The novel fusion transcript NR5A2-KLHL29FT is generated by an insertion at the KLHL29 locus

BACKGROUND: Novel fusion transcripts (FTs) caused by chromosomal rearrangement are common factors in the development of cancers. In the current study, the authors used massively parallel RNA sequencing to identify new FTs in colon cancers. METHODS: RNA sequencing (RNA-Seq) and TopHat-Fusion were used to identify new FTs in colon cancers. The authors then investigated whether the novel FT nuclear receptor subfamily 5, group A, member 2 (NR5A2)-Kelch-like family member 29 FT (KLHL29FT) was transcribed from a genomic chromosomal rearrangement. Next, the expression of NR5A2-KLHL29FT was measured by quantitative real-time polymerase chain reaction in colon cancers and matched corresponding normal epithelia. RESULTS: The authors identified the FT NR5A2-KLHL29FT in normal and cancerous epithelia. While investigating this transcript, it was unexpectedly found that it was due to an uncharacterized polymorphic germline insertion of the NR5A2 sequence from chromosome 1 into the KLHL29 locus at chromosome 2, rather than a chromosomal rearrangement. This germline insertion, which occurred at a population frequency of 0.40, appeared to bear no relationship to cancer development. Moreover, expression of NR5A2-KLHL29FT was validated in RNA specimens from samples with insertions of NR5A2 at the KLHL29 gene locus, but not from samples without this insertion. It is interesting to note that NR5A2-KLH29FT expression levels were significantly lower in colon cancers than in matched normal colonic epithelia (P =.029), suggesting the potential participation of NR5A2-KLHL29FT in the origin or progression of this tumor type. CONCLUSIONS: NR5A2-KLHL29FT was generated from a polymorphism insertion of the NR5A2 sequence into the KLHL29 locus. NR5A2-KLHL29FT may influence the origin or progression of colon cancer. Moreover, researchers should be aware that similar FTs may occur due to transchromosomal insertions that are not correctly annotated in genome databases, especially with current assembly algorithms. Cancer 2017;123:1507–1515.