Machine learning algorithm improved automated droplet classification of ddPCR for detection of BRAF V600E in paraffin-embedded samples

Autor(es)

Colozza-Gama, Gabriel A. ^[1] ; Callegari, Fabiano ^[2] ; Besic, Nikola ^[3] ; Paviza, Ana C. de J. ^[1] ; Cerutti, Janete M. ^[1]

Afiliação do(s) autor(es)

^[1] Univ Fed Sao Paulo, Dept Morphol & Genet, Div Genet, Genet Bases Thyroid Tumor Lab, Sao Paulo, SP - Brazil

^[2] Univ Fed Sao Paulo, Dept Pathol, Sao Paulo, SP - Brazil

^[3] Onkol Inst Ljubljana, Inst Oncol, Dept Surg Oncol, Ljubljana - Slovenia

• Tipo de documento: Artigo Científico
• Fonte: SCIENTIFIC REPORTS; v. 11, n. 1 JUN 16 2021.
• Citações Web of Science: 1

Resumo

Somatic mutations in cancer driver genes can help diagnosis, prognosis and treatment decisions. Formalin-fixed paraffin-embedded (FFPE) specimen is the main source of DNA for somatic mutation detection. 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. Sanger sequencing was able to detect BRAF V600E mutation only when it was present in more than 15% total alleles. 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. 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. 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. 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. (AU)

• Processo FAPESP: 18/13203-0 - Novos insights no prognóstico de microcarcinoma papilífero de tiroide: análise do status mutacional de BRAF V600E e sequenciamento de exoma completo
• Beneficiário: Gabriel Avelar Colozza Gama
• Modalidade de apoio: Bolsas no Brasil - Doutorado Direto
• Processo FAPESP: 14/06570-6 - Sequenciamento completo do exoma, Paired-end RNA e genoma: novos insights sobre a natureza genética do câncer de tiróide na idade adulta e na faixa etária pediátrica e aplicações na prática clínica
• Beneficiário: Janete Maria Cerutti
• Modalidade de apoio: Auxílio à Pesquisa - Temático
• Processo FAPESP: 18/23497-1 - Perfil genético identificado nos tumores da tiroide afetam o metabolismo das células tumorais?
• Beneficiário: Janete Maria Cerutti
• Modalidade de apoio: Auxílio à Pesquisa - Regular