Evidence map›Paper›PMID 35303493›Full record

ArticleLeukemia research2022

Accurate detection of subclonal variants in paired diagnosis-relapse acute myeloid leukemia samples by next generation Duplex Sequencing.

Ashwini S Kamath-Loeb, Jiang-Cheng Shen, Michael W Schmitt, Brendan F Kohrn, Keith R Loeb, Elihu H Estey, Jin Dai, Sylvia Chien, Lawrence A Loeb, Pamela S Becker

Open access · greenAbstract read
In one paragraph

Article in Leukemia research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
0.5field-weighted citation impact, top 44% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed, 6 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors at 2 institutions in 1 country.

Ashwini S Kamath-LoebDepartment of Laboratory Medicine and Pathology, University of Washington School of Medicine, Seattle, WA 98195, United States. Electronic address: askamath@uw.edu.
Jiang-Cheng ShenDepartment of Laboratory Medicine and Pathology, University of Washington School of Medicine, Seattle, WA 98195, United States.
Michael W SchmittDivision of Hematology, University of Washington School of Medicine, Seattle, WA 98195, United States.
Brendan F KohrnDepartment of Laboratory Medicine and Pathology, University of Washington School of Medicine, Seattle, WA 98195, United States.
Keith R LoebDepartment of Laboratory Medicine and Pathology, University of Washington School of Medicine, Seattle, WA 98195, United States; Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, United States.
Elihu H EsteyDivision of Hematology, University of Washington School of Medicine, Seattle, WA 98195, United States; Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, United States.
Jin DaiDivision of Hematology, University of Washington School of Medicine, Seattle, WA 98195, United States.
Sylvia ChienDivision of Hematology, University of Washington School of Medicine, Seattle, WA 98195, United States.
Lawrence A LoebDepartment of Laboratory Medicine and Pathology, University of Washington School of Medicine, Seattle, WA 98195, United States.
Pamela S BeckerDivision of Hematology, University of Washington School of Medicine, Seattle, WA 98195, United States; Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, United States. Electronic address: pbecker@coh.org.
University of Washington · USFred Hutch Cancer Center · US

Funding

Translational Bioimaging Core Shared ResourceP30CA015704 · NCI · FRED HUTCHINSON CANCER RESEARCH CENTER · PI Eric Collisson · 1985 to 2026
$296.4M
Tumor mutation signaturesP01CA077852 · NCI · UNIVERSITY OF WASHINGTON · PI SIDOROVA, JULIA · 1999 to 2019
$26.0M
CORE--VECTOR PRODUCTIONP30DK056465 · NIDDK · FRED HUTCHINSON CANCER RESEARCH CENTER · PI BERNSTEIN, IRWIN D · 1999 to 2014
$14.9M
Mutator Phenotype in Colon CancerR01CA193649 · NCI · UNIVERSITY OF WASHINGTON · PI LOEB, LAWRENCE A · 2016 to 2019
$1.4M
NCI NIH HHS P01 CA077852NCI NIH HHS P30 CA015704NCI NIH HHS R01 CA193649NIDDK NIH HHS P30 DK056465
6 · The paper itself

Abstract

Mutations characterize diverse human cancers; there is a positive correlation between elevated mutation frequency and tumor progression. One exception is acute myeloid leukemia (AML), which has few clonal single nucleotide mutations. We used highly sensitive and accurate Duplex Sequencing (DS) to show now that AML, in addition, has an extensive repertoire of variants with low allele frequencies, < 1%, which is below the accurate detection limit of most other sequencing methodologies. The subclonal variants are unique to each individual and change in composition, frequency, and sequence context from diagnosis to relapse. Their functional significance is apparent by the observation that many are known variants and cluster within functionally important protein domains. Subclones provide a reservoir of variants that could expand and contribute to the development of drug resistance and relapse. In accord, we accurately identified subclonal variants in AML driver genes NRAS and RUNX1 at allele frequencies between 0.1% and 0.3% at diagnosis, which expanded to comprise a major fraction (14-53%) of the blast population at relapse. Early and accurate detection of subclonal variants with low allele frequency thus offers the opportunity for early intervention, prior to detection of clinical relapse, to improve disease outcome and enhance patient survival.

Indexed as

Leukemia, Myeloid, AcuteAllelesHigh-Throughput Nucleotide SequencingHumansMutationRecurrenceAcute myeloid leukemiaDiagnosisDuplex SequencingEarly detectionRelapseSubclonal variants

Identifiers

PMID35303493
PMCPMC9014797
OpenAlexW4220986980

What OpenQuestion holds

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Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.