Multiplex Accurate Sensitive Quantitation (MASQ) With Application to Minimal Residual Disease in Acute Myeloid Leukemia

Moffitt, A. B., Spector, M. S., Andrews, P., Kendall, J., Alexander, J., Stepansky, A., Ma, B., Kolitz, J., Chiorazzi, N., Allen, S.L., Krasnitz, A., Wigler, M., Levy, D., Wang, Z. (February 2020) Multiplex Accurate Sensitive Quantitation (MASQ) With Application to Minimal Residual Disease in Acute Myeloid Leukemia. Nucleic Acids Research, 48 (7). e40. ISSN 0305-1048 (Public Dataset)

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DOI: 10.1093/nar/gkaa090


Measuring minimal residual disease in cancer has applications for prognosis, monitoring treatment and detection of recurrence. Simple sequence-based methods to detect nucleotide substitution variants have error rates (about 10-3) that limit sensitive detection. We developed and characterized the performance of MASQ (multiplex accurate sensitive quantitation), a method with an error rate below 10-6. MASQ counts variant templates accurately in the presence of millions of host genomes by using tags to identify each template and demanding consensus over multiple reads. Since the MASQ protocol multiplexes 50 target loci, we can both integrate signal from multiple variants and capture subclonal response to treatment. Compared to existing methods for variant detection, MASQ achieves an excellent combination of sensitivity, specificity and yield. We tested MASQ in a pilot study in acute myeloid leukemia (AML) patients who entered complete remission. We detect leukemic variants in the blood and bone marrow samples of all five patients, after induction therapy, at levels ranging from 10-2 to nearly 10-6. We observe evidence of sub-clonal structure and find higher target variant frequencies in patients who go on to relapse, demonstrating the potential for MASQ to quantify residual disease in AML.

Item Type: Paper
Subjects: diseases & disorders > cancer
diseases & disorders > cancer > cancer types > leukemia
bioinformatics > genomics and proteomics > annotation > variant calling
CSHL Authors:
Communities: CSHL labs > Krasnitz lab
CSHL labs > Levy lab
CSHL labs > Wigler lab
CSHL Cancer Center Program > Cancer Genetics and Genomics Program
Depositing User: Adrian Gomez
Date: 21 February 2020
Date Deposited: 06 Apr 2020 17:24
Last Modified: 05 Nov 2020 19:22
PMCID: PMC7144909
Related URLs:
Dataset ID:
  • EGA: EGAS00001003732

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