Technology

Powered by our ACE Technology, our DNA Assays provide more uniform coverage at high depths than standard assays.

Specifically, our ACE-enabled assays outperform conventional exome assays by enhances coverage across coding regions and supplementing more complex areas in the genomic architecture such as GC-rich content.

The Personalis solution  provides targeted sequencing to augment coverage gaps present in typical exomes. For example, in a paper published by van Buuren et al. in OncoImmunology, the PRDX5 gene is reported to harbor variants that result in immunogenic epitopes, the part of an antigen recognized by the immune system. Figure 1 highlights superior, uniform coverage of exonic regions with the ACE-enabled exome (green plus blue) compared to the most widely used standard exome (blue) for clinical and translational research. Variants residing in green regions (red rectangles) would be missed with a standard exome platform.

Figure 1: Coverage of the PRDX5 gene by the ACE Assay.

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• ACE Cancer Research Exome
• ACE Extended Cancer Panel for DNA

Many clinical studies depend on tissue archives that have been fixed using FFPE procedures. This preservation process makes it difficult to obtain a pure sample and often leads to RNA degradation. To overcome this challenge, Personalis has developed an exome-capture transcriptome protocol based on our ACE Technology that allows us to produce high-quality transcriptome sequencing results from challenging FFPE samples.

Our enrichment protocol directly selects for transcripts using the optimized ACE capture probes, eliminating background and focusing the sequencing on regions of interest. Sequencing using the ACE-enabled transcriptome protocol demonstrated that >90% of the bases mapped within the coding and UTR regions of the RNA (Figure 2).  Thus, the ACE approach results in high quality data and low off-target reads.

Figure 2: ACE-enabled Transcriptome Focuses on Regions of High Interest

Comprehensive characterization of tumor gene expression is an important overlay for interpreting somatic mutations, identifying neoantigens, assessing expression of checkpoint genes in immunotherapy, and identifying prognostic expression signatures. Whether Fresh Frozen or fixed specimens are available for your analysis, the ACE approach produces high quality transcriptome sequencing results.  Using paired FFPE and matched adjacent Fresh Frozen tissues; we found high correlation of normalized (TPM) gene expression (Figure 3.) across various tumor types.  This data demonstrates our ACE-enabled transcriptome for gene expression is an accurate, reliable method for characterizing expression in even challenging materials such as FFPE.

FIGURE 3: Correlation Plots of Log2 Transcripts Per Million (TPM) between matched FF (x-axis) and FFPE (y-axis) pairs in A–D) Colon E) Lung and F) Rectum tumor tissues.

• ACE Cancer Research Transcriptome
• ACE Cancer Panel for RNA

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.

Figure 4

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
  • 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 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

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• Solutions for Personalized Cancer Vaccines

HLA binding is currently the most well-established criteria for ranking neoantigen candidates. Recent advances in training data generated from mass spectrometry, provide a larger dataset of peptide binders and non-binders for individual HLA alleles. This new binding data takes two important additional components into consideration: cleavage and transportation, which are critically important for presentation assessment. We leverage this advancement, developing a brand new MHC binding prediction algorithm which outperforms both NetMHC predictors across the range of prediction score cut-offs (Figure 5).

Figure 5

• Accurately Identifying Neoantigens Utilizing Both DNA and RNA Somatic Variants in an Enhanced Platform
• Neontigen Discovery Report
• Neoantigen discovery: Prediction challenges
• Neoantigen discovery: Overcoming the challenges of neoantigen prediction
• 2017 ASCO: Validation of an Expanded Neoantigen Identication Platform for Therapeutic and Diagnostic Use in Immuno-oncology

We use machine learning and neural networks to provide you with advanced analytics to better inform your discovery and translational research programs.

ImmunogenomicsID guides the investigation of critical immuno-oncology genes with information including expression, variant effect impact, and DNA/RNA allelic fractions.  Unlike targeted therapies, there tends to be general agreement that it is unlikely that a single predictive biomarker in tumor biopsies will be found for determining response to immunotherapies. Thus, multidimensional biomarker analysis is needed to accurately assess patient response.  The Personalis Immunogenomics Engine enables the ability to look across critical areas to characterize tumor biology for focused analysis.

Rapidly evaluate the tumor biology of a sample in key areas including:

• Antigen Presentation
  • Translational research empowers a better understanding of the pathways that tumor cells use to evade immune surveillance. Detecting critical mutations in genes such as B2M are important to comprehend the mechanisms of acquired resistance to immunotherapies.  B2M deficiency has been shown in Adoptive Cell therapies (Restifo et al., 1996), Checkpoint Inhibitors (Zaretsky et al., 2016) and Neoantigen Vaccine strategies (Sahin et al., 2017)

• Repair and Replication
  • Microsatellite instability High (MSI- H) or DNA mismatch repair deficiency tumors are thought to be an important biomarker for patient response.  Recently the FDA approved the use of pembrolizumab based upon the tumor’s MSI status.
• Checkpoint Modulators
  • The activation of T-cells is regulated by both stimulatory and inhibitory signals. Understanding the tumors checkpoint ligand expression is key to understand the likely mechanisms of tumor escape.

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• ImmunogenomicsID Product Page
• Solutions for Personalized Cancer Vaccines

Our CAP-accredited, CLIA-certified Laboratory is equipped with robots for efficient scale up, our custom Genomics Workflow Management System, Symphony, and the latest technology including Illumina NovaSeq Instruments to ensure your projects are delivered with reliable high quality and rapid timelines.

1. Arrival: The moment samples arrive at our CAP-accredited, CLIA-certified laboratory, the samples are given a unique sample ID and are tracked in LIMS and Symphony.
2. Sample sparing preparation: Our laboratory staff bring a wealth of operational expertise, allowing us to hone our sample sparing methods.
3. Quality review: Prior to sequencing, samples undergo robust QC assessment.
4. Sequencing: The ImmunoID NeXT assays run simultaneously to streamline processes and save time.
5. Analysis: Data is then run on our somatic variant pipeline for standard data deliverables. Additional analytics provided through NeonatigenID, RepertoireID, and ImmunogenomicsID are also included.
6. FAS Support: Upon data delivery, your dedicated Field Application Scientist is available to walk through the data with you to answer any questions, and follow up with our scientific team as needed.