Using long-read CAGE sequencing to profile cryptic-promoter derived transcripts and their contribution to the immunopeptidome

Recent studies have demonstrated that the non-coding genome can produce unannotated proteins as antigens that induce immune response. One major source of this activity is the aberrant epigenetic reactivation of transposable elements (TEs). When happening in tumors, TEs can function as cryptic or alternate promoters and express tumor-specific unannotated proteins. Thus, TE-derived transcripts and their encoded tumor-specific protein sequences have the potential to produce recurrent antigens among many tumors. Identification of these TE-derived tumor antigens holds the promise to improve cancer immunotherapy; however, current genomics and computational tools are not optimized for their detection. Here we combined CAGE technology and third-generation long-read sequencing and developed a suite of computational tools to significantly enhance immunopeptidome analysis by incorporating TE-derived and other tumor transcripts in the proteome database. By applying our methods to human lung cancer cell line H1299 data, we demonstrated that long-read significantly improves mapping of promoters with low mappability score and CAGE guarantees accurate construction of novel 5' transcript structure. Peptides predicted from novel transcripts were readily detectable in whole cell lysate mass-spectrometry data, and they contributed a sizable fraction of tumor-specific antigens at the same scale to missense SNV-derived antigens.