Dataset Information

An in vivo mouse model of metastatic human thyroid cancer.

ABSTRACT:

Background

Mouse models of metastatic human cancers are important tools in preclinical studies for testing new systematic therapies and studying effectors of cancer metastasis. The major drawbacks of current mouse models for metastatic thyroid cancer are that they have low metastasis rates and do not allow in vivo tumor detection. Here, we report and characterize an in vivo detectable metastasis mouse model of human thyroid cancer using multiple thyroid cancer cell lines.

Methods

Human anaplastic thyroid cancer cell lines 8505C, C-643, SW-1736, and THJ-16T; follicular thyroid cancer cell lines FTC-133, FTC-236, and FTC-238; and Hürthle cell carcinoma cell line XTC-1 were transfected with a linearized pGL4.51[luc2/CMV/Neo] vector or transduced with lentivirus containing Luc2-eGFP reporter genes. The stably transfected cells were injected intravenously into NOD.Cg-Prkdc(scid) Il2rg(tm1Wjl)/SzJ mice. Tumors were detected with an in vivo imaging system-Xenogen IVIS. Vemurafenib, a BRAF inhibitor, was used to treat lung metastases generated from 8505C-Luc2 cells with a BRAF(V600E) mutation to test the accuracy of the model to evaluate response to therapy.

Results

Intravenous injection of as few as 30,000 8505C-Luc2 cells produced lung metastases in 100% of the injected mice, and many of these mice also developed bone metastases at a later stage of the disease. Similarly, metastatic tumors also developed in all mice injected with C-643-Luc2, THJ-16T-Luc2, FTC-133-Luc2, FTC-236-Luc2, FTC-238-Luc2, and XTC-1-Luc2 cells. The metastases were easily detectable in vivo, and tumor progression could be dynamically and accurately followed and correlated with the actual tumor burden. Furthermore, disease progression could be easily controlled by adjusting the number of injected cells. The in vivo treatment of 8505C xenograft lung metastases with vemurafenib dramatically reduced the growth and signal intensity with good correlation with actual tumor burden.

Conclusions

Herein we report an in vivo detectable mouse model of metastatic human thyroid cancer that is reliable and reproducible. It will serve as a useful tool in the preclinical testing of alternative systematic therapies for metastatic thyroid cancer, and for functional studies of thyroid cancer tumor biology in vivo.

SUBMITTER: Zhang L

PROVIDER: S-EPMC3993065 | biostudies-literature | 2014 Apr

REPOSITORIES: biostudies-literature

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Publications

An in vivo mouse model of metastatic human thyroid cancer.

Zhang Lisa L Gaskins Kelli K Yu Zhiya Z Xiong Yin Y Merino Maria J MJ Kebebew Electron E

Thyroid : official journal of the American Thyroid Association 20140310 4

<h4>Background</h4>Mouse models of metastatic human cancers are important tools in preclinical studies for testing new systematic therapies and studying effectors of cancer metastasis. The major drawbacks of current mouse models for metastatic thyroid cancer are that they have low metastasis rates and do not allow in vivo tumor detection. Here, we report and characterize an in vivo detectable metastasis mouse model of human thyroid cancer using multiple thyroid cancer cell lines.<h4>Methods</h4> ...[more]

PMID: 24262022

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Project description:BackgroundCancer metastasis is a complex process involving the spread of malignant cells from a primary tumor to distal organs. Understanding this cascade at a mechanistic level could provide critical new insights into the disease and potentially reveal new avenues for treatment. Transcriptome profiling of spontaneous cancer models is an attractive method to examine the dynamic changes accompanying tumor cell spread. However, such studies are complicated by the underlying heterogeneity of the cell types involved. The purpose of this study was to examine the transcriptomes of metastatic breast cancer cells using the well-established MMTV-PyMT mouse model.MethodsOrgan-derived metastatic cell lines were harvested from 10 female MMTV-PyMT mice. Cancer cells were isolated and sorted based on the expression of CD44low/EpCAMhigh or CD44high/EpCAMhigh surface markers. RNA from each cell line was extracted and sequenced using the NextSeq 500 Illumina platform. Tissue-specific genes were compared across the different metastatic and primary tumor samples. Reads were mapped to the mouse genome using STAR, and gene expression was quantified using RSEM. Single-cell RNA-seq (scRNA-seq) was performed on select samples using the ddSeq platform by BioRad and analyzed using Seurat v3.2.3. Monocle2 was used to infer pseudo-time progression.ResultsComparison of RNA sequencing data across all cell populations produced distinct gene clusters. Differential gene expression patterns related to CD44 expression, organ tropism, and immunomodulatory signatures were observed. scRNA-seq identified expression profiles based on tissue-dependent niches and clonal heterogeneity. These cohorts of data were narrowed down to identify subsets of genes with high expression and known metastatic propensity. Dot plot analyses further revealed clusters expressing cancer stem cell and cancer dormancy markers. Changes in relevant genes were investigated across pseudo-time and tissue origin using Monocle2. These data revealed transcriptomes that may contribute to sub-clonal evolution and treatment evasion during cancer progression.ConclusionsWe performed a comprehensive transcriptome analysis of tumor heterogeneity and organ tropism during breast cancer metastasis. These data add to our understanding of metastatic progression and highlight targets for breast cancer treatment. These markers could also be used to image the impact of tumor heterogeneity on metastases.

Dataset Information

An in vivo mouse model of metastatic human thyroid cancer.

Background

Methods

Results

Conclusions

Publications

An in vivo mouse model of metastatic human thyroid cancer.

Similar Datasets

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