A Culture of Rigorous Innovation

We’re always striving to build solutions that accelerate the discovery of new biomarkers, and the development and commercialization of next-generation cancer therapies.

From improved processes, to advanced assays and analytics, to investing in the latest technology to help scale efficiently, we’re helping biopharma harness the power of genomic data to make precision medicine a reality for all patients.

Our patented ACE® (Accuracy and Content Enhanced) Technology is the foundation of the Personalis® NeXT Platform™ and all our proprietary products. ACE improves processes from nucleic acid preparation, to sequencing, to analytics for superior sequencing results.

Nucleic Acid Extraction and Sample Preparation

Personalis has developed protocols to overcome the challenges of working with difficult, often degraded clinical samples including formalin-fixed paraffin-embedded (FFPE), fine needle aspirates (FNAs), fresh frozen, and PBMC sample types, and more.

We realize these are precious patient samples. In the past, traditional processes have required multiple samples sent to multiple vendors to obtain the data needed to meet study objectives. At Personalis, we have a simplified process using a dual simultaneous extraction of both DNA and RNA from challenging materials in a sparing manner.

Sequencing

Gaps or inconsistent coverage can result in missed content. Personalis’ ACE Technology augments sequencing gaps for more complete coverage across all ~20,000 genes in both DNA and RNA.

Alignment and Variant Discovery

Our state-of-the-art bioinformatics pipelines are optimized for accuracy and performance, resulting in superior sequence alignments and variant calls, including improved small and structural alteration detection for both clinical and translational applications.

Variant Annotation

We’ve implemented comprehensive annotation to overcome mapping/nomenclature issues and errors and inconsistencies in database curation using a combination of industry-leading public and proprietary databases.

Advanced Analytics

Our advanced, proprietary analytical algorithms enable us to leverage the augmented exome and transcriptome data to provide insights into next-generation biomarkers such as our own composite neoantigen burden, NEOantigen Presentation Score (NEOPS), HLA loss of heterozygosity, and many more.

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. 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), 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.
Figure 4: NeoantigenID Analytics Engine

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 including 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 relies upon a proprietary, high quality, and unambiguous training dataset generated by performing immunopeptidomics on a robust set of MHC Class I alleles using monoallelic cell lines (Figure 5).

Figure 5: Overview of SHERPA machine learning algorithm

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 peptide processing properties and gene expression data to predict a more comprehensive presentation rank (Figure 6).

Figure 6: SHERPA Models


The performance of SHERPA was evaluated using 10% of the monoallelic immunopeptidomics data (which had been held-out from the training data-set) mixed with synthetic negative examples in a 1:999 ratio (commonly assumed prevalence). SHERPA models have higher precision over all recall values compared to NetMHCPan-4.0, the most commonly used publicly available tool (Figure 7A), and significantly higher positive predictive values among the top 0.1% peptides in the test data (Figure 7B).

Figure 7A and B: SHERPA Enables Superior Neoantigen Presentation Prediction

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.

Table 1: HLA-Map’s HLA genotyping performance for both HLA Class I and Class II loci.

HLA LociNumber of AllelesNumber of Correct CallsHLAHM Concordance
All Class I9090100.0%
All Class II18017798.3%
All Class I + Class II27026798.9%

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 a lymphoblastic cell line paired tumor-normal sample that had HLA LOH in both HLA-B and HLA-C genes. We deeply sequenced the tumor and normal pairs with the NeXT Exome and sub-sampled the tumor sequencing data with the corresponding normal sequencing data to simulate various tumor purity and clonality levels. The sensitivity and specificity of the tool is demonstrated in the heatmaps shown in Figure 8 below. DASH retained 100% sensitivity until the mixture of reads was less than 18% tumor (as observed in several tumor purity and HLA LOH clonality combinations: 20% purity and 100% clonality; 50% purity and 30% clonality; 100% purity and 20% clonality). Additionally, DASH achieved 100% specificity until the read mixture was less than 21% derived from the tumor, highlighting DASH’s accuracy and low LOD.

Figure 8: DASH Heatmaps

Additional validation studies utilizing several novel, orthogonal methods are underway and the results of these studies will be shared once available.

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.

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.