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The quest for high confidence mutations in plasma: searching for a needle in a haystack
Next generation sequencing of circulating tumor DNA (ctDNA) from patient plasma is becoming more widespread in oncology clinical trials. The noninvasive nature of acquiring these samples is particularly important when resection of representative tumor samples is not advised or not possible. However, profiling of ctDNA has challenges to overcome, such as low concentration of ctDNA shed from the tumor and a low signal:noise ratio caused by somatic alterations with less than 1% variant allele fraction.
Improving the sensitivity of these assays to detect low allele frequency events with high confidence requires robust sequencing of low input libraries while employing error correction to reduce background noise.
To overcome these challenges, we have incorporated unique molecular identifiers (UMIs) into our NGS workflow. Using these novel adapters paired with our proprietary bioinformatics pipeline (AstraZeneca), the number of false positive variants reported for allele fractions less than 0.5% was reduced tenfold. We also refined our curation based on the mapping quality and strand bias in the vicinity of each variant to further reduce the background noise.
In this webinar we discuss our current NGS ctDNA workflow and our future plans to increase our sequencing sensitivity with these novel UMI adapters.
Topics covered in this video:
- NGS: Revolutionizing molecular biology and healthcare
- Tissue Molecular Profile to inform treatment (moving towards standard practice)
- Liquid biopsy - the Future of Personalised Medicine?
- Unique Molecular Identifiers (UMIs) - significant advantage in ctDNA sequencing workflow
- Unique Molecular Tagging - History and Principle
- Use of UMIs - best labratory practice
- Use of UMIs - best analysis practice
- UMI Sequencing - relation to input mass
- UMI analysis and panel size - irrespective of panel size, the application of the Q40 is beneficial in reducing the number of variants reported
- Use of UMIs - downstream manual and automated data curation
- Machine learning approach to assign confidence to variance
- EGFR C797S Resistance Mutation - one of the first examples highlighting the power of ctDNA sequencing
- Looking foward: Duplex Seq principle
- Looking forward: Duplex UMIs