Abstract 2496: Comparative transcriptome analysis of metastatic heterogeneity in a mouse model of breast cancer

Abstract

Abstract Introduction: Cancer 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. Methods: A suite of organ derived metastatic cell lines were harvested from 10 female PyMT mice and used for transcriptome profiling. The cells were first extracted from mouse organs with metastatic disease. Tissues were processed into single cell suspensions. Cancer cells were isolated and sorted based on the expression of specific cell surface markers. Cells exhibiting CD44low EpCAMhigh or CD44high EpCAMhigh profiles were collected. RNA from each cell line was extracted in biological replicates for bulk RNA sequencing. Samples were sequenced using the NextSeq 500 Illumina platform. Reads were mapped to the mouse genome using STAR and gene expression was quantified using RSEM. Tissue specific genes were compared across the different metastatic and primary tumor samples. Results: Cell lines harvested from PyMT mice were able to recapitulate the metastatic cascade in vivo. Expression of the oncogenic PyMT transgene was observed in all cell lines. Comparison of RNA sequencing data across all cell populations produced distinct gene clusters. Differential gene expression patterns related to organ tropism and immunomodulatory signatures were observed. Furthermore, the sequencing results identified expression profiles based on tissue dependent niches and clonal heterogeneity. These cohorts of data were narrowed down to identify a subset of genes with high expression and known metastatic propensity. These key genes are being used to image specific stages of the metastatic cascade. Conclusion: The collection of organ derived cancer cell lines enabled comprehensive transcriptomic analysis of different metastatic niches. This transcriptome pattern could contribute to subclonal evolution during cancer progression. In future work, we aim to investigate intratumoral heterogeneity by preforming single cell RNA sequencing on key metastatic samples and compare them to the primary tumor. The markers will also be used to image the impact of tumor heterogeneity on metastases. Citation Format: Anastasia A. Ionkina, Gabriela Balderrama-Gutierrez, Steve Huy Phan, Ali Mortazavi, Jennifer A. Prescher. Comparative transcriptome analysis of metastatic heterogeneity in a mouse model of breast cancer [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 2496.