Evidence map›Paper›PMID 38764048›Full record

ArticleCancer cell international2024

Comprehensive insights into AML relapse: genetic mutations, clonal evolution, and clinical outcomes.

Namsoo Kim, Seungmin Hahn, Yu Jeong Choi, Hyunsoo Cho, Haerim Chung, Ji Eun Jang, Chuhl Joo Lyu, Seung-Tae Lee, Jong Rak Choi, June-Won Cheong and 1 more

Abstract read
In one paragraph

Article in Cancer cell international, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

  1. Article
  2. Review
  3. A Relapsed AML Case Featuring MYC and MECOM Rearrangements.Diagnostics (Basel, Switzerland) · 2025
    Article
  4. Article
  5. Article
  6. Article
  7. Review
  8. Article
  9. 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

11 authors.

Namsoo Kim *Department of Laboratory Medicine, Yonsei University College of Medicine, Severance Hospital, 50 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Korea.
Seungmin Hahn *Department of Pediatric Hematology-Oncology, Severance Hospital, Yonsei Cancer Center, Yonsei University College of Medicine, Seoul, Korea.
Yu Jeong ChoiDepartment of Laboratory Medicine, Yonsei University College of Medicine, Severance Hospital, 50 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Korea.
Hyunsoo ChoDivision of Hematology, Department of Internal Medicine, Yonsei University College of Medicine, Severance Hospital, 50 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Korea.
Haerim ChungDivision of Hematology, Department of Internal Medicine, Yonsei University College of Medicine, Severance Hospital, 50 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Korea.
Ji Eun JangDivision of Hematology, Department of Internal Medicine, Yonsei University College of Medicine, Severance Hospital, 50 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Korea.
Chuhl Joo LyuDepartment of Pediatric Hematology-Oncology, Severance Hospital, Yonsei Cancer Center, Yonsei University College of Medicine, Seoul, Korea.
Seung-Tae LeeDepartment of Laboratory Medicine, Yonsei University College of Medicine, Severance Hospital, 50 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Korea.
Jong Rak ChoiDepartment of Laboratory Medicine, Yonsei University College of Medicine, Severance Hospital, 50 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Korea.
June-Won CheongDivision of Hematology, Department of Internal Medicine, Yonsei University College of Medicine, Severance Hospital, 50 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Korea. JWCHEONG70@yuhs.ac.
Saeam ShinDepartment of Laboratory Medicine, Yonsei University College of Medicine, Severance Hospital, 50 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Korea. saeam0304@yuhs.ac.

Funding

National Research Foundation of Korea NRF-2021R1I1A1A01045980Yonsei University College of Medicine 6-2022-0050
6 · The paper itself

Abstract

introductionAcute myeloid leukemia (AML) is a complex hematologic malignancy characterized by uncontrolled proliferation of myeloid precursor cells within bone marrow. Despite advances in understanding of its molecular underpinnings, AML remains a therapeutic challenge due to its high relapse rate and clonal evolution.

methodsIn this retrospective study, we analyzed data from 24 AML patients diagnosed at a single institution between January 2017 and August 2023. Comprehensive genetic analyses, including chromosomal karyotyping, next-generation sequencing, and gene fusion assays, were performed on bone marrow samples obtained at initial diagnosis and relapse. Clinical data, treatment regimens, and patient outcomes were also documented.

resultsMutations in core genes of FLT3, NPM1, DNMT3A, and IDH2 were frequently discovered in diagnostic sample and remained in relapse sample. FLT3-ITD, TP53, KIT, RUNX1, and WT1 mutation were acquired at relapse in one patient each. Gene fusion assays revealed stable patterns, while chromosomal karyotype analyses indicated a greater diversity of mutations in relapsed patients. Clonal evolution patterns varied, with some cases showing linear or branching evolution and others exhibiting no substantial change in core mutations between diagnosis and relapse.

conclusionsOur study integrates karyotype, gene rearrangements, and gene mutation results to provide a further understanding of AML heterogeneity and evolution. We demonstrate the clinical relevance of specific mutations and clonal evolution patterns, emphasizing the need for personalized therapies and measurable residual disease monitoring in AML management. By bridging the gap between genetics and clinical outcome, we move closer to tailored AML therapies and improved patient prognoses.

Indexed as

Acute myeloid leukemiaGene rearrangementNext-generation sequencingRelapseRNA sequencing

Identifiers

PMID38764048
PMCPMC11103850

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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.