Abstract P6-04-04: Comprehensive transcriptome analysis identifies novel molecular subtypes and subtype-specific lncRNAs of triple-negative breast cancer
Authors: Liu Y-R , Jiang Y-Z , Xu X-E , Zuo W-J , Yu K-D , Jin X , Hu X , Wu J , Liu G-Y , Di G-H , Shao Z-M 
Published: May 2016

Background: Triple-negative breast cancer (TNBC) is a highly heterogeneous group of cancers with no effective therapeutic targets hitherto. Thus molecular subtyping is necessary to better identify molecular-based therapies. While some classifiers have been established, no one has integrated the expression profiles of long-noncoding RNAs (lncRNAs) into such subtyping criterions. Considering the emerging important role of lncRNAs in gene regulation and other cellular processes, a novel classification integrating the transcriptome profiles of both messenger RNA (mRNA) and lncRNA would help us better understand the heterogeneity of TNBC and treat patients accordingly.

Methods: Using human transcriptome microarray, we retrieved the transcriptome profiles of 165 consecutive TNBC samples. We used k-means clustering to classify the samples based on the most differentially expressed genes (standard deviation&gt;0.65). Empirical cumulative distribution function was analyzed to determine the optimal number of subtypes. Then the new classifier was compared with the Lehmann/Pietenpol system, and survival analyses were performed to compare the recurrence-free survival (RFS) in different subtypes. Gene Ontology (GO) and pathway analyses were applied to determine the main function of the subtype-specific genes and pathways. We conducted co-expression network analysis to identify interactions between lncRNAs and mRNAs, and to predict possible functions of subtype specific lncRNAs.

Results: All 165 TNBC tumors were classified into four distinct clusters, each displaying unique GOs and pathways. These include an immunomodulatory (IM) subtype, a luminal androgen receptor (LAR) subtype, a mesenchymal-like (MES) subtype and a basal-like and immune suppressed (BLIS) subtype, accounting for 17.0%, 17.6%, 33.3%, and 32.2% of the patients, respectively. The IM subtype had unique GOs and pathways involving immune cell process. The LAR subtype was highly enriched in hormonally regulated pathways. Enriched pathways in the MES subtype included ECM-receptor interaction, focal adhesion, and processes linked to growth factor signaling pathways. The BLIS subtype was characterized by downregulation of immune response gene and activation of cell cycle and DNA repair, and patients in this subtype experienced worse RFS compared to other subtypes (log-rank test,P=0.045), which was in concordance with the highly proliferative and immune-suppressed nature of these tumors. When analyzing the distribution of the Lehmann/Pietenpol subtypes in our classification system, we found that the two classification systems were significantly correlated (P=0.039). However, our novel classification was more concise and significantly connected with survival outcome. Subtype-specific lncRNAs were identified and their possible functions were predicted using co-expression network analysis.

Conclusions: We developed a novel TNBC classification system integrating the expression profiles of both mRNAs and lncRNAs, and determined subtype-specific lncRNAs that are potential biomarkers and targets of TNBC. If further validated in larger population, our novel classification system could facilitate patient counseling and individualize treatment of TNBC.

Citation Format: Liu Y-R, Jiang Y-Z, Xu X-E, Zuo W-J, Yu K-D, Jin X, Hu X, Wu J, Liu G-Y, Di G-H, Shao Z-M. Comprehensive transcriptome analysis identifies novel molecular subtypes and subtype-specific lncRNAs of triple-negative breast cancer. [abstract]. In: Proceedings of the Thirty-Eighth Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2015 Dec 8-12; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2016;76(4 Suppl):Abstract nr P6-04-04.