Advancing Modern Precision Oncology

The emergence of immune checkpoint inhibitors has highlighted the potential of immuno-oncology therapeutics to produce unprecedented beneficial responses in cancer patients. In certain cases, these drugs are capable of effectively curing a patient’s disease. However, the ongoing issue is that the majority of patients who are administered these therapies enjoy no benefit whatsoever.

Therefore, it’s imperative that we increase response rates to existing immunotherapies and develop new compounds that will successfully treat a larger proportion of cancer patients. We believe that ImmunoID NeXT™ will assist in realizing both of these goals.

Figure 1: ImmunoID NeXT represents an end-to-end solution for immuno-oncology and all precision oncology applications. It combines the pioneering NeXT assay, sophisticated analytical engines, and quality support to provide researchers with the comprehensive immunogenomic data they need to drive their programs.

With ImmunoID NeXT, oncology researchers can comprehensively analyze both a tumor and its microenvironment from a single tumor sample. It enables customers to maximize the data generated from each sample while also decreasing the complexity of data interpretation by eliminating the need to integrate multiple assay technologies and reporting formats from several sources.

Specifically built for immuno-/precision oncology, Personalis NeXT Platform is used to investigate key areas of tumor biology; from elucidating mechanisms of tumor escape and detecting neoantigens, to identifying novel gene expression signatures and characterizing the immune repertoire. With these extensive capabilities, ImmunoID NeXT effectively consolidates multiple biomarker assays into one.

The unique design of the assay and analytical algorithms enables the delivery of critical tumor- and immune-related information including, but not limited to:

  • T-cell receptor (TCR) alpha & beta repertoire
  • BCR heavy chain (BCRh) repertoire and isotype composition
  • Neoantigen detection and neoantigen load
  • Tumor mutational burden (TMB)
  • Microsatellite instability (MSI) characterization
  • Human leukocyte antigens (HLA) typing, HLA and beta-2 microglobulin (B2M) somatic mutations, and HLA loss of heterozygosity (LOH)
  • Tumor escape and resistance mechanisms
  • Oncoviral detection
Deep Sequencing
~300X mean coverage across the entire footprint (>20,000 genes), ultra-deep coverage of TCR/BCR gene regions, as well as clinical-grade coverage across 247 cancer-related genes.
Augmented Coverage
ImmunoID NeXT utilizes our proprietary ACE Technology to provide augmented coverage of difficult-to-sequence gene regions across the entire ~20,000-gene footprint.
Specific Targeting
Enhanced targeting of HLA genes, MSI-related loci, as well as oncoviral genes to enable the accurate characterization of investigational and predictive precision oncology biomarkers.
Ultra-High Sensitivity
Accurate detection of SNVs, indels, gene fusions, and CNAs including low-abundance mutations, which is critical for the analysis of samples with low tumor content.
Optimized Algorithms
The Personalis framework of analytical pipelines integrates both proprietary and advanced, publicly-available in silico tools to generate the most informative and usable insights from the comprehensive raw DNA and RNA data.
Mastering Challenging Samples
Personalis’ protocols optimize nucleic acid extraction from difficult formalin-fixed paraffin-embedded (FFPE) samples. This approach enables dual extraction of both DNA and RNA from the same, precious tumor sample.

ImmunoID NeXT analytics leverage the accurate, raw data to evaluate the status of the most relevant oncology biomarkers, as have been identified and investigated in the literature. Figure 2 below illustrates how the analytics modules elucidate the complex interplay between tumor cells and immune cells of the TME.

Figure 2: ImmunoID NeXT analytics modules provide insights into the complex and dynamic interactions between tumor cells and the immune cells of the microenvironment.

Analytics Modules

Figure 3: Schematic of VDJ recombination to form hypervariable CDR3 regions
RepertoireID leverages RNA expression profiles to enable the detection and analysis of TCRα and TCRβ  clonotypes, together with BCR heavy chain (BCRh) clonotypes found within the TME of tumor samples. Additionally, RepertoireID also characterizes the isotype composition of BCRs. The RepertoireID analytics module is driven by an optimized algorithm that processes raw NGS data to deliver quantitated clonotypes and additional key information pertaining to the immune repertoire as part of a detailed report, enabling researchers to investigate the immune repertoire as a potential predictive biomarker of response to therapies.

ImmunoID NeXT is the first commercial solution that enables the comprehensive characterization of the immune repertoire using data derived from an augmented, exome-scale platform, designed specifically to explore multidimensional oncology biomarkers.

Figure 4: ImmunogenomicsID delivers biological insights into each of the highlighted biomarkers and functional areas of tumor biology.

ImmunogenomicsID provides an overview of TME-related biomarkers and critical genes that are involved in – or highly impact – key oncology functional groups including HLA (LOH and somatic mutations) and other antigen processing machinery (APM), DNA repair and replication, immune checkpoint modulation, tumor associated antigens (TAAs), adaptive and innate immune response, cytokines and chemokines, and cytotoxicity. Utilizing an industry-leading algorithm, ImmunogenoicsID also provides a characterization of MSI within a tumor sample, highlighting the stability status of five canonical loci, as well as the proportion of all microsatellite loci that are found to be unstable.

Additionally, ImmunogenomicsID — via the specific targeting of viral genomes in both DNA and RNA — reports out on the presence (or absence) of viruses that are known to contribute to oncogenesis in a broad variety of cancer types. These oncoviruses include HPV, HBV, HCV, EBV, KSHV, MCH, and HTLV, and their associated genotypes and subtypes.

Deep and uniform coverage of both DNA and RNA is critical for both comprehensive neoantigen identification and the accurate assessment of neoantigen load. ImmunoID NeXT ensures highly-sensitive variant detection via both the depth of sequencing (~300X mean coverage) and the augmented coverage of difficult-to-sequence regions across the entire ~20,000 gene footprint. Combined, these features reduce the chances of neoantigen-producing variants (SNVs, indels, and fusions) going undetected. These variants are a rich source of putative neoantigens.

Figure 5: Peptide-MHC (pMHC) complexes (for both MHC Class I and Class II).

Personalis has developed a Systematic HLA Epitope Ranking Pan Algorithm (SHERPA™) using one of the largest sets of immunopeptidomics training data to improve neoantigen presentation prediction based on MHC Class I binding potential, level of gene expression, and antigen presentation features such as proteasome cleavage and gene propensity. SHERPA is integrated into the NeoantigenID analytics engine for comprehensive characterization of putative neoantigens to inform the development of personalized therapeutics.

Accurate neoantigen prediction with SHERPA enables the determination of neoantigen burden as well as the generation of the Personalis Composite Neoantigen Presentation Score (NEOPS) which has the potential to improve the predictive and/or prognostic utility of these biomarkers for precision oncology and immunotherapy applications.

InfiltrateID leverages the augmented gene expression data derived from the NeXT Transcriptome to quantitate the presence of eight distinct immune cell populations in a single tumor specimen. InfiltrateID utilizes the single-sample gene set enrichment analysis (ssGSEA) approach to compute transcriptome-based enrichment scores for each of the immune cell type, thereby helping to delineate the underlying Immunocellular profile within the tumor microenvironment for a given tumor sample.

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