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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

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

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Colozza-Gama, Gabriel A. [1] ; Callegari, Fabiano [2] ; Besic, Nikola [3] ; Paviza, Ana C. de J. [1] ; Cerutti, Janete M. [1]
Total Authors: 5
[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
Total Affiliations: 3
Document type: Journal article
Source: SCIENTIFIC REPORTS; v. 11, n. 1 JUN 16 2021.
Web of Science Citations: 1

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)

FAPESP's process: 18/13203-0 - New insights into the prognosis of papillary thyroid microcarcinoma: mutational status analysis of BRAF V600E and whole exome sequencing
Grantee:Gabriel Avelar Colozza Gama
Support Opportunities: Scholarships in Brazil - Doctorate (Direct)
FAPESP's process: 14/06570-6 - Comprehensive whole exome, paired-end RNA and genome sequencing: new insights into genetic bases of thyroid carcinoma in pediatric and adult ages and applications in clinical practice
Grantee:Janete Maria Cerutti
Support Opportunities: Research Projects - Thematic Grants
FAPESP's process: 18/23497-1 - Genetic profile identified in thyroid tumors affect tumor cell metabolism?
Grantee:Janete Maria Cerutti
Support Opportunities: Regular Research Grants