Transcriptome Analysis Reveals MFGE8-HAPLN3 Fusion as a Novel Biomarker in Triple-Negative Breast Cancer

• Main Authors: Meng-Yuan Wang, Man Huang, Chao-Yi Wang, Xiao-Ying Tang, Jian-Gen Wang, Yong-De Yang, Xin Xiong, Chao-Wei Gao
• Format: Article
• Language: English
• Published: Frontiers Media S.A. 2021-06-01
• Series: Frontiers in Oncology
• Subjects: triple-negative breast cancer; MFGE8-HAPLN3; biomarker; target; precision treatment; fusion
• Online Access: https://www.frontiersin.org/articles/10.3389/fonc.2021.682021/full

BackgroundTriple-negative breast cancer (TNBC) is a highly aggressive cancer with poor prognosis. The lack of effective targeted therapies for TNBC remains a profound clinical challenge. Fusion transcripts play critical roles in carcinogenesis and serve as valuable diagnostic and therapeutic targets in cancer. The present study aimed to identify novel fusion transcripts in TNBC.MethodsWe analyzed the RNA sequencing data of 360 TNBC samples to identify and filter fusion candidates through SOAPfuse and ChimeraScan analysis. The characteristics, including recurrence, fusion type, chromosomal localization, TNBC subgroup distribution, and clinicopathological correlations, were analyzed in all candidates. Furthermore, we selected the promising fusion transcript and predicted its fusion type and protein coding capacity.ResultsUsing the RNA sequencing data, we identified 189 fusion transcripts in TNBC, among which 22 were recurrent fusions. Compared to para-tumor tissues, TNBC tumor tissues accumulated more fusion events, especially in high-grade tumors. Interestingly, these events were enriched at specific chromosomal loci, and the distribution pattern varied in different TNBC subtypes. The vast majority of fusion partners were discovered on chromosomes 1p, 11q, 19p, and 19q. Besides, fusion events mainly clustered on chromosome 11 in the immunomodulatory subtype and chromosome 19 in the luminal androgen receptor subtype of TNBC. Considering the tumor specificity and frameshift mutation, we selected MFGE8-HAPLN3 as a novel biomarker and further validated it in TNBC samples using PCR and Sanger sequencing. Further, we successfully identified three types of MFGE8-HAPLN3 (E6-E2, E5-E3, and E6-E3) and predicted the ORF of E6-E2, which could encode a protein of 712 amino acids, suggesting its critical role in TNBC.ConclusionsImproved bioinformatic stratification and comprehensive analysis identified the fusion transcript MFGE8-HAPLN3 as a novel biomarker with promising clinical application in the future.