Mouse Models of Breast Cancer Share Amplification and Deletion Events with Human Breast Cancer

  • Jonathan Rennhack
  • Briana To
  • Harrison Wermuth
  • Eran R. AndrechekEmail author


Breast tumor heterogeneity has been well documented through the use of multiplatform –omic studies in human tumors. However, there is no integrative database to capture the heterogeneity within mouse models of breast cancer. This project identifies genomic copy number alterations (CNAs) in 600 tumors across 27 major mouse models of breast cancer through the application of a predictive algorithm to publicly available gene expression data. It was found that despite the presence of strong oncogenic drivers in most mouse models, CNAs are extremely common but heterogeneous both between models and within models. Many mouse CNA events are largely conserved in human tumors and in the mouse we show that they are associated with secondary tumor characteristics such as tumor histology, metastasis, as well as enhanced oncogenic signaling. These data serve as an important resource in guiding investigators when choosing a mouse model to understand the gene copy number changes relevant to human breast cancer.


Copy number variation Mouse model Breast cancer Gene expression Metastasis 



We thank the members of the Andrechek laboratory for helpful discussions.

Authors’ Contributions

JR and EA collaborated on the study conception, design, and interpretation of results. BT provided histological coordination with CNA. HW provided the ACE analysis for many mouse models. JR performed all other experiments and drafted the manuscript. All authors have critically read, edited, and approved the final version of the manuscript.

Compliance with Ethical Standards

Conflict of Interest

The authors declare they have no conflict of interest.


This work was supported with NIH R01CA160514 to E.R. A and 1F99CA212221–01 to J.R.

Supplementary material

10911_2017_9374_MOESM1_ESM.pdf (424 kb)
Figure S1 – Correlation between copy number alterations and gene expression data. The TGCA data was queried for copy number alterations and protein levels in EGFR (a) and FOXO3 (b). These samples were separated in to five categories, Deep deletion (homozygous deletion), Shallow deletion (heterozygous deletion), diploid, Gain (low level amplification), and amplification (high level amplification). A positive correlation between increased copy number and protein level was identified. (PDF 423 kb)
10911_2017_9374_MOESM2_ESM.pdf (282 kb)
Figure S2 – Mouse genetic background and number of copy number alterations. To identify the effect of mouse strain on the stability of a mouse model we used mouse models with the same oncogenic driver on different mouse model backgrounds. This was done with the MMTV-PyMT (a), TAG (b), and p53/BRCA (c) models. It was found that in the PyMT model significantly more alterations were found in the FVB background (N = 66) when compared to the AKXD model (N = 55) (P < .01). A similar result was noted with the TAG model where the FVB background (N = 37) had significantly more alterations than TAG driven tumors in a Balb/C background (N = 3) (P < .05). In the BRCA/p53 models we found the C57 Bl/6 model (N = 12) to be more unstable compared to the Balb/C background (N = 73) (P < .01). (PDF 281 kb)
10911_2017_9374_MOESM3_ESM.pdf (493 kb)
Figure S3 – Amplification or Deletion in specific mouse models. Heatmap representation of the data in Figure 2B. Containing amplification or deletion percentages in specific mouse models. Percentages are displayed as a value between 0 (blue) and 100% (red). The figure is split into amplifications (left) and deletions (right) (PDF 492 kb)
10911_2017_9374_MOESM4_ESM.pdf (452 kb)
Figure 4S – Full heatmap associated with Figure 3A. (a) To assess the conservation of CNAs in mouse models and human patients unsupervised hierarchical complete linkage clustering of samples across human and mouse tumors were clustered by recurrent CNA events (N = 597) that were amplified or deleted in greater than 5% of mouse and human tumors. The dataset used the complete mouse models dataset of 27 mouse models (N = 600) and randomly chosen TCGA breast cancer tumors across all five major subtypes of breast cancer (N = 559). The clustering revealed three tight clusters composed of human and mouse samples as indicated by the purple, yellow, and green clusters. (PDF 451 kb)
10911_2017_9374_MOESM5_ESM.pdf (1.4 mb)
Figure S5 - Role of CNA in oncogenic signaling pathways. (a) Spearman’s rank correlation of amplification (red) or deletion (blue) events with high activity of oncogenic signaling pathways is shown. Events are arranged by chromosomal location as indicated at the top for the pathways indicated at the right. The String-DB derived connectivity map of RB-E2F (B) networks is depicted. Rb and E2F2 are denoted by black arrows. All other colored nodes are genes which have a copy number alteration significantly correlated with a particular signaling pathway indicated by black circles, with the exception of Rb and E2F2. (PDF 1479 kb)
10911_2017_9374_MOESM6_ESM.xlsx (7.3 mb)
Table S1 – Percent amplified or deleted at a particular genetic locus. Each locus identified by gene name, Affymetrix probe ID, as well as genomic location. Shows the percent amplified or deleted across the database and within each specific model. (XLSX 7459 kb)
10911_2017_9374_MOESM7_ESM.xlsx (557 kb)
Table S2 – Conserved metastasis related CNAs. Conserved amplified or deleted genes that are associated with high lung metastasis score that are highly conserved across mouse and human breast cancer samples. This is a searchable table with the coordination of mouse and human data (TCGA dataset). Genes are split into those which perform the same in each species and those that are different. Genes are searchable by Gene symbol or mouse and human locations. This table also shows the KMplot data and metabric data to place each gene into context of gene expression and Distant Metastasis Free Survival and Overall Survival. (XLSX 557 kb)


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Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  • Jonathan Rennhack
    • 1
  • Briana To
    • 1
  • Harrison Wermuth
    • 1
  • Eran R. Andrechek
    • 1
    Email author
  1. 1.Department of PhysiologyMichigan State UniversityEast LansingUSA

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