{"paper_id":"06f32825-e652-4d76-8d01-81d0c8c892b2","body_text":"Acute Myeloid Leukemia and Myelodysplastic Neoplasms: Clinical Implications of Myelodysplasia-Related Genes Mutations and TP53 Aberrations | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Acute Myeloid Leukemia and Myelodysplastic Neoplasms: Clinical Implications of Myelodysplasia-Related Genes Mutations and TP53 Aberrations Hyunwoo Kim, Ja Young Lee, Sinae Yu, Eunkyoung Yoo, Hye Ran Kim, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4974493/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Purpose The fifth World Health Organization (WHO) classification (2022 WHO) and International Consensus Classification (ICC) of myeloid neoplasms have recently been published. We reclassified patients according to the revised classification and analyzed their prognosis to confirm the clinical utility of the new classifications. Methods We included 101 adult patients, including 77 with acute myeloid leukemia (AML) and 24 with myelodysplastic neoplasms (MDS), who underwent bone marrow aspiration and next-generation sequencing (NGS) between August 2019 and July 2023. We reclassified patients according to the revised criteria, then examined the differences and analyzed the prognosis using survival analysis. Results According to the 2022 WHO and ICC, 23 (29.9%) patients and 32 (41.6%) patients were reclassified into different groups, respectively, due to the addition of myelodysplasia-related (MR) gene mutations to the diagnostic criteria or the addition of new entities associated with TP53 mutations. The median overall survival (OS) of patients with AML and MR gene mutations was shorter than those of other AML group; however, the difference was not significant. Patients with AML and TP53 mutation had a significantly shorter OS than the other AML group ( p = 0.0014, median OS 2.3 vs 10.3 months). They also had significantly shorter OS than the AML and MR mutation group ( p = 0.002, median OS 2.3 vs 9.6 months). Conclusion The revised classifications allow for more detailed categorization based on genetic abnormalities, which may be helpful in predicting prognosis. AML with TP53 mutations is a new ICC category that has shown high prognostic significance in a small number of cases. acute myeloid leukemia gene mutations International Concensus Classification World Health Organization Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction The World Health Organization (WHO) classification of myeloid neoplasms has been revised several times to improve our understanding of the molecular features of this disease [ 1 ]. Development of molecular genetic technology has advanced our understanding of myeloid neoplasms by adding distinct groups to their classification, such as acute myeloid leukemia (AML) with genetic abnormalities [ 2 – 4 ]. The commercialization of next-generation sequencing (NGS) has introduced genetic information into the diagnostic criteria for AML and myelodysplastic neoplasm (MDS). Recent studies have provided a detailed census of genes mutated in myeloid neoplasms; thus, the number of gene mutations incorporated into AML diagnosis and risk stratification has increased [ 5 ]. The fifth edition of the WHO classification (2022 WHO) and the International Consensus Classification (ICC) of myeloid neoplasms have been published [ 6 , 7 ]. Changes included lowering the blast threshold that defines AML and renaming “myelodysplastic syndrome” as “myelodysplastic neoplasm”. One of the largest differences between the revised fourth WHO classification (2016 WHO) and the 2022 WHO/ICC is the change in diagnostic criteria for AML associated with myelodysplasia. In the 2016 WHO, the main diagnostic criteria of AML with myelodysplasia-related changes (AML-MRC) were morphological changes in the bone marrow (BM), a history of MDS, and chromosomal abnormalities [ 8 ]. In the 2022 WHO, morphological dysplasia alone was excluded from the criteria and mutations in eight myelodysplasia-related (MR) genes ( ASXL1, BCOR, SF3B1, EZH2, SRSF2, STAG2, U2AF1 , and ZSZR2 ) were included [ 7 ]. RUNX1 mutations were added to the ICC criteria in addition to the eight MR genes [ 6 ]. Another significant difference is the addition of MDS and AML groups associated with TP53 mutations [ 6 , 7 ]. In the 2022 WHO, the subtype MDS with biallelic TP53 inactivation (MDS-bi TP53 ) was identified when there are two or more TP53 mutations or when there is one mutation with evidence of TP53 copy number loss [ 7 ]. A subtype of MDS with mutated TP53 (MDS- TP53 ) was added to the ICC. AML with mutated TP53 (AML- TP53 ) was added only to the ICC [ 6 ]. Subsequently, the 2022 European Leukemia Net (ELN) risk stratification was revised to include new diagnostic classifications [ 4 ]. In addition to ASXL1 and RUNX1 , which had already been classified as adverse in the 2017 ELN risk stratification, six other MR gene mutations were newly classified as adverse. These changes have advanced our understanding of the molecular genetic characteristics of AML and MDS and helped apply this knowledge to clinical diagnosis and therapeutic strategies. Several studies have primarily focused on reclassification, examining differences in diagnostic criteria between 2016 WHO and the two new classifications [ 8 – 10 ]. However, recent studies have targeted the prognostic effects of specific AML subtypes, such as AML, myelodysplasia-related (AML-MR), and AML- TP53 [ 11 – 13 ]. In Korea, a study found that patients with AML-MR according to the 2022 WHO had a shorter overall survival (OS) similar to that of patients with AML-MRC according to the 2016 WHO [ 14 ], and a study of a small group of patients with MDS reported that those with TP53 mutations had a worse prognosis than those without TP53 mutations [ 15 ]. In this study, we reclassified patients with myeloid neoplasms who were initially classified according to the 2016 WHO at our institution according to the revised criteria. We identified gene mutation rates in AML and MDS and performed survival analyses for the subgroups, particularly focusing on AML with MR gene and TP53 mutations. We compared the clinical outcomes of these groups with those of other AML subgroups to evaluate the clinical usefulness of the new classifications. Materials and Methods Patient selection and data collection We included all patients aged 18 years or older who had a BM examination and targeted NGS between August 2019 and July 2023. During this period, 288 patients underwent BM examination with suspicion of AML or MDS, of which 105 were tested with NGS at the time of initial diagnosis. The electronic medical records were retrospectively reviewed with respect to each patient’s demographic data and laboratory findings, including BM examination results, treatment information, and survival outcomes. After excluding four patients who subsequently relapsed and received the same NGS results as before, 101 patients were enrolled. This study was approved by the Institutional Review Board of Inje University, Busan Paik Hospital (No. 2023-10-026), which waived the need for informed consent. After reviewing the data, we reclassified the patients according to the 2022 WHO and ICC [ 6 , 7 ]. Molecular tests including targeted next-generation sequencing For fusion gene detection, RNA was isolated from BM using a QIAamp RNA Blood Mini Kit (Qiagen, Hilden, Germany). Multiplex reverse transcription polymerase chain reaction was performed using the HemaVision-28N Panel (DNA Diagnostic, Risskov, Denmark). NGS was performed using a Miseq Dx sequencing platform (Illumina Inc., San Diego, CA, USA). We identified 48 genes associated with AML and MDS (Supplementary Table S1 ). The targeted specimen was genomic DNA isolated from BM aspirates, and the target enrichment method was hybridized with oligonucleotide probes. The panel version and bioinformatics pipeline were NGB-Wet-V2.0 and NGB-DNA-somatic-V1.3, respectively. The sequence was aligned with the human reference genome GRCh37. The average depth of coverage was 592.8X. Among the NGS test results, gene mutations with Tier 3, unknown clinical significance, were excluded [ 16 ]. Karyotyping BM samples were processed after 24 and 48 hours of unstimulated culture using GTL-banding (Giemsa banding using trypsin and Leishman stain). The band resolution was 300 to 400 bands. Karyotypes were interpreted according to the International System for Human Cytogenetic Nomenclature [ 17 ]. Statistical analysis Chi-square and Fisher’s exact tests were used to detect differences in the gene mutation distribution in each classification system. We divided with AML patients into prognostic groups based on ELN risk stratifications [ 4 ]. The OS was defined as the period from the time of diagnosis to death or the end of follow-up. The endpoint was October 31, 2023. We used the Kaplan-Meier curve and log-rank test to analyze the OS of the patients in each group. All statistical analyses were performed using MedCalc (Ver 12.4, MedCalc Software, Ostend, Belgium) and R (R version 4.4.1; Rstudio version 2024. 04.2–764); p < 0.05 was considered statistically significant. Human Ethics and Consent to Participate declarations This study was a retrospective medical record analysis using only general clinical characteristics, molecular genetic test results, and charts of patients. No unnecessary blood collection or tests were performed. In addition, personal identification information was anonymized and managed. The study was conducted following the principles adopted in the Declaration of Helsinki, and was approved by the Institutional Review Board of Inje University, Busan Paik Hospital, Busan, Korea (2023-10-026). Results Reclassification of AML and MDS based on the revised criteria Seventy-seven patients with AML and 24 with MDS were enrolled in this study. The median age for the AML group was 67 years (range, 19–88), and that of the MDS group was 73 years (range, 54–86). Fifty-eight patients were male (57.4%). Among the 77 patients with AML, 23 (29.9%) and 32 (41.6%) patients were reclassified into other groups based on the 2022 WHO and ICC, respectively (Fig. 1 A). Using the revised criteria, 33 patients (42.9%) with AML and recurrent genetic abnormalities were classified the same as the 2016 WHO, with only the group names changing (Table 1 ). Of the 17 patients (22.1%) with AML-MRC in the 2016 WHO, 15 were reclassified as AML-MR based on the 2022 WHO. Seven of these had TP53 mutations and were reclassified as AML- TP53 according to the ICC. Of the 18 patients (23.4%) diagnosed with AML, not otherwise specified (NOS) according to the 2016 WHO, 11 (61.1%) were reclassified as AML-MR based on the 2022 WHO. For the ICC, 13 (72.2%) were reclassified as AML-MR due to the difference in MR gene mutations between the two classifications. One patient with t(1;11)(p32;q23) was reclassified as AML with KMT2A rearrangement (AML- KMT2A ) using the 2022 WHO and AML with other KMT2A rearrangements using the ICC. Table 1 Reclassification of acute myeloid leukemia and myelodysplastic neoplasm based on the 2022 WHO and ICC 2016 WHO N % 2022 WHO N % ICC N % AML RUNX1-RUNX1T1 3 3.9% RUNX1::RUNX1T1 3 3.9% RUNX1::RUNX1T1 3 3.9% CBFB-MYH11 2 2.6% CBFB::MYH11 2 2.6% CBFB::MYH11 2 2.6% PML-RARA 3 3.9% PML::RARA 4 5.2% PML::RARA 4 5.2% DEK-NUP214 2 2.6% DEK::NUP214 2 2.6% DEK::NUP214 2 2.6% MLLT3-KMT2A 4 5.2% KMT2A rearrangement 6 7.8% MLLT3::KMT2A 4 5.2% Other KMT2A 2 2.6% NPM1 13 16.9% NPM1 mutation 13 16.9% mutated NPM1 13 16.9% bi CEBPA 6 7.8% CEBPA mutation 6 7.8% bZIP CEBPA 6 7.8% Myelodysplasia-related changes 17 22.1% Myelodysplasia-related 30 39.0% MR gene 24 31.2% MR cytogenetic abnormalities 1 1.3% Panmyelosis with myelofibrosis 4 5.2% Blast-phase MPN 3 3.9% NA NA NA mutated TP53 10 13.0% Not otherwise specified 18 23.4% Defined by differentiation 8 10.4% Not otherwise specified 6 7.8% Therapy-related 5 6.5% NA NA MDS Single lineage dysplasia 1 4.2% Low blasts 10 41.7% NOS with single lineage dysplasia 1 4.2% Multilineage dysplasia 9 37.5% NOS with multilineage dysplasia 9 37.5% Excess blasts-1 2 8.3% Increased blasts-1 2 8.3% Excess blasts 2 8.3% Excess blasts-2 5 20.8% Increased blasts-2 6 25.0% MDS/AML 2 8.3% MDS/AML with MR gene mutations 4 16.7% MDS/AML with TP53 3 12.5% Ring sideroblasts 1 4.2% Low blasts and SF3B1 1 4.2% SF3B1 1 4.2% NA bi TP53 5 20.8% mutated with TP53 2 8.3% Unclassifiable 1 4.2% NA NA Therapy-related 5 20.8% NA NA Abbreviations: WHO, World Health Organization; ICC, International Consensus Classification; AML, acute myeloid leukemia; bZIP, basic leucine zipper; MR, myelodysplasia-related; NA, not applicable; MDS, myelodysplastic neoplasm. In the 2022 WHO and ICC, therapy-related AML (t-AML) was removed and added as an additional qualifier to other AML categories. Of the five patients who were diagnosed with t-AML in the 2016 WHO, three were reclassified as AML-MR, one as acute promyelocytic leukemia (APL), and one as AML- KMT2A according to the 2022 WHO. In the ICC, two patients diagnosed with t-AML according to 2016WHO were reclassified as the same with the 2022 WHO, whereas three patients with AML-MR according to the 2022 WHO were reclassified as AML- TP53 , AML-MR and AML with MR cytogenetic abnormalities in the ICC, respectively. Patients with panmyelosis and fibrosis was also excluded, and three patients had a history of myeloproliferative neoplasm (MPN). Therefore, they were reclassified as blast-phase MPN according to the 2022 WHO. In the ICC, these patients were reclassified as AML-MR and AML- TP53 , respectively, according on the presence of genetic mutations. Among the 24 patients with MDS, 7 (29.2%) and 12 (50.0%) patients were reclassified into other groups based on the 2022 WHO and ICC, respectively (Fig. 1 B). Despite adjustment of the blast threshold in the 2022 WHO and ICC schemes, no cases of MDS were reclassified as AML because of the absence of defined genetic abnormalities or rare gene fusion. Instead, in the ICC, one patient with MDS with excess blasts 2 (MDS-EB2) and one patient with t-MDS according to the 2016 WHO were reclassified into the MDS/AML group, and four patients with MDS-EB2 were reclassified into MDS/AML with MR gene mutations. One patient with MDS with multilineage dysplasia (MDS-MD) and one with MDS with excess blast 1 (MDS-EB1) in the 2016 WHO was reclassified as MDS-bi TP53 based on the 2022 WHO. Based on the ICC, they were reclassified as MDS- TP53 . Three patients with t-MDS were reclassified as having MDS/AML with mutated TP53 (MDS/AML- TP53 ) in the ICC and MDS-bi TP53 in the 2022 WHO. One patient with MDS, unclassifiable (MDS-U) was reclassified as having MDS with low blasts (MDS-LB) in the 2022 WHO and MDS, NOS with multilineage dysplasia in the ICC. MDS with ring sideroblasts (MDS-RS) was revised, and the name was changed to MDS with low blasts and SF3B1 mutation (MDS, LB and SF3B1 ) in the 2022 WHO and MDS with mutated SF3B1 (MDS- SF3B1 ) in the ICC. Genetic mutation landscape in AML and MDS Approximately 84.2% (85/101) of all patients had mutations in 29 genes. In total, 89.6% (69/77) of the patients with AML and 66.7% (16/24) of those with MDS had 211 mutations (median 2, range 0–9 per patient) and 36 mutations (median 2, range 0–7 per patient), respectively (Fig. 2 ). Among 69 patients with AML and mutations, 16 (20.8%) had 2 mutations and 36 patients (52.2%) had ≥ 3 mutations. The most common AML mutations were ASXL1 , IDH2 and NRAS (14 patients, 18.2%), followed by DNMT3A , FLT3 -ITD and NPM1 (13 patients, 16.9%). ASXL1 and IDH2 mutations were predominantly identified in AML-MR patients, whereas NRAS mutation were frequently detected in patients with AML, defining genetic abnormalities in the 2022 WHO. DNMT3A and FLT3 -ITD mutations were most commonly observed in patients with AML harboring NPM1 mutation. In a survival analysis of gene mutations present in more than 10% of patients with AML, we detected a statistically significant difference in median OS based on only FLT3-ITD and TP53 mutations ( p < 0.05; median OS 7.2 and 2.3 months with mutation vs. 11.7 and 10.3 months without mutation, respectively). In patients with MDS, the most frequently observed gene mutations were ASXL1 and TP53 , each found in five patients (20.8%, supplementary Fig. 1). Gene mutations in ASXL1 , RUNX1 , and STAG2 were significantly more common in patients with MDS with increased blasts (MDS-IB) than in those with MDS-LB in the 2022 WHO ( p < 0.05, Table 2 ). Table 2 Patient characteristics and gene mutations in myelodysplastic neoplasm All MDS 2022 WHO Low blasts Increased blasts bi TP53 P Patients 24 11 8 5 Sex, male: female 15:9 6:5 6:2 3:2 0.656 Age, years (range) 73(54–86) 73 (56–83) 70 (54–85) 70 (58–80) 0.930 Laboratory findings, median value White blood cell, ×10 9 /L, median (range) 2.48 (0.31–34.12) 2.44 (0.31–3.78) 2.27 (1.05–34.12) 3.46 (1.70–7.87) 0.406 Hemoglobin, g/dL, median (range) 8.3 (4.1–12.7) 9.1 (5.5–11.0) 6.9 (5.9–12.7) 7.5 (4.1–9.7) 0.537 Platelet, ×10 9 /L, median (range) 67.5 (6.0-264.0) 77.0 (6.0-259.0) 57.5 (10.0-264.0) 62.0 (21.0-102.0) 0.931 Blasts in peripheral blood, %, median (range) 0 (0–9) 0 (0–1) 1 (0–9) 3 (0–7) 0.004 Blasts in bone marrow, %, median (range) 4.8 (0-19.2) 2.2 (0-4.5) 14.5 (5.1–19.2) 13.0 (2.6–13.4) < 0.001 Cytogenetics Abnormal karyotype, N (%) 11 (45.8%) 4 (36.4%) 3 (37.5%) 4 (80.0%) 0.226 Complex karyotype, N (%) 6 (25.0%) 2 (18.2%) 0 (0%) 4 (80.0%) 0.003 Gene mutations N, median (range) 2 (0–7) 0 (0–3) 4 (0–7) 0 (0–2) 0.040 Tumor suppressor TP53 5 (20.8%) 0 0 5 (100%) < 0.001 Transcription factors (except MR gene) RUNX1 4 (16.7%) 0 4 (42.9%) 0 0.008 CEBPA 2 (8.3) 0 2 (28.6%) 0 0.113 Myelodysplasia related genes ASXL1 5 (20.8%) 0 5 (62.5%) 0 0.002 BCOR 3 (12.5%) 1 (9.1%) 2 (25.0%) 0 0.373 SF3B1 2 (8.3%) 1 (9.1%) 0 0 0.725 SRSF2 2 (8.3%) 0 2 (25.0%) 0 0.113 STAG2 4 (16.7%) 0 4 (50.0%) 0 0.008 U2AF1 1 (4.2%) 0 1 (12.5%) 0 0.352 ZRSR2 1 (4.2%) 1 (9.1%) 0 0 0.540 DNA methylation DNMT3A 3 (12.5%) 2 (18.2%) 1 (12.5%) 0 0.595 IDH2 1 (4.2%) 0 1 (12.5%) 0 0.352 TET2 1 (4.2%) 0 1 (12.5%) 0 0.352 RNA helicase DDX41 2 (8.3%) 2 (18.2%) 0 0 0.276 Abbreviations: MDS, myelodysplastic neoplasm; WHO, World Health Organization; MR, myelodysplasia-related. Patient characteristics and clinical outcomes in AML with MR gene mutations based on 2022 WHO classification Among 25 patients with AML and MR gene mutations, three had gene fusions, two had a previous history of myeloproliferative neoplasm, and one had an NPM1 mutation. Six patients were not classified as AML-MR according to the 2022 WHO. Thirty patients were classified as AML-MR according to the 2022 WHO, and MR gene mutations were present in 66.7% (20/30). The remaining nine patients had complex karyotypes, and one patient had a history of MDS. In our study, ASXL1 mutation (N = 9, 30.0%) was the most frequent among MR genes, followed by SRSF2 mutation (N = 6, 20.0%). Although RUNX1 mutation is included in MR genes only in the ICC, most of the RUNX1 mutations (75%, 6/8) were found in patients with AML-MR in the 2022 WHO. Patients with AML-MR were significantly older, had lower white blood cell (WBC) counts, and were more likely to have complex karyotypes than patients in the other groups (Table 3 ). When analyzing the frequency of gene mutations between the AML-MR and other groups, TP53 and SRSF2 mutations showed significantly higher rate in patients with AML-MR ( p < 0.05). Table 3 Patient characteristics and gene mutations in acute myeloid leukemia All AML 2022 WHO ICC AML-MR AML-others P AML-TP53 AML-MR AML-others P Patients 77 30 47 10 25 42 Sex, male: female 43:34 20:10 23:24 0.196 8:2 14:11 21:21 0.229 Age, years (range) 67 (19–88) 71 (32–87) 62 (19–88) 0.002 69.5 (57–87) 71 (32–83) 62 (19–88) 0.034 Laboratory findings, median value WBC, ×10 9 /L, median (range) 5.94 (0.81–231.1) 3.25 (1.03-224.18) 13.71(0.81-231.05) 0.005 3.29 (1.57–26.92) 3.33 (1.02-224.18) 15.27 (0.81-231.05) 0.009 Hemoglobin, g/dL, median (range) 8.0 (2.6–15.0) 7.9 (2.7–11.0) 8.3 (2.6–15.0) 0.154 8.3 (5.3–9.8) 7.8 (2.7–11.0) 8.3 (2.6–15.0) 0.647 Platelet, ×10 9 /L, median (range) 40.0 (3.0-344.0) 39.5 (3-143) 42.0 (6-344) 0.711 36.0 (3.0–48.0) 51.0 (4.0-143.0) 35.0 (6.0-344.0) 0.458 Blasts in PB, %, median (range) 27 (0–96) 18.5 (0–90) 30 (0–96) 0.339 4.5 (0–90) 27.0 (0–88) 30.0 (0–96) 0.218 Blasts in BM, %, median (range) 57.4 (13.7–92.7) 52.0 (21.2–92.7) 60.2 (13.7–91.8) 0.137 43.7 (23.2–90.3) 52.0 (21.2–92.7) 60.2 (13.7–91.8) 0.511 Cytogenetics Abnormal karyotype, N (%) 45 (58.4%) 20 (66.7%) 25 (53.2%) 0.458 8 (80.0%) 13 (52.0%) 24 (57.1%) 0.060 Complex karyotype, N (%) 14 (18.2%) 13 (43.3%) 1 (2.1%) < 0.001 8 (80.0%) 5 (20.0%) 1 (2.4%) < 0.001 Gene mutations N, median (range) 2 (0–9) 2.5 (0–6) 2 (0–9) 0.557 1 (1–6) 3 (0–6) 2 (0–9) 0.082 RAS pathway-related NRAS 14 (18.2%) 4 (13.3) 10 (21.3) 0.563 0 4 (16.0%) 10 (23.8%) 0.202 KRAS 5 (6.5%) 2 (6.7) 3 (6.4) 0.671 0 2 (8.0%) 3 (7.1%) 0.667 KIT 2 (2.6%) 0 2 (4.3) 0.682 0 0 2 (4.8%) 0.425 FLT3 -ITD 13 (16.9%) 4 (13.3) 9 (19.1) 0.725 0 4 (16.0%) 9 (21.4%) 0.264 FLT3 -TKD 7 (9.1%) 1 (3.3) 6 (12.8) 0.319 0 2 (8.0%) 5 (11.9%) 0.487 PTPN11 5 (6.5%) 0 5 (10.6) 0.170 1 (10.0) 0 4 (9.5%) 0.276 Tumor suppressor TP53 11 (14.3%) 8 (26.7) 3 (6.4) 0.032 10 (100.0) 0 1 (2.3) < 0.001 PHF6 1 (1.3%) 1 (3.3) 0 0.820 0 1 (4.0%) 0 0.349 WT1 6 (7.8%) 1 (3.3) 5 (10.6) 0.465 0 1 (4.0%) 5 (11.9%) 0.311 Transcription factors (except MR gene) RUNX1 8 (10.4%) 6 (20.0) 2 (4.3) 0.068 0 8 (32.0%) 0 < 0.001 CEBPA 6 (7.8%) 0 6 (12.8) 0.109 0 0 6 (14.3%) 0.067 SETBP1 1 (1.3%) 1 (3.3) 0 0.820 0 1 (4.0%) 0 0.349 GATA2 4 (5.2%) 1 (3.3) 3 (6.4) 0.951 0 1 (4.0%) 3 (7.1%) 0.624 Myelodysplasia related ASXL1 14 (18.2) 9 (30.0) 5 (10.6) 0.065 0 10 (43.5) 4 (9.5%) 0.002 BCOR 3 (3.9) 3 (10.0) 0 0.108 0 3 (12.0%) 0 0.039 SF3B1 2 (2.6) 2 (6.7) 0 0.290 0 2 (8.0%) 0 0.118 SRSF2 7 (9.1) 6 (20.0) 1 (2.1) 0.024 1 (10.0) 5 (20.0%) 1 (2.4%) 0.051 STAG2 2 (2.6) 1 (3.3) 1 (2.1) 0.682 0 1 (4.0%) 1 (2.4%) 0.791 U2AF1 3 (3.9) 2 (6.7) 1 (2.1) 0.689 1 (10.0) 2 (8.0%) 0 0.148 ZRSR2 3 (3.9) 3 (10.0) 0 0.108 0 3 (12.0%) 0 0.039 DNA methylation DNMT3A 13 (16.9) 5 (16.7) 8 (17.0) 0.786 1 (10.0) 4 (16.0%) 8 (19.0%) 0.782 IDH1 4 (5.2) 2 (6.7) 2 (4.3) 0.951 0 3 (12.0%) 1 (2.4%) 0.168 IDH2 14 (18.2) 6 (20.0) 8 (17.0) 0.978 1 (10.0) 8 (32.0%) 5 (11.9%) 0.092 TET2 9 (11.7) 4 (13.3) 5 (10.6) 0.996 1 (10.0) 4 (16.0%) 4 (9.5%) 0.716 Risk group by ELN 2022 guideline Favorable, N (%) 20 (26.0) 0 20 (42.6%) < 0.001 0 0 20 (47.6%) < 0.001 Intermediate, N (%) 18 (23.4) 0 18 (38.3%) 0 0 18 (42.9%) Adverse, N (%) 39 (50.6) 30 (100%) 9 (19.1%) 10 (100%) 25 (100%) 4 (9.5%) Abbreviations: AML, acute myeloid leukemia; WHO, World Health Organization; ICC, International Consensus Classification; MR, myelodysplasia-related; PB, peripheral blood; BM, bone marrow; ELN, European Leukemia Net. According to the 2022 ELN risk stratifications, all patients with AML-MR were classified into the adverse group. The median OS of the AML-MR group was shorter than that of other AML groups ( p = 0.464, median OS 7.5 vs. 10.3 months, Fig. 3 A). The median OS of patients with AML-MRC in the 2016 WHO and the AML-MR group in the 2022 WHO was shorter than that of patients with AML-NOS in the 2016 WHO classified as AML-MR in the 2022 WHO ( P = 0.174, median OS 4.6 vs 9.6 months, Fig. 3 B). In particular, the AML-MRC group in the 2016 WHO and the AML-MR group in the 2022 WHO demonstrated shorter OS than the group classified as AML-MRC in the 2016 WHO but not as AML-MR in the 2022 WHO (Fig. 3 C). When examining the OS of patients with AML those with cytogenetic abnormalities showed a relatively shorter median OS than those with MR gene mutations ( p = 0.473, median OS 2.4 vs. 9.6 months, Fig. 3 D). The median OS of patients with SRSF2 mutation (3.85 months) was the shortest among the patients with MR gene mutations; however, statistical significance was not reached because of the small sample size (Table 4 ). Table 4 Comparison of survival outcomes in patients with acute myeloid leukemia based on the presence of myelodysplasia-related gene mutations. Gene N Median OS (months) Range ASXL1 14 7.8 0.5–28.3 BCOR 3 NA 8.2–26.6 SF3B1 2 10.4 2.6–18.2 SRSF2 7 3.85 0.4–8.2 STAG2 2 6.7 3.3–10.1 U2AF1 3 NA 0.6–6.7 ZRSR2 3 7.8 7.2–14.1 Abbreviations: OS, overall survival; NA, not applicable Patient characteristics and clinical outcomes in AML and MDS patients with TP53 mutations based on the ICC classification TP53 mutations were found in 11 patients with AML (14.3%) and 5 with MDS (20.8%). The median variant allelic frequencies of TP53 mutation in AML and MDS were 53.97% and 38.16%, respectively. Biallelic TP53 mutations were observed in only four patients with MDS. Ten patients with AML and TP53 mutations were classified as AML- TP53 according to the ICC. One patient with MLLT3-KMT2A fusion was classified as AML with MLLT3::KMT2A . In the ICC, AML was divided into three groups; AML- TP53 , AML-MR and AML. The AML- TP53 and AML-MR groups were significantly older, had lower WBC counts, and exhibited higher rates of complex karyotypes than the other groups. We identified a significant negative correlation between TP53 mutation and the expression of other genes. According to the ICC, 25 patients with AML-MR showed a significantly higher frequency of mutations in ASXL1, BCOR , and ZRSR ( p < 0.05). Ten patients with AML- TP53 were classified into the adverse group according to the 2022 ELN risk stratification. The median OS of the AML- TP53 group was significantly shorter than that of the non-AML- TP53 group ( p = 0.0014, median OS 2.3 vs. 10.3 months, Fig. 4 A). They also had a significantly shorter OS than the AML-MR group ( p = 0.002, median OS 2.3 vs. 9.6 months, Fig. 4 B). Patients with MDS and TP53 mutations had shorter median OS than patients without TP53 mutations ( p = 0.1792; median OS 9.5 vs. 21.9 months). Discussion We conducted a retrospective study on the newly revised classification of myeloid neoplasms, focusing on two key genetic alterations, MR genes and TP53 mutations, at a single institution to identify differences and assess their clinical utility. The major changes in the 2022 WHO were the exclusion of morphologic dysplasia from the diagnostic criteria for AML-MR and the addition of eight MR gene mutations [ 7 ]. In this study, 11 patients were reclassified from AML, NOS to AML-MR in the 2022 WHO, resulting in a larger AML-MR group than the AML group. Based on the ICC, the RUNX1 mutation was added to the AML- MR criteria [ 6 ], and 13 patients in the AML, NOS group were reclassified as AML-MR. Differences in chromosomal abnormalities were observed in the diagnostic criteria for myelodysplasia-related AML included in the 2022 WHO and ICC, such as 11q deletion, monosomy 13, or 13q deletion in the 2022 WHO and trisomy 8 or 20q deletion in the ICC. In this study, one patient was reclassified as having AML with MR cytogenetic abnormalities based on an abnormal karyotype. In a similar single-center study, Zhou et al. [ 13 ] reported that the most common gene mutations in AML-MR were in TP53 , RUNX1 and ASXL1 . Our results are consistent with these findings, and ASXL1 and TP53 mutations were the most frequently found in patients with AML-MR. Among the MR genes, mutations in SRSF2 were the most common after ASXL1 , similar to the frequencies reported in the Korean study by Park et al. [ 14 ]. In survival analysis, patients with AML-MR showed worse outcome. When comparing the survival rates between patients classified as AML-MRC in the 2016 WHO but not AML-MR in the 2022 WHO and patients classified as AML-MRC and AML-MR in the 2022 WHO, we found that MR-associated cytogenetic abnormalities and the presence of MR genes were more significant prognostic factors than morphological abnormalities [ 18 , 19 ]. Additionally, patients with AML-MR with only cytogenetic abnormalities showed shorter survival outcome than those with only MR gene mutations. This finding aligns with those of previous reports, although statistical significance was not observed owing to the small sample size. Previous studies have reported a significantly shorter OS in patients with AML and ASXL1 , SRSF2 , and ZRSR2 mutations among MR genes than those without these mutations [ 14 , 19 , 20 ]. In our study, ZRSR2 mutation was detected in only three patients with AML-MR, and was found together with ASXL1 mutation in all cases. Consequently, despite its low detection frequency among MR genes, it significantly influences survival outcomes when co-occurring with ASXL1 . In our study, although none of the MR gene mutations showed a significant difference in median OS, there were differences in the median OS based on the MR genes. Even though RUNX1 mutations are only included as MR genes in ICC, we observed a shorter survival time in patients with AML and RUNX1 mutations than patients with other MR gene mutations in this study, and it was the second most frequent mutations after TP53 in AML-MR in the 2022 WHO. Large-scale studies are needed to assess the difference in frequency and prognosis of MR gene mutations, including RUNX1 . Another major change in the 2022 WHO and ICC classification is the new category for TP53 mutations. TP53 mutations are commonly associated with complex karyotype, negative correlation with other gene mutations, and poor prognosis in AML and MDS [ 6 , 21 , 22 ]. In our study, patients with AML and TP53 mutations showed similar characteristics and poor survival outcome. In the 2022 WHO, a distinct category of myeloid neoplasm with mutated TP53 is defined for MDS, but not for AML. Therefore, the AML-MR group included some patients with TP53 mutations, as they frequently occur in patients with complex chromosomal abnormalities. MR gene mutations are also associated with poor survival in AML; however, the survival analysis between the AML-MR and other AML groups did not show a statistically significant difference. In contrast, the ICC separated AML-MR and AML- TP53 , revealing a significant difference in the survival outcome between the two groups. This suggests that the AML- TP53 classification may provide a more precise stratification of patients with a poor prognosis based on the ICC. The MR genes has been reported to have better prognostic significance than the TP53 mutations and our study was consistent with that [ 12 ]. In our data, patients with AML- TP53 presented with extremely poor survival and a median OS of only 2.3 months. This is significantly lower than the survival rates reported in previous studies of AML and TP53 mutations [ 12 ]. For patients with high-risk AML, intensive chemotherapy, followed by allogenic hematopoietic stem-cell transplantation (allo-HSCT), is recommended as a potentially curative approach [ 23 ]. However, patients with AML harboring TP53 mutations exhibit a lower probability of achieving remission, and allo-HSCT has not been shown to improve OS [ 24 ]. In our study, all 10 patients with AML- TP53 received chemotherapy, with only one patient undergoing allo-HSCT. Although the median OS of the patients who underwent HSCT was longer than that of patients who received chemotherapy alone (10.8 vs 2.3 months), this difference was not statistically significant. Therefore, an optimal treatment strategy for patients with AML- TP53 remains to be established. This study had several limitations. First, although treatment guidelines may affect prognosis, we did not classify patients based on their treatment regimens or account for treatment modification according to age. Consequently, it was not possible to perform survival analysis stratified by treatment modality. Additionally, because the data were derived from a single institution with a short observation period, some subgroups analyses were limited by the small number of cases that showed statistical significance. Finally, we only used NGS data without conducting fluorescence in situ hybridization to identify TP53 mutations and related chromosomal abnormalities. In conclusion, the two newly revised classification criteria allowed us to further categorize patients diagnosed with AML, NOS as AML-MR or AML- TP53 . The AML- TP53 group, a new classification for ICC, had highly significant prognostic value, even with a small number of patients. As more data are accumulated and analyzed, the revised criteria will be more useful in predicting prognosis and facilitate a more detailed categorization of myeloid neoplasms at the gene mutation level. Declarations Acknowledgements: The authors declare that no funds, grants, or other support were received during the preparation of this article. Funding Declaration: No funding Competing interests: The authors have no conflict of interest to declare. Human Ethics and Consent to Participate declarations: Fully approved protocol and informed consent exemption. This study was a retrospective medical record analysis using only general clinical characteristics, molecular genetic test results, and charts of patients. No unnecessary blood collection or tests were performed. In addition, personal identification information was anonymized and managed. The study was conducted following the principles adopted in the Declaration of Helsinki, and was approved by the Institutional Review Board of Inje University, Busan Paik Hospital, Busan, Korea (2023-10-026). Author contributions : Yu S performed next-generation sequencing analysis; Lee S and Lee WS collected clinical data; Kim H analyzed data and wrote the original manuscript; Lee JY conceptualized, designed, supervised the study and reviewed and edited the manuscript; You E and Kim HR reviewed the manuscript. All authors have read and approved the final manuscript. Availability of data and materials : all data generated or analyzed during this study are included in this article and supplementary information files. References Arber DA, Orazi A, Hasserjian R, Thiele J, Borowitz MJ, Le Beau MM, et al. The 2016 revision to the World Health Organization classification of myeloid neoplasms and acute leukemia. Blood 2016;127:2391-405. Bernard E, Tuechler H, Greenberg PL, Hasserjian RP, Arango Ossa JE, Nannya Y, et al. Molecular International Prognostic Scoring System for Myelodysplastic Syndromes. NEJM Evid 2022;1:EVIDoa2200008. Cancer Genome Atlas Research N, Ley TJ, Miller C, Ding L, Raphael BJ, Mungall AJ, et al. Genomic and epigenomic landscapes of adult de novo acute myeloid leukemia. N Engl J Med 2013;368:2059-74. Dohner H, Wei AH, Appelbaum FR, Craddock C, DiNardo CD, Dombret H, et al. Diagnosis and management of AML in adults: 2022 recommendations from an international expert panel on behalf of the ELN. Blood 2022;140:1345-77. Papaemmanuil E, Gerstung M, Bullinger L, Gaidzik VI, Paschka P, Roberts ND, et al. Genomic Classification and Prognosis in Acute Myeloid Leukemia. N Engl J Med 2016;374:2209-21. Arber DA, Orazi A, Hasserjian RP, Borowitz MJ, Calvo KR, Kvasnicka HM, et al. International Consensus Classification of Myeloid Neoplasms and Acute Leukemias: integrating morphologic, clinical, and genomic data. Blood 2022;140:1200-28. Khoury JD, Solary E, Abla O, Akkari Y, Alaggio R, Apperley JF, et al. The 5th edition of the World Health Organization Classification of Haematolymphoid Tumours: Myeloid and Histiocytic/Dendritic Neoplasms. Leukemia 2022;36:1703-19. Wang X, Wang J, Wei S, Zhao J, Xin B, Li G, et al. The latest edition of WHO and ELN guidance and a new risk model for Chinese acute myeloid leukemia patients. Front Med (Lausanne) 2023;10:1165445. Chopra S and Bailey NG. Application of the International Consensus Classification and World Health Organization 5th edition classification to a series of myeloid neoplasms. Am J Clin Pathol 2023;160:566-70. Falini B and Martelli MP. Comparison of the International Consensus and 5th WHO edition classifications of adult myelodysplastic syndromes and acute myeloid leukemia. Am J Hematol 2023;98:481-92. Attardi E, Savi A, Borsellino B, Piciocchi A, Cipriani M, Ottone T, et al. Applicability of 2022 classifications of acute myeloid leukemia in the real-world setting. Blood Adv 2023;7:5122-31. Chen Y, Zheng J, Weng Y, Wu Z, Luo X, Qiu Y, et al. Myelodysplasia-related gene mutations are associated with favorable prognosis in patients with TP53 -mutant acute myeloid leukemia. Ann Hematol 2024;103:1211-20. Zhou Q, Zhao D, Zarif M, Davidson MB, Minden MD, Tierens A, et al. A real-world analysis of clinical outcomes in AML with myelodysplasia-related changes: a comparison of ICC and WHO-HAEM5 criteria. Blood Advances 2024;8:1760-71. Park HS, Kim HK, Kim HS, Yang Y, Han HS, Lee KH, et al. The new diagnostic criteria for myelodysplasia-related acute myeloid leukemia is useful for predicting clinical outcome: comparison of the 4th and 5th World Health Organization classifications. Ann Hematol 2022;101:2645-54. Lee C, Kim HN, Kwon JA, Yoon SY, Jeon MJ, Yu ES, et al. Implications of the 5(th) Edition of the World Health Organization Classification and International Consensus Classification of Myeloid Neoplasm in Myelodysplastic Syndrome With Excess Blasts and Acute Myeloid Leukemia. Ann Lab Med 2023;43:503-7. Kim J, Park WY, Kim NKD, Jang SJ, Chun SM, Sung CO, et al. Good Laboratory Standards for Clinical Next-Generation Sequencing Cancer Panel Tests. J Pathol Transl Med 2017;51:191-204. Jean M SA, Schmid M. ISCN, 2016: An International System for Human Cytogenomic Nomenclature. In: Basel, Switzerland: Karger, 2016. Fuhrmann I, Lenk M, Haferlach T, Stengel A, Hutter S, Baer C, et al. AML, NOS and AML-MRC as defined by multilineage dysplasia share a common mutation pattern which is distinct from AML-MRC as defined by MDS-related cytogenetics. Leukemia 2022;36:1939-42. Gao Y, Jia M, Mao Y, Cai H, Jiang X, Cao X, et al. Distinct Mutation Landscapes Between Acute Myeloid Leukemia With Myelodysplasia-Related Changes and De Novo Acute Myeloid Leukemia. Am J Clin Pathol 2022;157:691-700. Pratcorona M, Abbas S, Sanders MA, Koenders JE, Kavelaars FG, Erpelinck-Verschueren CA, et al. Acquired mutations in ASXL1 in acute myeloid leukemia: prevalence and prognostic value. Haematologica 2012;97:388-92. Weinberg OK, Siddon A, Madanat YF, Gagan J, Arber DA, Dal Cin P, et al. TP53 mutation defines a unique subgroup within complex karyotype de novo and therapy-related MDS/AML. Blood Adv 2022;6:2847-53. Zhao D, Eladl E, Zarif M, Capo-Chichi JM, Schuh A, Atenafu E, et al. Molecular characterization of AML-MRC reveals TP53 mutation as an adverse prognostic factor irrespective of MRC-defining criteria, TP53 allelic state, or TP53 variant allele frequency. Cancer Med 2023;12:6511-22. Cornelissen JJ and Blaise D. Hematopoietic stem cell transplantation for patients with AML in first complete remission. Blood 2016;127:62-70. Daver NG, Iqbal S, Renard C, Chan RJ, Hasegawa K, Hu H, et al. Treatment outcomes for newly diagnosed, treatment-naïve TP53 -mutated acute myeloid leukemia: a systematic review and meta-analysis. J Hematol Oncol 2023;16:19. Additional Declarations No competing interests reported. Supplementary Files SupplementaryFig.1.jpg Supplementary Fig.1. Molecular landscape of patients with myelodysplastic neoplasm. SupplementaryTableS1.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 02 Oct, 2024 Reviews received at journal 02 Oct, 2024 Reviews received at journal 12 Sep, 2024 Reviewers agreed at journal 11 Sep, 2024 Reviewers agreed at journal 06 Sep, 2024 Reviewers invited by journal 06 Sep, 2024 Editor assigned by journal 06 Sep, 2024 Submission checks completed at journal 06 Sep, 2024 First submitted to journal 25 Aug, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-4974493\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":361413694,\"identity\":\"c03176b0-1874-4f45-a5f2-238984f3a26d\",\"order_by\":0,\"name\":\"Hyunwoo Kim\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Inje University Busan Paik Hospital, Inje University College of 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University Busan Paik Hospital, Inje University College of Medicine\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Hye\",\"middleName\":\"Ran\",\"lastName\":\"Kim\",\"suffix\":\"\"},{\"id\":361413699,\"identity\":\"436c5951-eb7f-491e-b797-7c3da2d00dcf\",\"order_by\":5,\"name\":\"Sang-min Lee\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Inje University Busan Paik Hospital, Inje University College of Medicine\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Sang-min\",\"middleName\":\"\",\"lastName\":\"Lee\",\"suffix\":\"\"},{\"id\":361413700,\"identity\":\"06d32e38-03d1-4f40-8f23-4ddaee45b051\",\"order_by\":6,\"name\":\"Won Sik Lee\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Inje University Busan Paik Hospital, Inje University College of Medicine\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Won\",\"middleName\":\"Sik\",\"lastName\":\"Lee\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2024-08-26 01:24:02\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-4974493/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-4974493/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":67194341,\"identity\":\"6e10e4f7-7177-4586-bee2-69a3ba93b2f3\",\"added_by\":\"auto\",\"created_at\":\"2024-10-22 08:56:13\",\"extension\":\"jpg\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":1584009,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eSankey diagram showing the reclassification of patients with acute myeloid leukemia (A) and myelodysplastic neoplasm (B) from the 2016 World Health Organization (WHO) to the 2022 WHO and International Consensus Classifications.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Fig.1.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4974493/v1/56b79652b1f3aaaa55975b5b.jpg\"},{\"id\":67195005,\"identity\":\"f8a2bfe5-68fb-4b76-9a39-13d6c9a90c40\",\"added_by\":\"auto\",\"created_at\":\"2024-10-22 09:04:13\",\"extension\":\"jpg\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":2789345,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eMolecular landscape of patients with acute myeloid leukemia.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Fig.2.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4974493/v1/0b633eb0513d5e13d48f6a22.jpg\"},{\"id\":67196561,\"identity\":\"e0287b9a-2def-4757-a197-74ae119491e2\",\"added_by\":\"auto\",\"created_at\":\"2024-10-22 09:12:13\",\"extension\":\"jpg\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":1099240,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eSurvival analysis of patients with acute myeloid leukemia (AML) and myelodysplasia-related (MR) gene mutations based on the 2022 WHO classification. (A) survival outcomes between patients with AML and MR mutations and those with other types of AML (B) survival outcomes between groups of patients with AML with myelodysplastic changes (MRC), classified according to the 2016 WHO and MR gene mutation criteria in the 2022 WHO, and those with AML, not otherwise specified, according to the 2016 WHO and AML with MR gene mutation in the 2022 WHO (C) survival outcomes between groups of patients with AML with MRC based on the 2016 WHO and AML with MR gene mutations in the 2022 WHO, and those with AML with MRC in the 2016 WHO but without MR gene mutation in the 2022 WHO (D) survival outcomes between groups of patients of AML with MR gene mutations and cytogenetic abnormalities.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Fig.3.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4974493/v1/c5b4195049107c68332a63f3.jpg\"},{\"id\":67194343,\"identity\":\"296a0543-dbbf-4941-bee7-637bd8cc722a\",\"added_by\":\"auto\",\"created_at\":\"2024-10-22 08:56:13\",\"extension\":\"jpg\",\"order_by\":4,\"title\":\"Figure 4\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":501515,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eSurvival analysis of patients with AML and \\u003cem\\u003eTP53\\u003c/em\\u003e mutations based on the international consensus classification. (A) Survival outcomes between patients in AML with \\u003cem\\u003eTP53\\u003c/em\\u003emutations and other AML group (B) survival outcomes between patients in AML with\\u003cem\\u003e TP53\\u003c/em\\u003e mutation and AML with myelodysplasia-related gene mutations.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Fig.4.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4974493/v1/e8b1a969f65b88d7d2ab479b.jpg\"},{\"id\":67198332,\"identity\":\"2b77d7de-57a5-46f7-bda7-9aa06ab76c77\",\"added_by\":\"auto\",\"created_at\":\"2024-10-22 09:28:16\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":7176453,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4974493/v1/a1fcfcf3-4301-4544-b138-6a00fd38db91.pdf\"},{\"id\":67197335,\"identity\":\"54cfcd28-13e9-4259-9c86-d6dec6e8583c\",\"added_by\":\"auto\",\"created_at\":\"2024-10-22 09:20:13\",\"extension\":\"jpg\",\"order_by\":1,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":1712803,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eSupplementary Fig.1\\u003c/strong\\u003e. Molecular landscape of patients with myelodysplastic neoplasm.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"SupplementaryFig.1.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4974493/v1/728835fd8dde7ea9fc92ff3e.jpg\"},{\"id\":67195004,\"identity\":\"e4e55087-bda4-42f2-acfc-2f5cbed11d55\",\"added_by\":\"auto\",\"created_at\":\"2024-10-22 09:04:13\",\"extension\":\"docx\",\"order_by\":2,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":17775,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"SupplementaryTableS1.docx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4974493/v1/ca724b71a24fe79747051165.docx\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Acute Myeloid Leukemia and Myelodysplastic Neoplasms: Clinical Implications of Myelodysplasia-Related Genes Mutations and TP53 Aberrations \",\"fulltext\":[{\"header\":\"Introduction\",\"content\":\"\\u003cp\\u003eThe World Health Organization (WHO) classification of myeloid neoplasms has been revised several times to improve our understanding of the molecular features of this disease [\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e]. Development of molecular genetic technology has advanced our understanding of myeloid neoplasms by adding distinct groups to their classification, such as acute myeloid leukemia (AML) with genetic abnormalities [\\u003cspan additionalcitationids=\\\"CR3\\\" citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e]. The commercialization of next-generation sequencing (NGS) has introduced genetic information into the diagnostic criteria for AML and myelodysplastic neoplasm (MDS). Recent studies have provided a detailed census of genes mutated in myeloid neoplasms; thus, the number of gene mutations incorporated into AML diagnosis and risk stratification has increased [\\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e]. The fifth edition of the WHO classification (2022 WHO) and the International Consensus Classification (ICC) of myeloid neoplasms have been published [\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e]. Changes included lowering the blast threshold that defines AML and renaming \\u0026ldquo;myelodysplastic syndrome\\u0026rdquo; as \\u0026ldquo;myelodysplastic neoplasm\\u0026rdquo;.\\u003c/p\\u003e \\u003cp\\u003eOne of the largest differences between the revised fourth WHO classification (2016 WHO) and the 2022 WHO/ICC is the change in diagnostic criteria for AML associated with myelodysplasia. In the 2016 WHO, the main diagnostic criteria of AML with myelodysplasia-related changes (AML-MRC) were morphological changes in the bone marrow (BM), a history of MDS, and chromosomal abnormalities [\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e]. In the 2022 WHO, morphological dysplasia alone was excluded from the criteria and mutations in eight myelodysplasia-related (MR) genes (\\u003cem\\u003eASXL1, BCOR, SF3B1, EZH2, SRSF2, STAG2, U2AF1\\u003c/em\\u003e, and \\u003cem\\u003eZSZR2\\u003c/em\\u003e) were included [\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e]. \\u003cem\\u003eRUNX1\\u003c/em\\u003e mutations were added to the ICC criteria in addition to the eight MR genes [\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eAnother significant difference is the addition of MDS and AML groups associated with \\u003cem\\u003eTP53\\u003c/em\\u003e mutations [\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e]. In the 2022 WHO, the subtype MDS with biallelic \\u003cem\\u003eTP53\\u003c/em\\u003e inactivation (MDS-bi\\u003cem\\u003eTP53\\u003c/em\\u003e) was identified when there are two or more \\u003cem\\u003eTP53\\u003c/em\\u003e mutations or when there is one mutation with evidence of \\u003cem\\u003eTP53\\u003c/em\\u003e copy number loss [\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e]. A subtype of MDS with mutated \\u003cem\\u003eTP53\\u003c/em\\u003e (MDS-\\u003cem\\u003eTP53\\u003c/em\\u003e) was added to the ICC. AML with mutated \\u003cem\\u003eTP53\\u003c/em\\u003e (AML-\\u003cem\\u003eTP53\\u003c/em\\u003e) was added only to the ICC [\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e]. Subsequently, the 2022 European Leukemia Net (ELN) risk stratification was revised to include new diagnostic classifications [\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e]. In addition to \\u003cem\\u003eASXL1\\u003c/em\\u003e and \\u003cem\\u003eRUNX1\\u003c/em\\u003e, which had already been classified as adverse in the 2017 ELN risk stratification, six other MR gene mutations were newly classified as adverse.\\u003c/p\\u003e \\u003cp\\u003eThese changes have advanced our understanding of the molecular genetic characteristics of AML and MDS and helped apply this knowledge to clinical diagnosis and therapeutic strategies. Several studies have primarily focused on reclassification, examining differences in diagnostic criteria between 2016 WHO and the two new classifications [\\u003cspan additionalcitationids=\\\"CR9\\\" citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e]. However, recent studies have targeted the prognostic effects of specific AML subtypes, such as AML, myelodysplasia-related (AML-MR), and AML-\\u003cem\\u003eTP53\\u003c/em\\u003e [\\u003cspan additionalcitationids=\\\"CR12\\\" citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e]. In Korea, a study found that patients with AML-MR according to the 2022 WHO had a shorter overall survival (OS) similar to that of patients with AML-MRC according to the 2016 WHO [\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e], and a study of a small group of patients with MDS reported that those with \\u003cem\\u003eTP53\\u003c/em\\u003e mutations had a worse prognosis than those without \\u003cem\\u003eTP53\\u003c/em\\u003e mutations [\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eIn this study, we reclassified patients with myeloid neoplasms who were initially classified according to the 2016 WHO at our institution according to the revised criteria. We identified gene mutation rates in AML and MDS and performed survival analyses for the subgroups, particularly focusing on AML with MR gene and \\u003cem\\u003eTP53\\u003c/em\\u003e mutations. We compared the clinical outcomes of these groups with those of other AML subgroups to evaluate the clinical usefulness of the new classifications.\\u003c/p\\u003e\"},{\"header\":\"Materials and Methods\",\"content\":\"\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003ePatient selection and data collection\\u003c/h2\\u003e \\u003cp\\u003eWe included all patients aged 18 years or older who had a BM examination and targeted NGS between August 2019 and July 2023. During this period, 288 patients underwent BM examination with suspicion of AML or MDS, of which 105 were tested with NGS at the time of initial diagnosis. The electronic medical records were retrospectively reviewed with respect to each patient\\u0026rsquo;s demographic data and laboratory findings, including BM examination results, treatment information, and survival outcomes. After excluding four patients who subsequently relapsed and received the same NGS results as before, 101 patients were enrolled. This study was approved by the Institutional Review Board of Inje University, Busan Paik Hospital (No. 2023-10-026), which waived the need for informed consent. After reviewing the data, we reclassified the patients according to the 2022 WHO and ICC [\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e].\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec4\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eMolecular tests including targeted next-generation sequencing\\u003c/h2\\u003e \\u003cp\\u003eFor fusion gene detection, RNA was isolated from BM using a QIAamp RNA Blood Mini Kit (Qiagen, Hilden, Germany). Multiplex reverse transcription polymerase chain reaction was performed using the HemaVision-28N Panel (DNA Diagnostic, Risskov, Denmark).\\u003c/p\\u003e \\u003cp\\u003eNGS was performed using a Miseq Dx sequencing platform (Illumina Inc., San Diego, CA, USA). We identified 48 genes associated with AML and MDS (Supplementary Table \\u003cspan refid=\\\"MOESM1\\\" class=\\\"InternalRef\\\"\\u003eS1\\u003c/span\\u003e). The targeted specimen was genomic DNA isolated from BM aspirates, and the target enrichment method was hybridized with oligonucleotide probes. The panel version and bioinformatics pipeline were NGB-Wet-V2.0 and NGB-DNA-somatic-V1.3, respectively. The sequence was aligned with the human reference genome GRCh37. The average depth of coverage was 592.8X. Among the NGS test results, gene mutations with Tier 3, unknown clinical significance, were excluded [\\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e].\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec5\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eKaryotyping\\u003c/h2\\u003e \\u003cp\\u003eBM samples were processed after 24 and 48 hours of unstimulated culture using GTL-banding (Giemsa banding using trypsin and Leishman stain). The band resolution was 300 to 400 bands. Karyotypes were interpreted according to the International System for Human Cytogenetic Nomenclature [\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e].\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec6\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eStatistical analysis\\u003c/h2\\u003e \\u003cp\\u003eChi-square and Fisher\\u0026rsquo;s exact tests were used to detect differences in the gene mutation distribution in each classification system. We divided with AML patients into prognostic groups based on ELN risk stratifications [\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e]. The OS was defined as the period from the time of diagnosis to death or the end of follow-up. The endpoint was October 31, 2023. We used the Kaplan-Meier curve and log-rank test to analyze the OS of the patients in each group. All statistical analyses were performed using MedCalc (Ver 12.4, MedCalc Software, Ostend, Belgium) and R (R version 4.4.1; Rstudio version 2024. 04.2\\u0026ndash;764); \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05 was considered statistically significant.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec7\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eHuman Ethics and Consent to Participate declarations\\u003c/h2\\u003e \\u003cp\\u003eThis study was a retrospective medical record analysis using only general clinical characteristics, molecular genetic test results, and charts of patients. No unnecessary blood collection or tests were performed. In addition, personal identification information was anonymized and managed. The study was conducted following the principles adopted in the Declaration of Helsinki, and was approved by the Institutional Review Board of Inje University, Busan Paik Hospital, Busan, Korea (2023-10-026).\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"Results\",\"content\":\"\\u003cdiv id=\\\"Sec9\\\"\\u003e\\n \\u003ch2\\u003eReclassification of AML and MDS based on the revised criteria\\u003c/h2\\u003e\\n \\u003cp\\u003eSeventy-seven patients with AML and 24 with MDS were enrolled in this study. The median age for the AML group was 67 years (range, 19\\u0026ndash;88), and that of the MDS group was 73 years (range, 54\\u0026ndash;86). Fifty-eight patients were male (57.4%).\\u003c/p\\u003e\\n \\u003cp\\u003eAmong the 77 patients with AML, 23 (29.9%) and 32 (41.6%) patients were reclassified into other groups based on the 2022 WHO and ICC, respectively (Fig.\\u0026nbsp;\\u003cspan\\u003e1\\u003c/span\\u003eA). Using the revised criteria, 33 patients (42.9%) with AML and recurrent genetic abnormalities were classified the same as the 2016 WHO, with only the group names changing (Table\\u0026nbsp;\\u003cspan\\u003e1\\u003c/span\\u003e). Of the 17 patients (22.1%) with AML-MRC in the 2016 WHO, 15 were reclassified as AML-MR based on the 2022 WHO. Seven of these had \\u003cem\\u003eTP53\\u003c/em\\u003e mutations and were reclassified as AML-\\u003cem\\u003eTP53\\u003c/em\\u003e according to the ICC. Of the 18 patients (23.4%) diagnosed with AML, not otherwise specified (NOS) according to the 2016 WHO, 11 (61.1%) were reclassified as AML-MR based on the 2022 WHO. For the ICC, 13 (72.2%) were reclassified as AML-MR due to the difference in MR gene mutations between the two classifications. One patient with t(1;11)(p32;q23) was reclassified as AML with \\u003cem\\u003eKMT2A\\u003c/em\\u003e rearrangement (AML-\\u003cem\\u003eKMT2A\\u003c/em\\u003e) using the 2022 WHO and AML with other \\u003cem\\u003eKMT2A\\u003c/em\\u003e rearrangements using the ICC.\\u003c/p\\u003e\\n \\u003cdiv\\u003e\\n \\u003ctable id=\\\"Tab1\\\" border=\\\"1\\\"\\u003e\\n \\u003ccaption language=\\\"En\\\"\\u003e\\n \\u003cdiv\\u003eTable 1\\u003c/div\\u003e\\n \\u003cdiv\\u003e\\n \\u003cp\\u003eReclassification of acute myeloid leukemia and myelodysplastic neoplasm based on the 2022 WHO and ICC\\u003c/p\\u003e\\n \\u003c/div\\u003e\\n \\u003c/caption\\u003e\\n \\u003ccolgroup cols=\\\"10\\\"\\u003e\\u003c/colgroup\\u003e\\n \\u003cthead\\u003e\\n \\u003ctr\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2016 WHO\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eN\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e%\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2022 WHO\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eN\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e%\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eICC\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eN\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e%\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/thead\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" rowspan=\\\"14\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eAML\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eRUNX1-RUNX1T1\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e3\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e3.9%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eRUNX1::RUNX1T1\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e3\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e3.9%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eRUNX1::RUNX1T1\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e3\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e3.9%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eCBFB-MYH11\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e2.6%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eCBFB::MYH11\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e2.6%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eCBFB::MYH11\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e2.6%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003ePML-RARA\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e3\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e3.9%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003ePML::RARA\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e5.2%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003ePML::RARA\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e5.2%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eDEK-NUP214\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e2.6%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eDEK::NUP214\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e2.6%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eDEK::NUP214\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e2.6%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eMLLT3-KMT2A\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e5.2%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eKMT2A\\u003c/em\\u003e rearrangement\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e6\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e7.8%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eMLLT3::KMT2A\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e5.2%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eOther \\u003cem\\u003eKMT2A\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e2.6%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eNPM1\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e13\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e16.9%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eNPM1\\u003c/em\\u003e mutation\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e13\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e16.9%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003emutated \\u003cem\\u003eNPM1\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e13\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e16.9%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003ebi\\u003cem\\u003eCEBPA\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e6\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e7.8%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eCEBPA\\u003c/em\\u003e mutation\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e6\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e7.8%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003ebZIP \\u003cem\\u003eCEBPA\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e6\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e7.8%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eMyelodysplasia-related changes\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e17\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e22.1%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eMyelodysplasia-related\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e30\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e39.0%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eMR gene\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e24\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e31.2%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eMR cytogenetic abnormalities\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e1.3%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003ePanmyelosis with myelofibrosis\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e5.2%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eBlast-phase MPN\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e3\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e3.9%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eNA\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eNA\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eNA\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003emutated \\u003cem\\u003eTP53\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e10\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e13.0%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eNot otherwise specified\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e18\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e23.4%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eDefined by differentiation\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e8\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e10.4%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eNot otherwise specified\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e6\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e7.8%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eTherapy-related\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e5\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e6.5%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eNA\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eNA\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" rowspan=\\\"10\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eMDS\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eSingle lineage dysplasia\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e4.2%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eLow blasts\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e10\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e41.7%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eNOS with single lineage dysplasia\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e4.2%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eMultilineage dysplasia\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e9\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e37.5%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eNOS with multilineage dysplasia\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e9\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e37.5%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eExcess blasts-1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e8.3%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eIncreased blasts-1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e8.3%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eExcess blasts\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e8.3%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eExcess blasts-2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e5\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e20.8%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eIncreased blasts-2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e6\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e25.0%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eMDS/AML\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e8.3%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eMDS/AML with MR gene mutations\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e16.7%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eMDS/AML with \\u003cem\\u003eTP53\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e3\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e12.5%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eRing sideroblasts\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e4.2%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eLow blasts and \\u003cem\\u003eSF3B1\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e4.2%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eSF3B1\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e4.2%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eNA\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003ebi\\u003cem\\u003eTP53\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e5\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e20.8%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003emutated with \\u003cem\\u003eTP53\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e8.3%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eUnclassifiable\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e4.2%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eNA\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eNA\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eTherapy-related\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e5\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e20.8%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eNA\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eNA\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n \\u003ctfoot\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"10\\\"\\u003eAbbreviations: WHO, World Health Organization; ICC, International Consensus Classification; AML, acute myeloid leukemia; bZIP, basic leucine zipper; MR, myelodysplasia-related; NA, not applicable; MDS, myelodysplastic neoplasm.\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tfoot\\u003e\\n \\u003c/table\\u003e\\n \\u003c/div\\u003e\\n \\u003cp\\u003eIn the 2022 WHO and ICC, therapy-related AML (t-AML) was removed and added as an additional qualifier to other AML categories. Of the five patients who were diagnosed with t-AML in the 2016 WHO, three were reclassified as AML-MR, one as acute promyelocytic leukemia (APL), and one as AML-\\u003cem\\u003eKMT2A\\u003c/em\\u003e according to the 2022 WHO. In the ICC, two patients diagnosed with t-AML according to 2016WHO were reclassified as the same with the 2022 WHO, whereas three patients with AML-MR according to the 2022 WHO were reclassified as AML-\\u003cem\\u003eTP53\\u003c/em\\u003e, AML-MR and AML with MR cytogenetic abnormalities in the ICC, respectively. Patients with panmyelosis and fibrosis was also excluded, and three patients had a history of myeloproliferative neoplasm (MPN). Therefore, they were reclassified as blast-phase MPN according to the 2022 WHO. In the ICC, these patients were reclassified as AML-MR and AML-\\u003cem\\u003eTP53\\u003c/em\\u003e, respectively, according on the presence of genetic mutations.\\u003c/p\\u003e\\n \\u003cp\\u003eAmong the 24 patients with MDS, 7 (29.2%) and 12 (50.0%) patients were reclassified into other groups based on the 2022 WHO and ICC, respectively (Fig.\\u0026nbsp;\\u003cspan\\u003e1\\u003c/span\\u003eB). Despite adjustment of the blast threshold in the 2022 WHO and ICC schemes, no cases of MDS were reclassified as AML because of the absence of defined genetic abnormalities or rare gene fusion. Instead, in the ICC, one patient with MDS with excess blasts 2 (MDS-EB2) and one patient with t-MDS according to the 2016 WHO were reclassified into the MDS/AML group, and four patients with MDS-EB2 were reclassified into MDS/AML with MR gene mutations. One patient with MDS with multilineage dysplasia (MDS-MD) and one with MDS with excess blast 1 (MDS-EB1) in the 2016 WHO was reclassified as MDS-bi\\u003cem\\u003eTP53\\u003c/em\\u003e based on the 2022 WHO. Based on the ICC, they were reclassified as MDS-\\u003cem\\u003eTP53\\u003c/em\\u003e. Three patients with t-MDS were reclassified as having MDS/AML with mutated \\u003cem\\u003eTP53\\u003c/em\\u003e (MDS/AML-\\u003cem\\u003eTP53\\u003c/em\\u003e) in the ICC and MDS-bi\\u003cem\\u003eTP53\\u003c/em\\u003e in the 2022 WHO. One patient with MDS, unclassifiable (MDS-U) was reclassified as having MDS with low blasts (MDS-LB) in the 2022 WHO and MDS, NOS with multilineage dysplasia in the ICC. MDS with ring sideroblasts (MDS-RS) was revised, and the name was changed to MDS with low blasts and \\u003cem\\u003eSF3B1\\u003c/em\\u003e mutation (MDS, LB and \\u003cem\\u003eSF3B1\\u003c/em\\u003e) in the 2022 WHO and MDS with mutated \\u003cem\\u003eSF3B1\\u003c/em\\u003e (MDS-\\u003cem\\u003eSF3B1\\u003c/em\\u003e) in the ICC.\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec10\\\"\\u003e\\n \\u003ch2\\u003eGenetic mutation landscape in AML and MDS\\u003c/h2\\u003e\\n \\u003cp\\u003eApproximately 84.2% (85/101) of all patients had mutations in 29 genes. In total, 89.6% (69/77) of the patients with AML and 66.7% (16/24) of those with MDS had 211 mutations (median 2, range 0\\u0026ndash;9 per patient) and 36 mutations (median 2, range 0\\u0026ndash;7 per patient), respectively (Fig.\\u0026nbsp;\\u003cspan\\u003e2\\u003c/span\\u003e). Among 69 patients with AML and mutations, 16 (20.8%) had 2 mutations and 36 patients (52.2%) had\\u0026thinsp;\\u0026ge;\\u0026thinsp;3 mutations. The most common AML mutations were \\u003cem\\u003eASXL1\\u003c/em\\u003e, \\u003cem\\u003eIDH2\\u003c/em\\u003e and \\u003cem\\u003eNRAS\\u003c/em\\u003e (14 patients, 18.2%), followed by \\u003cem\\u003eDNMT3A\\u003c/em\\u003e, \\u003cem\\u003eFLT3\\u003c/em\\u003e-ITD and \\u003cem\\u003eNPM1\\u003c/em\\u003e (13 patients, 16.9%). \\u003cem\\u003eASXL1\\u003c/em\\u003e and \\u003cem\\u003eIDH2\\u003c/em\\u003e mutations were predominantly identified in AML-MR patients, whereas \\u003cem\\u003eNRAS\\u003c/em\\u003e mutation were frequently detected in patients with AML, defining genetic abnormalities in the 2022 WHO. \\u003cem\\u003eDNMT3A\\u003c/em\\u003e and \\u003cem\\u003eFLT3\\u003c/em\\u003e-ITD mutations were most commonly observed in patients with AML harboring \\u003cem\\u003eNPM1\\u003c/em\\u003e mutation. In a survival analysis of gene mutations present in more than 10% of patients with AML, we detected a statistically significant difference in median OS based on only \\u003cem\\u003eFLT3-ITD\\u003c/em\\u003e and \\u003cem\\u003eTP53\\u003c/em\\u003e mutations (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05; median OS 7.2 and 2.3 months with mutation vs. 11.7 and 10.3 months without mutation, respectively).\\u003c/p\\u003e\\n \\u003cp\\u003eIn patients with MDS, the most frequently observed gene mutations were \\u003cem\\u003eASXL1\\u003c/em\\u003e and \\u003cem\\u003eTP53\\u003c/em\\u003e, each found in five patients (20.8%, supplementary Fig.\\u0026nbsp;1). Gene mutations in \\u003cem\\u003eASXL1\\u003c/em\\u003e, \\u003cem\\u003eRUNX1\\u003c/em\\u003e, and \\u003cem\\u003eSTAG2\\u003c/em\\u003e were significantly more common in patients with MDS with increased blasts (MDS-IB) than in those with MDS-LB in the 2022 WHO (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05, Table\\u0026nbsp;\\u003cspan\\u003e2\\u003c/span\\u003e).\\u003c/p\\u003e\\n \\u003cdiv\\u003e\\n \\u003ctable id=\\\"Tab2\\\" border=\\\"1\\\"\\u003e\\n \\u003ccaption language=\\\"En\\\"\\u003e\\n \\u003cdiv\\u003eTable 2\\u003c/div\\u003e\\n \\u003cdiv\\u003e\\n \\u003cp\\u003ePatient characteristics and gene mutations in myelodysplastic neoplasm\\u003c/p\\u003e\\n \\u003c/div\\u003e\\n \\u003c/caption\\u003e\\n \\u003ccolgroup cols=\\\"6\\\"\\u003e\\u003c/colgroup\\u003e\\n \\u003cthead\\u003e\\n \\u003ctr\\u003e\\n \\u003cth align=\\\"left\\\" rowspan=\\\"2\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\" rowspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eAll MDS\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\" colspan=\\\"4\\\"\\u003e\\n \\u003cp\\u003e2022 WHO\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eLow blasts\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eIncreased blasts\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003ebi\\u003cem\\u003eTP53\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eP\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/thead\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003ePatients\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e24\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e11\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e8\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e5\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eSex, male: female\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e15:9\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e6:5\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e6:2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e3:2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003e0.656\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eAge, years (range)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e73(54\\u0026ndash;86)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e73 (56\\u0026ndash;83)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e70 (54\\u0026ndash;85)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e70 (58\\u0026ndash;80)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003e0.930\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eLaboratory findings, median value\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eWhite blood cell, \\u0026times;10\\u003csup\\u003e9\\u003c/sup\\u003e/L, median (range)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2.48 (0.31\\u0026ndash;34.12)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2.44 (0.31\\u0026ndash;3.78)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2.27 (1.05\\u0026ndash;34.12)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e3.46 (1.70\\u0026ndash;7.87)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003e0.406\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eHemoglobin, g/dL, median (range)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e8.3 (4.1\\u0026ndash;12.7)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e9.1 (5.5\\u0026ndash;11.0)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e6.9 (5.9\\u0026ndash;12.7)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e7.5 (4.1\\u0026ndash;9.7)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003e0.537\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003ePlatelet, \\u0026times;10\\u003csup\\u003e9\\u003c/sup\\u003e/L, median (range)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e67.5 (6.0-264.0)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e77.0 (6.0-259.0)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e57.5 (10.0-264.0)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e62.0 (21.0-102.0)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003e0.931\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eBlasts in peripheral blood, %, median (range)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0 (0\\u0026ndash;9)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0 (0\\u0026ndash;1)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1 (0\\u0026ndash;9)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e3 (0\\u0026ndash;7)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003e0.004\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eBlasts in bone marrow, %, median (range)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e4.8 (0-19.2)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2.2 (0-4.5)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e14.5 (5.1\\u0026ndash;19.2)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e13.0 (2.6\\u0026ndash;13.4)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eCytogenetics\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eAbnormal karyotype, N (%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e11 (45.8%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e4 (36.4%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e3 (37.5%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e4 (80.0%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003e0.226\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eComplex karyotype, N (%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e6 (25.0%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2 (18.2%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0 (0%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e4 (80.0%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003e0.003\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eGene mutations\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eN, median (range)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2 (0\\u0026ndash;7)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0 (0\\u0026ndash;3)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e4 (0\\u0026ndash;7)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0 (0\\u0026ndash;2)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003e0.040\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eTumor suppressor\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eTP53\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e5 (20.8%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e5 (100%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eTranscription factors (except MR gene)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eRUNX1\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e4 (16.7%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e4 (42.9%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003e0.008\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eCEBPA\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2 (8.3)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2 (28.6%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003e0.113\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eMyelodysplasia related genes\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eASXL1\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e5 (20.8%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e5 (62.5%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003e0.002\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eBCOR\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e3 (12.5%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1 (9.1%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2 (25.0%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003e0.373\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eSF3B1\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2 (8.3%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1 (9.1%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003e0.725\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eSRSF2\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2 (8.3%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2 (25.0%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003e0.113\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eSTAG2\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e4 (16.7%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e4 (50.0%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003e0.008\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eU2AF1\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1 (4.2%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1 (12.5%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003e0.352\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eZRSR2\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1 (4.2%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1 (9.1%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003e0.540\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eDNA methylation\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eDNMT3A\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e3 (12.5%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2 (18.2%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1 (12.5%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003e0.595\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eIDH2\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1 (4.2%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1 (12.5%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003e0.352\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eTET2\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1 (4.2%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1 (12.5%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003e0.352\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eRNA helicase\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eDDX41\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2 (8.3%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2 (18.2%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003e0.276\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n \\u003ctfoot\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"6\\\"\\u003eAbbreviations: MDS, myelodysplastic neoplasm; WHO, World Health Organization; MR, myelodysplasia-related.\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tfoot\\u003e\\n \\u003c/table\\u003e\\n \\u003c/div\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003ePatient characteristics and clinical outcomes in AML with MR gene mutations based on 2022 WHO classification\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003cp\\u003eAmong 25 patients with AML and MR gene mutations, three had gene fusions, two had a previous history of myeloproliferative neoplasm, and one had an \\u003cem\\u003eNPM1\\u003c/em\\u003e mutation. Six patients were not classified as AML-MR according to the 2022 WHO. Thirty patients were classified as AML-MR according to the 2022 WHO, and MR gene mutations were present in 66.7% (20/30). The remaining nine patients had complex karyotypes, and one patient had a history of MDS.\\u003c/p\\u003e\\n \\u003cp\\u003eIn our study, \\u003cem\\u003eASXL1\\u003c/em\\u003e mutation (N\\u0026thinsp;=\\u0026thinsp;9, 30.0%) was the most frequent among MR genes, followed by \\u003cem\\u003eSRSF2\\u003c/em\\u003e mutation (N\\u0026thinsp;=\\u0026thinsp;6, 20.0%). Although \\u003cem\\u003eRUNX1\\u003c/em\\u003e mutation is included in MR genes only in the ICC, most of the \\u003cem\\u003eRUNX1\\u003c/em\\u003e mutations (75%, 6/8) were found in patients with AML-MR in the 2022 WHO. Patients with AML-MR were significantly older, had lower white blood cell (WBC) counts, and were more likely to have complex karyotypes than patients in the other groups (Table\\u0026nbsp;\\u003cspan\\u003e3\\u003c/span\\u003e). When analyzing the frequency of gene mutations between the AML-MR and other groups, \\u003cem\\u003eTP53\\u003c/em\\u003e and \\u003cem\\u003eSRSF2\\u003c/em\\u003e mutations showed significantly higher rate in patients with AML-MR (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05).\\u003c/p\\u003e\\n \\u003cdiv\\u003e\\n \\u003ctable id=\\\"Tab3\\\" border=\\\"1\\\"\\u003e\\n \\u003ccaption language=\\\"En\\\"\\u003e\\n \\u003cdiv\\u003eTable 3\\u003c/div\\u003e\\n \\u003cdiv\\u003e\\n \\u003cp\\u003ePatient characteristics and gene mutations in acute myeloid leukemia\\u003c/p\\u003e\\n \\u003c/div\\u003e\\n \\u003c/caption\\u003e\\n \\u003ccolgroup cols=\\\"10\\\"\\u003e\\u003c/colgroup\\u003e\\n \\u003cthead\\u003e\\n \\u003ctr\\u003e\\n \\u003cth align=\\\"left\\\" rowspan=\\\"2\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\" rowspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eAll AML\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\" colspan=\\\"3\\\"\\u003e\\n \\u003cp\\u003e2022 WHO\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\" colspan=\\\"4\\\"\\u003e\\n \\u003cp\\u003eICC\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eAML-MR\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eAML-others\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eP\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eAML-TP53\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eAML-MR\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eAML-others\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eP\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/thead\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003ePatients\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e77\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e30\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e47\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e10\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e25\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e42\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eSex, male: female\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e43:34\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e20:10\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e23:24\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003e0.196\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e8:2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e14:11\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e21:21\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003e0.229\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eAge, years (range)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e67 (19\\u0026ndash;88)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e71 (32\\u0026ndash;87)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e62 (19\\u0026ndash;88)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003e0.002\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e69.5 (57\\u0026ndash;87)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e71 (32\\u0026ndash;83)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e62 (19\\u0026ndash;88)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003e0.034\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eLaboratory findings, median value\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eWBC, \\u0026times;10\\u003csup\\u003e9\\u003c/sup\\u003e/L, median (range)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e5.94 (0.81\\u0026ndash;231.1)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e3.25 (1.03-224.18)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e13.71(0.81-231.05)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003e0.005\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e3.29 (1.57\\u0026ndash;26.92)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e3.33 (1.02-224.18)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e15.27 (0.81-231.05)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003e0.009\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eHemoglobin, g/dL, median (range)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e8.0 (2.6\\u0026ndash;15.0)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e7.9 (2.7\\u0026ndash;11.0)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e8.3 (2.6\\u0026ndash;15.0)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003e0.154\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e8.3 (5.3\\u0026ndash;9.8)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e7.8 (2.7\\u0026ndash;11.0)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e8.3 (2.6\\u0026ndash;15.0)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003e0.647\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003ePlatelet, \\u0026times;10\\u003csup\\u003e9\\u003c/sup\\u003e/L, median (range)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e40.0 (3.0-344.0)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e39.5 (3-143)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e42.0 (6-344)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003e0.711\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e36.0 (3.0\\u0026ndash;48.0)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e51.0 (4.0-143.0)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e35.0 (6.0-344.0)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003e0.458\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eBlasts in PB, %, median (range)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e27 (0\\u0026ndash;96)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e18.5 (0\\u0026ndash;90)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e30 (0\\u0026ndash;96)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003e0.339\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e4.5 (0\\u0026ndash;90)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e27.0 (0\\u0026ndash;88)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e30.0 (0\\u0026ndash;96)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003e0.218\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eBlasts in BM, %, median (range)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e57.4 (13.7\\u0026ndash;92.7)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e52.0 (21.2\\u0026ndash;92.7)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e60.2 (13.7\\u0026ndash;91.8)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003e0.137\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e43.7 (23.2\\u0026ndash;90.3)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e52.0 (21.2\\u0026ndash;92.7)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e60.2 (13.7\\u0026ndash;91.8)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003e0.511\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eCytogenetics\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eAbnormal karyotype, N (%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e45 (58.4%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e20 (66.7%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e25 (53.2%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003e0.458\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e8 (80.0%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e13 (52.0%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e24 (57.1%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003e0.060\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eComplex karyotype, N (%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e14 (18.2%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e13 (43.3%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1 (2.1%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e8 (80.0%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e5 (20.0%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1 (2.4%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eGene mutations\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eN, median (range)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2 (0\\u0026ndash;9)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2.5 (0\\u0026ndash;6)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2 (0\\u0026ndash;9)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003e0.557\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1 (1\\u0026ndash;6)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e3 (0\\u0026ndash;6)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2 (0\\u0026ndash;9)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003e0.082\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eRAS pathway-related\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eNRAS\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e14 (18.2%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e4 (13.3)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e10 (21.3)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003e0.563\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e4 (16.0%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e10 (23.8%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003e0.202\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eKRAS\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e5 (6.5%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2 (6.7)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e3 (6.4)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003e0.671\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2 (8.0%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e3 (7.1%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003e0.667\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eKIT\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2 (2.6%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2 (4.3)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003e0.682\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2 (4.8%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003e0.425\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eFLT3\\u003c/em\\u003e-ITD\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e13 (16.9%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e4 (13.3)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e9 (19.1)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003e0.725\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e4 (16.0%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e9 (21.4%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003e0.264\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eFLT3\\u003c/em\\u003e-TKD\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e7 (9.1%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1 (3.3)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e6 (12.8)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003e0.319\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2 (8.0%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e5 (11.9%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003e0.487\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003ePTPN11\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e5 (6.5%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e5 (10.6)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003e0.170\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1 (10.0)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e4 (9.5%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003e0.276\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eTumor suppressor\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eTP53\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e11 (14.3%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e8 (26.7)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e3 (6.4)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003e0.032\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e10 (100.0)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1 (2.3)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003ePHF6\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1 (1.3%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1 (3.3)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003e0.820\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1 (4.0%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003e0.349\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eWT1\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e6 (7.8%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1 (3.3)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e5 (10.6)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003e0.465\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1 (4.0%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e5 (11.9%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003e0.311\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eTranscription factors (except MR gene)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eRUNX1\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e8 (10.4%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e6 (20.0)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2 (4.3)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003e0.068\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e8 (32.0%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eCEBPA\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e6 (7.8%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e6 (12.8)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003e0.109\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e6 (14.3%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003e0.067\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eSETBP1\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1 (1.3%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1 (3.3)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003e0.820\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n 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align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003e0.148\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eZRSR2\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e3 (3.9)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e3 (10.0)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003e0.108\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e3 (12.0%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003e0.039\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eDNA methylation\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eDNMT3A\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e13 (16.9)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e5 (16.7)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e8 (17.0)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003e0.786\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1 (10.0)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e4 (16.0%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e8 (19.0%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003e0.782\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eIDH1\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e4 (5.2)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2 (6.7)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2 (4.3)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003e0.951\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e3 (12.0%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1 (2.4%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003e0.168\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eIDH2\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e14 (18.2)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e6 (20.0)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e8 (17.0)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003e0.978\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1 (10.0)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e8 (32.0%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e5 (11.9%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003e0.092\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eTET2\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e9 (11.7)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e4 (13.3)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e5 (10.6)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003e0.996\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1 (10.0)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e4 (16.0%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e4 (9.5%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003e0.716\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eRisk group by ELN 2022 guideline\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eFavorable, N (%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e20 (26.0)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e20 (42.6%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e20 (47.6%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eIntermediate, N (%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e18 (23.4)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e18 (38.3%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e18 (42.9%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eAdverse, N (%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e39 (50.6)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e30 (100%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e9 (19.1%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e10 (100%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e25 (100%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e4 (9.5%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n \\u003ctfoot\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"10\\\"\\u003eAbbreviations: AML, acute myeloid leukemia; WHO, World Health Organization; ICC, International Consensus Classification; MR, myelodysplasia-related; PB, peripheral blood; BM, bone marrow; ELN, European Leukemia Net.\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tfoot\\u003e\\n \\u003c/table\\u003e\\n \\u003c/div\\u003e\\n \\u003cp\\u003eAccording to the 2022 ELN risk stratifications, all patients with AML-MR were classified into the adverse group. The median OS of the AML-MR group was shorter than that of other AML groups (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.464, median OS 7.5 vs. 10.3 months, Fig.\\u0026nbsp;\\u003cspan\\u003e3\\u003c/span\\u003eA). The median OS of patients with AML-MRC in the 2016 WHO and the AML-MR group in the 2022 WHO was shorter than that of patients with AML-NOS in the 2016 WHO classified as AML-MR in the 2022 WHO (\\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.174, median OS 4.6 vs 9.6 months, Fig.\\u0026nbsp;\\u003cspan\\u003e3\\u003c/span\\u003eB). In particular, the AML-MRC group in the 2016 WHO and the AML-MR group in the 2022 WHO demonstrated shorter OS than the group classified as AML-MRC in the 2016 WHO but not as AML-MR in the 2022 WHO (Fig.\\u0026nbsp;\\u003cspan\\u003e3\\u003c/span\\u003eC). When examining the OS of patients with AML those with cytogenetic abnormalities showed a relatively shorter median OS than those with MR gene mutations (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.473, median OS 2.4 vs. 9.6 months, Fig.\\u0026nbsp;\\u003cspan\\u003e3\\u003c/span\\u003eD). The median OS of patients with \\u003cem\\u003eSRSF2\\u003c/em\\u003e mutation (3.85 months) was the shortest among the patients with MR gene mutations; however, statistical significance was not reached because of the small sample size (Table\\u0026nbsp;\\u003cspan\\u003e4\\u003c/span\\u003e).\\u003c/p\\u003e\\n \\u003cdiv\\u003e\\n \\u003ctable id=\\\"Tab4\\\" border=\\\"1\\\"\\u003e\\n \\u003ccaption language=\\\"En\\\"\\u003e\\n \\u003cdiv\\u003eTable 4\\u003c/div\\u003e\\n \\u003cdiv\\u003e\\n \\u003cp\\u003eComparison of survival outcomes in patients with acute myeloid leukemia based on the presence of myelodysplasia-related gene mutations.\\u003c/p\\u003e\\n \\u003c/div\\u003e\\n \\u003c/caption\\u003e\\n \\u003ccolgroup cols=\\\"4\\\"\\u003e\\u003c/colgroup\\u003e\\n \\u003cthead\\u003e\\n \\u003ctr\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eGene\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eN\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eMedian OS (months)\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eRange\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/thead\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eASXL1\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e14\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e7.8\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.5\\u0026ndash;28.3\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eBCOR\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e3\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eNA\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e8.2\\u0026ndash;26.6\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eSF3B1\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e10.4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e2.6\\u0026ndash;18.2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eSRSF2\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e7\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e3.85\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.4\\u0026ndash;8.2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eSTAG2\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e6.7\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e3.3\\u0026ndash;10.1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eU2AF1\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e3\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eNA\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.6\\u0026ndash;6.7\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eZRSR2\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e3\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e7.8\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e7.2\\u0026ndash;14.1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n \\u003ctfoot\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"4\\\"\\u003eAbbreviations: OS, overall survival; NA, not applicable\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tfoot\\u003e\\n \\u003c/table\\u003e\\n \\u003c/div\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003ePatient characteristics and clinical outcomes in AML and MDS patients with TP53 mutations based on the ICC classification\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eTP53\\u003c/em\\u003e mutations were found in 11 patients with AML (14.3%) and 5 with MDS (20.8%). The median variant allelic frequencies of \\u003cem\\u003eTP53\\u003c/em\\u003e mutation in AML and MDS were 53.97% and 38.16%, respectively. Biallelic \\u003cem\\u003eTP53\\u003c/em\\u003e mutations were observed in only four patients with MDS. Ten patients with AML and \\u003cem\\u003eTP53\\u003c/em\\u003e mutations were classified as AML-\\u003cem\\u003eTP53\\u003c/em\\u003e according to the ICC. One patient with \\u003cem\\u003eMLLT3-KMT2A\\u003c/em\\u003e fusion was classified as AML with \\u003cem\\u003eMLLT3::KMT2A\\u003c/em\\u003e. In the ICC, AML was divided into three groups; AML-\\u003cem\\u003eTP53\\u003c/em\\u003e, AML-MR and AML. The AML-\\u003cem\\u003eTP53\\u003c/em\\u003e and AML-MR groups were significantly older, had lower WBC counts, and exhibited higher rates of complex karyotypes than the other groups. We identified a significant negative correlation between \\u003cem\\u003eTP53\\u003c/em\\u003e mutation and the expression of other genes. According to the ICC, 25 patients with AML-MR showed a significantly higher frequency of mutations in \\u003cem\\u003eASXL1, BCOR\\u003c/em\\u003e, and \\u003cem\\u003eZRSR\\u003c/em\\u003e (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05). Ten patients with AML-\\u003cem\\u003eTP53\\u003c/em\\u003e were classified into the adverse group according to the 2022 ELN risk stratification. The median OS of the AML-\\u003cem\\u003eTP53\\u003c/em\\u003e group was significantly shorter than that of the non-AML-\\u003cem\\u003eTP53\\u003c/em\\u003e group (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.0014, median OS 2.3 vs. 10.3 months, Fig.\\u0026nbsp;\\u003cspan\\u003e4\\u003c/span\\u003eA). They also had a significantly shorter OS than the AML-MR group (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.002, median OS 2.3 vs. 9.6 months, Fig.\\u0026nbsp;\\u003cspan\\u003e4\\u003c/span\\u003eB). Patients with MDS and \\u003cem\\u003eTP53\\u003c/em\\u003e mutations had shorter median OS than patients without \\u003cem\\u003eTP53\\u003c/em\\u003e mutations (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.1792; median OS 9.5 vs. 21.9 months).\\u003c/p\\u003e\\n\\u003c/div\\u003e\"},{\"header\":\"Discussion\",\"content\":\"\\u003cp\\u003eWe conducted a retrospective study on the newly revised classification of myeloid neoplasms, focusing on two key genetic alterations, MR genes and \\u003cem\\u003eTP53\\u003c/em\\u003e mutations, at a single institution to identify differences and assess their clinical utility.\\u003c/p\\u003e \\u003cp\\u003eThe major changes in the 2022 WHO were the exclusion of morphologic dysplasia from the diagnostic criteria for AML-MR and the addition of eight MR gene mutations [\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e]. In this study, 11 patients were reclassified from AML, NOS to AML-MR in the 2022 WHO, resulting in a larger AML-MR group than the AML group. Based on the ICC, the \\u003cem\\u003eRUNX1\\u003c/em\\u003e mutation was added to the AML- MR criteria [\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e], and 13 patients in the AML, NOS group were reclassified as AML-MR. Differences in chromosomal abnormalities were observed in the diagnostic criteria for myelodysplasia-related AML included in the 2022 WHO and ICC, such as 11q deletion, monosomy 13, or 13q deletion in the 2022 WHO and trisomy 8 or 20q deletion in the ICC. In this study, one patient was reclassified as having AML with MR cytogenetic abnormalities based on an abnormal karyotype. In a similar single-center study, Zhou et al. [\\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e] reported that the most common gene mutations in AML-MR were in \\u003cem\\u003eTP53\\u003c/em\\u003e, \\u003cem\\u003eRUNX1\\u003c/em\\u003e and \\u003cem\\u003eASXL1\\u003c/em\\u003e. Our results are consistent with these findings, and \\u003cem\\u003eASXL1\\u003c/em\\u003e and \\u003cem\\u003eTP53\\u003c/em\\u003e mutations were the most frequently found in patients with AML-MR. Among the MR genes, mutations in \\u003cem\\u003eSRSF2\\u003c/em\\u003e were the most common after \\u003cem\\u003eASXL1\\u003c/em\\u003e, similar to the frequencies reported in the Korean study by Park et al. [\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e]. In survival analysis, patients with AML-MR showed worse outcome. When comparing the survival rates between patients classified as AML-MRC in the 2016 WHO but not AML-MR in the 2022 WHO and patients classified as AML-MRC and AML-MR in the 2022 WHO, we found that MR-associated cytogenetic abnormalities and the presence of MR genes were more significant prognostic factors than morphological abnormalities [\\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e]. Additionally, patients with AML-MR with only cytogenetic abnormalities showed shorter survival outcome than those with only MR gene mutations. This finding aligns with those of previous reports, although statistical significance was not observed owing to the small sample size. Previous studies have reported a significantly shorter OS in patients with AML and \\u003cem\\u003eASXL1\\u003c/em\\u003e, \\u003cem\\u003eSRSF2\\u003c/em\\u003e, and \\u003cem\\u003eZRSR2\\u003c/em\\u003e mutations among MR genes than those without these mutations [\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e]. In our study, \\u003cem\\u003eZRSR2\\u003c/em\\u003e mutation was detected in only three patients with AML-MR, and was found together with \\u003cem\\u003eASXL1\\u003c/em\\u003e mutation in all cases. Consequently, despite its low detection frequency among MR genes, it significantly influences survival outcomes when co-occurring with \\u003cem\\u003eASXL1\\u003c/em\\u003e. In our study, although none of the MR gene mutations showed a significant difference in median OS, there were differences in the median OS based on the MR genes. Even though \\u003cem\\u003eRUNX1\\u003c/em\\u003e mutations are only included as MR genes in ICC, we observed a shorter survival time in patients with AML and \\u003cem\\u003eRUNX1\\u003c/em\\u003e mutations than patients with other MR gene mutations in this study, and it was the second most frequent mutations after \\u003cem\\u003eTP53\\u003c/em\\u003e in AML-MR in the 2022 WHO. Large-scale studies are needed to assess the difference in frequency and prognosis of MR gene mutations, including \\u003cem\\u003eRUNX1\\u003c/em\\u003e.\\u003c/p\\u003e \\u003cp\\u003eAnother major change in the 2022 WHO and ICC classification is the new category for \\u003cem\\u003eTP53\\u003c/em\\u003e mutations. \\u003cem\\u003eTP53\\u003c/em\\u003e mutations are commonly associated with complex karyotype, negative correlation with other gene mutations, and poor prognosis in AML and MDS [\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e22\\u003c/span\\u003e]. In our study, patients with AML and \\u003cem\\u003eTP53\\u003c/em\\u003e mutations showed similar characteristics and poor survival outcome. In the 2022 WHO, a distinct category of myeloid neoplasm with mutated \\u003cem\\u003eTP53\\u003c/em\\u003e is defined for MDS, but not for AML. Therefore, the AML-MR group included some patients with \\u003cem\\u003eTP53\\u003c/em\\u003e mutations, as they frequently occur in patients with complex chromosomal abnormalities. MR gene mutations are also associated with poor survival in AML; however, the survival analysis between the AML-MR and other AML groups did not show a statistically significant difference. In contrast, the ICC separated AML-MR and AML-\\u003cem\\u003eTP53\\u003c/em\\u003e, revealing a significant difference in the survival outcome between the two groups. This suggests that the AML-\\u003cem\\u003eTP53\\u003c/em\\u003e classification may provide a more precise stratification of patients with a poor prognosis based on the ICC. The MR genes has been reported to have better prognostic significance than the \\u003cem\\u003eTP53\\u003c/em\\u003e mutations and our study was consistent with that [\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eIn our data, patients with AML-\\u003cem\\u003eTP53\\u003c/em\\u003e presented with extremely poor survival and a median OS of only 2.3 months. This is significantly lower than the survival rates reported in previous studies of AML and \\u003cem\\u003eTP53\\u003c/em\\u003e mutations [\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e]. For patients with high-risk AML, intensive chemotherapy, followed by allogenic hematopoietic stem-cell transplantation (allo-HSCT), is recommended as a potentially curative approach [\\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e]. However, patients with AML harboring \\u003cem\\u003eTP53\\u003c/em\\u003e mutations exhibit a lower probability of achieving remission, and allo-HSCT has not been shown to improve OS [\\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e]. In our study, all 10 patients with AML-\\u003cem\\u003eTP53\\u003c/em\\u003e received chemotherapy, with only one patient undergoing allo-HSCT. Although the median OS of the patients who underwent HSCT was longer than that of patients who received chemotherapy alone (10.8 vs 2.3 months), this difference was not statistically significant. Therefore, an optimal treatment strategy for patients with AML-\\u003cem\\u003eTP53\\u003c/em\\u003e remains to be established.\\u003c/p\\u003e \\u003cp\\u003eThis study had several limitations. First, although treatment guidelines may affect prognosis, we did not classify patients based on their treatment regimens or account for treatment modification according to age. Consequently, it was not possible to perform survival analysis stratified by treatment modality. Additionally, because the data were derived from a single institution with a short observation period, some subgroups analyses were limited by the small number of cases that showed statistical significance. Finally, we only used NGS data without conducting fluorescence \\u003cem\\u003ein situ\\u003c/em\\u003e hybridization to identify \\u003cem\\u003eTP53\\u003c/em\\u003e mutations and related chromosomal abnormalities.\\u003c/p\\u003e \\u003cp\\u003eIn conclusion, the two newly revised classification criteria allowed us to further categorize patients diagnosed with AML, NOS as AML-MR or AML-\\u003cem\\u003eTP53\\u003c/em\\u003e. The AML-\\u003cem\\u003eTP53\\u003c/em\\u003e group, a new classification for ICC, had highly significant prognostic value, even with a small number of patients. As more data are accumulated and analyzed, the revised criteria will be more useful in predicting prognosis and facilitate a more detailed categorization of myeloid neoplasms at the gene mutation level.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eAcknowledgements:\\u0026nbsp;\\u003c/strong\\u003eThe authors declare that no funds, grants, or other support were received during the preparation of this article.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eFunding Declaration:\\u0026nbsp;\\u003c/strong\\u003eNo funding\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eCompeting interests:\\u0026nbsp;\\u003c/strong\\u003eThe authors have no conflict of interest to declare.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eHuman Ethics and Consent to Participate declarations:\\u0026nbsp;\\u003c/strong\\u003eFully approved protocol and informed consent exemption. This study was a retrospective medical record analysis using only general clinical characteristics, molecular genetic test results, and charts of patients. No unnecessary blood collection or tests were performed. In addition, personal identification information was anonymized and managed. The study was conducted following the principles adopted in the Declaration of Helsinki, and was approved by the Institutional Review Board of Inje University, Busan Paik Hospital, Busan, Korea (2023-10-026).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp;\\u003cstrong\\u003eAuthor contributions\\u003c/strong\\u003e: Yu S performed next-generation sequencing analysis; Lee S and Lee WS collected clinical data; Kim H analyzed data and wrote the original manuscript; Lee JY conceptualized, designed, supervised the study and reviewed and edited the manuscript; You E and Kim HR reviewed the manuscript. All authors have read and approved the final manuscript.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAvailability of data and materials\\u003c/strong\\u003e: all data generated or analyzed during this study are included in this article and supplementary information files.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\n\\u003cli\\u003eArber DA, Orazi A, Hasserjian R, Thiele J, Borowitz MJ, Le Beau MM, et al. The 2016 revision to the World Health Organization classification of myeloid neoplasms and acute leukemia. Blood 2016;127:2391-405.\\u003c/li\\u003e\\n\\u003cli\\u003eBernard E, Tuechler H, Greenberg PL, Hasserjian RP, Arango Ossa JE, Nannya Y, et al. Molecular International Prognostic Scoring System for Myelodysplastic Syndromes. NEJM Evid 2022;1:EVIDoa2200008.\\u003c/li\\u003e\\n\\u003cli\\u003eCancer Genome Atlas Research N, Ley TJ, Miller C, Ding L, Raphael BJ, Mungall AJ, et al. Genomic and epigenomic landscapes of adult de novo acute myeloid leukemia. N Engl J Med 2013;368:2059-74.\\u003c/li\\u003e\\n\\u003cli\\u003eDohner H, Wei AH, Appelbaum FR, Craddock C, DiNardo CD, Dombret H, et al. Diagnosis and management of AML in adults: 2022 recommendations from an international expert panel on behalf of the ELN. Blood 2022;140:1345-77.\\u003c/li\\u003e\\n\\u003cli\\u003ePapaemmanuil E, Gerstung M, Bullinger L, Gaidzik VI, Paschka P, Roberts ND, et al. Genomic Classification and Prognosis in Acute Myeloid Leukemia. N Engl J Med 2016;374:2209-21.\\u003c/li\\u003e\\n\\u003cli\\u003eArber DA, Orazi A, Hasserjian RP, Borowitz MJ, Calvo KR, Kvasnicka HM, et al. International Consensus Classification of Myeloid Neoplasms and Acute Leukemias: integrating morphologic, clinical, and genomic data. Blood 2022;140:1200-28.\\u003c/li\\u003e\\n\\u003cli\\u003eKhoury JD, Solary E, Abla O, Akkari Y, Alaggio R, Apperley JF, et al. The 5th edition of the World Health Organization Classification of Haematolymphoid Tumours: Myeloid and Histiocytic/Dendritic Neoplasms. Leukemia 2022;36:1703-19.\\u003c/li\\u003e\\n\\u003cli\\u003eWang X, Wang J, Wei S, Zhao J, Xin B, Li G, et al. The latest edition of WHO and ELN guidance and a new risk model for Chinese acute myeloid leukemia patients. Front Med (Lausanne) 2023;10:1165445.\\u003c/li\\u003e\\n\\u003cli\\u003eChopra S and Bailey NG. Application of the International Consensus Classification and World Health Organization 5th edition classification to a series of myeloid neoplasms. Am J Clin Pathol 2023;160:566-70.\\u003c/li\\u003e\\n\\u003cli\\u003eFalini B and Martelli MP. Comparison of the International Consensus and 5th WHO edition classifications of adult myelodysplastic syndromes and acute myeloid leukemia. Am J Hematol 2023;98:481-92.\\u003c/li\\u003e\\n\\u003cli\\u003eAttardi E, Savi A, Borsellino B, Piciocchi A, Cipriani M, Ottone T, et al. Applicability of 2022 classifications of acute myeloid leukemia in the real-world setting. Blood Adv 2023;7:5122-31.\\u003c/li\\u003e\\n\\u003cli\\u003eChen Y, Zheng J, Weng Y, Wu Z, Luo X, Qiu Y, et al. Myelodysplasia-related gene mutations are associated with favorable prognosis in patients with \\u003cem\\u003eTP53\\u003c/em\\u003e-mutant acute myeloid leukemia. Ann Hematol 2024;103:1211-20.\\u003c/li\\u003e\\n\\u003cli\\u003eZhou Q, Zhao D, Zarif M, Davidson MB, Minden MD, Tierens A, et al. A real-world analysis of clinical outcomes in AML with myelodysplasia-related changes: a comparison of ICC and WHO-HAEM5 criteria. Blood Advances 2024;8:1760-71.\\u003c/li\\u003e\\n\\u003cli\\u003ePark HS, Kim HK, Kim HS, Yang Y, Han HS, Lee KH, et al. The new diagnostic criteria for myelodysplasia-related acute myeloid leukemia is useful for predicting clinical outcome: comparison of the 4th and 5th World Health Organization classifications. Ann Hematol 2022;101:2645-54.\\u003c/li\\u003e\\n\\u003cli\\u003eLee C, Kim HN, Kwon JA, Yoon SY, Jeon MJ, Yu ES, et al. Implications of the 5(th) Edition of the World Health Organization Classification and International Consensus Classification of Myeloid Neoplasm in Myelodysplastic Syndrome With Excess Blasts and Acute Myeloid Leukemia. Ann Lab Med 2023;43:503-7.\\u003c/li\\u003e\\n\\u003cli\\u003eKim J, Park WY, Kim NKD, Jang SJ, Chun SM, Sung CO, et al. Good Laboratory Standards for Clinical Next-Generation Sequencing Cancer Panel Tests. J Pathol Transl Med 2017;51:191-204.\\u003c/li\\u003e\\n\\u003cli\\u003eJean M SA, Schmid M. ISCN, 2016: An International System for Human Cytogenomic Nomenclature. In: Basel, Switzerland: Karger, 2016.\\u003c/li\\u003e\\n\\u003cli\\u003eFuhrmann I, Lenk M, Haferlach T, Stengel A, Hutter S, Baer C, et al. AML, NOS and AML-MRC as defined by multilineage dysplasia share a common mutation pattern which is distinct from AML-MRC as defined by MDS-related cytogenetics. Leukemia 2022;36:1939-42.\\u003c/li\\u003e\\n\\u003cli\\u003eGao Y, Jia M, Mao Y, Cai H, Jiang X, Cao X, et al. Distinct Mutation Landscapes Between Acute Myeloid Leukemia With Myelodysplasia-Related Changes and De Novo Acute Myeloid Leukemia. Am J Clin Pathol 2022;157:691-700.\\u003c/li\\u003e\\n\\u003cli\\u003ePratcorona M, Abbas S, Sanders MA, Koenders JE, Kavelaars FG, Erpelinck-Verschueren CA, et al. Acquired mutations in \\u003cem\\u003eASXL1\\u003c/em\\u003e in acute myeloid leukemia: prevalence and prognostic value. Haematologica 2012;97:388-92.\\u003c/li\\u003e\\n\\u003cli\\u003eWeinberg OK, Siddon A, Madanat YF, Gagan J, Arber DA, Dal Cin P, et al. \\u003cem\\u003eTP53\\u003c/em\\u003e mutation defines a unique subgroup within complex karyotype de novo and therapy-related MDS/AML. Blood Adv 2022;6:2847-53.\\u003c/li\\u003e\\n\\u003cli\\u003eZhao D, Eladl E, Zarif M, Capo-Chichi JM, Schuh A, Atenafu E, et al. Molecular characterization of AML-MRC reveals \\u003cem\\u003eTP53 \\u003c/em\\u003emutation as an adverse prognostic factor irrespective of MRC-defining criteria, \\u003cem\\u003eTP53\\u003c/em\\u003e allelic state, or \\u003cem\\u003eTP53 \\u003c/em\\u003evariant allele frequency. Cancer Med 2023;12:6511-22.\\u003c/li\\u003e\\n\\u003cli\\u003eCornelissen JJ and Blaise D. Hematopoietic stem cell transplantation for patients with AML in first complete remission. Blood 2016;127:62-70.\\u003c/li\\u003e\\n\\u003cli\\u003eDaver NG, Iqbal S, Renard C, Chan RJ, Hasegawa K, Hu H, et al. Treatment outcomes for newly diagnosed, treatment-na\\u0026iuml;ve \\u003cem\\u003eTP53\\u003c/em\\u003e-mutated acute myeloid leukemia: a systematic review and meta-analysis. J Hematol Oncol 2023;16:19.\\u003c/li\\u003e\\n\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":false,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":true,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":false,\"email\":\"\",\"identity\":\"blood-research\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"\",\"title\":\"BLOOD RESEARCH\",\"twitterHandle\":\"\",\"acdcEnabled\":false,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"VoR Journals\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":false},\"keywords\":\"acute myeloid leukemia, gene mutations, International Concensus Classification, World Health Organization\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-4974493/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-4974493/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003ch2\\u003ePurpose\\u003c/h2\\u003e \\u003cp\\u003eThe fifth World Health Organization (WHO) classification (2022 WHO) and International Consensus Classification (ICC) of myeloid neoplasms have recently been published. We reclassified patients according to the revised classification and analyzed their prognosis to confirm the clinical utility of the new classifications.\\u003c/p\\u003e\\u003ch2\\u003eMethods\\u003c/h2\\u003e \\u003cp\\u003eWe included 101 adult patients, including 77 with acute myeloid leukemia (AML) and 24 with myelodysplastic neoplasms (MDS), who underwent bone marrow aspiration and next-generation sequencing (NGS) between August 2019 and July 2023. We reclassified patients according to the revised criteria, then examined the differences and analyzed the prognosis using survival analysis.\\u003c/p\\u003e\\u003ch2\\u003eResults\\u003c/h2\\u003e \\u003cp\\u003eAccording to the 2022 WHO and ICC, 23 (29.9%) patients and 32 (41.6%) patients were reclassified into different groups, respectively, due to the addition of myelodysplasia-related (MR) gene mutations to the diagnostic criteria or the addition of new entities associated with \\u003cem\\u003eTP53\\u003c/em\\u003e mutations. The median overall survival (OS) of patients with AML and MR gene mutations was shorter than those of other AML group; however, the difference was not significant. Patients with AML and \\u003cem\\u003eTP53\\u003c/em\\u003e mutation had a significantly shorter OS than the other AML group (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.0014, median OS 2.3 vs 10.3 months). They also had significantly shorter OS than the AML and MR mutation group (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.002, median OS 2.3 vs 9.6 months).\\u003c/p\\u003e\\u003ch2\\u003eConclusion\\u003c/h2\\u003e \\u003cp\\u003eThe revised classifications allow for more detailed categorization based on genetic abnormalities, which may be helpful in predicting prognosis. AML with \\u003cem\\u003eTP53\\u003c/em\\u003e mutations is a new ICC category that has shown high prognostic significance in a small number of cases.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Acute Myeloid Leukemia and Myelodysplastic Neoplasms: Clinical Implications of Myelodysplasia-Related Genes Mutations and TP53 Aberrations \",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2024-10-22 08:56:09\",\"doi\":\"10.21203/rs.3.rs-4974493/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0},{\"type\":\"decision\",\"content\":\"Revision requested\",\"date\":\"2024-10-02T08:15:02+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2024-10-02T07:41:32+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2024-09-12T05:59:21+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"235230301858799877363825758939196555887\",\"date\":\"2024-09-11T07:04:39+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"108730329450313127699900450551807702116\",\"date\":\"2024-09-06T09:40:14+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewersInvited\",\"content\":\"\",\"date\":\"2024-09-06T06:01:13+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorAssigned\",\"content\":\"\",\"date\":\"2024-09-06T05:38:50+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"checksComplete\",\"content\":\"\",\"date\":\"2024-09-06T05:23:23+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"submitted\",\"content\":\"BLOOD RESEARCH\",\"date\":\"2024-08-26T01:22:47+00:00\",\"index\":\"\",\"fulltext\":\"\"}],\"status\":\"published\",\"journal\":{\"display\":false,\"email\":\"\",\"identity\":\"blood-research\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"\",\"title\":\"BLOOD RESEARCH\",\"twitterHandle\":\"\",\"acdcEnabled\":false,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"VoR Journals\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":false}}],\"origin\":\"\",\"ownerIdentity\":\"f577cd28-c56a-4f61-ba6a-e7c06f16b821\",\"owner\":[],\"postedDate\":\"October 22nd, 2024\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"under-review\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2024-11-05T18:53:31+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2024-10-22 08:56:09\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-4974493\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-4974493\",\"identity\":\"rs-4974493\",\"version\":[\"v1\"]},\"buildId\":\"qtupq5eGEP_6zYnWcrvyt\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}