Dataset Information

Intratumoral heterogeneity in a Trp53-null mouse model of human breast cancer.

ABSTRACT: Intratumoral heterogeneity correlates with clinical outcome and reflects the cellular complexity and dynamics within a tumor. Such heterogeneity is thought to contribute to radio- and chemoresistance because many treatments may target only certain tumor cell subpopulations. A better understanding of the functional interactions between various subpopulations of cells, therefore, may help in the development of effective cancer treatments. We identified a unique subpopulation of tumor cells expressing mesenchymal-like markers in a Trp53-null mouse model of basal-like breast cancer using fluorescence-activated cell sorting and microarray analysis. Both in vitro and in vivo experiments revealed the existence of cross-talk between these "mesenchymal-like" cells and tumor-initiating cells. Knockdown of genes encoding ligands upregulated in the mesenchymal cells and their corresponding receptors in the tumor-initiating cells resulted in reduced tumorigenicity and increased tumor latency. These studies illustrate the non-cell-autonomous properties and importance of cooperativity between tumor subpopulations.Intratumoral heterogeneity has been considered one important factor in assessing a patient's initial response to treatment and selecting drug regimens to effectively increase tumor response rate. Elucidating the functional interactions between various subpopulations of tumor cells will help provide important new insights in understanding treatment response and tumor progression.

SUBMITTER: Zhang M

PROVIDER: S-EPMC4420701 | biostudies-literature | 2015 May

REPOSITORIES: biostudies-literature

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Intratumoral heterogeneity in a Trp53-null mouse model of human breast cancer.

Zhang Mei M Tsimelzon Anna A Chang Chi-Hsuan CH Fan Cheng C Wolff Andrew A Perou Charles M CM Hilsenbeck Susan G SG Rosen Jeffrey M JM

Cancer discovery 20150303 5

<h4>Unlabelled</h4>Intratumoral heterogeneity correlates with clinical outcome and reflects the cellular complexity and dynamics within a tumor. Such heterogeneity is thought to contribute to radio- and chemoresistance because many treatments may target only certain tumor cell subpopulations. A better understanding of the functional interactions between various subpopulations of cells, therefore, may help in the development of effective cancer treatments. We identified a unique subpopulation of ...[more]

PMID: 25735774

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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

Intratumoral heterogeneity in a Trp53-null mouse model of human breast cancer.

Publications

Intratumoral heterogeneity in a Trp53-null mouse model of human breast cancer.

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