Browsing by Author "Han, Leng"

Now showing 1 - 6 of 6
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    Allele-Specific Reprogramming of Cancer Metabolism by the Long Non-coding RNA CCAT2
    (Elsevier, 2016-02-18) Redis, Roxana S.; Vela, Luz E.; Lu, Weiqin; de Oliveira, Juliana Ferreira; Ivan, Cristina; Rodriguez-Aguayo, Cristian; Adamoski, Douglas; Pasculli, Barbara; Taguchi, Ayumu; Chen, Yunyun; Fernandez, Agustin F.; Valledor, Luis; Van Roosbroeck, Katrien; Chang, Samuel; Shah, Maitri; Kinnebrew, Garrett; Han, Leng; Atlasi, Yaser; Cheung, Lawrence H.; Huang, Gilbert Yuanjay; Monroig, Paloma; Ramirez, Marc S.; Ivkovic, Tina Catela; Van, Long; Ling, Hui; Gafà, Roberta; Kapitanovic, Sanja; Lanza, Giovanni; Bankson, James A.; Huang, Peng; Lai, Stephan Y.; Bast, Robert C.; Rosenblum, Michael G.; Radovich, Milan; Ivan, Mircea; Bartholomeusz, Geoffrey; Liang, Han; Fraga, Mario F.; Widger, William R.; Hanash, Samir; Berindan-Neagoe, Ioana; Lopez-Berestein, Gabriel; Ambrosio, Andre L.B.; Dias, Sandra M Gomes; Calin, George A.; Department of Surgery, IU School of Medicine
    Altered energy metabolism is a cancer hallmark as malignant cells tailor their metabolic pathways to meet their energy requirements. Glucose and glutamine are the major nutrients that fuel cellular metabolism, and the pathways utilizing these nutrients are often altered in cancer. Here, we show that the long ncRNA CCAT2, located at the 8q24 amplicon on cancer risk-associated rs6983267 SNP, regulates cancer metabolism in vitro and in vivo in an allele-specific manner by binding the Cleavage Factor I (CFIm) complex with distinct affinities for the two subunits (CFIm25 and CFIm68). The CCAT2 interaction with the CFIm complex fine-tunes the alternative splicing of Glutaminase (GLS) by selecting the poly(A) site in intron 14 of the precursor mRNA. These findings uncover a complex, allele-specific regulatory mechanism of cancer metabolism orchestrated by the two alleles of a long ncRNA.
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    Characterization of intratumor microbiome in cancer immunotherapy
    (Elsevier, 2023-07-12) Zhang, Zhao; Gao, Qian; Ren, Xiangmei; Luo, Mei; Liu, Yuan; Liu, Peilin; Liu, Yun; Ye, Youqiong; Che, Xiang; Liu, Hong; Han, Leng; Biostatistics and Health Data Science, School of Medicine
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    Effective combinatorial immunotherapy for penile squamous cell carcinoma
    (Springer Nature, 2020-05-01) Huang, Tianhe; Cheng, Xi; Chahoud, Jad; Sarhan, Ahmed; Tamboli, Pheroze; Rao, Priya; Guo, Ming; Manyam, Ganiraju; Zhang, Li; Xiang, Yu; Han, Leng; Shang, Xiaoying; Deng, Pingna; Luo, Yanting; Lu, Xuemin; Feng, Shan; Ferrer, Magaly Martinez; Wang, Y. Alan; DePinho, Ronald A.; Pettaway, Curtis A.; Lu, Xin; Medicine, School of Medicine
    Penile squamous cell carcinoma (PSCC) accounts for over 95% of penile malignancies and causes significant mortality and morbidity in developing countries. Molecular mechanisms and therapies of PSCC are understudied, owing to scarcity of laboratory models. Herein, we describe a genetically engineered mouse model of PSCC, by co-deletion of Smad4 and Apc in the androgen-responsive epithelium of the penis. Mouse PSCC fosters an immunosuppressive microenvironment with myeloid-derived suppressor cells (MDSCs) as a dominant population. Preclinical trials in the model demonstrate synergistic efficacy of immune checkpoint blockade with the MDSC-diminishing drugs cabozantinib or celecoxib. A critical clinical problem of PSCC is chemoresistance to cisplatin, which is induced by Pten deficiency on the backdrop of Smad4/Apc co-deletion. Drug screen studies informed by targeted proteomics identify a few potential therapeutic strategies for PSCC. Our studies have established what we believe to be essential resources for studying PSCC biology and developing therapeutic strategies.
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    Identification of Hypoxia-ALCAMhigh Macrophage- Exhausted T Cell Axis in Tumor Microenvironment Remodeling for Immunotherapy Resistance
    (Wiley, 2024) Xun, Zhenzhen; Zhou, Huanran; Shen, Mingyi; Liu, Yao; Sun, Chengcao; Du, Yanhua; Jiang, Zhou; Yang, Liuqing; Zhang, Qing; Lin, Chunru; Hu, Qingsong; Ye, Youqiong; Han, Leng; Biostatistics and Health Data Science, School of Medicine
    Although hypoxia is known to be associated with immune resistance, the adaptability to hypoxia by different cell populations in the tumor microenvironment and the underlying mechanisms remain elusive. This knowledge gap has hindered the development of therapeutic strategies to overcome tumor immune resistance induced by hypoxia. Here, bulk, single‐cell, and spatial transcriptomics are integrated to characterize hypoxia associated with immune escape during carcinogenesis and reveal a hypoxia‐based intercellular communication hub consisting of malignant cells, ALCAM high macrophages, and exhausted CD8+ T cells around the tumor boundary. A hypoxic microenvironment promotes binding of HIF‐1α complex is demonstrated to the ALCAM promoter therefore increasing its expression in macrophages, and the ALCAM high macrophages co‐localize with exhausted CD8+ T cells in the tumor spatial microenvironment and promote T cell exhaustion. Preclinically, HIF‐1ɑ inhibition reduces ALCAM expression in macrophages and exhausted CD8+ T cells and potentiates T cell antitumor function to enhance immunotherapy efficacy. This study reveals the systematic landscape of hypoxia at single‐cell resolution and spatial architecture and highlights the effect of hypoxia on immunotherapy resistance through the ALCAM high macrophage‐exhausted T cell axis, providing a novel immunotherapeutic strategy to overcome hypoxia‐induced resistance in cancers.
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    PancanQTLv2.0: a comprehensive resource for expression quantitative trait loci across human cancers
    (Oxford University Press, 2024) Chen, Chengxuan; Liu, Yuan; Luo, Mei; Yang, Jingwen; Chen, Yamei; Wang, Runhao; Zhou, Joseph; Zang, Yong; Diao, Lixia; Han, Leng; Biostatistics and Health Data Science, Richard M. Fairbanks School of Public Health
    Expression quantitative trait locus (eQTL) analysis is a powerful tool used to investigate genetic variations in complex diseases, including cancer. We previously developed a comprehensive database, PancanQTL, to characterize cancer eQTLs using The Cancer Genome Atlas (TCGA) dataset, and linked eQTLs with patient survival and GWAS risk variants. Here, we present an updated version, PancanQTLv2.0 (https://hanlaboratory.com/PancanQTLv2/), with advancements in fine-mapping causal variants for eQTLs, updating eQTLs overlapping with GWAS linkage disequilibrium regions and identifying eQTLs associated with drug response and immune infiltration. Through fine-mapping analysis, we identified 58 747 fine-mapped eQTLs credible sets, providing mechanic insights of gene regulation in cancer. We further integrated the latest GWAS Catalog and identified a total of 84 592 135 linkage associations between eQTLs and the existing GWAS loci, which represents a remarkable ∼50-fold increase compared to the previous version. Additionally, PancanQTLv2.0 uncovered 659516 associations between eQTLs and drug response and identified 146948 associations between eQTLs and immune cell abundance, providing potentially clinical utility of eQTLs in cancer therapy. PancanQTLv2.0 expanded the resources available for investigating gene expression regulation in human cancers, leading to advancements in cancer research and precision oncology.
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    The Genetic, Pharmacogenomic, and Immune Landscapes Associated with Protein Expression across Human Cancers
    (American Association for Cancer Research, 2023) Chen, Chengxuan; Liu, Yuan; Li, Qiang; Zhang, Zhao; Luo, Mei; Liu, Yaoming; Han, Leng; Biostatistics and Health Data Science, Richard M. Fairbanks School of Public Health
    Proteomics is a powerful approach that can rapidly enhance our understanding of cancer development. Detailed characterization of the genetic, pharmacogenomic, and immune landscape in relation to protein expression in cancer patients could provide new insights into the functional roles of proteins in cancer. By taking advantage of the genotype data from The Cancer Genome Atlas (TCGA) and protein expression data from The Cancer Proteome Atlas (TCPA), we characterized the effects of genetic variants on protein expression across 31 cancer types and identified approximately 100,000 protein quantitative trait loci (pQTL). Among these, over 8000 pQTL were associated with patient overall survival. Furthermore, characterization of the impact of protein expression on more than 350 imputed anticancer drug responses in patients revealed nearly 230,000 significant associations. In addition, approximately 21,000 significant associations were identified between protein expression and immune cell abundance. Finally, a user-friendly data portal, GPIP (https://hanlaboratory.com/GPIP), was developed featuring multiple modules that enable researchers to explore, visualize, and browse multidimensional data. This detailed analysis reveals the associations between the proteomic landscape and genetic variation, patient outcome, the immune microenvironment, and drug response across cancer types, providing a resource that may offer valuable clinical insights and encourage further functional investigations of proteins in cancer.