Introduction: Ultrasensitive point mutation detection can facilitate minimal residual disease detection, early detection of cancer, and therapeutic monitoring of cancer patients. However, nearly identical mutant and wildtype molecules exhibit crosstalk with most technologies, thus producing high background levels. To evaluate technologies against each other for accurate ultrasensitive point mutation detection it is crucial to supply laboratories with reference samples of various dilutions.
Methods: We tested a series of sample mixes with digital-droplet PCR and next generation sequencing. Using linearity and accuracy as the gold standard, we empirically introduced two corrections to generate improved quality control reagents. Baseline variant allele frequency (VAF) in the parental cell line was used to correct for copy number variation of variant, while haplotype counting was used to correct technical errors (cell counting and pipetting).
Results: Correlation between the level of dilution and the measured VAF was relatively good (R2 = 0.80) when measured using ddPCR. However, correcting for the parental VAFs substantially improved the correlation (R2 = 0.97). In contrast, correlation of the cell count dilution and the measured VAF, as determined by next generation sequencing (AmpliSeq), was initially relatively poor (R2 = 0.63). However, substantial improvement in correlation was achieved by correcting for the parental VAFs (R2 = 0.94). In addition, correcting by haplotype counting alone only slightly improved accuracy in both technologies (ddPCR, from R2 = 0.80 to R2 = 0.81; AmpliSeq, from R2 = 0.63 to R2 = 0.64). Furthermore, correcting for both factors resulted in the best correlation for ddPCR-generated data (R2 = 0.99); however, introducing both corrections for AmpliSeq data did not prove more beneficial than correcting for parental VAFs alone (both corrections, R2 = 0.94 compared to parental VAF correction only, R2 = 0.94).
Conclusions: Validation of ultrasensitive variant detection requires standardized reference samples. These will also permit assessing various ultrasensitive detection technologies against one another.
Citation Format: Marija Debeljak, Lisa Haley, Derek A. Anderson, Emily M. Adams, Masaya Suenaga, Katie Beierl, Ming-Tseh Lin, Michael Goggins, Christopher D. Gocke, James R. Eshleman. Generating quality control reference standards to evaluate ultrasensitive variant detection. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 1378.