Views provided by UsageCounts
Tumor-specific antigens (TSAs) represent ideal targets for cancer immunotherapy, but few have been identified thus far. We therefore developed a proteogenomic approach to enable the high-throughput discovery of TSAs coded by potentially all genomic regions. In two murine cancer cell lines and seven human primary tumors, we identified a total of 40 TSAs, about 90% of which derived from allegedly non-coding regions and would have been missed by standard exome-based approaches. Moreover, the majority of these TSAs derived from non-mutated yet aberrantly expressed transcripts (such as endogenous retroelements) that could be shared by multiple tumor types. In mice, the efficacy of TSA vaccination was influenced by two parameters that can be estimated in humans and could serve for TSA prioritization in clinical studies: TSA expression and the frequency of TSA-responsive T cells in the pre-immune repertoire. In conclusion, the strategy reported herein could considerably facilitate the identification and prioritization of actionable human TSAs.
k-mer database (k=24 nucleotides) generated from concatenated RNA-sequencing reads from human TEC (n=2) and mTEC (n=4), which was used in the following publication Laumont C.M., Vincent K. et al. Sci Trans Med (2018). If you use the data, please cite this paper. For methodological details, see section Study design of the Materials and Methods and sections Human TEC and mTEC extraction, RNA extraction, library preparation and sequencing and Generation of cancer and normal k-mer databases of the Supplementary Materials and Methods of the article cited above. For any questions or for assistance, please contact us using the following email address: perreault.lab@iric.ca
Thymic epithelial cells, Mass spectrometry, RNA-sequencing, MHC class I, Identification of tumor-specific antigens, k-mers
Thymic epithelial cells, Mass spectrometry, RNA-sequencing, MHC class I, Identification of tumor-specific antigens, k-mers
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
| views | 14 |

Views provided by UsageCounts