B-232 One Year Post-Implementation Experience Using a Custom Designed DNA Contamination Detection Module for a Clinical Myeloid Neoplasm Next Generation Sequencing Assay

Abstract Background In complex automated clinical laboratory Next Generation Sequencing (NGS) workflows, numerous opportunities exist for contamination events to occur. For oncology testing applications, detection of these events is crucial for accurate results reporting. A contamination detection m...

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Veröffentlicht in:Clinical chemistry (Baltimore, Md.) Md.), 2024-10, Vol.70 (Supplement_1)
Hauptverfasser: Dina, M A, Berry, P L, Koganti, T, Balan, J, Basu, S, Artymiuk, C J, Bessonen, K R, Kodah, M, Bailey, B R, Daede, B C, Carlson, R G, Ross, G C, Zoromski, R J, Paladino, L P, He, R, Viswanatha, D S
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Sprache:eng
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Zusammenfassung:Abstract Background In complex automated clinical laboratory Next Generation Sequencing (NGS) workflows, numerous opportunities exist for contamination events to occur. For oncology testing applications, detection of these events is crucial for accurate results reporting. A contamination detection module (MICon) using microhaplotype (MH) regions and an in-house designed analysis model was developed for use in a NGS assay for myeloid neoplasms (NGSHM) [1]. Variant detection for NGSHM has a validated analytical sensitivity of 2% variant allele frequency (VAF). Gross contamination events can potentially cause erroneous false positive variant detection whereas true low VAF mutations may be masked and evade detection. The following study was performed to evaluate MICon module performance on samples processed over the course of a year. Methods MH regions contain highly conserved tandem single nucleotide polymorphisms (SNPs) with high global heterogeneity occurring within a 300-nucleotide span. From the work of Kidd et al [2], 27 MH regions covering 92 SNPs and spanning 16 chromosomes were selected for inclusion in the 47 gene NGSHM target-capture panel. Sequencing was performed on a NovaSeq 6000 and analyzed through an internal bioinformatics pipeline, which incorporates the MICon module. MICon uses a binary classification model incorporating VAFs from MH regions, number of MH genotypes, and the contamination estimation score from verifyBamID to compute a single value on a 0-100 scale. Scores
ISSN:0009-9147
1530-8561
DOI:10.1093/clinchem/hvae106.591