Evidence map›Paper›PMID 37620601›Full record

ArticleNature genetics2023

Multiparameter prediction of myeloid neoplasia risk.

Muxin Gu, Sruthi Cheloor Kovilakam, William G Dunn, Ludovica Marando, Clea Barcena, Irina Mohorianu, Alexandra Smith, Siddhartha P Kar, Margarete A Fabre, Moritz Gerstung and 4 more

Erratum issuedOpen access · hybridAbstract read
In one paragraph

Article in Nature genetics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 79 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
79citing papers in PubMed, 2 pooled it
28.5field-weighted citation impact, top 1% 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

79 citing papers in PubMed, 2 syntheses or guidelines pooled it, 98 citations in OpenAlex.

  1. Pooled it
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  6. Genetic influences on haematopoiesis.Nature reviews. Genetics · 2026
    Review
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19 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

14 authors at 8 institutions in 4 countries.

Muxin GuWellcome-MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK.
Sruthi Cheloor KovilakamWellcome-MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK.
William G DunnWellcome-MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK.
Ludovica MarandoWellcome-MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK.
Clea BarcenaWellcome-MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK.ORCID http://orcid.org/0000-0002-7452-2536
Irina MohorianuWellcome-MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK.
Alexandra SmithEpidemiology and Cancer Statistics Group, University of York, York, UK.
Siddhartha P KarMRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK.ORCID http://orcid.org/0000-0002-2314-1426
Margarete A FabreWellcome-MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK.
Moritz GerstungDivision of Artificial Intelligence in Oncology, DKFZ, Heidelberg, Germany.
Catherine A CargoHaematological Malignancy Diagnostic Service, St James's Hospital, Leeds, UK.
Luca MalcovatiDepartment of Molecular Medicine, University of Pavia, Pavia, Italy.ORCID http://orcid.org/0000-0002-1460-1611
Pedro M QuirosWellcome-MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK. pmquiros@ispasturias.es.ORCID http://orcid.org/0000-0002-7793-6291
George S VassiliouWellcome-MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK. gsv20@cam.ac.uk.ORCID http://orcid.org/0000-0003-4337-8022
Wellcome/MRC Cambridge Stem Cell Institute · GBAstraZeneca (United Kingdom) · GBHeidelberg University · DELeeds Teaching Hospitals NHS Trust · GBUniversidad de Oviedo · ESUniversity of Cambridge · GBUniversity of Pavia · ITUniversity of York · GB

Funding

Cancer Research UK 23015Cancer Research UK 29685Medical Research Council MC_PC_17230Medical Research Council MR/T043202/1
6 · The paper itself

Abstract

The myeloid neoplasms encompass acute myeloid leukemia, myelodysplastic syndromes and myeloproliferative neoplasms. Most cases arise from the shared ancestor of clonal hematopoiesis (CH). Here we analyze data from 454,340 UK Biobank participants, of whom 1,808 developed a myeloid neoplasm 0-15 years after recruitment. We describe the differences in CH mutational landscapes and hematology/biochemistry test parameters among individuals that later develop myeloid neoplasms (pre-MN) versus controls, finding that disease-specific changes are detectable years before diagnosis. By analyzing differences between 'pre-MN' and controls, we develop and validate Cox regression models quantifying the risk of progression to each myeloid neoplasm subtype. We construct 'MN-predict', a web application that generates time-dependent predictions with the input of basic blood tests and genetic data. Our study demonstrates that many individuals that develop myeloid neoplasms can be identified years in advance and provides a framework for disease-specific prognostication that will be of substantial use to researchers and physicians.

Indexed as

Clonal HematopoiesisNeoplasmsFamilyHumansMutationSoftware

Identifiers

PMID37620601
PMCPMC10484784
OpenAlexW4386142415

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.