Leveraging our enhanced exome and transcriptome, we’ve applied these assays for deeper bioinformatics analysis to accurately predict neoantigens. Using neoantigens as a predictor for determining likelihood of response to immunotherapies is one exciting application (Rizvi et al., 2015), but also using these analyses to develop personalized cancer vaccines is increasingly critical. Although standard research pipelines have been developed to assist in neoantigen identification, developing a strong analytically validated neoantigen identification platform suitable for clinical applications is complex.
Within our neoantigen workflow (Figure 4), somatic DNA and RNA variants are processed for antigen identification, including SNVs, indels, and fusion events. Importantly, both in-frame and out-of-frame events are accurately considered by transcript, allowing for a rich source of putative candidate neoantigens. Our pipeline includes assessment of potentially important criteria including HLA prediction, MHC binding (class I and II), immunogenicity, similarity to self, and similarity to known antigens.
Further, peptides are evaluated for both gene and variant-level expression.
Taken together, the ImmunoID NeXT Platform provides a comprehensive assessment of features allowing the ability to identify and rank potentially immunogenic neoantigens.
NeoantigenID enables the identification of neoantigens and allows you to assess:
- The mutational landscape with neoantigen load and mutational burden
- The peptides derived from SNVs, indels and gene fusions
- Critical metrics such as:
- Gene- and variant-level expression
- MHC Class I and select Class II binding affinity
Correct HLA Typing for accurate neoantigen prediction
Additional features within our Neoantigen Discovery Engine are the in silico HLA Typing of both Class I and select Class II alleles and further refinement of peptide prediction by the incorporation of phasing within the pipeline.
HLA typing is an essential component of the neoantigen prediction process. Incorrect typing can lead to downstream inaccuracies in binding predictions. In order to provide accurate HLA typing within ImmunoID NeXT, we have optimized and validated an extremely accurate tool. To accomplish, we validated hundreds of HLA alleles across both Class I and select Class II that had previously been typed with clinical grade PCR-based methods. We performed a blinded clinical validation with 19 orthogonally typed samples involving >300 HLA alleles, achieving high concordance.
Table 1: Results of HLA Typing
|HLA Locus||Number of Calls||Number in Agreement||Concordance|
|Class I + II||334||322||96.4%|