Calibration-free NGS quantitation of mutations below 0.01% VAF

The quantitation of rare somatic mutations is essential for basic research and translational clinical applications including minimal residual disease (MRD) detection. Though unique molecular identifier (UMI) has suppressed sequencing error and allowed detection rare mutation, the sequencing depth requirement is high. The blocker displacement amplification (BDA) allele enrichment method allows detection of rare mutations using low sequencing depth, but requires calibration to accurately quantitate the VAF of novel mutations. Here, we present Quantitative Blocker Displacement Amplification (QBDA), a method that allows accurate detection and quantitation of mutations below 0.01% VAF at only 23,000X depth. QBDA integrates sequence-selective variant enrichment into UMI quantitation allowing confident detection of rare mutations and reduced sequencing depth. Using a panel of 20 genes recurrently altered in acute myeloid leukemia, we demonstrate quantitation of various mutations including single base substitutions and indels down to a VAF of 0.001% at a single locus with less than 4 million sequencing reads, allowing a sensitive minimal residual disease (MRD) detection in patients during complete remission. In a comprehensive pan-cancer panel covering 61 genes and a melanoma hotspot panel covering 8 genes, we detect mutations down to 0.1% VAF using only 1 million reads in a broad range of clinical samples including cell-free DNA and FFPE DNA, enabling tissue or liquid biopsy genetic tests with de-centralized sequencing instruments. QBDA thus provides a convenient and versatile method for sensitive mutation quantitation using low-depth sequencing.

[1]  B. Gruhn,et al.  Prevalence and dynamics of clonal hematopoiesis caused by leukemia-associated mutations in elderly individuals without hematologic disorders , 2020, Leukemia.

[2]  Kari Stefansson,et al.  Clonal hematopoiesis, with and without candidate driver mutations, is common in the elderly. , 2017, Blood.

[3]  J. Jorgensen,et al.  How Do We Use Multicolor Flow Cytometry to Detect Minimal Residual Disease in Acute Myeloid Leukemia? , 2017, Clinics in laboratory medicine.

[4]  M. McCarthy,et al.  Age-related clonal hematopoiesis associated with adverse outcomes. , 2014, The New England journal of medicine.

[5]  T. Druley,et al.  Clonal haematopoiesis harbouring AML-associated mutations is ubiquitous in healthy adults , 2016, Nature Communications.

[6]  J. Ptak,et al.  Detection of low-frequency DNA variants by targeted sequencing of the Watson and Crick strands , 2021, Nature Biotechnology.

[7]  D. Berry,et al.  Association of Measurable Residual Disease With Survival Outcomes in Patients With Acute Myeloid Leukemia: A Systematic Review and Meta-analysis. , 2020, JAMA oncology.

[8]  M. Meyerson,et al.  EGFR mutation and resistance of non-small-cell lung cancer to gefitinib. , 2005, The New England journal of medicine.

[9]  Yashma Patel,et al.  Assessment of Minimal Residual Disease in Standard-Risk AML. , 2016, The New England journal of medicine.

[10]  S. Gabriel,et al.  Clonal hematopoiesis and blood-cancer risk inferred from blood DNA sequence. , 2014, The New England journal of medicine.

[11]  Behzad Baradaran,et al.  The Different Mechanisms of Cancer Drug Resistance: A Brief Review , 2017, Advanced pharmaceutical bulletin.

[12]  P. A. Futreal,et al.  Clearance of Somatic Mutations at Remission and the Risk of Relapse in Acute Myeloid Leukemia. , 2018, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[13]  C. Quince,et al.  Insight into biases and sequencing errors for amplicon sequencing with the Illumina MiSeq platform , 2015, Nucleic acids research.

[14]  H. Kantarjian,et al.  Next-generation sequencing-based multigene mutational screening for acute myeloid leukemia using MiSeq: applicability for diagnostics and disease monitoring , 2014, Haematologica.

[15]  Markus G. Manz,et al.  Molecular Minimal Residual Disease in Acute Myeloid Leukemia , 2018, The New England journal of medicine.

[16]  R. DeSalle,et al.  Co-existence of BRAF and NRAS driver mutations in the same melanoma cells results in heterogeneity of targeted therapy resistance , 2016, Oncotarget.

[17]  Vladimir Potapov,et al.  Examining Sources of Error in PCR by Single-Molecule Sequencing , 2017, PloS one.

[18]  K. Kinzler,et al.  Detection and quantification of rare mutations with massively parallel sequencing , 2011, Proceedings of the National Academy of Sciences.

[19]  M. Dubé,et al.  DNMT3A and TET2 dominate clonal hematopoiesis and demonstrate benign phenotypes and different genetic predispositions. , 2017, Blood.

[20]  K. Lewis Persister cells, dormancy and infectious disease , 2007, Nature Reviews Microbiology.

[21]  Jesse J. Salk,et al.  Detection of ultra-rare mutations by next-generation sequencing , 2012, Proceedings of the National Academy of Sciences.

[22]  Clonal evolution of acute myeloid leukemia revealed by high-throughput single-cell genomics , 2020, Nature communications.

[23]  Endre Kiss,et al.  Comprehensive human cell-type methylation atlas reveals origins of circulating cell-free DNA in health and disease , 2018, Nature Communications.

[24]  Verena I Gaidzik,et al.  Clonal evolution patterns in acute myeloid leukemia with NPM1 mutation , 2019, Nature Communications.

[25]  M. Stratton,et al.  Somatic mutation landscapes at single-molecule resolution , 2021, Nature.

[26]  James D. Brenton,et al.  Liquid biopsies come of age: towards implementation of circulating tumour DNA , 2017, Nature Reviews Cancer.

[27]  Klaus Pantel,et al.  Cell-free nucleic acids as biomarkers in cancer patients , 2011, Nature Reviews Cancer.

[28]  T. Kunkel,et al.  DNA polymerase fidelity and the polymerase chain reaction. , 1991, PCR methods and applications.

[29]  L. Shlush Age-related clonal hematopoiesis. , 2018, Blood.

[30]  Umer Zeeshan Ijaz,et al.  Illumina error profiles: resolving fine-scale variation in metagenomic sequencing data , 2016, BMC Bioinformatics.

[31]  J. Jorgensen,et al.  Multi-color flow cytometric immunophenotyping for detection of minimal residual disease in AML: past, present and future , 2014, Bone Marrow Transplantation.

[32]  Yalei Wu,et al.  Multiplexed enrichment of rare DNA variants via sequence-selective and temperature-robust amplification , 2017, Nature Biomedical Engineering.

[33]  Marc Lipsitch,et al.  Mycobacterium tuberculosis mutation rate estimates from different lineages predict substantial differences in the emergence of drug resistant tuberculosis , 2013, Nature Genetics.

[34]  Ash A. Alizadeh,et al.  Integrated digital error suppression for improved detection of circulating tumor DNA , 2016, Nature Biotechnology.

[35]  A. Shaw,et al.  Tumour heterogeneity and resistance to cancer therapies , 2018, Nature Reviews Clinical Oncology.