Abyzov, Alexej

AbstractPaclitaxel‐induced peripheral neuropathy (PIPN) cannot be predicted from clinical parameters and might have a pharmacogenomic basis. Previous studies identified single nucleotide variants (SNV) associated with PIPN. However, only a subset of findings has been confirmed to date in more than one study, suggesting a need for further re‐testing and validation in additional clinical cohorts. Candidate PIPN‐associated SNVs were identified from the literature. SNVs were retested in 119 patients selected by extreme phenotyping from 269 in NCCTG N08C1 (Alliance) as previously reported. SNV genotyping was performed by a combination of short‐read sequencing analysis and Taqman PCR. These 22 candidate PIPN SNVs were genotyped. Two of these, rs7349683 in the EPHA5 and rs3213619 in ABCB1 were found to be significantly associated with PIPN with an Odds ratios OR = 2.07 (P = 0.02) and OR = 0.12 (P = 0.03), respectively. In addition, three SNVs showed a trend toward a risk‐ or protective effect that was consistent with previous reports. The rs10509681 and rs11572080 in the gene CYP2C8*3 showed risk effect with an OR = 1.49 and rs1056836 in CYP1B1 showed a protective effect with an OR = 0.66. None of the other results supported the previously reported associations, including some SNVs displaying an opposite direction of effect from previous reports, including rs1058930 in CYP2C8, rs17222723 and rs8187710 in ABCC2, rs10771973 in FGD4, rs16916932 in CACNB2 and rs16948748 in PITPNA. Alliance N08C1 validated or supported a minority of previously reported SNV‐PIPN associations. Associations previously reported by multiple studies appeared to have a higher likelihood to be validated by Alliance N08C1.

Abstract Summary: Structural variations (SVs) are large genomic rearrangements that vary significantly in size, making them challenging to detect with the relatively short reads from next-generation sequencing (NGS). Different SV detection methods have been developed; however, each is limited to specific kinds of SVs with varying accuracy and resolution. Previous works have attempted to combine different methods, but they still suffer from poor accuracy particularly for insertions. We propose MetaSV, an integrated SV caller which leverages multiple orthogonal SV signals for high accuracy and resolution. MetaSV proceeds by merging SVs from multiple tools for all types of SVs. It also analyzes soft-clipped reads from alignment to detect insertions accurately since existing tools underestimate insertion SVs. Local assembly in combination with dynamic programming is used to improve breakpoint resolution. Paired-end and coverage information is used to predict SV genotypes. Using simulation and experimental data, we demonstrate the effectiveness of MetaSV across various SV types and sizes. Availability and implementation: Code in Python is at http://bioinform.github.io/metasv/. Contact: rd@bina.com Supplementary information: Supplementary data are available at Bioinformatics online.