Journal
BRIEFINGS IN BIOINFORMATICS
Volume 22, Issue 6, Pages -Publisher
OXFORD UNIV PRESS
DOI: 10.1093/bib/bbab221
Keywords
DCIS; NGS; exome; heterogeneity
Funding
- CDMRP Breast Cancer Research Program Award [BC132057]
- National Institutes of Health [U54 CA217376, U2C CA233254, P01 CA91955, R01 CA170595, R01 CA185138, R01 CA140657]
- Arizona Biomedical Research Commission [ADHS18-198847]
Ask authors/readers for more resources
A new bioinformatic pipeline was developed to accurately identify somatic single nucleotide variants (SNVs) from archival FFPE samples of breast cancers, resulting in a significant improvement in SNV detection compared to existing tools. The method demonstrated increased accuracy and efficiency in detecting mutations, particularly in comparing different stages of breast cancer.
Most tissue collections of neoplasms are composed of formalin-fixed and paraffin-embedded (FFPE) excised tumor samples used for routine diagnostics. DNA sequencing is becoming increasingly important in cancer research and clinical management; however it is difficult to accurately sequence DNA from FFPE samples. We developed and validated a new bioinformatic pipeline to use existing variant-calling strategies to robustly identify somatic single nucleotide variants (SNVs) from whole exome sequencing using small amounts of DNA extracted from archival FFPE samples of breast cancers. We optimized this strategy using 28 pairs of technical replicates. After optimization, the mean similarity between replicates increased 5-fold, reaching 88% (range 0-100%), with a mean of 21.4 SNVs (range 1-68) per sample, representing a markedly superior performance to existing tools. We found that the SNV-identification accuracy declined when there was less than 40 ng of DNA available and that insertion-deletion variant calls are less reliable than single base substitutions. As the first application of the new algorithm, we compared samples of ductal carcinoma in situ of the breast to their adjacent invasive ductal carcinoma samples. We observed an increased number of mutations (paired-samples sign test, P<0.05), and a higher genetic divergence in the invasive samples (paired-samples sign test, P<0.01). Our method provides a significant improvement in detecting SNVs in FFPE samples over previous approaches.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available