Fusion transcriptome profiling provides insights into alveolar rhabdomyosarcoma

Abstract

Gene fusions and fusion products were thought to be unique features of neoplasia. However, more and more studies have identified fusion RNAs in normal physiology. Through RNA sequencing of 27 human noncancer tissues, a large number of fusion RNAs were found. By analyzing fusion transcriptome, we observed close clusterings between samples of same or similar tissues, supporting the feasibility of using fusion RNA profiling to reveal connections between biological samples. To put the concept into use, we selected alveolar rhabdomyosarcoma (ARMS), a myogenic pediatric cancer whose exact cell of origin is not clear. PAX3-FOXO1 (paired box gene 3 fused with forkhead box O1) fusion RNA, which is considered a hallmark of ARMS, was recently found during normal muscle cell differentiation. We performed and analyzed RNA sequencing from various time points during myogenesis and uncovered many chimeric fusion RNAs. Interestingly, we found that the fusion RNA profile of RH30, an ARMS cell line, is most similar to the myogenesis time point when PAX3-FOXO1 is expressed. In contrast, full transcriptome clustering analysis failed to uncover this connection. Strikingly, all of the 18 chimeric RNAs in RH30 cells could be detected at the same myogenic time point(s). In addition, the seven chimeric RNAs that follow the exact transient expression pattern as PAX3-FOXO1 are specific to rhabdomyosarcoma cells. Further testing with clinical samples also confirmed their specificity to rhabdomyosarcoma. These results provide further support for the link between at least some ARMSs and the PAX3-FOXO1-expressing myogenic cells and demonstrate that fusion RNA profiling can be used to investigate the etiology of fusion-gene-associated cancers.rhabdomyosarcoma | gene fusion | chimeric RNA | myogenesis | PAX3-FOXO1

Fusion RNA profiling provides hints on cell of origin of mysterious tumor

“…The fusion transcriptome clustering with a binary input (present or absence of a chimera) behaved as well as traditional transcriptome clustering in human body maps 3 and uncovered a connection between normal myogenesis and ARMS 3 that could not be revealed by whole transcriptome analyses. Three factors may contribute to these results: (1) Gene expression profiles are highly influenced by changes in genes that are not associated with specific cell types; (2) In contrast, chimeric RNAs in general are often tightly associated with specific cancer types, and/or tissue differentiation lineages (manuscript in preparation); (3) Unlike the expression of most cellular genes, where the level of expression is used as a quantitative trait, the chimeric RNAs may be viewed as potential qualitative traits.…”

“…2 Recently, we demonstrated that fusion RNA profiling is a novel way to group biological samples and can reveal connections between samples. 3 Contradictory to the prevailing view that fusion RNAs are cancer-unique phenomena, we and others have showed that they are widespread in non-cancer cells and tissues. [4][5][6] We coined the phrase "fusion transcriptome," and demonstrated that "fusion RNA profiling" can cluster biological samples into separate groups of different tissues.…”

“…3 We performed paired-end RNA-sequencing at 4 time points throughout skeletal muscle differentiation from mesenchymal stem cells (MSCs), in addition to an ARMS cell line, RH30. We found hundreds of fusion RNAs.…”

Fusion gene addiction: can tumours be forced to give up the habit?

“…PAX3–FOXO1 transcripts appeared to be limited to particular time points in the differentiation pathway, suggesting a role at certain stages of myogenic differentiation. Cells at these time points shared gene expression patterns with a fusion‐positive ARMS cell line . This suggests a non‐oncogenic role of the fusion protein and underlines the importance of cellular context and cooperating events.…”

Trefoil factor 3 contributes to the malignancy of glioma via regulating HIF-1α

“…The following primers were used: TFF3 mRNA forward: 5′- TGAACTGACCTCTCCCCTTT -3′, reverse: 5′- CTGCTCTGGATTGTTTGCTTG -3′, VEGF mRNA forward: 5’- GCTACTGCCATCCAATCGAGAC-3’, reverse: 5’- CTATGTGCTGGCCTTGGTGA G-3’, CA9 mRNA forward: 5’- ACCTGGTGACTC TCGGCTACAG-3’, reverse: 5’- CAG CCAGGCAGGAA TTCAGC-3’, MMP2 mRNA forward: 5’- CCCACACTGGGCCCTGTCACT-3’, reverse: 5’- TGGGCTTGTCACGTGGCGTC-3’, GAPDH mRNA forward: 5′- CGGAGTC AACGGATTTGGTCGTAT -3′, reverse: 5′-AGCCTTCTCCATGGTGGTGAAGAC -3′ (TaKaRa). The comparative Ct (2 -ΔΔCt ) method was used [ 41 ].…”