TY - GEN
T1 - Excerno
T2 - 11th International Conference on Computational Advances in Bio and Medical Sciences, ICCABS 2021
AU - Mitchell, Audrey
AU - Ruiz, Marco
AU - Yang, Soua
AU - Wang, Chen
AU - Davila, Jaime
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - The accurate detection of mutations from clinical samples using Next Generation Sequencing (NGS) is of great importance in the clinical treatment of cancer patients. Clinical tests use archival pathology slides, which are preserved by Formalin-Fixation Paraffin Embedding (FFPE). The FFPE process introduces spurious C > T mutations hindering accurate cancer diagnosis. FFPE mutational artifacts occur in a well-defined pattern called a mutational signature. By quantifying the abundance of the FFPE mutational signature and using Bayes’ formula we developed a method to filter FFPE artifacts. We implemented this method as the excerno package in the R statistical language. We tested our method by generating mutations that follow the FFPE mutational signature and combining them with variants produced by other mutational signatures from the Catalog of Somatic Mutations in Cancer (COSMIC). First, we mixed an equal number of FFPE variants and mutations from a single COSMIC mutational signature and tested excerno across all of the 60 COSMIC mutational signatures. Our median sensitivity, specificity, and Area Under the Curve (AUC) were 0.89, 0.99, and 0.96 respectively. Furthermore, our performance characteristics decrease as a linear function of the similarity between the COSMIC and the FFPE mutational signatures (R2 = 0.90). We also tested our method by mixing different proportions of mutations from the COSMIC and FFPE mutational signatures. As we increased the proportion of FFPE variants our sensitivity increased while our specificity decreased. In conclusion, we developed and implemented excerno, an accurate method to filter FFPE artifactual mutations and characterized its performance characteristics using simulated datasets.
AB - The accurate detection of mutations from clinical samples using Next Generation Sequencing (NGS) is of great importance in the clinical treatment of cancer patients. Clinical tests use archival pathology slides, which are preserved by Formalin-Fixation Paraffin Embedding (FFPE). The FFPE process introduces spurious C > T mutations hindering accurate cancer diagnosis. FFPE mutational artifacts occur in a well-defined pattern called a mutational signature. By quantifying the abundance of the FFPE mutational signature and using Bayes’ formula we developed a method to filter FFPE artifacts. We implemented this method as the excerno package in the R statistical language. We tested our method by generating mutations that follow the FFPE mutational signature and combining them with variants produced by other mutational signatures from the Catalog of Somatic Mutations in Cancer (COSMIC). First, we mixed an equal number of FFPE variants and mutations from a single COSMIC mutational signature and tested excerno across all of the 60 COSMIC mutational signatures. Our median sensitivity, specificity, and Area Under the Curve (AUC) were 0.89, 0.99, and 0.96 respectively. Furthermore, our performance characteristics decrease as a linear function of the similarity between the COSMIC and the FFPE mutational signatures (R2 = 0.90). We also tested our method by mixing different proportions of mutations from the COSMIC and FFPE mutational signatures. As we increased the proportion of FFPE variants our sensitivity increased while our specificity decreased. In conclusion, we developed and implemented excerno, an accurate method to filter FFPE artifactual mutations and characterized its performance characteristics using simulated datasets.
KW - Formalin-Fixation Paraffin-Embedded (FFPE)
KW - Mutational signatures
KW - Next Generation Sequencing (NGS)
UR - http://www.scopus.com/inward/record.url?scp=85142729239&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85142729239&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-17531-2_3
DO - 10.1007/978-3-031-17531-2_3
M3 - Conference contribution
AN - SCOPUS:85142729239
SN - 9783031175305
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 29
EP - 37
BT - Computational Advances in Bio and Medical Sciences - 11th International Conference, ICCABS 2021, Revised Selected Papers
A2 - Bansal, Mukul S.
A2 - Măndoiu, Ion
A2 - Rajasekaran, Sanguthevar
A2 - Moussa, Marmar
A2 - Patterson, Murray
A2 - Skums, Pavel
A2 - Zelikovsky, Alexander
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 16 December 2021 through 18 December 2021
ER -