Technology

NeXT Exome

By applying our ACE Technology to all ~20,000 genes, our NeXT Exome provides deeper, more uniform coverage at high depths than standard assays. Specifically, the NeXT Exome outperforms conventional exome assays by enhancing coverage across coding regions and supplementing, via a novel, proprietary probe design, 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, thus reducing the likelihood of the non-detection of potentially significant somatic variants present in patients’ tumors. 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 that can be recognized by the immune system. Figure 1 highlights the superior, uniform coverage of exonic regions achieved by the ACE-enabled exome (green plus blue) compared to the most widely-used standard exomes (blue) for clinical and translational oncology research. Variants residing in green regions (red rectangles) would likely be missed with a standard exome platform.

In addition to ACE augmentation, the unique NeXT Exome probe design also facilitates the enhanced targeting of areas of the genome that are particularly pertinent to modern precision oncology research and clinical applications including the HLA genes, exome-wide MSI-related loci, as well as oncoviral genomes. This enables the more accurate characterization of established and investigational oncology biomarkers in every tumor sample processed with the NeXT Exome.

The innovative assay design also incorporates boosted coverage of 247 targeted therapy cancer-related genes. Going to a sequencing depth of >1,000X in these genes enables the clinical-grade sensitivity and specificity that’s required to identify potentially clinically-significant small and structural variants for clinical diagnostic applications such as therapy selection and clinical trial matching.

NeXT Transcriptome

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 our NeXT Transcriptome with an exome-capture protocol, again based on our ACE Technology, that allows us to produce consistently high-quality transcriptome sequencing results from FFPE samples and other challenging sample types.

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 >84% of the bases mapped within the coding region of the RNA (Figure 2). Thus, the ACE approach results in high quality data and low off-target reads, particularly impressive when compared to other conventional approaches such as ribosomal RNA (rRNA)-depletion-based methodologies.

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-enabled 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.

Immune Repertoire Profiling

The NeXT Platform has been configured and purpose-built to cater to precision/immuno-oncology applications. One particularly exciting, emerging research area of focus has been immune repertoire profiling. Metrics such as TCRβ clonality have been identified as biomarkers with clinical potential for predicting response to immune checkpoint blockade, as well as other types of immunotherapies and combination treatment regimens. Therefore, we endeavored to enable the comprehensive analysis of the immune repertoire with the NeXT Platform. Once again owing to the novel probe design of the NeXT assays, the NeXT Transcriptome achieves boosted, ultra-deep read depth across the T-cell receptor (TCR) and B-cell receptor (BCR) gene regions, providing our partners with the required sensitivity to assess clonality and the most highly-abundant clonotypes found in the tumor microenvironment of individual patients’ tumors; something which is beyond the capabilities of conventional broad-content (i.e. ~20,000-gene) assays.

NeoantigenID

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. ImmunoID NeXT™ 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), as follows:

• First, the sequencing data from the NeXT Exome and Transcriptome are run through the NeXT DNA and RNA pipeline in order to identify tumor-specific small variants and fusions.
• 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™).
• 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 candidate neoantigens for rapid development of personalized cancer therapies, as well as facilitating the generation of neoantigen burden-based composite biomarkers such as the Personalis NEOantigen Presentation Score (NEOPS) that can potentially better predict response to immunotherapies.

The NeoantigenID reporting output consists of:

1. A list of all putative neoantigens and their comprehensive characterization
2. In silico HLA genotyping, as well as somatic alterations impacting HLA genes incuding SNVs, indels, and HLA LOH
3. A NeoantigenID Summary Report containing NEOPS, neoantigen burden, and tumor mutational burden (TMB).

SHERPA™: Systematic HLA Epitope Ranking Pan Algorithm

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 leveraged this advancement by developing our Systematic HLA Epitope Ranking Pan Algorithm (SHERPA), our pan-predictive machine learning model for predicting MHC class I presentation.

SHERPA utilizes proprietary, high quality immunopeptidomics data, publicly available & curated mono- and multi-allelic data, as well as binding affinity data as a training set (Figure 5). Publicly available multi-allelic data from several tissue types were systematically reprocessed and deconvoluted to capture the diverse facets of antigen processing and presentation. The integration of different training data types resulted into decreased bias, increased generalizability, and improved performance of SHERPA.

Multiple modeling strategies were combined to accurately predict neoantigens for all known alleles. The SHERPA-Binding algorithm uses both the peptide and binding pocket information to predict a binding rank. The SHERPA-Presentation algorithm incorporates additional, critical features such as expression level of the source protein, proteasomal cleavage, and gene propensity to predict a more comprehensive presentation rank (Figure 6).

The performance of SHERPA was evaluated on ~10% held-out mono-allelic data set, mixed with negative examples in a 1:999 ratio (commonly assumed prevalence). The Precision-recall curves demonstrate that SHERPA models have consistently higher precision at all recall values compared to other publicly available prediction algorithms (Figure 7A). Both SHERPA models also have better PPV compared to publicly available prediction tools (Figure 7B). SHERPA-Presentation has a better PPV compared to SHERPA-Binding model, attesting to the utility of presentation-specific features. Boxplots in Figure 7B denote the distributions of positive predictive values (top 0.1%) across alleles within the mono-allelic immunopeptidomics held-out test data. Distributions are shown to compare SHERPA with other publicly available models.

HLA-Map

In addition to its status as an emerging biomarker of interest in the era of cancer immunotherapy, HLA genotyping is also an essential component of the neoantigen prediction process. Personalis’ HLA typing tool, HLA-Map, has been integrated into the NeoantigenID analytics engine; enabling the highly-accurate in silico typing of all HLA Class I and Class II loci, which is critical for ensuring the precision of downstream peptide-MHC-binding predictions.

To confirm the accuracy of HLA-Map, we performed a comprehensive analytical validation study. This validation study was performed on a total of 15 proficiency testing samples with known, but blinded HLA genotype profiles. Ten of these samples were sourced from the American Society of Histocompatibility and Immunogenetics (ASHI) and five additional samples were obtained from the College of American Pathologists (CAP). Each of these samples had previously been independently genotyped via various orthogonal clinical tests, and these results against which our own results were compared. As is demonstrated in the table below, HLA-Map performed exceptionally well in accurately genotyping not only the HLA Class I loci, but also the more challenging HLA Class II loci.

DASH: Deletion of Allele-Specific HLAs

The success of immune checkpoint blockade (ICB) has revolutionized cancer treatment. However, the fact that the majority of cancer patients do not respond favorably to such immunotherapies has resulted in an explosion in the breadth of research efforts to identify new biomarkers of response and/or resistance to these new class of cancer therapeutics.

Given that the mechanism of action of these therapies is contingent on the dynamic interplay between the tumor and the host’s immune system, the role of the antigen processing machinery (APM) in ensuring that tumor-specific neoantigens are successfully presented to the adaptive immune cells has garnered increasing attention in the search for more effective biomarkers. More specifically, loss of heterozygosity (LOH) impacting the HLA Class I genes has emerged as a means by which solid tumors can evade immunosurveillance by reducing the repertoire of neoantigens that can be presented to the immune system, and this phenomenon is now recognized as a key resistance mechanism to ICB (McGranahan et al., 2017; Tran et al., 2016).

In line with our goal to provide our partners with the most comprehensive cancer immunogenomics platform, Personalis has endeavored to enable the accurate assessment of HLA LOH with NeXT. Through DASH (Deletion of Allele-Specific HLAs), we have created a machine-learning-based tool to capture the unique features associated with each individual HLA Class I region which, when combined with the ACE-augmented sequencing data generated by the NeXT assays, enables us to accurately assess HLA LOH using a novel NGS-based approach.

In order to validate our performance, we assessed the limit of detection (LOD) of DASH using three tumor-Normal cell line pairs with HLA LOH in at least one locus. we sub-sampled the tumor sequencing data and mixed it with complementary normal sequencing data to achieve simulated purity levels. Next, we mixed the HLA-mapping reads across a range of ratios to simulate the potential spectrum of tumor purities and sub-clonalities. Both LOHHLA and DASH have nearly perfect specificity (>99%, data not shown) across tumor purities and sub-clonalities.

For fully clonal HLA LOH events, consistent sensitivity is achieved with >25% tumor purity for both algorithms. However, DASH has significantly higher sensitivity to detect sub-clonal events than LOHHLA (Figure 8).

Additional validation studies utilizing several novel, orthogonal methods have been completed and the results of these studies can be found here.

ImmunogenomicsID

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 Processing Machinery (APM)
  Translational research empowers a better understanding of the pathways that tumor cells use to evade immunosurveillance. Detecting critical mutations in genes such as HLA and B2M are important to comprehend potential mechanisms of acquired resistance to immunotherapies
• Repair and Replication
  Microsatellite instability High (MSI-H) or DNA mismatch repair-deficiency have emerged as promising predictive biomarkers of response or non-response to ICB. In 2017, the FDA approved the use of pembrolizumab for any MSI-H, advanced stage solid tumor in what was the first tumor site-agnostic, biomarker-based FDA-approval for a cancer drug.
• Checkpoint Modulation
  The activation of T-cells is regulated by both stimulatory (e.g. OX40, 4-1BB, etc.) and inhibitory (e.g. PD-L1, IDO1, etc.) signals. Evaluating the tumors checkpoint ligand expression is key to understanding the likely mechanisms of tumor escape. ImmunogenomicsID provides insights into each of these ligands and their respective pathways, providing comprehensive gene and expression information for each relevant gene.
• Oncoviruses
  Since roughly 12% of all cancers are associated with the presence of an oncogenic viral infection, it is imperative for any comprehensive immunogenomics platform to enable the detection of oncoviral genomes. the detection of seven of the most common oncoviruses – as well as their associated genotypes and/or subtypes – in both DNA and RNA derived from tumor samples: HPV, HBV, HCV, EBV, KSHV, MCV, and HTLV.
• Tumor-associated
  Shared antigens or TAAs are common to specific tumor lineages and can be used as targets for adoptive cell therapies and non-personalized cancer vaccines. ImmunogenomicsID includes data on critical genes associated with shared antigens including PRAME, MAGE, SSX2, MUC1, etc.
• Adaptive and Innate Immune Response
  Mounting an effective immune response following the administration of immunotherapies relies on the coordinated impact of not only the adaptive immune system, but also the innate immune system. ImmunogenomicsID reports on genes associated with both types of immune response to better understand the activity profile of the immune infiltrate through DNA and RNA data on genes such as AIF1, IL2, IRF1, and VCAM1.
• Cytokines and Chemokines
  ImmunogenomicsID reports on interleukins and chemokines to further elucidate the tumor microenvironment. Chemokines such as CXCL10, CXCL9, and CXCL11 stimulate cytocidal activity in the immune infiltrate. Additionally, there is also a strong association between cytotoxic activity and the expression of genes involved in attracting T-helper cells to the tumor site including interleukins and CSCL1, 9, 10, 11, and CXCR3 among others.
• Cytotoxicity
  Cytotoxic factors such as granzymes (GZMs), perforin 1 (PRF1) and granulysin (GNLY) are released by cells of the immune system (e.g. NK cells or cytotoxic T-cells) and are essential for their cytotoxic activity against cancer cells. Evaluating the expression of these factors can help to determine the degree of cytotoxic activity within a tumor. ImmunogenomicsID provides information on genes such as GNLY, GZMA, GZMB, and PRF1.

InfiltrateID

InfiltrateID utilizes the single-sample gene set enrichment analysis (ssGSEA) approach to compute transcriptome-based enrichment scores for eight distinct immune cell types (Table 1) from a single tumor sample, quantifying the abundance of those populations within the TME of that sample. For this purpose, we created in-house proprietary cell type-specific signatures for the eight distinct immune cell types usingNeXT Transcriptome gene expression data, derived from purified immune cell populations. Each signature consists of genes curated based on a strict selection criterion, requiring consistent expression and low expression variability in each of the eight immune cell types.

Our CAP-accredited, CLIA-certified laboratory is equipped with robotics for efficient scale-up, our custom Genomics Workflow Management System, Symphony, and the latest technology including Illumina NovaSeq instrumentation 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 NeXT assays run simultaneously to streamline processes and save time.
5. Analysis: Sequencing data is then run through our framework of analytical pipelines and tools to provide a wealth of precision oncology-related biomarker information for each individual patient sample
6. FAS Support: Every aspect of every project is overseen and managed by a designated PhD-level Project Manager. In addition, upon data delivery, your dedicated Field Applications Scientist will walk through the comprehensive dataset, and is available to answer any technical or commercial questions, and to follow-up with our internal scientific teams as needed.