Systematic Review of Patient-Derived Xenograft Models for Preclinical Studies of Anti-Cancer Drugs in Solid Tumors

Koga, Yoshikatsu; Ochiai, Atsushi

doi:10.3390/cells8050418

Cited by 93 publications

(86 citation statements)

References 123 publications

(188 reference statements)

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“…In contrast, SCID/beige mice, Rag2 KO mice, and Rag2 KO/Jak3 KO mice were used only by 2 facilities (11.8%), one facility (5.9%), and one facility (5.9%), respectively. These were similar to the trend of the mouse strains used in recent PDX models found in a literature search 8 . As shown in Table , the majority of the respondents used either nude mice because of the cost and ease of handling or NOG or NSG mice because they are the most immune deficient, allowing for more efficient engraftment of human‐derived cancer tissue.…”

Section: Mouse Strains Used In Pdx Models and Housing Environments Fosupporting

confidence: 75%

Report of the use of patient‐derived xenograft models in the development of anticancer drugs in Japan

Tsumura

Koga

Hamada³

et al. 2020

Cancer Science

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Cell line‐derived xenograft (CDX) models created by implanting cancer cell lines into immunodeficient mice have contributed largely to the development of cancer drug therapies. However, cell lines often lose their original biological characteristics through many passages and cancer tissues in CDX models have many cancer cells and few cancer stromal cells, therefore CDX models are currently considered not suitable for predicting the results of clinical studies. Conversely, patient‐derived xenograft (PDX) models are gaining importance, as human cancer biological characteristics and microenvironments are recreated by implanting tumor tissue into immunodeficient mice. These highly expected, evidently beneficial PDX models have been used in some basic research and are becoming more generalized. However, quality control and quality assurance criteria have not been established for them, and challenges and problems in the utilization of valuable PDX models in drug development have yet to be clarified. In this report, we conducted a questionnaire survey among researchers in Japanese academic institutions and pharmaceutical companies to understand the current status of PDX models in Japan. Based on the questionnaire results, we summarized the situations surrounding respondent's utilization and quality control in the development of anticancer drugs and proposed several measures to facilitate the utilization of PDX models in the development of anticancer drugs.

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Section: Mouse Strains Used In Pdx Models and Housing Environments Fosupporting

confidence: 75%

Report of the use of patient‐derived xenograft models in the development of anticancer drugs in Japan

Tsumura

Koga

Hamada³

et al. 2020

Cancer Science

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“…Drug-adapted cancer cell lines are, like every model system, associated with specific advantages and limitations. Models including primary cancer cell cultures, three-dimensional cell (co-) culture systems, tumour-derived organoids, and animal models better reflect certain aspects of tumour growth such as intra-tumour heterogeneity, three-dimensional architecture, cancer cell interaction with the cancer microenvironment, and/ or metastatic behaviour [105][106][107][108][109][110][111][112][113][114] . Such models can be used to study processes that cannot be studied in cell lines.…”

Section: Resultsmentioning

confidence: 99%

Drug-adapted cancer cell lines as preclinical models of acquired resistance

Michaelis¹,

Wass²,

Činátl³

2019

CDR

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Acquired resistance formation limits the efficacy of anti-cancer therapies. Acquired and intrinsic resistance differ conceptually. Acquired resistance is the consequence of directed evolution, whereas intrinsic resistance depends on the (stochastic) presence of pre-existing resistance mechanisms. Preclinical model systems are needed to study acquired drug resistance because they enable: (1) in depth functional studies; (2) the investigation of non-standard treatments for a certain disease condition (which is necessary to identify small groups of responders); and (3) the comparison of multiple therapies in the same system. Hence, they complement data derived from clinical trials and clinical specimens, including liquid biopsies. Many groups have successfully used drug-adapted cancer cell lines to identify and elucidate clinically relevant resistance mechanisms to targeted and cytotoxic anti-cancer drugs. Hence, we argue that drug-adapted cancer cell lines represent a preclinical model system in their own right that is complementary to other preclinical model systems and clinical data.

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“…Patient-derived xenograft (PDX) models are valuable tools for preclinical cancer research studies (1). Several studies have reported that patient-derived glioma stem cells (GSCs) maintain the phenotype and genotype characteristics of the original tumors (2)(3)(4), and GSC-derived xenografts recapitulate the distinctive cytological hallmarks and diverse histological variants of the original tumors (5).…”

Section: Introductionmentioning

confidence: 99%

Patient‑derived orthotopic xenograft glioma models fail to replicate the magnetic resonance imaging features of the original patient tumor

et al. 2020

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Patient-derived orthotopic glioma xenograft models are important platforms used for pre-clinical research of glioma. In the present study, the diagnostic ability of magnetic resonance imaging (MRI) was examined with regard to the identification of biomarkers obtained from patient-derived glioma xenografts and human tumors. Conventional MRI, diffusion weighted imaging and dynamic contrast-enhanced (DCE)-MRI were used to analyze seven pairs of high grade gliomas with their corresponding xenografts obtained from non-obese diabetic-severe-combined immunodeficiency nude mice. Tumor samples were collected for transcriptome sequencing and histopathological staining, and differentially expressed genes were screened between the original tumors and the corresponding xenografts. Gene Ontology (GO) analysis was performed to predict the functions of these genes. In 6 cases of xenografts with diffuse growth, the degree of enhancement was significantly lower compared with the original tumors. Histopathological staining indicated that the microvascular area and microvascular diameter of the xenografts were significantly lower compared with the original tumors (P=0.009 and P=0.007, respectively). In one case, there was evidence of nodular tumor growth in the mouse. Both MRI and histopathological staining showed a clear demarcation between the transplanted tumors and the normal brain tissues. The relative apparent diffusion coefficient values of the 7 cases examined were significantly higher compared with the corresponding original tumors (P=0.001) and transfer coefficient values derived from DCE-MRI of the tumor area was significantly lower compared with the original tumors (P=0.016). GO analysis indicated that the expression levels of extracellular matrix-associated genes, angiogenesis-associated genes and immune function-associated genes in the original tumors were higher compared with the corresponding xenografts. In conclusion, the data demonstrated that the MRI features of patient-derived xenograft glioma models in mice were different compared with those of the original patient tumors. Differential gene expression may underlie the differences noted in the MRI features between original tumors and corresponding xenografts. The results of the present study highlight the precautions that should be taken when extrapolating data from patient-derived xenograft studies, and their applicability to humans.

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Systematic Review of Patient-Derived Xenograft Models for Preclinical Studies of Anti-Cancer Drugs in Solid Tumors

Cited by 93 publications

References 123 publications

Report of the use of patient‐derived xenograft models in the development of anticancer drugs in Japan

Report of the use of patient‐derived xenograft models in the development of anticancer drugs in Japan

Drug-adapted cancer cell lines as preclinical models of acquired resistance

Patient‑derived orthotopic xenograft glioma models fail to replicate the magnetic resonance imaging features of the original patient tumor

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