Augmented Cancer Panel for Cancer Research and Clinical Trials
Key Features
- > 1,300 cancer genes plus > 200 miRNA genes
- Augmented for accuracy with ACE technology
- Comprehensive coverage of key cancer pathway genes
- > 500x mean coverage over the extended footprint (DNA)
- Sensitivity for both known and novel fusion events (RNA)
- Complete informatics analysis
- Alterations reported include SNVs, LOH, CNVs, gene fusions and low-level variant expression
- Most accurate and extensive variant annotation with > 40 public and proprietary databases including COSMIC,The Cancer Genome Atlas, Cancer Gene Census and PharmGKB™
ACE Extended Cancer Panel for DNA Analysis
The most advanced of its kind, the ACE Extended Cancer Panel includes a core set of clinically actionable* genes, all genes in the Cancer Gene Census, genes from TCGA reports, those within canonical cancer pathways proposed by Vogelstein et. al., (Science, Mar 29 2013) and other leading academic groups, as well as an additional 200 cancer related microRNA genes and important pharmacogenomic genes*.
The ACE Extended Cancer Panel has been designed to support both discovery research and clinical trials. It provides more coverage of gene pathways and functions known to be involved in cancer biology than any other panel commercially available (TABLE 1). Up until now discovery researchers have had to make a choice between the breadth of sequencing used to find novel variants in cancer-related genes, and the depth of sequencing needed to identify variants present at low allelic fraction. The Personalis Extended Cancer Panel is the first service to combine the breadth of coverage for novel variant discovery across key cancer pathway genes, with the sequencing depth needed to detect variants present at low allelic fraction.
The accuracy of our ACE Cancer Panel has also been enhanced over standard NGS panel approaches by augmenting and repairing coverage gaps, especially in targeted regions with high-GC content. This is accomplished by performing separate targeted capture under optimized sample prep conditions and combining data from these separate targeted preps into a single high quality sequencing dataset. This results in genes with more complete coverage.
