Systematic functional perturbations uncover a prognostic genetic network driving human breast cancer.

Tristan Gallenne,Salony Salony,Sridhar Ramaswamy,Kenneth N Ross,Christophe J Desmet,Lodewyk F.A Wessels,Nils L Visser,Daniel S Peeper,Ben S Wittner

doi:10.18632/oncotarget.16244

Abstract

Prognostic classifiers conceivably comprise biomarker genes that functionally contribute to the oncogenic and metastatic properties of cancer, but this has not been investigated systematically. The transcription factor Fra-1 not only has an essential role in breast cancer, but also drives the expression of a highly prognostic gene set. Here, we systematically perturbed the function of 31 individual Fra-1-dependent poor-prognosis genes and examined their impact on breast cancer growth in vivo. We find that stable shRNA depletion of each of nine individual signature genes strongly inhibits breast cancer growth and aggressiveness. Several factors within this nine-gene set regulate each others expression, suggesting that together they form a network. The nine-gene set is regulated by estrogen, ERBB2 and EGF signaling, all established breast cancer factors. We also uncover three transcription factors, MYC, E2F1 and TP53, which act alongside Fra-1 at the core of this network. ChIP-Seq analysis reveals that a substantial number of genes are bound, and regulated, by all four transcription factors. The nine-gene set retains significant prognostic power and includes several potential therapeutic targets, including the bifunctional enzyme PAICS, which catalyzes purine biosynthesis. Depletion of PAICS largely cancelled breast cancer expansion, exemplifying a prognostic gene with breast cancer activity. Our data uncover a core genetic and prognostic network driving human breast cancer. We propose that pharmacological inhibition of components within this network, such as PAICS, may be used in conjunction with the Fra-1 prognostic classifier towards personalized management of poor prognosis breast cancer.

Highlights

Gene-expression patterns of primary breast cancers aid clinicians in predicting the risk of metastatic disease [1,2,3,4,5,6]
Among the 158 genes common between the Fra-1 signatures in the two cell lines, we selected those that were downregulated by both Fra-1 shRNAs. This yielded 52 genes (Figure 1, middle panel), from which we selected those that were highly expressed in poor prognosis breast cancer patients. This selection produced a set of 31 genes (Table 1) that were expressed at higher levels than the median in the poor prognosis patients group, and lower than the median in the good prognosis patients group
Whereas prognostic classifiers have proven useful in identifying good-prognosis patients who should be spared from adjuvant chemotherapy [1, 2, 4, 24,25,26], they have not yet been explored in guiding the best therapeutic options for poor-prognosis patients

Summary

Introduction

Gene-expression patterns of primary breast cancers aid clinicians in predicting the risk of metastatic disease [1,2,3,4,5,6]. Some prognostic signatures have recently been prospectively validated, highlighting their clinical value [7, 8]. Such classifiers conceivably comprise biomarker genes that, functionally contribute to the oncogenic www.impactjournals.com/oncotarget and metastatic properties of the tumors, but this has not been investigated systematically. Subsequent work suggested a role for Fra-1 in breast cancer stem cells [10]. We hypothesized that, in addition to its prognostic value, the Fra-1 dependent transcriptome may harbor one or more genes that drive breast cancer

Methods

Results

Conclusion