Immuno-Oncology

ACE ImmunoID Platform 2017-09-29T20:32:08+00:00

Overview

Clinical and biomarker discovery studies are aggressively investigating many areas of immuno-oncology including cancer vaccines, checkpoint inhibitors, adoptive T cell transfer, combination therapies, and biomarkers for predicting patient response.

ACE ImmunoID is a comprehensive immunogenomics platform that enables researchers to overcome the following challenges of complex immuno-oncology research:

  • Discovery of neoantigens, biomarkers, and genomic signatures.

  • Determining rational personal cancer vaccine design.

  • Understanding the potential mechanisms of tumor escape.

  • Elucidating the tumor microenvironment.

The ACE ImmunoID Platform combines the ACE Cancer Exome and ACE Cancer Transcriptome assays to enable broad tumor immunogenomic characterization.

ACE ImmunoID includes comprehensive bioinformatics reporting, provided for both DNA and RNA, utilizing our robust somatic pipelines. The platform can be further customized based on your unique research and project needs by using our Neoantigen Discovery Engine to enable neoantigen identification, and/or the Immunogenomics Analytics Engine for the assessment of tumor immunogenomics in critical genes, providing information including expression, variant effect impact, and DNA/RNA allelic fractions.

Technical Details
Assay Configuration ACE Cancer Exome and ACE Cancer Transcriptome
Sequencing depth DNA: ≥200x (tumor)/≥70x (normal)
RNA: 100M total reads (tumor)
Sensitivity* 99% SNV
>94% indel
Sample source FFPE, fresh frozen, fine needle aspirates, PBMCs
Analysis configuration Paired tumor and normal only
*20% Mean Allelic Frequency

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 ACE ImmunoID product provides a comprehensive assessment of features allowing the ability to identify and rank potentially immunogenic neoantigens.

Figure 4

The Neoantigen Discovery Engine 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
    • Immunogenicity

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 ACE ImmunoID, 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 62 orthogonally typed samples involving ~700 HLA alleles, achieving high concordance with the ACE Exome.

Table 1: Results of HLA Typing

HLA Locus Number of Calls Number in Agreement Concordance
Class I 318 311 98%
Class II 378 355 94%
Class I + II 696 666 96%
122/23 experimentally-validated immunogenic neoantigens accurately predicted
2DRB1, DPA1, DPB1, DQA1, DQB1, and DRB5 included

Cancer biology is inherently complicated and content rich assays such as ACE ImmunoID provide an expansive dataset to aid in understanding the individual tumor’s biology.

The Immunogenomics Analytics Report is an optional output of the ACE ImmunoID Platform. This annotated report provides deeper insights into the tumor biology of a sample, focusing on critical areas such as Tumor Associated Antigens, Antigen Presentation Machinery, DNA Repair and Replication, Immune Modulators, Adaptive and Innate Immune Response, Cytokines and Chemokines, and Cytotoxicity. Each area of interest consists of a curated list of genes that are involved directly in, or highly impact, the specific functional area. The report output includes:

  • Gene expression (TPM)
  • Variant type
  • Variant effect impact
  • Allelic fraction (DNA/RNA)
DNA Analysis RNA Analysis
  • Raw data files: FASTQ, BAM files
  • Somatic variant (SNVs, indels) analysis and report: VCF file
  • Somatic variant annotation: VAR file
  • Filtering and annotation of variants by cancer relevance and frequency
  • Quality Control report and Statistical Summary Report
  • Raw data files: FASTQ, BAM files
  • Variant (SNVs, indels) analysis: VCF file
  • Gene-associated variant analysis with additional filtering by cancer relevance
  • Fusion gene analysis and report
  • Gene-based expression results
  • Quality Control report and Statistical Summary report

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