Andrechek, E. | Search bioRxiv

bioRxiv2018

Integrated sequence and gene expression analysis of mouse models of breast cancer reveals critical events with human parallels

Mouse models have an essential role in cancer research, yet little is known about how various models resemble human cancer at a genomic level. However, the shared genomic alterations in each model and corresponding human cancer are critical for translating findings in mice to the clinic. We have completed whole genome sequencing and transcriptome profiling of two widely used mouse models of breast cancer, MMTV-Neu and MMTV-PyMT. This genomic information was integrated with phenotypic data and CRISPR/Cas9 studies to understand the impact of key events on tumor biology. Despite the engineered initiating transgenic event in these mouse models, they contain similar copy number alterations, single nucleotide variants, and translocation events as human breast cancer. Through integrative in vitro and in vivo studies, we identified copy number alterations in key extracellular matrix proteins including Collagen 1 Type 1 alpha 1 (Col1a1) and Chondroadherin (CHAD) that drive metastasis in these mouse models. Importantly this amplification is also found in 25% of HER2+ human breast cancer and is associated with increased metastasis. In addition to copy number alterations, we observed a propensity of the tumors to modulate tyrosine kinase mediated signaling through mutation of phosphatases. Specifically, we found that 81% of MMTV-PyMT tumors have a mutation in the EGFR regulatory phosphatase, PTPRH. Mutation in PTPRH led to increased phospho-EGFR levels and decreased latency. Moreover, PTPRH mutations increased response to EGFR kinase inhibitors. Analogous PTPRH mutations are present in lung cancer patients and together this data suggests that a previously unidentified population of human lung cancer patients may respond to EGFR targeted therapy. These findings underscore the importance of understanding the complete genomic landscape of a mouse model and illustrate the utility this has in understanding human cancers.

cancer biology↗

bioRxiv2018

Evaluating cancer cell lines as models for metastatic breast cancer

Metastasis is the most common cause of cancer-related death and, as such, there is an urgent need to discover new therapies to treat metastasized cancers. Cancer cell lines are widely-used models to study cancer biology and test drug candidates. However, it is still unknown to what extent they adequately resemble the disease in patients. The recent accumulation of large-scale genomic data in cell lines, mouse models, and patient tissue samples provides an unprecedented opportunity to evaluate the suitability of cell lines for metastatic cancer research. In this work, we used breast cancer as a case study. The comprehensive comparison of the genetic profiles of 57 breast cancer cell lines with those of metastatic breast cancer samples revealed substantial genetic differences. In addition, we identified cell lines that more closely resemble different subtypes of metastatic breast cancer. Surprisingly, a combined analysis of mutation, copy number variation and gene expression data suggested that MDA-MB-231, the most commonly used triple negative cell line for metastatic breast cancer research, had little genomic similarity with Basal-like metastatic breast cancer samples. We further compared cell lines with organoids, a new type of preclinical model which are becoming more popular in recent years. We found that organoids outperformed cell lines in resembling the transcriptome of metastatic breast cancer samples. However, additional differential expression analysis suggested that both types of models could not mimic the effects of tumor microenvironment and meanwhile had their own bias towards modeling specific biological processes. Our work provides a guide of cell line selection in metastasis-related study and sheds light on the potential of organoids in translational research.

bioinformatics↗