The advent of Next-Generation Sequencing (NGS) and targeted library preparation represents a major advance for the study of cancer genomics. However, NGS-enabled cancer genomics presents two unique challenges. First, tumor samples are highly heterogeneous, and key mutations are often found in a very small fraction of tumor cells. Second, tumor samples are often processed for histological analysis and archiving prior to molecular testing. Formalin fixation and paraffin embedding (FFPE) of biopsies is the standard means of preservation and archiving of samples in clinical oncology; however, this harsh chemical treatment causes significant damage to the nucleic acid in the tissue. Here we discuss sample heterogeneity, input nucleic acid damage, and their implications for the detection of single nucleotide variants (SNVs) and copy number variations (CNVs). We also introduce the PreSeq™ DNA QC Assay, a simple qPCR assay that facilitates the quantification of the number of sequenceable copies of input genomic DNA, thereby maximizing information recovery from NGS libraries.
Idea in Brief
- The sensitivity of NGS-based variant detectino is derived from the number of unique fragments present in a region of interest
- Traditional amplicon-based enrichment techniques lack the ability to count how many unique fragments are sequenced
- Mass is not predictive - it is the number of amplifiable genomes that determines the library complexity and ultimately, the sensitivity of NGS assays used in somatic mutation testing
- The PreSeq DNA QC assay provides a method of quantifying the number of input molecules that are present in a given sample, and the use of this assy can help rescue or avoid libraries that would otherwise fail Archer Analysis QC.
Key points from the PreSeq DNA technical note
The theoretical likelihood of sampling at least 10 mutant molecules as a function of allelic frequency (AF) and number of molecules integrated
PreSeq DNA Assay is a simple assay for quantifying the concentration of available genomic DNA molecules in a sample
DNA mass is not an accurate predictor of library yield or complexity
qPCR quantification of amplifiable genomes identifies samples that will yield sequenceable library with Archer VariantPlex Solid Tumor panel
Input complexity is predictive of Archer Analysis QC Pass
Input complexity drives library complexity, per-base unique coverage, and variant detection sensitivity
Increasing number of amplifiable genomes of input improves CNV sensitivity