![]() In the routine cancer histology, tissue biopsies and surgical specimens are fixed in formalin-fixed paraffin-embedded (FFPE) sections for diagnostic purposes. Although Sanger can uncover new mutations and is affordable, it has specific drawbacks. Sanger sequencing is still considered the gold standard for detecting mutations when small fragments of DNA are analyzed for specific single nucleotide variants (SNVs). Notable, only the Machine learning k-NN algorithm was able to automatically classify the samples, surpassing manual classification based on no-template controls, which shows promise in clinical practice. By examining the addition of non-classifiable droplets (rain) in ddPCR, it was possible to obtain better qualitative classification of droplets and better quantitative classification compared to no rain droplets, when considering pyrosequencing results. To address the droplet allocation bias in ddPCR analysis, we have compared different algorithms for automated droplet classification and next correlated these findings with those obtained from pyrosequencing. Although the sensitivity of ddPCR is higher than that observed for Sanger, it was less consistent than pyrosequencing, likely due to droplet classification bias of FFPE-derived DNA. Sanger sequencing was able to detect BRAF V600E mutation only when it was present in more than 15% total alleles. To overcome constraints of DNA isolated from FFPE, we compared pyrosequencing and ddPCR analysis for absolute quantification of BRAF V600E mutation in the DNA extracted from FFPE specimens and compared the results to the qualitative detection information obtained by Sanger Sequencing. Formalin-fixed paraffin-embedded (FFPE) specimen is the main source of DNA for somatic mutation detection. Somatic mutations in cancer driver genes can help diagnosis, prognosis and treatment decisions.
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