Minimizing Sample Failure Rates for Challenging Clinical Tumor Samples.

J Mol Diagn

Australian Translational Genomics Centre, Queensland University of Technology, Woolloongabba, Queensland, Australia; The University of Queensland Diamantina Institute, Faculty of Medicine, The University of Queensland, Woolloongabba, Queensland, Australia; Princess Alexandra Hospital, Woolloongabba, Queensland, Australia.

Published: May 2023

Identification of somatic variants in cancer by high-throughput sequencing has become common clinical practice, largely because many of these variants may be predictive biomarkers for targeted therapies. However, there can be high sample quality control (QC) failure rates for some tests that prevent the return of results. Stem-loop inhibition mediated amplification (SLIMamp) is a patented technology that has been incorporated into commercially available cancer next-generation sequencing testing kits. The claimed advantage is that these kits can interrogate challenging formalin-fixed, paraffin-embedded tissue samples with low tumor purity, poor-quality DNA, and/or low-input DNA, resulting in a high sample QC pass rate. The study aimed to substantiate that claim using Pillar Biosciences oncoReveal Solid Tumor Panel. Forty-eight samples that had failed one or more preanalytical QC sample parameters for whole-exome sequencing from the Australian Translational Genomics Centre's ISO15189-accredited diagnostic genomics laboratory were acquired. XING Genomic Services performed an exploratory data analysis to characterize the samples and then tested the samples in their ISO15189-accredited laboratory. Clinical reports could be generated for 37 (77%) samples, of which 29 (60%) contained clinically actionable or significant variants that would not otherwise have been identified. Eleven samples were deemed unreportable, and the sequencing data were likely dominated by artifacts. A novel postsequencing QC metric was developed that can discriminate between clinically reportable and unreportable samples.

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Source
http://dx.doi.org/10.1016/j.jmoldx.2023.01.008DOI Listing

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