Evidence map›Paper›PMID 40361241›Full record

ReviewBiomarker research2025

Insights into KMT2A rearrangements in acute myeloid leukemia: from molecular characteristics to targeted therapies.

Sara Zehtabcheh, Hamed Soleimani Samarkhazan, Marjan Asadi, Mitra Zabihi, Sahar Parkhideh, Mohammad Hossein Mohammadi

Abstract readReview
In one paragraph

Review in Biomarker research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.

0numbers the graph read from it
0cells of the map it votes in
32citing 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

32 citing papers in PubMed.

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  10. A Rare Case of De Novo Acute Myeloid Leukemia, Featuring aDiagnostics (Basel, Switzerland) · 2026
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  17. CharacterizingCancers · 2026
    Article
  18. Epigenetic alterations in Myeloid Malignancies.Advances in experimental medicine and biology · 2026
    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

6 authors.

Sara ZehtabchehStudent Research Committee, Department of Hematology and Blood Banking, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.ORCID http://orcid.org/0009-0006-5998-1722
Hamed Soleimani SamarkhazanStudent Research Committee, Department of Hematology and Blood Banking, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.ORCID http://orcid.org/0000-0003-1045-7613
Marjan AsadiDepartment of Hematology and Blood Banking, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.ORCID http://orcid.org/0000-0003-2789-8271
Mitra ZabihiDepartment of Hematology and Blood Banking, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Sahar ParkhidehHematopoetic Stem Cell Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran.ORCID http://orcid.org/0000-0001-7846-3222
Mohammad Hossein MohammadiDepartment of Hematology and Blood Banking, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran. Drmohammadi@sbmu.ac.ir.ORCID http://orcid.org/0009-0005-4884-5218

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Acute myeloid leukemia (AML) with KMT2A rearrangements (KMT2A-r) represents a highly aggressive and prognostically unfavorable subtype of leukemia, often resistant to standard treatments and associated with high relapse rates. KMT2A-r, found in 3-10% of adult AML cases, disrupt epigenetic regulation by forming chimeric proteins that activate oncogenic pathways like HOXA and MEIS1. These fusion proteins recruit cofactors such as Menin and DOT1L, driving leukemogenesis through abnormal histone methylation. Diagnosing KMT2A-r AML requires precision, with traditional methods like FISH and RT-PCR being complemented by advanced technologies such as next-generation sequencing (NGS) and machine learning (ML). ML models, leveraging transcriptomic data, can predict KMT2A-r and identify biomarkers like LAMP5 and SKIDA1, improving risk stratification. Therapeutically, there is a shift from chemotherapy to targeted therapies. Menin inhibitors (e.g., Revumenib, Ziftomenib) disrupt the Menin-KMT2A interaction, suppressing HOXA/MEIS1 and promoting differentiation. DOT1L inhibitors (e.g., Pinometostat) show promise in combination therapies, while novel approaches like WDR5 inhibitors and PROTAC-mediated degradation are expanding treatment options. Despite progress, challenges remain, including optimizing minimal residual disease monitoring, overcoming resistance, and validating biomarkers. This review emphasizes the imperative to translate molecular insights into personalized therapeutic regimens, offering renewed hope for patients afflicted by this historically refractory malignancy.

Indexed as

Acute myeloid leukemiaDOT1L inhibitorsKMT2A rearrangementMenin inhibitors

Identifiers

PMID40361241
PMCPMC12077025

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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.