Background: Triple-negative breast cancer (TNBC) is an aggressive disease. Recent studies have identified genome instability-derived genes for patient outcomes. However, most of them mainly focus on only one or a few genes related to genome instability. Comprehensive genome instability analysis for the prognostic potential and clinical significance of genome instability-associated genes have not been well explored in TNBC. Methods: In this study, we developed a computational approach to identify TNBC prognostic signature. It consisted of 1) using somatic mutations and copy number variations (CNVs) in TNBC to build a binary matrix and identifying the top and bottom 25% mutated samples; 2) Comparing the gene expression between the top and bottom 25% samples to identify genome instability-related genes; 3) Performing univariate Cox proportional hazards regression analysis to identify survival-associated gene signature, and Kaplan-Meier, log-rank test and multivariate Cox regression analyses of overall survival (OS) for TNBC outcome prediction. Results: From the identified 111 genome instability-related genes, we extracted a genome instability-derived gene signature (GIGenSig) of 11 genes. Through survival analysis, we were able to significantly classify TNBC cases into high- and low-risk groups by the signature in the training dataset (log-rank test p = 2.66e-04), and validated its prognostic performance in the testing (log-rank test p = 2.45e-02) and METABRIC (log-rank test p = 2.57e-05) datasets. We further validated the predictive power of the signature in five independent datasets. Conclusion: The identified novel signature provides a better understanding of genome instability in TNBC and can be used as prognostic markers for clinical TNBC management.