianfu; Yang, Kai; Ni, Dong; Huang, Kun; Zhang, Jie; Biostatistics, School of Public Health
    Alzheimer’s disease (AD) brains are characterized by progressive neuron loss and gliosis. Previous studies of gene expression using bulk tissue samples often fail to consider changes in cell-type composition when comparing AD versus control, which can lead to differences in expression levels that are not due to transcriptional regulation. We mined five large transcriptomic AD datasets for conserved gene co-expression module, then analyzed differential expression and differential co-expression within the modules between AD samples and controls. We performed cell-type deconvolution analysis to determine whether the observed differential expression was due to changes in cell-type proportions in the samples or to transcriptional regulation. Our findings were validated using four additional datasets. We discovered that the increased expression of microglia modules in the AD samples can be explained by increased microglia proportions in the AD samples. In contrast, decreased expression and perturbed co-expression within neuron modules in the AD samples was likely due in part to altered regulation of neuronal pathways. Several transcription factors that are differentially expressed in AD might account for such altered gene regulation. Similarly, changes in gene expression and co-expression within astrocyte modules could be attributed to combined effects of astrogliosis and astrocyte gene activation. Gene expression in the astrocyte modules was also strongly correlated with clinicopathological biomarkers. Through this work, we demonstrated that combinatorial analysis can delineate the origins of transcriptomic changes in bulk tissue data and shed light on key genes and pathways involved in AD.
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    Computational analysis of pathological images enables a better diagnosis of TFE3 Xp11.2 translocation renal cell carcinoma
    (Nature Research, 2020) Cheng, Jun; Han, Zhi; Mehra, Rohit; Shao, Wei; Cheng, Michael; Feng, Qianjin; Ni, Dong; Huang, Kun; Cheng, Liang; Zhang, Jie; Medicine, School of Medicine
    TFE3 Xp11.2 translocation renal cell carcinoma (TFE3-RCC) generally progresses more aggressively compared with other RCC subtypes, but it is challenging to diagnose TFE3-RCC by traditional visual inspection of pathological images. In this study, we collect hematoxylin and eosin- stained histopathology whole-slide images of 74 TFE3-RCC cases (the largest cohort to date) and 74 clear cell RCC cases (ccRCC, the most common RCC subtype) with matched gender and tumor grade. An automatic computational pipeline is implemented to extract image features. Comparative study identifies 52 image features with significant differences between TFE3-RCC and ccRCC. Machine learning models are built to distinguish TFE3-RCC from ccRCC. Tests of the classification models on an external validation set reveal high accuracy with areas under ROC curve ranging from 0.842 to 0.894. Our results suggest that automatically derived image features can capture subtle morphological differences between TFE3-RCC and ccRCC and contribute to a potential guideline for TFE3-RCC diagnosis.
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    Machine Learning Based Classification from Whole-Slide Histopathological Images Enables Reliable and Interpretable Diagnosis of Inverted Urothelial Papilloma
    (Elsevier, 2021-11-05) Shao, Wei; Cheng, Michael; Huang, Zhi; Han, Zhi; Wang, Tongxin; Lopez-Beltran, Antonio; Osunkoya, Adeboye O.; Zhang, Jie; Cheng, Liang; Huang, Kun; Medicine, School of Medicine
    Inverted urothelial papilloma (IUP) is a benign neoplasm of the urinary tract that accounts for less than 1% of urothelial tumors. It is diagnostically challenging for pathologists to distinguish histological features of IUP from other subtypes of non-invasive urothelial carcinoma, such as inverted Ta urothelial carcinoma (UCInv) and low-grade Ta urothelial carcinoma (UCLG). Using a machine learning approach, we analyzed the H&E-stained whole-slide histopathological images of 64 IUP (the largest cohort to date), 69 UCInv, and 92 UCLG samples, and propose a reliable, reproducible, and interpretable machine learning pipeline to classify IUP from other non-invasive urothelial carcinomas. The results showed that our method could achieve area under the ROC of 0.913 and 0.920 for classifying IUP from UCInv and UCLG, respectively, which is superior to competing methods, including deep learning-based methods. Testing of the classification models on an external validation dataset confirmed that our model can effectively identify IUP with high accuracy. Our results suggest that the proposed machine learning pipeline can robustly and accurately capture histopathological differences between IUP and other urothelial carcinoma subtypes, which can be extended to identify other rare cancer subtypes with limited samples and has the potential to expand the clinician’s armamentarium for accurate diagnosis.
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    Recent Advances of Deep Learning for Computational Histopathology: Principles and Applications
    (MDPI, 2022-02-25) Wu, Yawen; Cheng, Michael; Huang, Shuo; Pei, Zongxiang; Zuo, Yingli; Liu, Jianxin; Yang, Kai; Zhu, Qi; Zhang, Jie; Hong, Honghai; Zhang, Daoqiang; Huang, Kun; Cheng, Liang; Shao, Wei; Medicine, School of Medicine
    With the remarkable success of digital histopathology, we have witnessed a rapid expansion of the use of computational methods for the analysis of digital pathology and biopsy image patches. However, the unprecedented scale and heterogeneous patterns of histopathological images have presented critical computational bottlenecks requiring new computational histopathology tools. Recently, deep learning technology has been extremely successful in the field of computer vision, which has also boosted considerable interest in digital pathology applications. Deep learning and its extensions have opened several avenues to tackle many challenging histopathological image analysis problems including color normalization, image segmentation, and the diagnosis/prognosis of human cancers. In this paper, we provide a comprehensive up-to-date review of the deep learning methods for digital H&E-stained pathology image analysis. Specifically, we first describe recent literature that uses deep learning for color normalization, which is one essential research direction for H&E-stained histopathological image analysis. Followed by the discussion of color normalization, we review applications of the deep learning method for various H&E-stained image analysis tasks such as nuclei and tissue segmentation. We also summarize several key clinical studies that use deep learning for the diagnosis and prognosis of human cancers from H&E-stained histopathological images. Finally, online resources and open research problems on pathological image analysis are also provided in this review for the convenience of researchers who are interested in this exciting field.
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    Targeting the chromatin effector Pygo2 promotes cytotoxic T cell responses and overcomes immunotherapy resistance in prostate cancer
    (American Association for the Advancement of Science, 2023) Zhu, Yini; Zhao, Yun; Wen, Jiling; Liu, Sheng; Huang, Tianhe; Hatial, Ishita; Peng, Xiaoxia; Al Janabi, Hawraa; Huang, Gang; Mittlesteadt, Jackson; Cheng, Michael; Bhardwaj, Atul; Ashfeld, Brandon L.; Kao, Kenneth R.; Maeda, Dean Y.; Dai, Xing; Wiest, Olaf; Blagg, Brian S. J.; Lu, Xuemin; Cheng, Liang; Wan, Jun; Lu, Xin; Medical and Molecular Genetics, School of Medicine
    The noninflamed microenvironment in prostate cancer represents a barrier to immunotherapy. Genetic alterations underlying cancer cell-intrinsic oncogenic signaling are increasingly appreciated for their role in shaping the immune landscape. Recently, we identified Pygopus 2 (PYGO2) as the driver oncogene for the amplicon at 1q21.3 in prostate cancer. Here, using transgenic mouse models of metastatic prostate adenocarcinoma, we found that Pygo2 deletion decelerated tumor progression, diminished metastases, and extended survival. Pygo2 loss augmented the activation and infiltration of cytotoxic T lymphocytes (CTLs) and sensitized tumor cells to T cell killing. Mechanistically, Pygo2 orchestrated a p53/Sp1/Kit/Ido1 signaling network to foster a microenvironment hostile to CTLs. Genetic or pharmacological inhibition of Pygo2 enhanced the antitumor efficacy of immunotherapies using immune checkpoint blockade (ICB), adoptive cell transfer, or agents inhibiting myeloid-derived suppressor cells. In human prostate cancer samples, Pygo2 expression was inversely correlated with the infiltration of CD8+ T cells. Analysis of the ICB clinical data showed association between high PYGO2 level and worse outcome. Together, our results highlight a potential path to improve immunotherapy using Pygo2-targeted therapy for advanced prostate cancer.