Publikation
Systematic identification of cancer-specific MHC-binding peptides with RAVEN
Wissenschaftlicher Artikel/Review - 23.07.2018
Baldauf Michaela, Hasegawa Tadashi, Sugimura Haruhiko, Baumhoer Daniel, Knott Maximilian M L, Sannino Giuseppina, Marchetto Aruna, Li Jing, Busch Dirk H, Feuchtinger Tobias, Ohmura Shunya, Orth Martin F, Thiel Uwe, Kirchner Thomas, Sugita Shintaro, Özen Özlem, Gerke Julia S, Kirschner Andreas, Blaeschke Franziska, Effenberger Manuel, Schober Kilian, Rubio Rebeca Alba, Kanaseki Takayuki, Kiran Merve M, Dallmayer Marlene, Musa Julian, Akpolat Nurset, Akatli Ayse N, Rosman Fernando C, Grünewald Thomas G P
Bereiche
PubMed
DOI
Zitation
Art
Zeitschrift
Veröffentlichungsdatum
ISSN (Druck)
Seiten
Kurzbeschreibung/Zielsetzung
Immunotherapy can revolutionize anti-cancer therapy if specific targets are available. Immunogenic peptides encoded by cancer-specific genes (CSGs) may enable targeted immunotherapy, even of oligo-mutated cancers, which lack neo-antigens generated by protein-coding missense mutations. Here, we describe an algorithm and user-friendly software named RAVEN (Rich Analysis of Variable gene Expressions in Numerous tissues) that automatizes the systematic and fast identification of CSG-encoded peptides highly affine to Major Histocompatibility Complexes (MHC) starting from transcriptome data. We applied RAVEN to a dataset assembled from 2,678 simultaneously normalized gene expression microarrays comprising 50 tumor entities, with a focus on oligo-mutated pediatric cancers, and 71 normal tissue types. RAVEN performed a transcriptome-wide scan in each cancer entity for gender-specific CSGs, and identified several established CSGs, but also many novel candidates potentially suitable for targeting multiple cancer types. The specific expression of the most promising CSGs was validated in cancer cell lines and in a comprehensive tissue-microarray. Subsequently, RAVEN identified likely immunogenic CSG-encoded peptides by predicting their affinity to MHCs and excluded sequence identity to abundantly expressed proteins by interrogating the UniProt protein-database. The predicted affinity of selected peptides was validated in T2-cell peptide-binding assays in which many showed binding-kinetics like a very immunogenic influenza control peptide. Collectively, we provide an exquisitely curated catalogue of cancer-specific and highly MHC-affine peptides across 50 cancer types, and a freely available software (https://github.com/JSGerke/RAVENsoftware) to easily apply our algorithm to any gene expression dataset. We anticipate that our peptide libraries and software constitute a rich resource to advance anti-cancer immunotherapy.