A variety of genetic alterations exist in human cancer, including deletions, amplifications, rearrangements and point mutations. DNA from tumor cells is released into clinical samples such as blood, lymph, stools and urine. In order to use these mutations as biomarkers, they must be detected in a large excess of non-mutated DNA from normal cells. Sensitive methods for detecting these somatic mutations can serve an important tool for clinical decision making and outcome in the oncology patient population.
Traditional attempts to detect low frequency alleles have been met with two fundamental limitations. The assay may yield a false negative because the amount of starting DNA is too low to detect the rare mutation (inadequate sensitivity) or a stochastic false positive result because rare random mutations are present (inadequate specificity). Quantitative PCR assays are typically reported to provide 0.5 to 1% sensitivity levels.
The RainDrop System leverages the power of up to 10 million individual PCR reactions to overcome these challenges to provide confident detection and quantification of rare mutations in high background of wild-type DNA at levels better than 1 part in 200,0001. Additionally, digital PCR methods do not rely on standard curves for quantitation, which improves the ability to make run-to-run or cross-laboratory comparisons, thereby improving the “transportability” of low frequency allele results.
RainDance continues to develop and report performance on important cancer assays including variants in EGFR, BRAF, KRAS, PIK3CA, ALK and MLH1 to demonstrate the power of 10 million droplets.
The combination of superior sensitivity, unprecedented multiplexing, and flexibility in experiment design make the RainDrop System a powerful genomic analysis platform for ground-breaking research in cancer including rare variant detection, absolute quantitation of biomarkers, tumor profiling, and the ability to monitor residual disease.
1. Pezkin D, et. al. Quantitative and sensitive detection of rare mutations using droplet-based microfluidics, Lab on a Chip, 2011