Neoantigen prediction in human breast cancer using RNA sequencing data.

Neoantigens have attracted attention as biomarkers or therapeutic targets. However, accurate prediction of neoantigens is still challenging, especially in terms of its accuracy and cost. Variant detection using RNA sequencing (RNA-seq) data has been reported to be a low-accuracy but cost-effective tool, but the feasibility of RNA-seq data for neoantigen detection has not been fully examined. In this study, we used whole-exome sequencing (WES) and RNA-seq data of tumor and matched normal samples from six breast cancer patients to evaluate the utility of RNA-seq data instead of WES data in variant calling to detect neoantigen candidates. Somatic variants were called in three protocols using: (1) tumor and normal WES data (DNA method, Dm); (2) tumor and normal RNA-seq data (RNA method, Rm); and (3) combination of tumor RNA-seq and normal WES data (Combination method, Cm). We found that the Rm had both high false-positive and high false-negative rates because this method depended greatly on the expression status of normal transcripts. When we compared the results of the Dm with those of the Cm, only 14% of the neoantigen candidates detected in the Dm were identified in the Cm, but the majority of the missed candidates lacked coverage or variant allele reads in the tumor RNA. On the other hand, about 70% of the neoepitopes with higher expression and rich mutant transcripts could be detected in the Cm. Our results showed that the Cm could be an efficient and a cost-effective approach to predict highly expressed neoantigens in tumor samples.