Integrated Analysis of Copy Number Variation, Microsatellite Instability, and Tumor Mutation Burden Identifies an 11-Gene Signature Predicting Survival in Breast Cancer.

Yi Chen,Xin Jin,Junfeng Yan,Chuanzhi Chen,Wen-Kuan Huang

doi:10.3389/fcell.2021.721505

Abstract

Genetic variants such as copy number variation (CNV), microsatellite instability (MSI), and tumor mutation burden (TMB) have been reported to associate with the immune microenvironment and prognosis of patients with breast cancer. In this study, we performed an integrated analysis of CNV, MSI, and TMB data obtained from The Cancer Genome Atlas, thereby generating two genetic variants-related subgroups. We characterized the differences between the two subgroups in terms of prognosis, MSI burden, TMB, CNV, mutation landscape, and immune landscape. We found that cluster 2 was marked by a worse prognosis and lower TMB. According to these groupings, we identified 130 differentially expressed genes, which were subjected to univariate and least absolute shrinkage and selection operator-penalized multivariate modeling. Consequently, we constructed an 11-gene signature risk model called the genomic variation-related prognostic risk model (GVRM). Using ROC analysis and a calibration plot, we estimated the prognostic prediction of this GVRM. We confirmed the predictive efficiency of this GVRM by validating it in another independent International Cancer Genome Consortium cohort. Our results conclude that an 11-gene signature developed by integrated analysis of CNV, MSI, and TMB has a high potential to predict breast cancer prognosis, which provided a strong rationale for further investigating molecular mechanisms and guiding clinical decision-making in breast cancer.

Highlights

Breast cancer is the most commonly diagnosed cancer and the second leading cause of cancerrelated deaths in women worldwide (DeSantis et al, 2019)
Consensus clustering based on k-means revealed two subgroups. We further explored their differences in survival, mutation pattern, tumor mutation burden (TMB), copy number variation (CNV), immune cell infiltration, and potential immune response of immune checkpoint blockade (ICB) by the Tumor Immune Dysfunction and Exclusion (TIDE) algorithm
We characterized the three genome variants based on the Microsatellite instability (MSI) data, CNV data, and SNV data of the The Cancer Genome Atlas (TCGA)-BRCA dataset

Summary

Introduction

Breast cancer is the most commonly diagnosed cancer and the second leading cause of cancerrelated deaths in women worldwide (DeSantis et al, 2019). The success of immune checkpoint blockade (ICB) in melanoma, non-small cell lung cancer, and other solid tumors has led to emerging enthusiasm for investigating immunotherapy to treat patients with breast cancer. High tumor mutation burden (TMB) has emerged as a biomarker of responsiveness to immunotherapy in several tumor types (Rizvi et al, 2015; Samstein et al, 2019). CNV, TMB, and MSI were found to be associated with breast cancer prognosis (Horlings et al, 2010; Fusco et al, 2018; Thomas et al, 2018). Integrated analysis of gene expression based on these three genetic variants may more accurately identify a gene signature model to predict the immune response and prognosis in breast cancer

Methods

Results

Conclusion