Prognostic impact of KMT2A-PTD in acute myeloid leukemia in the NGS era: a multicenter retrospective study

Taichiro Tokura; Satoshi Wakita; Keiko Fukunaga; Yasunobu Nagata; Shunsuke Yui; Atsushi Marumo; Tomoaki Kitano; Mayumi Yogosawa; Daisuke Minakata; Shinichi Kako; Shinya Kimura; Sho Okamoto; Taku Tsukamoto; Junya Kanda; Atsushi Satake; Haruko Tashiro; Masao Ogata; Kuniko Takano; Jun Ando; Taro Edahiro; Tatsuo Ichinohe; Yuri Kamitsuji; Nobuhiko Uoshima; Eri Kawata; Hitoji Uchiyama; Keiichi Moriya; Noriko Doki; Yuho Najima; Kazutaka Nakayama; Toshimitsu Ueki; Katsuhiro Shono; Shinichiro Mori; Masao Hagihara; Akiko Hashimoto; Hideharu Muto; Daichi Nishiyama; Sayuri Motomura; Yoshinobu Kanda; Hiroki Yamaguchi

doi:10.1007/s00277-026-07158-y

. 2026 Jul 7;105(10):433. doi: 10.1007/s00277-026-07158-y

Prognostic impact of KMT2A-PTD in acute myeloid leukemia in the NGS era: a multicenter retrospective study

Taichiro Tokura ^1,^✉, Satoshi Wakita ^1,^✉, Keiko Fukunaga ¹, Yasunobu Nagata ¹, Shunsuke Yui ¹, Atsushi Marumo ¹, Tomoaki Kitano ¹, Mayumi Yogosawa ¹, Daisuke Minakata ², Shinichi Kako ³, Shinya Kimura ⁴, Sho Okamoto ⁴, Taku Tsukamoto ⁵, Junya Kanda ⁶, Atsushi Satake ⁷, Haruko Tashiro ⁸, Masao Ogata ⁹, Kuniko Takano ⁹, Jun Ando ¹⁰, Taro Edahiro ¹¹, Tatsuo Ichinohe ¹¹, Yuri Kamitsuji ¹², Nobuhiko Uoshima ¹³, Eri Kawata ¹³, Hitoji Uchiyama ¹⁴, Keiichi Moriya ¹⁵, Noriko Doki ¹⁶, Yuho Najima ¹⁶, Kazutaka Nakayama ¹⁷, Toshimitsu Ueki ¹⁸, Katsuhiro Shono ¹⁹, Shinichiro Mori ²⁰, Masao Hagihara ²¹, Akiko Hashimoto ²², Hideharu Muto ²³, Daichi Nishiyama ²⁴, Sayuri Motomura ²⁵, Yoshinobu Kanda ^2,³, Hiroki Yamaguchi ¹

¹Department of Hematology, Nippon Medical School, 1-1-5 Sendagi, Bunkyo-ku, Tokyo, 113-8603 Japan

²Division of Hematology, Department of Medicine, Jichi Medical University, Tochigi, Japan

³Division of Hematology, Jichi Medical University Saitama Medical Center, Saitama, Japan

⁴Division of Hematology, Respiratory Medicine and Oncology, Department of Internal Medicine, Faculty of Medicine, Saga University, Saga, Japan

⁵Division of Hematology and Oncology, Kyoto Prefectural University of Medicine, Kyoto, Japan

⁶Department of Hematology, Graduate School of Medicine, Kyoto University, Kyoto, Japan

⁷First Department of Internal Medicine, Kansai Medical University, Osaka, Japan

⁸Department of Hematology/Oncology, Teikyo University School of Medicine, Tokyo, Japan

⁹Department of Hematology, Oita University Hospital, Oita, Japan

¹⁰Division of Hematology, Department of Internal Medicine, Juntendo University School of Medicine, Tokyo, Japan

¹¹Department of Hematology and Oncology, Research Institute for Radiation Biology and Medicine, Hiroshima University, Hiroshima, Japan

¹²Department of Hematology, Panasonic Health Insurance Organization Matsushita Memorial Hospital, Osaka, Japan

¹³Department of Hematology, Japanese Red Cross Kyoto Daini Hospital, Kyoto, Japan

¹⁴Department of Hematology, Japanese Red Cross Kyoto Daiichi Hospital, Kyoto, Japan

¹⁵Department of Hematology, IMS Group Shinmatsudo Central General Hospital, Chiba, Japan

¹⁶Hematology Division, Tokyo Metropolitan Cancer and Infectious Diseases Center, Komagome Hospital, Tokyo, Japan

¹⁷Department of Hematology, Yokohama Minami Kyousai Hospital, Yokohama, Kanagawa Japan

¹⁸Department of Hematology, Nagano Red Cross Hospital, Nagano, Japan

¹⁹Department of Hematology, Chiba Aoba Municipal Hospital, Chiba, Japan

²⁰Hematology Department, St. Luke’s International Hospital, Tokyo, Japan

²¹Department of Hematology, Eiju General Hospital, Tokyo, Japan

²²Department of Immunology and Hematology, Kobe City Nishi-Kobe Medical Center, Kobe, Japan

²³Division of Hematology, Tokyo Metropolitan Ohtsuka Hospital, Tokyo, Japan

²⁴Department of Hematology, Fukuchiyama City Hospital, Fukuchiyama, Japan

²⁵Department of Hematology, Tokyo Metropolitan Tama-Hokubu Medical Center, Tokyo, Japan

^✉

Corresponding author.

PMCID: PMC13619693 PMID: 42410272

Abstract

The KMT2A gene is frequently altered in acute myeloid leukemia (AML). KMT2A abnormalities include partial tandem duplication (PTD), single-nucleotide variants (SNVs), and chromosomal rearrangements such as t(9;11)(p21.3;q23.3) and t(v;11q23.3). However, with the widespread adoption of next-generation sequencing (NGS), PTD has been assessed less frequently. We evaluated the clinical features and prognostic impact of PTD and other KMT2A abnormalities. We analyzed the KMT2A abnormalities and coexisting genetic alterations in 585 patients with de novo AML diagnosed across 25 institutions (1991–2020). Using bone marrow and/or peripheral blood samples, we identified PTD by targeted polymerase chain reaction-based assay, chromosomal rearrangements by karyotyping, and SNVs and other genetic alterations by targeted NGS. KMT2A abnormalities were detected in 18.3% of de novo AML, comprising PTD in 6.7%, SNVs in 5.1%, t(9;11)(p21.3;q23.3) in 2.2%, and t(v;11q23.3) in 4.8%; 0.5% had concurrent KMT2A abnormalities. Patients with PTD, t(9;11)(p21.3;q23.3), or t(v;11q23.3) had significantly shorter relapse-free survival (RFS) than the KMT2A wild-type group (hazard ratio [HR] 1.99, p = 0.048; HR 3.13, p = 0.009; HR 2.41, p = 0.006, respectively). Patients with PTD, t(9;11)(p21.3;q23.3), or t(v;11q23.3) had significantly shorter overall survival (OS) than the KMT2A wild-type group (HR 3.92, p < 0.001; HR 4.58, p < 0.001; HR 3.73, p < 0.001, respectively). Within the PTD group, older age (p = 0.030) and higher lactate dehydrogenase (p = 0.005) remained independent adverse prognostic factors. KMT2A-PTD is associated with inferior RFS and OS in de novo AML. Our findings support routine PTD testing at diagnosis and the incorporation of KMT2A-PTD into prognostic risk stratification and clinical decision-making.

Supplementary Information

The online version contains supplementary material available at https://doi.org/10.1007/s00277-026-07158-y.

Keywords: Acute myeloid leukemia (AML), KMT2A, MLL, Partial tandem duplication (PTD), Prognostic factors, Risk stratification

Introduction

Acute myeloid leukemia (AML) is a heterogeneous hematologic malignancy driven by diverse chromosomal and genetic abnormalities that stratify patients into favorable, intermediate, or adverse prognostic risk groups. The 2022 European LeukemiaNet (ELN) classification recommends molecular screening at diagnosis for mutations such as FLT3, NPM1, and CEBPA, and for recurrent gene rearrangements including RUNX1::RUNX1T1 [1]. Nevertheless, risk assessment remains incomplete when additional lesions that influence prognosis, treatment, and hematopoietic stem cell transplantation (HSCT) decisions are not systematically evaluated. Among these, abnormalities of lysine (K)-specific methyltransferase 2A (KMT2A), formerly mixed-lineage leukemia (MLL), are critically important. Recent development of menin inhibitors, including revumenib and ziftomenib, which block the menin–KMT2A interaction, further highlights comprehensive KMT2A assessment [2, 3].

The KMT2A gene is located on chromosome 11q23.3 and encodes an approximately 500-kDa protein comprising 3,969 amino acids with several functional domains [4–6]. The N-terminus contains DNA-binding elements, including the menin-binding motif and AT-hook domains, involved in hematopoiesis [7]. The C-terminus contains transcriptional activation and SET domains, the latter catalyzing histone 3 lysine 4 methylation and thereby regulating HOX gene expression [8]. After taspase-1 cleavage, the KMT2A-N fragment may fuse with more than 120 partner genes, generating KMT2A rearrangements, most of which are adverse prognostic factors in AML [1, 5, 6, 9]. The 2022 ELN classification therefore assigns t(v;11q23.3) to the adverse-risk group [1]. In contrast, t(9;11)(p21.3;q23.3), known as KMT2A::MLLT3, which accounts for 20–30% of KMT2A-rearranged AML, is classified as intermediate risk [1, 10].

Another distinct KMT2A abnormality is partial tandem duplication (PTD), an in-frame duplication involving the N-terminal AT-hook region and CXXC domain (Fig. 1).

Fig. 1 — Schematic representation of *KMT2A* wild-type, PTD, rearrangements, and SNVs. Abbreviations for domains and motifs: MBM, menin-binding motif; AT, AT-hook DNA-binding motifs; SNL, speckled nuclear localization domains; CXXC, CXXC-type zinc-finger DNA-binding domain; PHD, plant homeodomain fingers; BRD, bromodomain; FYRN, phenylalanine/tyrosine-rich N-terminal domain; FYRC, phenylalanine/tyrosine-rich C-terminal domain; Win, WDR5-interacting motif; SET, Su(var)3–9, Enhancer-of-zeste, Trithorax methyltransferase domain. Other abbreviations: PTD, partial tandem duplication; SNVs, single-nucleotide variants

Unlike KMT2A rearrangements, PTD preserves full-length KMT2A, including the transcriptional activation and SET domains [11, 12]. PTD occurs in approximately 5–11% of AML, predominantly in adults, and often coexists with FLT3-internal tandem duplication (ITD) or TP53 mutations [10, 13–15]. Although previously reported as an independent adverse prognostic factor, KMT2A-PTD is not included in the 2022 ELN risk classification [1, 14, 15]. This omission may partly reflect the technical limitations of short-read next-generation sequencing (NGS), which often fails to detect PTD, excluding it from targeted sequencing panels. PTD has expression signatures similar to KMT2A rearrangements, and preclinical studies have shown that menin inhibitors may also be effective for PTD-positive AML. This diagnostic blind spot is therefore consequential in the current era, as accurate recognition of PTD may influence both risk classification and eligibility for targeted strategies. We therefore evaluated the prognostic significance of PTD.

Expanded NGS has enabled detection of KMT2A single-nucleotide variants (SNVs) in several malignancies, including high-grade B-cell lymphoma, T-cell lymphoblastic leukemia, and AML [4, 16]. Their prognostic significance in AML, however, remains unclear. We therefore comprehensively analyzed KMT2A-PTD, SNVs, and rearrangements, to evaluate their clinical characteristics and prognostic impact in de novo AML in the NGS era.

Materials and methods

Study design and participants

We conducted a retrospective, multicenter study of patients with de novo AML diagnosed and treated at Nippon Medical School and 24 affiliated or partner institutions between 1991 and 2020. Diagnostic bone marrow and/or peripheral blood samples were used.

De novo AML was defined as AML without prior myeloid neoplasm or cytotoxic chemotherapy/radiation exposure. Therapy-related AML, AML arising from myelodysplastic syndrome/myeloproliferative neoplasm, mixed-phenotype acute leukemia, and relapsed AML were excluded [1]. Overall, 585 consecutive eligible patients with available diagnostic material underwent cytogenetic and/or molecular testing and were included in at least one analysis set.

The study was performed in accordance with the Declaration of Helsinki and was approved by the institutional ethics committee and, where applicable, participating centers. Written informed consent was obtained when available. For other patients, additional written informed consent was waived because of the retrospective design, with opt-out applied when appropriate.

Cytogenetic testing

Conventional G-banded karyotyping identified numerical and structural chromosomal abnormalities. Fluorescence in situ hybridization for RUNX1::RUNX1T1, PML::RARA, CBFB::MYH11, and KMT2A rearrangements was performed when clinically indicated. Cytogenetic risk was assigned according to the 2022 ELN classification [1].

Molecular testing

Targeted NGS for KMT2A SNVs and variants in other genes was performed using the Ion PGM™ platform (Thermo Fisher Scientific, Waltham, MA, USA). Oligonucleotide libraries were generated by multiplex polymerase chain reaction (PCR) using custom-designed primers targeting exons of KMT2A, TET2, DNMT3A, ASXL1, RUNX1, KIT, TP53, PTPN11, GATA2, WT1, STAG2, RAD21, SMC1A, SMC3, BCOR, BCORL1, EZH2, NF1, DDX41, and PHF6. Libraries were clonally amplified on Ion Sphere Particles by emulsion PCR and sequenced on the Ion PGM™ platform. Detected variants were annotated using the National Center for Biotechnology Information and Catalogue of Somatic Mutations in Cancer databases to distinguish polymorphisms from pathogenic variants. Newly identified variants of potential significance were confirmed by Sanger sequencing of remission-phase DNA. Other genes located at known hotspots (FLT3-ITD, FLT3-TKD, NPM1, IDH1, IDH2, NRAS, and KRAS) and CEBPA, for which primer design for emulsion-based sequencing was difficult, were analyzed using previously reported methods [17].

KMT2A-PTD, not analyzable by the Ion PGM™ platform, was assessed with a PCR-based assay using designed primers. Because many PTDs join exons 2–6 or 2–8 to intron 1 near exon 2, a forward primer (5’-GTCCAGAGCAGAGCAAACAG-3’) targeting exon 7 and a reverse primer (5’-GATGAAGAAGTCAGAGTGCG-3’) targeting exon 2 were used to amplify the PTD-specific junction and determine the PTD insertion size [18, 19]. A reverse primer targeting intron 8 served as an internal control. PCR products were separated by agarose gel electrophoresis and visualized by nucleic acid staining. A representative KMT2A-PTD gel electrophoresis, including controls and patient samples, is provided in Online Resource 1 (Supplementary Fig. S1).

Statistical analyses

Categorical variables were compared using Pearson’s chi-square test or Fisher’s exact test, as appropriate. When omnibus testing yielded a two-sided p-value < 0.05, post hoc residual analysis with adjusted standardized residuals was used to identify cells that were overrepresented or underrepresented relative to expected counts. Adjusted standardized residuals were interpreted as cell-level deviations from the overall distribution, not pairwise comparisons. For baseline variables in Tables 1, 2, and 3, all 10 group pairs were assessed, and Bonferroni-adjusted p-values are provided in Online Resource 1. Continuous variables were compared after assessment of normality by the Shapiro-Wilk test and homogeneity of variance by Levene’s test. Omnibus tests first assessed whether each continuous variable differed across the five KMT2A groups. When both assumptions were satisfied, a one-way analysis of variance (ANOVA) was performed, followed by Tukey’s post hoc test. If the assumption of normality was violated, the Kruskal–Wallis test was used, followed by the Dunn post hoc test with Bonferroni adjustment. If the assumption of homogeneity of variances was violated, Welch’s ANOVA was used, followed by the Games–Howell post hoc test.

Table 1.

Clinical characteristics according to KMT2A abnormalities

	t(9;11)(p21.3;q23.3) (n = 12)	t(v;11q23.3) (n = 27)	PTD (n = 38)	SNVs (n = 27)	Wild-type (n = 478)	p-value
Sex (%)						0.888
Male	58.3	59.3	63.2	51.9	55.6
Female	41.7	40.7	36.8	48.1	44.4
Age	49.4 ± 18.0	50.0 ± 15.0	51.1 ± 15.6	51.7 ± 16.3	54.4 ± 16.4	0.280
WBC (×10⁹/µL)	64.4 ± 74.1	67.0 ± 83.9	60.2 ± 95.8	46.4 ± 73.0	53.2 ± 84.8	0.711
PB Blasts (%)	56.3 ± 33.0	49.9 ± 39.6	50.8 ± 29.7	49.4 ± 34.4	49.8 ± 32.7	0.967
BM Blasts (%)	76.5 ± 20.0	78.5 ± 18.1	67.7 ± 19.0	65.1 ± 24.0	66.2 ± 23.0	0.079
Hb (g/dL)	9.8 ± 3.4	7.8 ± 2.2	8.5 ± 2.1	9.0 ± 2.6	8.6 ± 2.4	0.171
Platelets (×10⁴/µL)	6.7 ± 4.4	5.9 ± 5.2	9.3 ± 6.3	5.3 ± 4.9	6.9 ± 7.0	0.044
LDH (U/L)	1878 ± 2044	719 ± 615	964 ± 949	575 ± 553	730 ± 753	0.085
CNS lesions (%)	0	0	0	5.9	1.4	0.602
Extramedullary lesions (%)	0	26.3	9.5	11.8	9.1	0.139
HSCT (%)	54.5	66.7	62.5	41.7	41.4	0.024
FAB subtype (%)
M0	0	8.0	5.3	3.7	6.6	0.975
M1	0	20.0	31.6	22.2	21.5	0.254
M2	9.1	12.0	39.5	51.9	42.1	0.003
M3	0	0	0	0	0	1.000
M4	18.2	12.0	10.5	14.8	15.6	0.923
M5	72.7	44.0	10.5	3.7	11.7	< 0.0001
M6	0	4.0	2.6	3.7	2.1	0.533
M7	0	0	0	0	0.4	1.000

Open in a new tab

Continuous variables are presented as mean ± SD. Abbreviations: PTD, partial tandem duplication; SNVs, single-nucleotide variants; WBC, white blood cell; PB, peripheral blood; BM, bone marrow; Hb, hemoglobin; LDH, lactate dehydrogenase; CNS, central nervous system; HSCT, hematopoietic stem cell transplantation; FAB, French–American–British classification

Table 2.

Cytogenetics according to KMT2A abnormalities

	t(9;11)(p21.3;q23.3) (n = 12)	t(v;11q23.3) (n = 27)	PTD (n = 38)	SNVs (n = 27)	Wild-type (n = 478)	p-value
Normal karyotype (%)	0	0	60.5	57.7	53.4	< 0.0001
Complex karyotype (%)	8.3	33.3	18.4	11.5	10.3	0.011
Monosomal karyotype (%)	0	22.2	5.3	0	5.5	0.022
t(8;21) (%)	0	0	2.6	23.1	12.8	0.014
Monosomy 5 (%)	0	0	0	0	0.4	1.000
Monosomy 7 (%)	0	14.8	2.6	0	3.4	0.079
Trisomy 8 (%)	41.7	3.7	5.3	7.7	5.9	0.004
Trisomy 11 (%)	0	0	10.3	0	1.0	0.017
Trisomy 13 (%)	8.3	0	7.9	0	1.0	0.020
Trisomy 21 (%)	0	7.4	5.3	3.9	2.7	0.302
Trisomy 22 (%)	0	3.7	2.6	3.8	0.6	0.075
Cytogenetics (%)
Favorable	0	0	2.6	19.2	14.1	0.016
Intermediate	91.7	0	79.0	69.2	72.8	< 0.0001
Adverse	8.3	100	18.4	11.6	13.1	< 0.0001

Open in a new tab

Abbreviations: PTD, partial tandem duplication; SNVs, single-nucleotide variants

Table 3.

Genetics according to KMT2A abnormalities

	t(9;11)(p21.3;q23.3) (n = 12)	t(v;11q23.3) (n = 27)	PTD (n = 38)	SNVs (n = 27)	Wild-type (n = 478)	p-value
FLT3-ITD (%)	0	0	44.7	14.8	24.2	< 0.0001
FLT3-TKD (%)	9.1	5.0	0	3.7	4.0	0.429
NPM1 (%)	0	0	2.6	25.9	32.3	< 0.0001
CEBPA (%)	0	0	10.5	22.2	16.0	0.073
DNMT3A (%)	0	5.0	25.0	18.5	19.1	0.207
ASXL1 (%)	9.1	5.0	0	0	2.9	0.371
BCOR (%)	0	0	2.8	3.7	3.6	1.000
EZH2 (%)	0	0	2.8	0	1.7	0.804
GATA2 (%)	0	15.0	2.8	0	4.4	0.213
IDH1 (%)	0	0	11.1	0	5.2	0.314
IDH2 (%)	0	0	5.6	14.8	9.9	0.381
KIT (%)	0	10.0	5.6	11.1	6.9	0.733
KRAS (%)	9.1	15.0	2.8	3.7	3.1	0.066
NRAS (%)	9.1	5.0	0	7.4	12.2	0.128
NF1 (%)	0	0	0	7.4	4.0	0.575
RUNX1 (%)	0	5.0	11.1	0	7.6	0.502
TET2 (%)	0	10.0	22.2	11.1	20.9	0.266
TP53 (%)	0	10.0	5.6	3.7	3.4	0.323
WT1 (%)	0	10.0	13.9	3.7	5.2	0.174
STAG2 (%)	0	5.0	16.7	0	1.3	< 0.001

Open in a new tab

Abbreviations: PTD, partial tandem duplication; SNVs, single-nucleotide variants

Relapse-free survival (RFS) was defined from first complete remission to relapse or death. Overall survival (OS) was defined from diagnosis to death. Event-free patients were censored at last follow-up. RFS and OS were estimated using the Kaplan-Meier method and compared with the log-rank test. Median follow-up was estimated using the reverse Kaplan-Meier method. Pairwise hazard ratios (HR) were estimated using Bonferroni-adjusted Cox models.

Candidate covariates for multivariable Cox regression were prespecified clinical, cytogenetic, and molecular variables associated with OS at p < 0.20 in univariable analyses. White blood cell (WBC) and lactate dehydrogenase (LDH) were log2-transformed; age was modeled per 10-year increase. Results are reported as HR with 95% confidence intervals (CI) and two-sided p-values. Two-sided p < 0.05 was considered significant. Statistical analyses were performed using R software version 4.4.3.

Results

Among 585 patients with de novo AML, 107 (18.3%) had KMT2A abnormalities: t(9;11)(p21.3;q23.3) in 13 (2.2%), t(v;11q23.3) in 28 (4.8%), PTD in 39 (6.7%), and SNVs in 30 (5.1%). Three patients (0.5%) had concurrent KMT2A abnormalities and were excluded from subsequent analyses to maintain mutually exclusive groups.

Clinical characteristics

Clinical characteristics are summarized in Table 1, and Bonferroni-adjusted pairwise comparisons are provided in Online Resource 1, Supplementary Table S1. Platelet counts differed among the five groups (p = 0.044), although no Bonferroni-adjusted pairwise comparison was significant. Allogeneic HSCT use also differed (p = 0.024), with overrepresentation by residual analysis in the t(v;11q23.3) group (adjusted standardized residual + 2.27, p < 0.05) and PTD group (+ 2.16, p < 0.05), although no Bonferroni-adjusted pairwise comparison was significant. FAB M2 differed (p = 0.003), with underrepresentation in t(9;11)(p21.3;q23.3) (-2.14, p < 0.05) and t(v;11q23.3) (-2.96, p < 0.01). FAB M5 also differed (p < 0.0001), with overrepresentation in t(9;11)(p21.3;q23.3) (+ 5.71, p < 0.001) and t(v;11q23.3) (+ 4.47, p < 0.001), and underrepresentation in KMT2A wild-type (-3.18, p < 0.01). A stacked bar plot of FAB subtype distribution is provided in Online Resource 1 (Supplementary Fig. S2), illustrating that M1/M2 accounted for 71.1% of PTD, whereas M5 accounted for 72.7% and 44.0% of t(9;11)(p21.3;q23.3) and t(v;11q23.3), respectively.

Cytogenetics

Cytogenetic findings are summarized in Table 2, with Bonferroni-adjusted pairwise comparisons in Online Resource 1, Supplementary Table S2. Normal karyotype differed across groups (p < 0.0001). Complex karyotype and monosomal karyotype differed (p = 0.011 and p = 0.022, respectively), both showing overrepresentation in t(v;11q23.3) group by residual analysis (+ 3.51 and + 3.71, respectively; both p < 0.001). Trisomy 8 differed (p = 0.004) and was overrepresented in t(9;11)(p21.3;q23.3) (+ 4.97, p < 0.001). Trisomy 11 and trisomy 13 differed (p = 0.017 and p = 0.020, respectively) and were overrepresented in PTD (+ 4.63, p < 0.001; +3.27, p < 0.01). In Bonferroni-adjusted pairwise comparisons, PTD versus KMT2A wild-type was significant for trisomy 11 but not for trisomy 13. The frequency of t(8;21) differed (p = 0.014), although post hoc residual analysis was not significant (all p > 0.05).

Genetics

Genetic findings are summarized in Table 3, with pairwise comparisons in Online Resource 1, Supplementary Table S3. FLT3-ITD differed across groups (p < 0.0001), showing underrepresentation in t(v;11q23.3) (-2.72, p < 0.01) and overrepresentation in PTD (+ 3.17, p < 0.01). NPM1 mutation differed (p < 0.0001), with underrepresentation in t(9;11)(p21.3;q23.3), t(v;11q23.3), and PTD (-2.19, p < 0.05; -3.06, p < 0.01; and − 3.61, p < 0.001, respectively), and overrepresentation in KMT2A wild-type (+ 4.91, p < 0.001). STAG2 mutation differed (p < 0.001), with overrepresentation in PTD (+ 5.98, p < 0.001) and underrepresentation in KMT2A wild-type (-3.68, p < 0.001).

Relapse-free survival (RFS)

RFS differed significantly across the five groups (log-rank χ²=26.1, degrees of freedom [df] = 4, p < 0.001; Fig. 2).

Median follow-up for RFS was 4.6 years (95% CI 3.8–5.1). Median RFS (years) was 2.4 (95% CI 1.4–6.5) in KMT2A wild-type, 0.3 (0.2–not reached [NR]) in t(9;11)(p21.3;q23.3), 0.6 (0.4–2.9) in t(v;11q23.3), 0.7 (0.4–3.1) in PTD, and 1.3 (0.7–NR) in SNVs. Three-year RFS rates were 48.4% (95% CI 42.8–54.6) in KMT2A wild-type, 11.1% (1.8–70.5) in t(9;11)(p21.3;q23.3), 15.1% (4.7–48.3) in t(v;11q23.3), 26.5% (13.4–52.6) in PTD, and 43.5% (26.7–70.8) in SNVs.

Compared with KMT2A wild-type, RFS was inferior in t(9;11)(p21.3;q23.3) (HR 3.13, 95% CI 1.54–6.39, p = 0.0009), t(v;11q23.3) (HR 2.41, 95% CI 1.44–4.04, p = 0.0006), and PTD (HR 1.99, 95% CI 1.22–3.25, p = 0.0048). The SNVs group did not differ from KMT2A wild-type (HR 1.12, 95% CI 0.62–2.02, p = 0.701). After Bonferroni correction for all 10 pairwise comparisons, t(v;11q23.3) (p = 0.006), t(9;11)(p21.3;q23.3) (p = 0.009), and PTD (p = 0.048) remained significantly different from KMT2A wild-type. Events were 157/323 in KMT2A wild-type, 8/10 in t(9;11)(p21.3;q23.3), 16/21 in t(v;11q23.3), 18/25 in PTD, and 12/24 in SNVs.

Overall survival (OS)

OS also differed significantly across the five groups (log-rank χ²=73.8, df = 4, p < 0.001; Fig. 3).

Median follow-up for OS was 3.9 years (95% CI 3.6–4.5). Median OS (years) was NR in KMT2A wild-type, 1.2 (0.7–NR) in t(9;11)(p21.3;q23.3), 1.4 (1.0–5.0) in t(v;11q23.3), 1.1 (0.9–2.8) in PTD, and 3.2 (1.0–NR) in SNVs. Five-year OS rates were 66.0% (95% CI 60.5–72.1) in KMT2A wild-type, 20.2% (5.9–69.6) in t(9;11)(p21.3;q23.3), 23.6% (10.3–54.1) in t(v;11q23.3), 20.1% (9.7–41.3) in PTD, and 46.1% (30.1–70.7) in SNVs.

Compared with KMT2A wild-type, OS was inferior in t(9;11)(p21.3;q23.3) (HR 4.58, 95% CI 2.22–9.47, p < 0.001), t(v;11q23.3) (HR 3.73, 95% CI 2.27–6.12, p < 0.001), PTD (HR 3.92, 95% CI 2.55–6.02, p < 0.001), and SNVs (HR 2.04, 95% CI 1.16–3.58, p = 0.0109). After Bonferroni correction for all 10 pairwise comparisons, t(9;11)(p21.3;q23.3), t(v;11q23.3), and PTD remained significantly different from KMT2A wild-type (all p < 0.001), whereas SNVs did not (p = 0.109). Deaths were 95/366 in KMT2A wild-type, 8/11 in t(9;11)(p21.3;q23.3), 19/27 in t(v;11q23.3), 27/37 in PTD, and 14/30 in SNVs.

Multivariable analyses

We performed multivariable Cox proportional hazards analyses for OS in the t(v;11q23.3) and PTD groups, using prespecified covariates selected from univariable screening (p < 0.20) for clinical characteristics, cytogenetics, and genetics. The t(9;11)(p21.3;q23.3) group had too few events for reliable multivariable modeling; therefore, only univariable results are provided in Online Resource 1, Supplementary Table S4. In this group, higher LDH was associated with inferior OS (HR 1.84 per twofold increase, 95% CI 0.98–3.45, p = 0.044), whereas other covariates were not significant.

In the t(v;11q23.3) group, based on univariable Cox regression analyses (Online Resource 1, Supplementary Table S5), we identified age, complex karyotype, and LDH as prespecified clinically relevant covariates for the multivariable Cox model. Because LDH violated the proportional hazards assumption, it was modeled with a time-varying coefficient. Age remained an independent adverse factor (HR 4.69 per 10-year increase, 95% CI 2.01–10.94, p < 0.001), whereas complex karyotype did not (HR 1.96, 95% CI 0.56–6.88, p = 0.291). The time-varying effect of LDH was significant (p = 0.004). The HR per twofold increase in LDH was 6.90 (95% CI 1.13–41.99) at day 30, 1.56 (0.71–3.40) at day 180, and 0.86 (0.40–1.84) at day 365, indicating that the adverse association of higher LDH was strongest early and attenuated over time.

In the PTD group, based on univariable Cox regression analyses (Online Resource 1, Supplementary Table S6), we identified age, complex karyotype, LDH, and FLT3-ITD as prespecified clinically relevant covariates for the multivariable Cox model. The proportional hazards assumption was satisfied. Age (HR 1.37 per 10-year increase, 95% CI 1.03–1.82, p = 0.030) and LDH (HR 1.95 per twofold increase, 95% CI 1.23–3.10, p = 0.005) remained independent adverse factors. FLT3-ITD was not significant (HR 0.90, 95% CI 0.31–2.67, p = 0.852). Complex karyotype showed a nonsignificant trend toward inferior OS (HR 2.82, 95% CI 0.89–8.96, p = 0.078).

Additional and sensitivity analyses

We performed two sensitivity analyses to assess the robustness of our findings and two additional PTD-restricted subgroup analyses to address the prognostic impact of coexisting FLT3-ITD and DNMT3A alterations. First, because allogeneic HSCT use differed among KMT2A subgroups (Table 1) and could influence OS, follow-up was censored at allogeneic HSCT. In this HSCT-censored analysis (n = 471; deaths = 94), OS still differed among the five KMT2A subgroups (log-rank χ²=51.1, df = 4; p < 0.001; Fig. 4).

Fig. 4 — HSCT-censored overall survival by *KMT2A* abnormalities

Median HSCT-censored OS was not reached in KMT2A wild-type, 1.02 years (95% CI 0.95–NE) in PTD, 2.40 years (0.89–NE) in SNVs, 0.72 years (0.46–NE) in t(9;11)(p21.3;q23.3), and 1.45 years (0.82–NE) in t(v;11q23.3). Compared with KMT2A wild-type, HSCT-censored OS remained inferior in PTD (HR 4.47; 95% CI 2.51–7.95; p < 0.001), t(v;11q23.3) (HR 4.45; 95% CI 2.19–9.05; p < 0.001), and t(9;11)(p21.3;q23.3) (HR 6.88; 95% CI 2.07–22.86; p = 0.002). The SNVs group showed inferior HSCT-censored OS in unadjusted models (HR 2.51, 95% CI 1.28–4.93, p = 0.007), but not after Bonferroni correction (adjusted p = 0.072).

Second, because FLT3-ITD was overrepresented in PTD (Table 3) and has established prognostic relevance, we performed a sensitivity analysis restricted to FLT3-ITD–negative cases to evaluate whether the adverse association of PTD with OS persisted independent of FLT3-ITD. OS remained significantly different among KMT2A subgroups (log-rank χ²=54.7, df = 4; p < 0.001; Fig. 5).

Fig. 5 — Overall survival by *KMT2A* abnormalities in *FLT3*-ITD-negative patients

Compared with KMT2A wild-type, OS was still inferior in PTD (HR 3.53, 95% CI 1.99–6.27, p < 0.001), t(9;11)(p21.3;q23.3) (HR 5.14, 95% CI 2.46–10.75, p < 0.001) and t(v;11q23.3) (HR 4.03, 95% CI 2.30–7.07, p < 0.001), whereas SNVs were not significantly different (HR 1.70, 95% CI 0.88–3.30, p = 0.117).

Third, we examined whether coexisting FLT3-ITD further stratified OS within PTD-positive AML. OS did not differ significantly between FLT3-ITD-positive and FLT3-ITD-negative patients (log-rank χ²=1.46, df = 1, p = 0.227; Fig. 6). FLT3-ITD-positive patients had a numerically higher risk of death than FLT3-ITD-negative patients, but the difference was not significant (HR 1.62, 95% CI 0.74–3.54, p = 0.231).

Fig. 6 — Overall survival in PTD-positive patients according to *FLT3*-ITD status

Finally, because DNMT3A showed a numerically elevated HR in univariable analysis within the PTD group, we analyzed OS according to DNMT3A status among PTD-positive patients. DNMT3A-positive patients showed a trend toward inferior OS compared with DNMT3A-negative patients, but the difference did not reach statistical significance (log-rank χ²=3.56, df = 1, p = 0.059; Fig. 7). Compared with DNMT3A-negative patients, DNMT3A-positive patients had a numerically higher risk of death, although this association did not reach significance (HR 2.28; 95% CI 0.95–5.46; p = 0.066).

Fig. 7 — Overall survival in PTD-positive patients according to *DNMT3A* status

Discussion

In this multicenter cohort of de novo AML, we evaluated the clinical, cytogenetic, molecular, and prognostic impact of four KMT2A abnormalities: t(9;11)(p21.3;q23.3), t(v;11q23.3), KMT2A-PTD, and KMT2A SNVs. Three principal findings emerged. First, KMT2A-PTD showed a distinct genomic background, with overrepresentation of trisomy 11, trisomy 13, FLT3-ITD, and STAG2 mutations, along with underrepresentation of NPM1 mutation. Second, KMT2A-PTD was associated with inferior RFS and OS compared with KMT2A wild-type, and this association remained robust after censoring at HSCT and after restriction to FLT3-ITD-negative patients. Third, both translocation groups showed consistently inferior RFS and OS; notably, t(9;11)(p21.3;q23.3), currently categorized as intermediate risk by the 2022 ELN classification, behaved as an adverse factor in our cohort.

Overrepresentation in residual analysis of trisomy 11, trisomy 13, FLT3-ITD, and STAG2 mutation, together with the low frequency of NPM1 mutation in the KMT2A-PTD group, delineates a genetic background distinct from that of KMT2A rearrangements. This non-overlapping pattern is consistent with distinct leukemogenic mechanisms: preserved full-length KMT2A with tandem duplication versus chimeric fusion proteins. Importantly, the adverse association of PTD persisted after adjustment for baseline covariates. Within the PTD group, older age and higher LDH were independently associated with inferior OS, whereas FLT3-ITD was not significant in the multivariable model. In addition, PTD remained associated with inferior OS even in FLT3-ITD–negative patients. These findings indicate that the prognostic impact of PTD is not explained solely by concomitant FLT3-ITD and may reflect intrinsic high-risk biology.

These findings have direct diagnostic and therapeutic implications. Because short-read NGS may miss PTD, routine PTD assessment using PCR-based assays, long-read DNA sequencing, or targeted RNA approaches should be considered at diagnosis, given (i) its robust association with inferior RFS and OS and (ii) the expanding therapeutic landscape for KMT2A-driven leukemias. Accurate identification of PTD may therefore refine risk stratification and may become increasingly relevant as menin-directed therapies expand.

The greater impairment in OS than in RFS in the PTD group may also be clinically meaningful. This pattern could arise from, first, excess early mortality during induction therapy (i.e., deaths before achieving complete remission), given that RFS was defined from the first complete remission and therefore does not capture pre-remission deaths. Second, even after complete remission, the OS disadvantage could reflect poorer survival after relapse, because RFS records time to the first event (relapse or death) and does not capture survival time from relapse to death. Accordingly, the observed pattern may reflect a combination of higher pre-remission mortality and poorer survival after relapse, rather than relapse risk alone. In particular, because PTD is not explicitly included in the 2022 ELN risk classification, clinicians may underestimate its prognostic impact. Greater recognition of PTD as a high-risk feature could support more timely and intensive post-relapse strategies, including effective salvage regimens and allogeneic HSCT, which may help improve OS in PTD.

Finally, the poor outcome of t(v;11q23.3) was consistent with prior evidence supporting adverse risk for most KMT2A rearrangements. More unexpectedly, t(9;11)(p21.3;q23.3) was also associated with inferior OS, despite its current intermediate-risk designation. Differences in age distribution, HSCT use, and small sample size may partly explain variability across studies. Even so, our findings highlight that nominally intermediate-risk t(9;11)(p21.3;q23.3) may carry substantial risk in adult practice, warranting careful clinical appraisal and consideration of intensified or targeted approaches. Indeed, some previous studies have also identified t(9;11)(p21.3;q23.3) as an adverse prognostic factor [20, 21], and further investigation is warranted to clarify its prognostic impact.

This study has several strengths, including a large, multi-institutional design, integrated cytogenetic and molecular profiling, and systematic assessment of multiple KMT2A abnormalities within the same framework. However, limitations warrant caution. First, potential confounding by indication (e.g., selection for allogeneic HSCT) cannot be excluded in this retrospective design. Although HSCT-censored sensitivity analysis supported the robustness of the main findings, transplantation was not modeled as a time-dependent treatment effect, and residual confounding remains possible. Second, generalizability beyond a predominantly Japanese cohort requires external validation. Future studies should prospectively standardize PTD detection at diagnosis, explore its utility as a measurable residual disease marker, and evaluate menin inhibitors, which disrupt the menin–KMT2A interaction, in PTD-positive AML.

Conclusion

KMT2A-PTD is associated with inferior RFS and OS in de novo AML. These findings support routine PTD testing at diagnosis and the incorporation of KMT2A-PTD into prognostic risk stratification and clinical decision-making.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary Material 1^{(769.1KB, pdf)}

Acknowledgements

We would like to thank the clinicians and acknowledge the contributions of all patients who provided samples.

Abbreviations

KMT2A: Lysine (K)-specific methyltransferase 2A
AML: Acute myeloid leukemia
NGS: Next-generation sequencing
PTD: Partial tandem duplication
SNVs: Single-nucleotide variants
WT: Wild-type

Author contributions

T.T. and S.W. were the principal investigators and took primary responsibility for the paper. S.W. and H.Y. designed the study and supervised the experiments. T.T., T.K., and M.Y. performed the laboratory work. T.T., S.W., and A.M. curated the data. T.T. performed the statistical analyses and visualization. T.T. wrote the manuscript. T.T., S.W., and H.Y. reviewed and edited the manuscript. Patient recruitment and clinical data collection were performed by T.T., S.W., K.F., Y.N., S.Y., H.Y., D.M., S.K., S.K., S.O., T.T., J.K., A.S., H.T., M.O., K.T., J.A., T.E., T.I., Y.K., N.U., E.K., H.U., K.M., N.D., Y.N., K.N., T.U., K.S., S.M., M.H., A.H., H.M., D.N., S.M., and Y.K. All authors reviewed and approved the manuscript.

Funding

This research received no external funding.

Data availability

The datasets generated and/or analyzed during the current study are available from the corresponding author upon reasonable request. Public sharing of the datasets is restricted because they contain potentially sensitive clinical and genetic information.

Declarations

Ethics statement

Approval of the research protocol by an Institutional Review Board: This study was reviewed and approved by the Human Subjects Institutional Review Board of the Nippon Medical School (Tokyo, Japan).

Informed consent

Informed consent was obtained when available, including from patients enrolled in institutional protocols. For other patients, additional written informed consent was not required under the approved retrospective study protocol, and an opt-out approach was used where applicable.

Registry and the registration no. of the study/trial

N/A.

Animal studies

N/A.

Competing interests

The authors declare no competing interests.

Footnotes

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Contributor Information

Taichiro Tokura, Email: t-tokura@nms.ac.jp.

Satoshi Wakita, Email: aaa11140@nms.ac.jp.

References

1.Döhner H, Wei AH, Appelbaum FR, Craddock C, DiNardo CD, Dombret H et al (2022) Diagnosis and management of AML in adults: 2022 recommendations from an international expert panel on behalf of the ELN. Blood 140:1345–1377. 10.1182/blood.2022016867 [DOI] [PubMed] [Google Scholar]
2.Issa GC, Aldoss I, DiPersio J, Cuglievan B, Stone R, Arellano M et al (2023) The menin inhibitor revumenib in KMT2A-rearranged or NPM1-mutant leukaemia. Nature 615:920–924. 10.1038/s41586-023-05812-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
3.Candoni A, Coppola G (2024) A 2024 update on menin inhibitors: a new class of target agents against KMT2A-rearranged and NPM1-mutated acute myeloid leukemia. Hematol Rep 16:244–254. 10.3390/hematolrep16020024 [DOI] [PMC free article] [PubMed] [Google Scholar]
4.Castiglioni S, Di Fede E, Bernardelli C, Lettieri A, Parodi C, Grazioli P et al (2022) KMT2A: umbrella gene for multiple diseases. Genes (Basel) 13:514. 10.3390/genes13030514 [DOI] [PMC free article] [PubMed] [Google Scholar]
5.Milan T, Celton M, Lagacé K, Roques É, Safa-Tahar-Henni S, Bresson E et al (2022) Epigenetic changes in human model KMT2A leukemias highlight early events during leukemogenesis. Haematologica 107:86–99. 10.3324/haematol.2020.271619 [DOI] [PMC free article] [PubMed] [Google Scholar]
6.Hsieh JJ, Ernst P, Erdjument-Bromage H, Tempst P, Korsmeyer SJ (2003) Proteolytic cleavage of MLL generates a complex of N- and C-terminal fragments that confers protein stability and subnuclear localization. Mol Cell Biol 23:186–194. 10.1128/MCB.23.1.186-194.2003 [DOI] [PMC free article] [PubMed] [Google Scholar]
7.Caslini C, Shilatifard A, Yang L, Hess JL (2000) The amino terminus of the mixed lineage leukemia protein (MLL) promotes cell cycle arrest and monocytic differentiation. Proc Natl Acad Sci U S A 97:2797–2802. 10.1073/pnas.040574897 [DOI] [PMC free article] [PubMed] [Google Scholar]
8.Milne TA, Briggs SD, Brock HW, Martin ME, Gibbs D, Allis CD et al (2002) MLL targets SET domain methyltransferase activity to Hox gene promoters. Mol Cell 10:1107–1117. 10.1016/S1097-2765(02)00741-4 [DOI] [PubMed] [Google Scholar]
9.Winters AC, Bernt KM (2017) MLL-rearranged leukemias-an update on science and clinical approaches. Front Pediatr 5:4. 10.3389/fped.2017.00004 [DOI] [PMC free article] [PubMed] [Google Scholar]
10.Meyer C, Larghero P, Almeida Lopes B, Burmeister T, Gröger D, Sutton R et al (2023) The KMT2A recombinome of acute leukemias in 2023. Leukemia 37:988–1005. 10.1038/s41375-023-01877-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
11.Whitman SP, Liu S, Vukosavljevic T, Rush LJ, Yu L, Liu C et al (2005) The MLL partial tandem duplication: evidence for recessive gain-of-function in acute myeloid leukemia identifies a novel patient subgroup for molecular-targeted therapy. Blood 106:345–352. 10.1182/blood-2005-01-0204 [DOI] [PMC free article] [PubMed] [Google Scholar]
12.Yip BH, So CW (2013) Mixed lineage leukemia protein in normal and leukemic stem cells. Exp Biol Med (Maywood) 238:315–323. 10.1177/1535370213480717 [DOI] [PubMed] [Google Scholar]
13.Döhner K, Döhner H (2008) Molecular characterization of acute myeloid leukemia. Haematologica 93:976–982. 10.3324/haematol.13345 [DOI] [PubMed] [Google Scholar]
14.Patel JP, Gönen M, Figueroa ME, Fernandez H, Sun Z, Racevskis J et al (2012) Prognostic relevance of integrated genetic profiling in acute myeloid leukemia. N Engl J Med 366:1079–1089. 10.1056/NEJMoa1112304 [DOI] [PMC free article] [PubMed] [Google Scholar]
15.Kihara R, Nagata Y, Kiyoi H, Kato T, Yamamoto E, Suzuki K et al (2014) Comprehensive analysis of genetic alterations and their prognostic impacts in adult acute myeloid leukemia patients. Leukemia 28:1586–1595. 10.1038/leu.2014.55 [DOI] [PubMed] [Google Scholar]
16.AACR Project GENIE Consortium (2017) AACR Project GENIE: powering precision medicine through an international consortium. Cancer Discov 7:818–831. 10.1158/2159-8290.CD-17-0151 [DOI] [PMC free article] [PubMed] [Google Scholar]
17.Wakita S, Yamaguchi H, Omori I, Terada K, Ueda T, Manabe E et al (2013) Mutations of the epigenetics-modifying gene (DNMT3a, TET2, IDH1/2) at diagnosis may induce FLT3-ITD at relapse in de novo acute myeloid leukemia. Leukemia 27:1044–1052. 10.1038/leu.2012.317 [DOI] [PubMed] [Google Scholar]
18.Döhner K, Tobis K, Ulrich R, Fröhling S, Benner A, Schlenk RF et al (2002) Prognostic significance of partial tandem duplications of the MLL gene in adult patients 16 to 60 years old with acute myeloid leukemia and normal cytogenetics: a study of the Acute Myeloid Leukemia Study Group Ulm. J Clin Oncol 20:3254–3261. 10.1200/JCO.2002.09.088 [DOI] [PubMed] [Google Scholar]
19.Schnittger S, Kinkelin U, Schoch C, Heinecke A, Haase D, Haferlach T et al (2000) Screening for MLL tandem duplication in 387 unselected patients with AML identify a prognostically unfavorable subset of AML. Leukemia 14:796–804. 10.1038/sj.leu.2401773 [DOI] [PubMed] [Google Scholar]
20.Issa GC, Zarka J, Sasaki K, Qiao W, Pak D, Ning J et al (2021) Predictors of outcomes in adults with acute myeloid leukemia and KMT2A rearrangements. Blood Cancer J 11:162. 10.1038/s41408-021-00557-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
21.Chandra P, Luthra R, Zuo Z, Yao H, Ravandi F, Reddy N et al (2010) Acute myeloid leukemia with t(9;11)(p21-22;q23): common properties of dysregulated ras pathway signaling and genomic progression characterize de novo and therapy-related cases. Am J Clin Pathol 133:686–693. 10.1309/AJCPGII1TT4NYOGI [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Material 1^{(769.1KB, pdf)}

Data Availability Statement

[CR1] 1.Döhner H, Wei AH, Appelbaum FR, Craddock C, DiNardo CD, Dombret H et al (2022) Diagnosis and management of AML in adults: 2022 recommendations from an international expert panel on behalf of the ELN. Blood 140:1345–1377. 10.1182/blood.2022016867 [DOI] [PubMed] [Google Scholar]

[CR2] 2.Issa GC, Aldoss I, DiPersio J, Cuglievan B, Stone R, Arellano M et al (2023) The menin inhibitor revumenib in KMT2A-rearranged or NPM1-mutant leukaemia. Nature 615:920–924. 10.1038/s41586-023-05812-3 [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR3] 3.Candoni A, Coppola G (2024) A 2024 update on menin inhibitors: a new class of target agents against KMT2A-rearranged and NPM1-mutated acute myeloid leukemia. Hematol Rep 16:244–254. 10.3390/hematolrep16020024 [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR4] 4.Castiglioni S, Di Fede E, Bernardelli C, Lettieri A, Parodi C, Grazioli P et al (2022) KMT2A: umbrella gene for multiple diseases. Genes (Basel) 13:514. 10.3390/genes13030514 [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR5] 5.Milan T, Celton M, Lagacé K, Roques É, Safa-Tahar-Henni S, Bresson E et al (2022) Epigenetic changes in human model KMT2A leukemias highlight early events during leukemogenesis. Haematologica 107:86–99. 10.3324/haematol.2020.271619 [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR6] 6.Hsieh JJ, Ernst P, Erdjument-Bromage H, Tempst P, Korsmeyer SJ (2003) Proteolytic cleavage of MLL generates a complex of N- and C-terminal fragments that confers protein stability and subnuclear localization. Mol Cell Biol 23:186–194. 10.1128/MCB.23.1.186-194.2003 [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR7] 7.Caslini C, Shilatifard A, Yang L, Hess JL (2000) The amino terminus of the mixed lineage leukemia protein (MLL) promotes cell cycle arrest and monocytic differentiation. Proc Natl Acad Sci U S A 97:2797–2802. 10.1073/pnas.040574897 [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR8] 8.Milne TA, Briggs SD, Brock HW, Martin ME, Gibbs D, Allis CD et al (2002) MLL targets SET domain methyltransferase activity to Hox gene promoters. Mol Cell 10:1107–1117. 10.1016/S1097-2765(02)00741-4 [DOI] [PubMed] [Google Scholar]

[CR9] 9.Winters AC, Bernt KM (2017) MLL-rearranged leukemias-an update on science and clinical approaches. Front Pediatr 5:4. 10.3389/fped.2017.00004 [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR10] 10.Meyer C, Larghero P, Almeida Lopes B, Burmeister T, Gröger D, Sutton R et al (2023) The KMT2A recombinome of acute leukemias in 2023. Leukemia 37:988–1005. 10.1038/s41375-023-01877-1 [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR11] 11.Whitman SP, Liu S, Vukosavljevic T, Rush LJ, Yu L, Liu C et al (2005) The MLL partial tandem duplication: evidence for recessive gain-of-function in acute myeloid leukemia identifies a novel patient subgroup for molecular-targeted therapy. Blood 106:345–352. 10.1182/blood-2005-01-0204 [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR12] 12.Yip BH, So CW (2013) Mixed lineage leukemia protein in normal and leukemic stem cells. Exp Biol Med (Maywood) 238:315–323. 10.1177/1535370213480717 [DOI] [PubMed] [Google Scholar]

[CR13] 13.Döhner K, Döhner H (2008) Molecular characterization of acute myeloid leukemia. Haematologica 93:976–982. 10.3324/haematol.13345 [DOI] [PubMed] [Google Scholar]

[CR14] 14.Patel JP, Gönen M, Figueroa ME, Fernandez H, Sun Z, Racevskis J et al (2012) Prognostic relevance of integrated genetic profiling in acute myeloid leukemia. N Engl J Med 366:1079–1089. 10.1056/NEJMoa1112304 [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR15] 15.Kihara R, Nagata Y, Kiyoi H, Kato T, Yamamoto E, Suzuki K et al (2014) Comprehensive analysis of genetic alterations and their prognostic impacts in adult acute myeloid leukemia patients. Leukemia 28:1586–1595. 10.1038/leu.2014.55 [DOI] [PubMed] [Google Scholar]

[CR16] 16.AACR Project GENIE Consortium (2017) AACR Project GENIE: powering precision medicine through an international consortium. Cancer Discov 7:818–831. 10.1158/2159-8290.CD-17-0151 [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR17] 17.Wakita S, Yamaguchi H, Omori I, Terada K, Ueda T, Manabe E et al (2013) Mutations of the epigenetics-modifying gene (DNMT3a, TET2, IDH1/2) at diagnosis may induce FLT3-ITD at relapse in de novo acute myeloid leukemia. Leukemia 27:1044–1052. 10.1038/leu.2012.317 [DOI] [PubMed] [Google Scholar]

[CR18] 18.Döhner K, Tobis K, Ulrich R, Fröhling S, Benner A, Schlenk RF et al (2002) Prognostic significance of partial tandem duplications of the MLL gene in adult patients 16 to 60 years old with acute myeloid leukemia and normal cytogenetics: a study of the Acute Myeloid Leukemia Study Group Ulm. J Clin Oncol 20:3254–3261. 10.1200/JCO.2002.09.088 [DOI] [PubMed] [Google Scholar]

[CR19] 19.Schnittger S, Kinkelin U, Schoch C, Heinecke A, Haase D, Haferlach T et al (2000) Screening for MLL tandem duplication in 387 unselected patients with AML identify a prognostically unfavorable subset of AML. Leukemia 14:796–804. 10.1038/sj.leu.2401773 [DOI] [PubMed] [Google Scholar]

[CR20] 20.Issa GC, Zarka J, Sasaki K, Qiao W, Pak D, Ning J et al (2021) Predictors of outcomes in adults with acute myeloid leukemia and KMT2A rearrangements. Blood Cancer J 11:162. 10.1038/s41408-021-00557-6 [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR21] 21.Chandra P, Luthra R, Zuo Z, Yao H, Ravandi F, Reddy N et al (2010) Acute myeloid leukemia with t(9;11)(p21-22;q23): common properties of dysregulated ras pathway signaling and genomic progression characterize de novo and therapy-related cases. Am J Clin Pathol 133:686–693. 10.1309/AJCPGII1TT4NYOGI [DOI] [PubMed] [Google Scholar]

PERMALINK

Prognostic impact of KMT2A-PTD in acute myeloid leukemia in the NGS era: a multicenter retrospective study

Taichiro Tokura

Satoshi Wakita

Keiko Fukunaga

Yasunobu Nagata

Shunsuke Yui

Atsushi Marumo

Tomoaki Kitano

Mayumi Yogosawa

Daisuke Minakata

Shinichi Kako

Shinya Kimura

Sho Okamoto

Taku Tsukamoto

Junya Kanda

Atsushi Satake

Haruko Tashiro

Masao Ogata

Kuniko Takano

Jun Ando

Taro Edahiro

Tatsuo Ichinohe

Yuri Kamitsuji

Nobuhiko Uoshima

Eri Kawata

Hitoji Uchiyama

Keiichi Moriya

Noriko Doki

Yuho Najima

Kazutaka Nakayama

Toshimitsu Ueki

Katsuhiro Shono

Shinichiro Mori

Masao Hagihara

Akiko Hashimoto

Hideharu Muto

Daichi Nishiyama

Sayuri Motomura

Yoshinobu Kanda

Hiroki Yamaguchi

Abstract

Supplementary Information

Introduction

Fig. 1.

Materials and methods

Study design and participants

Cytogenetic testing

Molecular testing

Statistical analyses

Table 1.

Table 2.

Table 3.

Results

Clinical characteristics

Cytogenetics

Genetics

Relapse-free survival (RFS)

Fig. 2.

Overall survival (OS)

Fig. 3.

Multivariable analyses

Additional and sensitivity analyses

Fig. 4.

Fig. 5.

Fig. 6.

Fig. 7.

Discussion

Conclusion

Supplementary Information

Acknowledgements

Abbreviations

Author contributions

Funding

Data availability

Declarations

Ethics statement

Informed consent

Registry and the registration no. of the study/trial

Animal studies