Evidence map›Paper›PMID 38322990›Full record

ArticleiScience2024

A machine learning model identifies M3-like subtype in AML based on PML/RARα targets.

Tingting Shao, Jianing Li, Minghai Su, Changbo Yang, Yingying Ma, Chongwen Lv, Wei Wang, Yunjin Xie, Gang Xu, Ce Shi and 4 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in iScience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
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

1 citing paper in PubMed.

  1. Review
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

14 authors.

Tingting ShaoCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang Province 150001, China.
Jianing LiCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang Province 150001, China.
Minghai SuCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang Province 150001, China.
Changbo YangCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang Province 150001, China.
Yingying MaCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang Province 150001, China.
Chongwen LvCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang Province 150001, China.
Wei WangThe Second Affiliated Hospital, Harbin Medical University, Harbin, Heilongjiang Province 150001, China.
Yunjin XieCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang Province 150001, China.
Gang XuCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang Province 150001, China.
Ce ShiKey Laboratory of Hepatosplenic Surgery of Ministry of Education, NHC Key Laboratory of Cell Transplantation, the First Affiliated Hospital, Harbin Medical University, Harbin, Heilongjiang Province 150001, China.
Xinying ZhouKey Laboratory of Hepatosplenic Surgery of Ministry of Education, NHC Key Laboratory of Cell Transplantation, the First Affiliated Hospital, Harbin Medical University, Harbin, Heilongjiang Province 150001, China.
Huitao FanKey Laboratory of Hepatosplenic Surgery of Ministry of Education, NHC Key Laboratory of Cell Transplantation, the First Affiliated Hospital, Harbin Medical University, Harbin, Heilongjiang Province 150001, China.
Yongsheng LiSchool of Interdisciplinary Medicine and Engineering, Harbin Medical University, Harbin 150001, China.
Juan XuCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang Province 150001, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The typical genomic feature of acute myeloid leukemia (AML) M3 subtype is the fusion event of PML/RARα, and ATRA/ATO-based combination therapy is current standard treatment regimen for M3 subtype. Here, a machine-learning model based on expressions of PML/RARα targets was developed to identify M3 patients by analyzing 1228 AML patients. Our model exhibited high accuracy. To enable more non-M3 AML patients to potentially benefit from ATRA/ATO therapy, M3-like patients were further identified. We found that M3-like patients had strong GMP features, including the expression patterns of M3 subtype marker genes, the proportion of myeloid progenitor cells, and deconvolution of AML constituent cell populations. M3-like patients exhibited distinct genomic features, low immune activity and better clinical survival. The initiative identification of patients similar to M3 subtype may help to identify more patients that would benefit from ATO/ATRA treatment and deepen our understanding of the molecular mechanism of AML pathogenesis.

Indexed as

GenomicsMachine learningMedical science

Identifiers

PMID38322990
PMCPMC10844831

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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.