The Generation and Application of Patient-Derived Xenograft Model for Cancer Research.
The Generation and Application of Patient-Derived Xenograft Model for Cancer Research.
复制标题
患者衍生的异种移植模型在癌症研究中的产生和应用。
DOI:
10.4143/crt.2017.307
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发表时间:
2018-01
影响因子:
4.6
通讯作者:
Chang S
中科院分区:
文献类型:
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作者:
Jung J;Seol HS;Chang S
Establishing an appropriate preclinical model is crucial for translational cancer research. The most common way that has been adopted by far is grafting cancer cell lines, derived from patients. Although this xenograft model is easy to generate, but has several limitations because this cancer model could not represent the unique features of each cancer patient sufficiently. Moreover, accumulating evidences demonstrate cancer is a highly heterogeneous disease so that a tumor is comprised of cancer cells with diverse characteristics. In attempt to avoid these discrepancies between xenograft model and patients’ tumor, a patient-derived xenograft (PDX) model has been actively generated and applied. The PDX model can be developed by the implantation of cancerous tissue from a patient’s tumor into an immune-deficient mouse directly, thereby it preserves both cell-cell interactions and tumor microenvironment. In addition, the PDX model has shown advantages as a preclinical model in drug screening, biomarker development and co-clinical trial. In this review, we will summarize the methodology and applications of PDX in detail, and cover critical issues for the development of this model for preclinical research.
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影响因子:
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作者:
Gonçalves A;Bertucci F;Guille A;Garnier S;Adelaide J;Carbuccia N;Cabaud O;Finetti P;Brunelle S;Piana G;Tomassin-Piana J;Paciencia M;Lambaudie E;Popovici C;Sabatier R;Tarpin C;Provansal M;Extra JM;Eisinger F;Sobol H;Viens P;Lopez M;Ginestier C;Charafe-Jauffret E;Chaffanet M;Birnbaum D
通讯作者:
Birnbaum D
DOI:
10.1136/jim-2016-000076
发表时间:
2016-03
期刊:
Journal of investigative medicine : the official publication of the American Federation for Clinical Research
影响因子:
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作者:
Francis OL;Milford TA;Beldiman C;Payne KJ
通讯作者:
Payne KJ
影响因子:
4.6
作者:
Aytes, Alvaro;Mollevi, David G.;Villanueva, Alberto
通讯作者:
Villanueva, Alberto
影响因子:
8.8
作者:
Garrido-Laguna, I.;Tan, A. C.;Hidalgo, M.
通讯作者:
Hidalgo, M.
影响因子:
64.8
作者:
通讯作者:
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