Evidence map›Paper›PMID 34422660›Full record

ArticleFrontiers in oncology2021

Decoupling Lineage-Associated Genes in Acute Myeloid Leukemia Reveals Inflammatory and Metabolic Signatures Associated With Outcomes.

Hussein A Abbas, Vakul Mohanty, Ruiping Wang, Yuefan Huang, Shaoheng Liang, Feng Wang, Jianhua Zhang, Yihua Qiu, Chenyue W Hu, Amina A Qutub and 8 more

Abstract read
In one paragraph

Article in Frontiers in oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

10 citing papers in PubMed.

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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

18 authors.

Hussein A AbbasDepartment of Leukemia, The University of Texas MD Anderson Cancer Center, Houston, TX, United States.
Vakul MohantyDepartment of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States.
Ruiping WangDepartment of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, United States.
Yuefan HuangDepartment of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States.
Shaoheng LiangDepartment of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States.
Feng WangDepartment of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, United States.
Jianhua ZhangDepartment of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, United States.
Yihua QiuDepartment of Leukemia, The University of Texas MD Anderson Cancer Center, Houston, TX, United States.
Chenyue W HuDepartment of Leukemia, The University of Texas MD Anderson Cancer Center, Houston, TX, United States.
Amina A QutubDepartment of Bioengineering, Rice University, Houston, TX, United States.
Monique DailOncology Biomarker Development, Genentech Inc, South San Francisco, CA, United States.
Christopher R BolenOncology Bioinformatics, Genentech Inc, South San Francisco, CA, United States.
Naval DaverDepartment of Leukemia, The University of Texas MD Anderson Cancer Center, Houston, TX, United States.
Marina KonoplevaDepartment of Leukemia, The University of Texas MD Anderson Cancer Center, Houston, TX, United States.
Andrew FutrealDepartment of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, United States.
Ken ChenDepartment of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States.
Linghua WangDepartment of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, United States.
Steven M KornblauDepartment of Leukemia, The University of Texas MD Anderson Cancer Center, Houston, TX, United States.

Funding

TRAINING FOR ACADEMIC ONCOLOGY/HEMATOLOGYT32CA009666 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI Michael Davies, Courtney DiNardo · 1994 to 2026
$9.8M
Informatics for Functional Integration of Heterogeneous Cancer Genome and Transcriptome Sequencing DataU01CA247760 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI CHEN, KEN · 2020 to 2022
$1.2M
NCI NIH HHS T32 CA009666NCI NIH HHS U01 CA247760
6 · The paper itself

Abstract

Acute myeloid leukemia (AML) is a heterogeneous disease with variable responses to therapy. Cytogenetic and genomic features are used to classify AML patients into prognostic and treatment groups. However, these molecular characteristics harbor significant patient-to-patient variability and do not fully account for AML heterogeneity. RNA-based classifications have also been applied in AML as an alternative approach, but transcriptomic grouping is strongly associated with AML morphologic lineages. We used a training cohort of newly diagnosed AML patients and conducted unsupervised RNA-based classification after excluding lineage-associated genes. We identified three AML patient groups that have distinct biological pathways associated with outcomes. Enrichment of inflammatory pathways and downregulation of

Indexed as

acute myeloid leukemiainflammationlineagemetabolismmultiplatform analysis

Identifiers

PMID34422660
PMCPMC8372368

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Registered trials

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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.