TY  - THES
N2  - With the growing value of next generation sequencing (NGS) assays for the determination of minimal residual disease (MRD) in the clinic, the con?dent and sensitive detection of low frequency variants is crucial to the treatment of cancer. Current in silico pipelines often lack the sensitivity to detect low frequency variants, whose variant allele frequencies (VAFs) covary with sample purity (i.e. tumor-normal and/or normal-tumor contamination), sample clonality, and copy number variations. Sensitivity is also confounded by the background error inherent to sequencing data, which may be introduced by systematic platform error, library ampli?cation, and errors in sample preparation. Attempting to mitigate background error in sequencing data, researchers have developed many software error correction programs that model sources of error to mitigate its impact on downstream processing. While these models have been developed for de novo assembly, metagenomics research, and viral haplotype reconstruction, their application to the use case of low frequency variant detection has yet to be explored in-depth. For this research, we sought to develop a software framework for the evaluation of background error models in the low frequency variant use case, with a speci?c focus on their potential value to MRD monitoring.
DO  - 10.6083/m4z31xrz
DO  - DOI
AB  - With the growing value of next generation sequencing (NGS) assays for the determination of minimal residual disease (MRD) in the clinic, the con?dent and sensitive detection of low frequency variants is crucial to the treatment of cancer. Current in silico pipelines often lack the sensitivity to detect low frequency variants, whose variant allele frequencies (VAFs) covary with sample purity (i.e. tumor-normal and/or normal-tumor contamination), sample clonality, and copy number variations. Sensitivity is also confounded by the background error inherent to sequencing data, which may be introduced by systematic platform error, library ampli?cation, and errors in sample preparation. Attempting to mitigate background error in sequencing data, researchers have developed many software error correction programs that model sources of error to mitigate its impact on downstream processing. While these models have been developed for de novo assembly, metagenomics research, and viral haplotype reconstruction, their application to the use case of low frequency variant detection has yet to be explored in-depth. For this research, we sought to develop a software framework for the evaluation of background error models in the low frequency variant use case, with a speci?c focus on their potential value to MRD monitoring.
T1  - Evaluation of background prediction for variant detection in a clinical context : towards improved NGS monitoring of minimal residual disease in hematological malignancies
DA  - 2017
AU  - Cordier, Benjamin A.
L1  - https://digitalcollections.ohsu.edu/record/7682/files/Cordier.Benjamin.2017.pdf
PB  - Marylhurst University: Oregon Health and Science University
PY  - 2017
ID  - 7682
L4  - https://digitalcollections.ohsu.edu/record/7682/files/Cordier.Benjamin.2017.pdf
KW  - Leukemia 
KW  - Precision Medicine
KW  -  Genomics
KW  - residual neoplasm
TI  - Evaluation of background prediction for variant detection in a clinical context : towards improved NGS monitoring of minimal residual disease in hematological malignancies
Y1  - 2017
L2  - https://digitalcollections.ohsu.edu/record/7682/files/Cordier.Benjamin.2017.pdf
LK  - https://digitalcollections.ohsu.edu/record/7682/files/Cordier.Benjamin.2017.pdf
UR  - https://digitalcollections.ohsu.edu/record/7682/files/Cordier.Benjamin.2017.pdf
ER  -