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A High Throughput MHC II Binding Assay for Quantitative Analysis of Peptide Epitopes

A High Throughput MHC II Binding Assay for Quantitative Analysis of Peptide Epitopes

Published on: March 25, 2014

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

Michaela C Baldauf¹, Julia S Gerke¹, Andreas Kirschner²

¹Faculty of Medicine, Max-Eder Research Group for Pediatric Sarcoma Biology, Institute of Pathology, LMU Munich, Munich, Germany.

|September 20, 2018

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.

Insights

Researchers developed RAVEN software to identify cancer-specific gene (CSG)-encoded peptides for targeted immunotherapy. This tool analyzes transcriptome data to find peptides with high Major Histocompatibility Complex (MHC) affinity, aiding in treating cancers lacking traditional neo-antigens.

Area of Science:

Oncology
Immunology
Bioinformatics

Background:

Immunotherapy offers revolutionary potential in anti-cancer treatment, contingent on identifying specific therapeutic targets.
Oligo-mutated cancers, lacking neo-antigens from protein mutations, present a challenge for current targeted immunotherapies.
Cancer-specific genes (CSGs) encode peptides that can serve as potential targets for novel immunotherapeutic strategies.

Purpose of the Study:

To develop and present an automated algorithm and user-friendly software, RAVEN, for identifying cancer-specific gene (CSG)-encoded peptides with high Major Histocompatibility Complex (MHC) affinity.
To create a comprehensive catalog of cancer-specific, highly MHC-affine peptides across diverse cancer types.
To provide a freely accessible software tool for researchers to apply the RAVEN algorithm to any gene expression dataset for advancing anti-cancer immunotherapy.

Main Methods:

Developed RAVEN (Rich Analysis of Variable gene Expressions in Numerous tissues) software for automated identification of CSG-encoded peptides from transcriptome data.
Applied RAVEN to a dataset of 2,678 microarrays from 50 tumor entities and 71 normal tissues, focusing on oligo-mutated pediatric cancers.
Validated CSG expression in cell lines and tissue microarrays, predicted peptide-MHC affinity, and excluded sequence identity using the UniProt database; validated peptide binding in T2-cell assays.

Main Results:

RAVEN successfully identified established and novel cancer-specific genes (CSGs) across 50 tumor types, including those relevant to oligo-mutated cancers.
The software predicted numerous CSG-encoded peptides with high affinity to Major Histocompatibility Complexes (MHCs).
Experimental validation confirmed the binding kinetics of selected peptides to MHCs, comparable to a known immunogenic peptide.

Conclusions:

RAVEN software provides an efficient and systematic method for discovering cancer-specific peptides with high MHC affinity from transcriptome data.
The study generated a valuable catalog of cancer-specific, MHC-affine peptides across 50 cancer types, suitable for targeted immunotherapy development.
The freely available RAVEN software and peptide libraries represent a significant resource for advancing anti-cancer immunotherapy, particularly for challenging cancer types.

Read full analysis

Keywords:

Immunotherapy bioinformatics cancer-specific genes microarray

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