Browsing by Author "Lim, Dae-Sik"

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    PRMT1 promotes pancreatic cancer development and resistance to chemotherapy
    (Elsevier, 2024) Ku, Bomin; Eisenbarth, David; Baek, Seonguk; Jeong, Tae-Keun; Kang, Ju-Gyeong; Hwang, Daehee; Noh, Myung-Giun; Choi, Chan; Choi, Sungwoo; Seol, Taejun; Kim, Yun-Hee; Woo, Sang Myung; Kong, Sun-Young; Lim, Dae-Sik; Medicine, School of Medicine
    Pancreatic ductal adenocarcinoma (PDAC) remains one of the most lethal types of cancer, and novel treatment regimens are direly needed. Epigenetic regulation contributes to the development of various cancer types, but its role in the development of and potential as a therapeutic target for PDAC remains underexplored. Here, we show that PRMT1 is highly expressed in murine and human pancreatic cancer and is essential for cancer cell proliferation and tumorigenesis. Deletion of PRMT1 delays pancreatic cancer development in a KRAS-dependent mouse model, and multi-omics analyses reveal that PRMT1 depletion leads to global changes in chromatin accessibility and transcription, resulting in reduced glycolysis and a decrease in tumorigenic capacity. Pharmacological inhibition of PRMT1 in combination with gemcitabine has a synergistic effect on pancreatic tumor growth in vitro and in vivo. Collectively, our findings implicate PRMT1 as a key regulator of pancreatic cancer development and a promising target for combination therapy.
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    Protocol to calculate the synergy of drug combinations in organoids and primary cells from murine tumors
    (Elsevier, 2024-10-01) Eisenbarth, David; Ku, Bomin; Lim, Dae-Sik; Medicine, School of Medicine
    Evaluating the synergy of drug combinations is crucial in advancing treatment regimens. Here, we present a protocol to establish primary cells and organoids from murine tumors and calculate drug synergy. We describe all necessary cell culture procedures, including establishing primary cultures, setting up treatment groups, and detecting cell viability. We then outline how to calculate the synergy score based on a bioinformatical pipeline. This approach applies to any disease model in which a combination of drugs needs to be evaluated. For complete details on the use and execution of this protocol, please refer to Ku et al.1.