Evidence map›Paper›PMID 37699001›Full record

ArticlePLOS global public health2023

Rapid detection of myeloid neoplasm fusions using single-molecule long-read sequencing.

Olga Sala-Torra, Shishir Reddy, Ling-Hong Hung, Lan Beppu, David Wu, Jerald Radich, Ka Yee Yeung, Cecilia C S Yeung

Open access · goldAbstract read
In one paragraph

Article in PLOS global public health, 2023. 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
1.2field-weighted citation impact, top 20% 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, 8 citations in OpenAlex.

  1. Review
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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

8 authors at 3 institutions in 1 country.

Olga Sala-TorraTranslational Science and Therapeutics Division, Fred Hutchinson Cancer Center, Seattle, Washington, United States of America.
Shishir ReddyUniversity of Washington, Seattle, Washington, United States of America.
Ling-Hong HungUniversity of Washington, Seattle, Washington, United States of America.
Lan BeppuTranslational Science and Therapeutics Division, Fred Hutchinson Cancer Center, Seattle, Washington, United States of America.ORCID https://orcid.org/0000-0002-0401-1159
David WuSchool of Engineering and Technology, University of Washington Tacoma, Tacoma, Washington, United States of America.
Jerald RadichTranslational Science and Therapeutics Division, Fred Hutchinson Cancer Center, Seattle, Washington, United States of America.
Ka Yee YeungUniversity of Washington, Seattle, Washington, United States of America.ORCID https://orcid.org/0000-0002-1754-7577
Cecilia C S YeungTranslational Science and Therapeutics Division, Fred Hutchinson Cancer Center, Seattle, Washington, United States of America.
University of Washington · USFred Hutch Cancer Center · USUniversity of Washington Tacoma · US

Funding

VIRUS-MEDIATED MYELOSUPPRESSIONP01CA018029 · NCI · FRED HUTCHINSON CANCER RESEARCH CENTER · PI STEPHANIE J LEE · 1985 to 2026
$128.4M
MorPhiC Data Resource and Administrative Coordinating CenterU24HG012674 · NHGRI · UNIVERSITY OF MIAMI SCHOOL OF MEDICINE · PI Helen Elizabeth Parkinson, Stephan C Schurer · 2022 to 2026
$6.9M
ITSC for Leukemia: Novel Molecular strategies for NCTN "Individualized" TherapiesUG1CA233338 · NCI · UNIVERSITY OF CINCINNATI · PI BYRD, JOHN C. · 2019 to 2025
$4.5M
Understanding and predicting relapse in acute myeloid leukemiaR01CA175008 · NCI · FRED HUTCHINSON CANCER RESEARCH CENTER · PI RADICH, JERALD PATRICK · 2013 to 2023
$3.9M
Intelligent deployment of containerized bioinformatics workflows on the cloudR01GM126019 · NIGMS · UNIVERSITY OF WASHINGTON · PI YEUNG-RHEE, KA YEE · 2018 to 2020
$1.1M
Rapid Acute Leukemia Genomic Profiling with CRISPR enrichment and Real-time long-read sequencingR21CA280520 · NCI · FRED HUTCHINSON CANCER CENTER · PI YEUNG, CECILIA C · 2023 to 2024
$687k
NCI NIH HHS P01 CA018029NCI NIH HHS R01 CA175008NCI NIH HHS R21 CA280520NCI NIH HHS UG1 CA233338NHGRI NIH HHS U24 HG012674NIGMS NIH HHS R01 GM126019
6 · The paper itself

Abstract

Recurrent gene fusions are common drivers of disease pathophysiology in leukemias. Identifying these structural variants helps stratify disease by risk and assists with therapy choice. Precise molecular diagnosis in low-and-middle-income countries (LMIC) is challenging given the complexity of assays, trained technical support, and the availability of reliable electricity. Current fusion detection methods require a long turnaround time (7-10 days) or advance knowledge of the genes involved in the fusions. Recent technology developments have made sequencing possible without a sophisticated molecular laboratory, potentially making molecular diagnosis accessible to remote areas and low-income settings. We describe a long-read sequencing DNA assay designed with CRISPR guides to select and enrich for recurrent leukemia fusion genes, that does not need a priori knowledge of the abnormality present. By applying rapid sequencing technology based on nanopores, we sequenced long pieces of genomic DNA and successfully detected fusion genes in cell lines and primary specimens (e.g., BCR::ABL1, PML::RARA, CBFB::MYH11, KMT2A::AFF1) using cloud-based bioinformatics workflows with novel custom fusion finder software. We detected fusion genes in 100% of cell lines with the expected breakpoints and confirmed the presence or absence of a recurrent fusion gene in 12 of 14 patient cases. With our optimized assay and cloud-based bioinformatics workflow, these assays and analyses could be performed in under 8 hours. The platform's portability, potential for adaptation to lower-cost devices, and integrated cloud analysis make this assay a candidate to be placed in settings like LMIC to bridge the need of bedside rapid molecular diagnostics.

Identifiers

PMID37699001
PMCPMC10497132
OpenAlexW4386691187

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.