Highly robust model of transcription regulator activity predicts breast cancer overall survival

Chuanpeng Dong,Huanmei Wu,Guanglong Jiang,Tianhan Dong,Yunlong Liu,Jiannan Liu,Steven X Chen,Yue Wang,Jill L Reiter

doi:10.1186/s12920-020-0688-z

Abstract

BackgroundWhile several multigene signatures are available for predicting breast cancer prognosis, particularly in early stage disease, effective molecular indicators are needed, especially for triple-negative carcinomas, to improve treatments and predict diagnostic outcomes. The objective of this study was to identify transcriptional regulatory networks to better understand mechanisms giving rise to breast cancer development and to incorporate this information into a model for predicting clinical outcomes.MethodsGene expression profiles from 1097 breast cancer patients were retrieved from The Cancer Genome Atlas (TCGA). Breast cancer-specific transcription regulatory information was identified by considering the binding site information from ENCODE and the top co-expressed targets in TCGA using a nonlinear approach. We then used this information to predict breast cancer patient survival outcome.ResultWe built a multiple regulator-based prediction model for breast cancer. This model was validated in more than 5000 breast cancer patients from the Gene Expression Omnibus (GEO) databases. We demonstrated our regulator model was significantly associated with clinical stage and that cell cycle and DNA replication related pathways were significantly enriched in high regulator risk patients.ConclusionOur findings demonstrate that transcriptional regulator activities can predict patient survival. This finding provides additional biological insights into the mechanisms of breast cancer progression.

Highlights

While several multigene signatures are available for predicting breast cancer prognosis, in early stage disease, effective molecular indicators are needed, especially for triple-negative carcinomas, to improve treatments and predict diagnostic outcomes
Dataset and workflow for estimating transcription regulator activities RNA-seq profiles from 1097 primary breast cancer patients were used as the training set
Validation of the transcription regulator model with independent datasets To test whether the high performance of the riskscore model in the training dataset might have resulted from overfitting, we evaluated the performance of the transcription regulator activity model using independent breast cancer datasets from the Gene Expression Omnibus (GEO)

Summary

Introduction

While several multigene signatures are available for predicting breast cancer prognosis, in early stage disease, effective molecular indicators are needed, especially for triple-negative carcinomas, to improve treatments and predict diagnostic outcomes. Breast tumor classification is primarily based on histopathologic features and the expression of estrogen receptor (ER), progesterone receptor (PR) and human epidermal growth factor receptor 2 (HER2) [3] These subtypes differ with respect to available receptor-targeted therapies, response to treatment, clinical outcomes and risk of acquiring resistance to therapy [4]. The MammaPrint assay categorized patients into good or poor risk groups using 70 genes and has been approved by the Food and Drug Administration (FDA) to aid in predicting prognosis for breast cancer patients with specific clinical characteristics [9, 10] These gene-based risk models have certain limitations and to date, there is no multigene test that has been approved for recommending adjuvant treatment for triple-negative (ER/PR/HER2-negative) breast tumors. There remains a critical need for the development of a robust model that can aid in effectively predicting individual patient prognosis for hormonereceptor-negative breast tumors that can convey additional biological information from gene expression profiles

Objectives

Methods

Results

Discussion

Conclusion