Transposon insertional mutagenesis in mice identifies human breast cancer susceptibility genes and signatures for stratification

Author:

Chen Liming,Jenjaroenpun Piroon,Pillai Andrea Mun Ching,Ivshina Anna V.,Ow Ghim Siong,Efthimios Motakis,Zhiqun Tang,Tan Tuan ZeaORCID,Lee Song-Choon,Rogers Keith,Ward Jerrold M.,Mori Seiichi,Adams David J.,Jenkins Nancy A.,Copeland Neal G.,Ban Kenneth Hon-Kim,Kuznetsov Vladimir A.,Thiery Jean Paul

Abstract

Robust prognostic gene signatures and therapeutic targets are difficult to derive from expression profiling because of the significant heterogeneity within breast cancer (BC) subtypes. Here, we performed forward genetic screening in mice using Sleeping Beauty transposon mutagenesis to identify candidate BC driver genes in an unbiased manner, using a stabilized N-terminal truncated β-catenin gene as a sensitizer. We identified 134 mouse susceptibility genes from 129 common insertion sites within 34 mammary tumors. Of these, 126 genes were orthologous to protein-coding genes in the human genome (hereafter, human BC susceptibility genes, hBCSGs), 70% of which are previously reported cancer-associated genes, and ∼16% are known BC suppressor genes. Network analysis revealed a gene hub consisting of E1A binding protein P300 (EP300), CD44 molecule (CD44), neurofibromin (NF1) and phosphatase and tensin homolog (PTEN), which are linked to a significant number of mutated hBCSGs. From our survival prediction analysis of the expression of human BC genes in 2,333 BC cases, we isolated a six-gene-pair classifier that stratifies BC patients with high confidence into prognostically distinct low-, moderate-, and high-risk subgroups. Furthermore, we proposed prognostic classifiers identifying three basal and three claudin-low tumor subgroups. Intriguingly, our hBCSGs are mostly unrelated to cell cycle/mitosis genes and are distinct from the prognostic signatures currently used for stratifying BC patients. Our findings illustrate the strength and validity of integrating functional mutagenesis screens in mice with human cancer transcriptomic data to identify highly prognostic BC subtyping biomarkers.

Funder

Agency for Science, Technology and Research

Publisher

Proceedings of the National Academy of Sciences

Subject

Multidisciplinary

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