When one biomarker doesn’t tell the whole story

Understanding response to cancer immunotherapies involves examining the interaction between mechanisms of tumor escape, the tumor microenvironment, and neoantigens. Gaining these molecular insights often requires analyzing data sourced from multiple platforms and vendors. When samples are precious and limited, researchers need a way to simplify.

That’s where Personalis can help. Our assays combine advanced sequencing with analytics for comprehensive tumor immunogenomics that better informs your biomarker strategy.

Paired Tumor/

Normal Analysis of DNA/RNA


Comprehensive Analytics


More Informed Biomarker Strategy

Our ACE ImmunoID Platform provides paired tumor/normal analysis of DNA and RNA through our ACE (Accuracy Content Enhanced) Cancer Exome and ACE Cancer Transcriptome assays.

ACE Cancer Exome

The ACE Cancer Exome outperforms conventional exome assays by augmenting coverage across genes poorly covered by standard approaches.

Key features:
  • Broad coverage across all 20,000 genes and enhanced coverage of >8,000 biomedically-important genes, including >1,400 cancer-related genes
  • Augmentation and repair of coverage gaps, especially in high-GC regions
  • Improved somatic variant detection of SNPs and indels

Conventional sequencing has gaps that result in possible missed variants as seen below.

ACE augments these sequencing gaps (green regions), providing you with more complete coverage across the entire gene. The ACE Exome includes both the blue and green regions of the gene as seen below.

ACE Cancer Transcriptome

The same accuracy and coverage improvements are available through the ACE Cancer 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 an exome-capture transcriptome protocol based on our ACE Technology that allows us to produce high-quality transcriptome sequencing results from challenging FFPE samples (Figure 1).

Key features:
  • Multiple probes target each transcript, capturing transcripts even when the poly-A tail is lost due to RNA degradation, making it ideal for cancer FFPE samples
  • Sequencing protocol demonstrates that >95% of the bases are mapped within the coding and untranslated regions (UTR) of the RNA
  • Fusion detection and gene expression analysis

Figure 1. ACE Cancer Transcriptome Enrichment Workflow

For RNASeq, looking at the uniformity in sequencing coverage across the transcript can provide insight into the data quality. Transcript coverage plots below show representative ACE Cancer Transcriptome performance.  Each colored line denotes a different sample in the study. As shown, RNAseq sequencing coverage performance is very uniform across the transcripts from the 5’ to 3’ ends using the ACE enrichment protocol. Whether we started with a fixed sample (top panel) or a frozen specimen (bottom panel), coverage is uniform.

Figure 2. Uniform ACE Cancer Transcriptome Coverage


Developed for use with ACE ImmunoID, ImmunogenomicsID guides the investigation of critical immuno-oncology genes.  This report interrogates exome and transcriptome data to allow you to go beyond gene expression, with information including variant effect impact, DNA/RNA allelic fractions, and population metrics (COSMIC and dbSNP).  This information can be used to evaluate a variant’s potential influence on the tumor’s biology (Figure 3).

Our gene lists were designed from recent thought leader publications and provide an overview of pathways and genes that have been implicated in immuno-oncology (Concha-Benavente et al., 2016; Milner and Holland, 2013; Mendez, R et al., 2009).

Figure 3.  Subset of Data Available in the Immunogenomics Report


Tumor Mutational Burden (TMB) has been shown to correlate with clinical response to immune checkpoint blockade (Snyder et al., 2016), yet overall elevated neoantigen load is also thought to be a promising predictive signature for determining patient response. Within NeoantigenID, we provide high-level metrics such as both TMB and neoantigen load, as well as a deep dive into the neoantigen landscape:

  • Identifies neoantigens resulting from SNVs, indels and fusions
  • Tumor mutational burden and neoantigen load
  • Allelic fraction and gene expression of variants
  • Phasing for allele-specific expression determination
  • MHC Class I and MHC Class II peptide binding affinities (based on the patient’s HLA alleles)

ACE ImmunoID includes raw data and variant annotation deliverables through our validated somatic variant analysis pipeline.

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

Obtaining research samples can be challenging. Evaluating data across diverse biomarkers typically involves sending multiple sections to several different vendors. This not only increases complexity, but can waste your precious samples.

ACE ImmunoID simplifies this situation by requiring only one paired tumor and normal sample, allowing you to do more with less sample.

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