Abstract
Basal-like breast cancer (BLBC) is a molecular subtype of breast cancer associated with poor clinical outcome, although some patients with BLBC experience long-term survival. Apart from nodal status, current clinical/histopathological variables show little capacity to identify BLBC patients at either high- or low-risk of disease recurrence. Accordingly, we sought to develop a network based genomic predictor for predicting the outcome of patients with BLBC. We performed network analysis on global gene expression profiling data of BLBCs, and identified BLBC network modules associated with AP-1 transcription, G-protein coupled receptors, and T-, B-, and NK-cells that are significant predictors of BLBC patient survival. In gene expression and tissue microarray (TMA) validation cohorts of 210 and 102 BLBC patients, respectively, the identified network modules were robustly associated with patient outcome. In the gene expression validation cohort, the Kaplan-Meier estimate for 10-year survival in the low-risk group was 90%, whereas in the high-risk group it was a 56%. In the TMA cohort, the Kaplan-Meier estimate for 10-year survival in the low-risk group was 98%, whereas in the high-risk group it was 71%. The capacity to distinguish between patients with BLBC at high- or low-risk of recurrence at the time of diagnosis could permit timely intervention with more aggressive therapeutic regimens in those patients predicted to be high-risk, and to avoid such therapy in low-risk patients.
Highlights
Prognostic stratification of breast cancer patients is traditionally based on a variety of factors such as tumor size, grade, hormone receptor status, HER2 status, lympho-vascular space invasion and lymph node involvement [1, 2]
In gene expression and tissue microarray (TMA) validation cohorts of 210 and 102 Basal-like breast cancer (BLBC) patients, respectively, the identified network modules were robustly associated with patient outcome
The network was clustered using Markov Clustering (MCL) (Markov clustering), to identify candidate interaction modules associated with outcome (Figure 1C)
Summary
Prognostic stratification of breast cancer patients is traditionally based on a variety of factors such as tumor size, grade, hormone receptor status, HER2 status, lympho-vascular space invasion and lymph node involvement [1, 2]. Detailed reports on the prognosis of BLBC suggest that patients with BLBCs experience high relapse rates within the first 3-5 years following diagnosis. After this period the recurrence risk rapidly declines such that over the long term BLBC patients have outcomes similar to those of patients with luminal A disease [15,16,17,18]. Www.impactjournals.com/oncotarget these findings demonstrate that patients with BLBCs can be stratified into two clinically distinct groups; those at high-risk of early recurrence and death, and those at lowrisk of such an outcome and likely to experience long term survival
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