Transcript-targeted analysis reveals isoform alterations and double-hop fusions in breast cancer

Namba et al develop a new pipeline called MuSTA to enable the efficient assembly of transcriptome from long-read sequencing data of breast cancer samples. This method enables the authors to discover subtype-specific isoforms, find that fusion transcript structures depend on their genomic context and identify a double-hop fusion that results in aberrant expression of an endogenous retroviral gene. Although transcriptome alteration is an essential driver of carcinogenesis, the effects of chromosomal structural alterations on the cancer transcriptome are not yet fully understood. Short-read transcript sequencing has prevented researchers from directly exploring full-length transcripts, forcing them to focus on individual splice sites. Here, we develop a pipeline for Multi-Sample long-read Transcriptome Assembly (MuSTA), which enables construction of a transcriptome from long-read sequence data. Using the constructed transcriptome as a reference, we analyze RNA extracted from 22 clinical breast cancer specimens. We identify a comprehensive set of subtype-specific and differentially used isoforms, which extended our knowledge of isoform regulation to unannotated isoforms including a short form TNS3. We also find that the exon-intron structure of fusion transcripts depends on their genomic context, and we identify double-hop fusion transcripts that are transcribed from complex structural rearrangements. For example, a double-hop fusion results in aberrant expression of an endogenous retroviral gene, ERVFRD-1, which is normally expressed exclusively in placenta and is thought to protect fetus from maternal rejection; expression is elevated in several TCGA samples with ERVFRD-1 fusions. Our analyses provide direct evidence that full-length transcript sequencing of clinical samples can add to our understanding of cancer biology and genomics in general.

Automated summary

The MuSTA pipeline enables efficient transcriptome assembly from long-read sequencing data of breast cancer samples, leading to the discovery of subtype-specific isoforms and genomic context-dependent fusion transcript structures. This method sheds light on the influence of chromosomal structural alterations on cancer transcriptomes.