Abstract
Sensitive mutation detection by next-generation sequencing is critical for early cancer detection, monitoring minimal/measurable residual disease (MRD), and guiding precision oncology. Nevertheless, because of artifacts introduced during library preparation and sequencing, the detection of low-frequency variants at high specificity is problematic. Here, we present Espresso, an error suppression method that considers local sequence features to accurately detect single-nucleotide variants (SNVs). Compared to other advanced error suppression techniques, Espresso consistently demonstrated lower numbers of false-positive mutation calls and greater sensitivity. We demonstrated Espresso’s superior performance in detecting MRD in the peripheral blood of patients with acute myeloid leukemia (AML) throughout their treatment course. Furthermore, we showed that accurate mutation calling in a small number of informative genomic loci might provide a cost-efficient strategy for pragmatic risk prediction of AML development in healthy individuals. More broadly, we aim for Espresso to aid with accurate mutation detection in many other research and clinical settings.
| Original language | English |
|---|---|
| Article number | 3722 |
| Number of pages | 15 |
| Journal | Science advances |
| Volume | 6 |
| Issue number | 50 |
| DOIs | |
| Publication status | Published - 9 Dec 2020 |
Funding
We acknowledge the support from the Princess Margaret Cancer Foundation. We thank the Genome Technologies team at the Ontario Institute for Cancer Research as well as the Princess Margaret Genomics Centre for technical expertise with sequencing, data management, and QC analysis. We acknowledge the dedicated work of G. Vassiliou from the Wellcome Trust Sanger Institute for supervising sample acquisition from the EPIC-Norfolk longitudinal cohort and for the management of the sequencing data, its creation, and its sharing. Funding: S.A. received support from the Benjamin Pearl fellowship from the McEwen Centre for Regenerative Medicine and is funded by the Ontario Institute for Cancer Research. A.G.X.Z. was supported by the CIHR Vanier Scholarship. S.V.B., S.M.C., and T.J.P. were supported by the Gattuso-Slaight Personalized Cancer Medicine Fund at the Princess Margaret Cancer Centre. J.E.D. was supported by funds from the Princess Margaret Cancer Centre Foundation, Ontario Institute for Cancer Research, with funding from the Province of Ontario, Canadian Institutes for Health Research, Canadian Cancer Society Research Institute, Terry Fox Foundation, Genome Canada through the Ontario Genomics Institute, and the Canada Research Chair. Author contributions: S.A. developed the concept and the code, led and supervised the analysis of the data, and wrote the manuscript. A.G.X.Z. created the R package and contributed to the development of the analytical pipeline. I.N.-M. contributed to the development of the R package and the correspondence with the reviewers. S.W.K.N. provided support for genetic predictive model derivation. T.T.W. and T.J.P. provided bioinformatics support and contributed to the analysis of the data. M.D.M., T.M., and S.M.C. enabled sample acquisition and clinical data curation and provided clinical expertise. L.I.S. and P.A. contributed to the study design and/or in obtaining data and materials. J.E.D. and S.V.B. supervised all aspects of the study and wrote the manuscript. All authors commented on the manuscript at all stages.
All Science Journal Classification (ASJC) codes
- General