Novel Predictors of Breast Cancer Survival Derived from miRNA Activity Analysis

Abstract

Purpose: Breast cancer is among the leading causes of cancer-related death; discovery of novel prognostic markers is needed to improve outcomes. Combining systems biology and epidemiology, we investigated miRNA-associated genes and breast cancer survival in a well-characterized population-based study.Experimental Design: A recently developed algorithm, ActMiR, was used to identify key miRNA "activities" corresponding to target gene degradation, which were predictive of breast cancer mortality in published databases. We profiled miRNA-associated genes in tumors from our well-characterized population-based cohort of 606 women with first primary breast cancer. Cox proportional hazards models were used to estimate HRs and 95% confidence intervals (CI), after 15+ years of follow-up with 119 breast cancer-specific deaths.Results: miR-500a activity was identified as a key miRNA for estrogen receptor-positive breast cancer mortality using public databases. From a panel of 161 miR-500a-associated genes profiled, 73 were significantly associated with breast cancer-specific mortality (FDR < 0.05) in our population, among which two clusters were observed to have opposing directions of association. For example, high level of SUSD3 was associated with reduced breast cancer-specific mortality (HR = 0.3; 95% CI, 0.2-0.4), whereas the opposite was observed for TPX2 (HR = 2.7; 95% CI, 1.8-3.9). Most importantly, we identified set of genes for which associations with breast cancer-specific mortality were independent of known prognostic factors, including hormone receptor status and PAM50-derived risk-of-recurrence scores. These results are validated in independent datasets.Conclusions: We identified novel markers that may improve prognostic efficiency while shedding light on molecular mechanisms of breast cancer progression. Clin Cancer Res; 24(3); 581-91. ©2017 AACR.

Fig. 1.

Survival curves associated with inferred activity (A) and expression levels (B) of miR-500a-5p, based on published datasets of TCGA.

Fig. 2.

Age- and stage- adjusted correlation of individual miR500a-related genes expression levels with long-term breast cancer-specific survival of LIBCSP patients. Each dot corresponds to a gene, with horizontal axis displaying hazard ratio of high expression group comparing to low expression group (i.e. genes on the left are associated with better survival, genes on the right are associated with worse survival) and vertical axis displaying logarithm of p-value of the same Cox model. Genes included in the PAM50 panel are marked with pink color.

Fig. 3.

Co-expression of miR-500a-related genes with strongest association with survival, according to LIBCSP results. Blue color in gene labels and horizontal bar indicates genes associated with worse survival, red color associated with better survival. Color of the plot cells shows the correlation in respective pair of genes (red color indicating positive correlation and blue color – negative correlation).

Fig. 4.

Concordance between two characteristics: association of gene expression with miR-500a activity (vertical axis: correlation coefficient) calculated by ActMiR algorithm for TCGA dataset, and association of gene expression with survival (horizontal axis: hazards ratio) calculated for Cox model for LIBCSP dataset.

Fig. 5.

ROC curves for the SUSD3 expression level (left) and risk-of-recurrence score (right). (A) Survival as a binary outcome without adjustment for the length of survival. (B) Survival within one year time window.