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Non-coding regions are the main source of targetable tumor-specific antigens – CODES

Céline M. Laumont; Krystel Vincent; Leslie Hesnard; Éric Audemard; Éric Bonneil; Jean-Philippe Laverdure; Patrick Gendron; Mathieu Courcelles; Marie-Pierre Hardy; Caroline Côté; Chantal Durette; Charles St-Pierre; Mohamed Benhammadi; Joël Lanoix; Suzanne Vobecky; Elie Haddad; Sébastien Lemieux; Pierre Thibault; Claude Perreault

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.

Codes used to generate databases for mass spectrometry and perform the subsequent identification of TSAs from immunopeptidomic data extracted from 2 murine cell lines and 7 primary human samples. km (doi: https://doi.org/10.1101/295808) and Nektar packages required to run this workflow are available on GitHub: https://github.com/iric-soft/. Results are presented in the following publication Laumont C.M., Vincent K. et al. Sci Trans Med (2018). If you use those codes, please cite this paper. For methodological details, see sections Generation of canonical cancer and normal proteomes, Generation of cancer and normal k-mer databases, k-mer filtering and generation of cancer-specific proteomes, Identification and validation of TSA candidates and Peripheral expression of MHC peptide-coding sequences 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
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cDNAperso.py
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createFm.py
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extractNbReads.py
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getReads.py
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getSRR.sh
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humanTAApcr.py
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k-mer-profiling_pipeline.txt
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kmerAssembly.sh
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kmerFiltering.sh
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kmerGeneration.sh
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mapclassif.py
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mapclassif_HUMAN.py
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pepPrior.py
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queryHumanTSA.py
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runPersoTranscr.sh
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runQuery.sh
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translation.py
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tsa-annotation_pipeline.txt
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