Technologies for neoantigen discovery are critical for the development of personalized cancer therapies and neoantigen-based biomarkers. Precision neoantigen discovery entails comprehensive detection of tumor-specific genomic variants and accurate prediction of MHC presentation of epitopes originating from such variants. Our ImmunoID NeXT Platform® enables a comprehensive survey of putative neoantigens by combining highly sensitive exome- scale DNA and RNA sequencing with the NeoantigenID™ analytics engine.
The NeoantigenID analytics engine flows seamlessly from biological samples to neoantigen prediction (Figure 4).
- First, tumor-specific small variants and fusions are identified utilizing the Personalis® NeXT DNA and RNA pipelines.
- Highly accurate germline in silico HLA typing is performed, followed by somatic mutation and allele-specific loss of heterozygosity (LOH) detection in the HLA genes.
- Tumor-specific small variants and fusions are combined with the patient HLA types and gene expression information to predict neoantigens using our internally developed Systematic HLA Epitope Ranking Pan Algorithm (SHERPA™), as well as publicly available in silico prediction algorithms.
- SHERPA has been integrated into Personalis’ NeoantigenID analytics engine along with additional secondary metrics that enable further prioritization of the predicted putative neoantigen candidates.
- Accurate neoantigen prediction with SHERPA enables the determination of neoantigen burden as well as the generation of the Personalis Composite Neoantigen Presentation Score (NEOPS) that can potentially better predict response to immunotherapies.
Figure 4: NeoantigenID Analytics Engine
The NeoantigenID report consists of:
- A list of all putative neoantigens and their comprehensive characterization
- In silico HLA Typing, somatic mutations in HLA genes, and HLA LOH
- A NeoantigenID Summary Report containing mutation burden, neoantigen burden, and the Personalis Composite Neoantigen Presentation Score.
Correct HLA Typing for accurate neoantigen prediction
Additional features with the NeoantigenID analytics engine are the in silico HLA Typing of both Class I and 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.
The HLA typing validation study combined a total of 15 reference samples from ASHI and CAP. These samples were independently genotyped via various orthogonal clinical tests and compared with the HLA typing results from ImmunoID NeXT Platform. HLA typing was performed for HLA Class I (A, B, C) and Class II alleles (DRB1, DPA1, DPB1, DQA1, DQB1, DRB3, DRB4, DRB5). The results in Table 1 demonstrate that the Personalis ImmunoID NeXT Platform produces robust and accurate HLA typing on both class I and class II MHC loci.
Table 1: HLA Genotyping Validation Study