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Faithful preclinical mouse models for better translation to bedside in the field of immuno-oncology
The success of immunotherapy using immune checkpoint inhibitors has changed the practice of cancer treatment tremendously. However, there are still many clinical challenges, such as drug resistance, predictive biomarker development, exploration of combination therapies, and prediction of immune-rela...
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Published in: | International journal of clinical oncology 2020-05, Vol.25 (5), p.831-841 |
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container_title | International journal of clinical oncology |
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creator | Saito, Ryoichi Kobayashi, Takashi Kashima, Soki Matsumoto, Keiyu Ogawa, Osamu |
description | The success of immunotherapy using immune checkpoint inhibitors has changed the practice of cancer treatment tremendously. However, there are still many clinical challenges, such as drug resistance, predictive biomarker development, exploration of combination therapies, and prediction of immune-related adverse events in preclinical settings. To overcome these problems, it is essential to establish faithful preclinical mouse models that recapitulate the clinical features, molecular genetics, biological heterogeneity, and immune microenvironment of human cancers. Here we review the advantages and disadvantages of current preclinical mouse models, including syngeneic murine tumor cell lines, autochthonous tumor models, cancer cell line-derived xenografts, patient-derived-xenografts, and various kinds of immunologically humanized mice. We discuss how these models should be characterized and applied in preclinical settings, and how we should prepare preclinical studies for successful translation from bench to bedside. |
doi_str_mv | 10.1007/s10147-019-01520-z |
format | article |
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subjects | Animal models Cancer immunotherapy Cancer Research Cell culture Drug resistance Immune checkpoint inhibitors Immunotherapy Invited Review Article Medicine Medicine & Public Health Oncology Surgical Oncology Translation Tumor cell lines Xenografts |
title | Faithful preclinical mouse models for better translation to bedside in the field of immuno-oncology |
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