AUTHOR=Shugg Tyler , Ly Reynold C. , Osei Wilberforce , Rowe Elizabeth J. , Granfield Caitlin A. , Lynnes Ty C. , Medeiros Elizabeth B. , Hodge Jennelle C. , Breman Amy M. , Schneider Bryan P. , Sahinalp S. Cenk , Numanagić Ibrahim , Salisbury Benjamin A. , Bray Steven M. , Ratcliff Ryan , Skaar Todd C. 

TITLE=Computational pharmacogenotype extraction from clinical next-generation sequencing

JOURNAL=Frontiers in Oncology

VOLUME=13

YEAR=2023

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2023.1199741

DOI=10.3389/fonc.2023.1199741

ISSN=2234-943X

ABSTRACT=<sec><title>Background</title><p>Next-generation sequencing (NGS), including whole genome sequencing (WGS) and whole exome sequencing (WES), is increasingly being used for clinic care. While NGS data have the potential to be repurposed to support clinical pharmacogenomics (PGx), current computational approaches have not been widely validated using clinical data. In this study, we assessed the accuracy of the Aldy computational method to extract PGx genotypes from WGS and WES data for 14 and 13 major pharmacogenes, respectively.</p></sec><sec><title>Methods</title><p>Germline DNA was isolated from whole blood samples collected for 264 patients seen at our institutional molecular solid tumor board. DNA was used for panel-based genotyping within our institutional Clinical Laboratory Improvement Amendments- (CLIA-) certified PGx laboratory. DNA was also sent to other CLIA-certified commercial laboratories for clinical WGS or WES. Aldy v3.3 and v4.4 were used to extract PGx genotypes from these NGS data, and results were compared to the panel-based genotyping reference standard that contained 45 star allele-defining variants within <italic>CYP2B6</italic>, <italic>CYP2C8</italic>, <italic>CYP2C9</italic>, <italic>CYP2C19</italic>, <italic>CYP2D6</italic>, <italic>CYP3A4</italic>, <italic>CYP3A5</italic>, <italic>CYP4F2</italic>, <italic>DPYD</italic>, <italic>G6PD</italic>, <italic>NUDT15</italic>, <italic>SLCO1B1</italic>, <italic>TPMT</italic>, and <italic>VKORC1</italic>.</p></sec><sec><title>Results</title><p>Mean WGS read depth was &gt;30x for all variant regions except for <italic>G6PD</italic> (average read depth was 29 reads), and mean WES read depth was &gt;30x for all variant regions. For 94 patients with WGS, Aldy v3.3 diplotype calls were concordant with those from the genotyping reference standard in 99.5% of cases when excluding diplotypes with additional major star alleles not tested by targeted genotyping, ambiguous phasing, and <italic>CYP2D6</italic> hybrid alleles. Aldy v3.3 identified 15 additional clinically actionable star alleles not covered by genotyping within <italic>CYP2B6</italic>, <italic>CYP2C19</italic>, <italic>DPYD</italic>, <italic>SLCO1B1</italic>, and <italic>NUDT15</italic>. Within the WGS cohort, Aldy v4.4 diplotype calls were concordant with those from genotyping in 99.7% of cases. When excluding patients with <italic>CYP2D6</italic> copy number variation, all Aldy v4.4 diplotype calls except for one <italic>CYP3A4</italic> diplotype call were concordant with genotyping for 161 patients in the WES cohort.</p></sec><sec><title>Conclusion</title><p>Aldy v3.3 and v4.4 called diplotypes for major pharmacogenes from clinical WES and WGS data with &gt;99% accuracy. These findings support the use of Aldy to repurpose clinical NGS data to inform clinical PGx.</p></sec>