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. 2018 Jul 23;7(9):e1481558.
doi: 10.1080/2162402X.2018.1481558. eCollection 2018.

Systematic identification of cancer-specific MHC-binding peptides with RAVEN

Michaela C Baldauf  1 , Julia S Gerke  1 , Andreas Kirschner  2 , Franziska Blaeschke  3 , Manuel Effenberger  4 , Kilian Schober  4 , Rebeca Alba Rubio  1 , Takayuki Kanaseki  5 , Merve M Kiran  6 , Marlene Dallmayer  1 , Julian Musa  1 , Nurset Akpolat  7 , Ayse N Akatli  7 , Fernando C Rosman  8 , Özlem Özen  9 , Shintaro Sugita  5 , Tadashi Hasegawa  5 , Haruhiko Sugimura  10 , Daniel Baumhoer  11 , Maximilian M L Knott  1 , Giuseppina Sannino  1 , Aruna Marchetto  1 , Jing Li  1 , Dirk H Busch  4 , Tobias Feuchtinger  3 , Shunya Ohmura  1 , Martin F Orth  1 , Uwe Thiel  2 , Thomas Kirchner  12   13   14 , Thomas G P Grünewald  1   12   13   14
Affiliations

Systematic identification of cancer-specific MHC-binding peptides with RAVEN

Michaela C Baldauf et al. Oncoimmunology. .

Abstract

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.

Keywords: Immunotherapy; bioinformatics; cancer-specific genes; microarray.

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Figures

Figure 1.
Figure 1.
Schematic illustration of the assembly, quality-check, and normalization of gene expression data as well as tasks executed by RAVEN.
Figure 2.
Figure 2.
Overexpressed CSGs in multiple cancer entities identified with RAVEN. Relative gene expression intensities of the top-5 CSGs for each cancer entity (excluding overlapping CSGs with other tumor entities) indicated in greyscale with black color representing high and white color low expression. Each line represents an individual CSG (for a complete list see Supplementary Table 5); each column represents one primary tumor/leukemia/normal tissue sample. The bar graph on the right displays the number of different cancer entities in which the corresponding CSG reached a CSG-score above the 99.9th percentile of all CSG-scores. ALL, acute lymphoblastic leukemia; AML, acute myeloid leukemia; ATRT, atypical teratoid/rhabdoid tumor; CLL, chronic lymphatic leukemia; CML, chronic myeloid leukemia; DLBCL, diffuse large B cell lymphoma; GIST, gastrointestinal stromal tumor; MALT, mucosa associated lymphatic tissue; MPNST, malignant peripheral nerve sheath tumor; PNET, primitive neuroectodermal tumor.
Figure 3.
Figure 3.
Validation of the expression pattern of selected CSGs by qRT-PCR and IHC. A) Upper and middle panel: CSG-scores and corresponding expression intensities (natural scale) of selected genes in primary Ewing sarcoma (EwS, n = 50), neuroblastoma (NB; n = 49), rhabdomyosarcoma (RMS; n = 101), liposarcoma (LPS; n = 50), leiomyosarcoma (LMS, n = 50) and osteosarcoma tumors (OS, n = 40). Lower panel: Relative expression levels of the same genes as determined by qRT-PCR in EwS (n = 9), NB (n = 4), RMS (n = 5) and LPS (n = 3), LMS (n = 3) and OS (n = 6) cell lines. B) Analysis of nuclear PAX7 immunoreactivity by IHC in indicated primary tumors and normal tissues. ASPS, alveolar soft part sarcoma; GIST, gastrointestinal stromal tumor. Numbers of analyzed samples are given in parentheses. C) Representative images of nuclear PAX7 IHC staining in cancer and selected normal tissues. Scale bar = 300 µm. UPS, undifferentiated pleomorphic sarcoma. Note: In renal proximal tubules non-specific cytoplasmic staining for PAX7 was observed, while all nuclei showed no PAX7 immunoreactivity. This non-specific cytoplasmic stain has been previously described for the employed anti-PAX7-antibody.56
Figure 4.
Figure 4.
Validation of MHC-affinity of CSG-encoded peptides in a T2-binding assay. A) Relative MHC-I-affinity of 79 selected peptides at 100 µM in T2-binding assays as compared to a highly affine influenza peptide (peptide sequences are given in Supplementary Table 6). The colored boxes at the right side of the graph represent the number and type of cancer entities in which the corresponding CSG encoding the indicated peptide is overexpressed. Peptides with an MHC-affinity of ≥ 50% of the influenza peptide are highlighted in red color. Data are presented as mean and SEM of n ≥ 3 experiments. ALL, acute lymphoblastic leukemia; AML, acute myeloid leukemia; ATRT, atypical teratoid/rhabdoid tumor; CLL, chronic lymphatic leukemia; CML, chronic myeloid leukemia; DLBCL, diffuse large B cell lymphoma; GIST, gastrointestinal stromal tumor; MALT, mucosa associated lymphatic tissue; MPNST, malignant peripheral nerve sheath tumor; PNET, primitive neuroectodermal tumor. B) Normalized fluorescence signals of 16 selected peptides with high MHC-affinity as compared to that of a highly affine influenza peptide in T2-binding assays. Data are presented as mean and SEM of n ≥ 3 experiments. P values of a Spearman’s rank-order correlation are reported.
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Grants and funding

The laboratory of TGPG is supported by grants from the ‘Verein zur Förderung von Wissenschaft und Forschung an der Medizinischen Fakultät der LMU München (WiFoMed)’, the Daimler and Benz Foundation in cooperation with the Reinhard Frank Foundation, by LMU Munich’s Institutional Strategy LMUexcellent within the framework of the German Excellence Initiative, the ‘Mehr LEBEN für krebskranke Kinder – Bettina-Bräu-Stiftung’, the Walter Schulz Foundation, the Kind-Philipp Foundation, the Friedrich-Baur Foundation, the Fritz Thyssen Foundation (FTF-2015-01046), the Dr. Leopold and Carmen Ellinger Foundation, the Wilhelm Sander-Foundation (2016.167.1), the Matthias-Lackas Foundation, the Barbara und Hubertus Trettner Foundation, the Deutsche Forschungsgemeinschaft (DFG 391665916) and by the German Cancer Aid (DKH-111886 and DKH-70112257). Deutsche Krebshilfe [70112257].

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