A novel scoring estimator to screening for oncogenic chimeric transcripts in cancer transcriptome sequencing

Based on various genomic information of chimeric transcript, recent studies used machine-learning methods to predict the oncogenic potentials for chimeric transcripts, however these works ignored transcriptional signature of those chimeric transcripts. Based on clonal evolution theory, we hypothesized that a chimeric transcript is more likely to be an oncogenic ‘driver’ mutation, if the neoplastic cells harboring this chimeric mutation has larger clonal size than other neoplastic cells in a particular tumor. Here we proposed a novel method, called iFCR (internal Fusion Clone Ratio), to estimate the ratio of subclone carrying chimeric transcripts to the rest of neoplastic cells in transcriptome sequencing data. To evaluate our hypothesis, we applied iFCR method on two public cancer transcriptome sequencing datasets, one for breast cancer cell line and the other for prostate tumors with adjacent normal tissues. Our results demonstrated that the chimeric transcripts in tumor samples appear to have higher iFCR value than normal tissues, the most frequent prostate cancer fusion mutation, TMPRSS2- ERG, has remarkably higher iFCR value in all three independent patients. Our work providing a novel point of view for screening oncogenesis chimeric transcripts in cancer research.