Increased expression of peptides from non-coding genes in cancer proteomics datasets suggests potential tumor neoantigens.
Rong XiangLeyao MaMingyu YangZetian ZhengXiaofang ChenFujian JiaFanfan XieYiming ZhouFuqiang LiKui WuYafeng ZhuPublished in: Communications biology (2021)
Neoantigen-based immunotherapy has yielded promising results in clinical trials. However, it is limited to tumor-specific mutations, and is often tailored to individual patients. Identifying suitable tumor-specific antigens is still a major challenge. Previous proteogenomics studies have identified peptides encoded by predicted non-coding sequences in human genome. To investigate whether tumors express specific peptides encoded by non-coding genes, we analyzed published proteomics data from five cancer types including 933 tumor samples and 275 matched normal samples and compared these to data from 31 different healthy human tissues. Our results reveal that many predicted non-coding genes such as DGCR9 and RHOXF1P3 encode peptides that are overexpressed in tumors compared to normal controls. Furthermore, from the non-coding genes-encoded peptides specifically detected in cancers, we predict a large number of "dark antigens" (neoantigens from non-coding genomic regions), which may provide an alternative source of neoantigens beyond standard tumor specific mutations.
Keyphrases
- genome wide
- clinical trial
- endothelial cells
- amino acid
- papillary thyroid
- mass spectrometry
- end stage renal disease
- ejection fraction
- gene expression
- machine learning
- electronic health record
- genome wide identification
- dendritic cells
- randomized controlled trial
- dna methylation
- big data
- chronic kidney disease
- risk assessment
- genome wide analysis
- peritoneal dialysis
- rna seq
- pluripotent stem cells
- single cell
- prognostic factors
- binding protein
- induced pluripotent stem cells
- patient reported outcomes
- lymph node metastasis