Epidemiology and prognostic factors of acute erythroid leukemia

Introduction

Acute erythroid leukemia (AEL) is a rare, highly aggressive subtype of acute myeloid leukemia (AML) characterized by the proliferation of immature erythroid cells within the bone marrow. AEL constitutes approximately 2% of all AML cases and has a poor prognosis, with a median survival of only 6–8 months (1,2).

The pathogenesis of AEL is frequently associated with complex or monosomal karyotypes, biallelic loss of TP53 function, and other genetic variants. Single or multiple TP53 mutations serve as significant molecular markers (3,4). However, AEL possesses distinct molecular characteristics that differentiate it from other TP53-mutated AML. Recent studies indicate that the distribution of key driver gene mutations within the hematopoietic hierarchy of AEL shows erythroid lineage specificity. For instance, in M6-AML (AEL), driver mutations such as in NPM1 and TP53 can be found in megakaryocyte-erythroid progenitors (MEPs) and mature CD235⁺ erythroid cells, a pattern not observed in non-M6-AML (5). This suggests that the cell of origin in AEL may be a progenitor with erythroid differentiation potential, or that the genetic events specifically disrupt the erythroid developmental pathway, correlating with its unique morphological phenotype. Furthermore, mutations in genes like GATA2 and CEBPA also contribute to the onset and progression of AEL (6). In AEL patients, the Para Hox gene CDX4 is upregulated, while its target genes GATA1 and GATA2 are downregulated, thereby inhibiting erythrocyte differentiation (7). Nonetheless, due to the rarity and biological complexity of AEL, its pathogenesis remains incompletely understood, creating a pressing need to identify specific biomarkers for early diagnosis and to develop proactive treatment strategies to improve prognosis.

The diagnostic classification criteria for AEL have evolved. Significant revisions were introduced in the World Health Organization (WHO) 2016 classification and the updated international consensus classification (ICC) (8). The core change is that diagnosis has moved away from relying solely on the proportion of erythroid precursors and now places greater emphasis on the percentage of blasts among bone marrow nucleated cells as the key threshold for distinguishing AML from myelodysplastic syndrome (MDS). Specifically, a case with ≥30% erythroid precursors and ≥20% blasts should be diagnosed as AML (corresponding to the former “pure erythroid leukemia”). If the blast percentage is between 10% and 19%, it may be classified as MDS with erythroid hyperplasia. This revision aims to make diagnosis more reproducible and better aligned with the disease’s biological behavior. It is crucial to emphasize that although molecular features are increasingly important for subclassification and prognosis, examination of bone marrow cell morphology remains the cornerstone and starting point for the diagnosis of AML, including AEL. The latest diagnostic frameworks (such as WHO and ICC) all require an integrated approach combining morphology, immunophenotype, genetics, and clinical features (9). For AEL, the morphological identification of a predominant population of dysplastic proerythroblasts in the bone marrow is the essential initial step that triggers subsequent immunophenotyping (for CD71, CD235a) and molecular testing (focused on screening for mutations in TP53, GATA2, etc).

Certain clinical and sociodemographic factors have been implicated in the development of leukemia. For example, Qiu et al. (10) identified cytogenetics as an independent prognostic factor in AEL in their study of 97 patients. Gera et al. (11) demonstrated that chemotherapy can ameliorate the poor prognosis associated with AEL. Nevertheless, the influence of socio-demographic factors remains poorly understood. Previous population-level study has highlighted variables such as race and sex as potentially significant (12). Due to the low incidence of AEL, much prior research has been limited by small sample sizes and single-center designs. Consequently, we used data from the Surveillance, Epidemiology, and End Results (SEER) database to examine the epidemiology, clinical characteristics, and survival trends associated with AEL in order to identify adverse prognostic factors early and assist clinicians in enhancing prognostic assessment. We present this article in accordance with the STROBE reporting checklist (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2026-0452/rc).

Methods

Patients and ethics

This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. We used the SEER database along with SEER statistical software (SEER*Stat version 8.3.9) to identify and extract data on patients diagnosed with AEL from 2000 to 2021, as classified by the International Classification of Diseases for Oncology, Third Edition (ICD-O-3). The dataset included variables such as the time of diagnosis, sex, age, race, marital status, chemotherapy administration, second malignant neoplasm (SMN), and family income. The inclusion criteria for splicing regulatory elements are patients aged ≥18 years with a medical history/behavioral code corresponding to AEL (ICD-O-3-9840) and complete follow-up data, with a sample size of 947. Exclusion criteria were a missing or an unknown diagnosis (N=23) and unknown cause of death (N=5). Early death was defined as death from any cause within 30 days of diagnosis (13,14).

Figure 1 illustrates the study methodology. Due to the anonymized nature of SEER data, ethical approval was not required.

Figure 1 Flow chart of the overall study design. AEL, acute erythroid leukemia; ICD-O-3, International Classification of Diseases for Oncology, Third Edition; SEER, Surveillance, Epidemiology, and End Results.

Statistical analysis

Using SEER data, we determined the age-standardized incidence of AEL based on the 2000 U.S. standard population criteria. Incidence was calculated using weighted least squares, and Kaplan-Meier survival analysis was conducted. Overall survival (OS): defined as the time from the date of the patient’s diagnosis with AEL to death from any cause. Cancer-specific survival (CSS): defined as the time from the date of the patient’s diagnosis with AEL to death directly attributable to AEL. Deaths from other identifiable causes (other diseases, accidents) are treated as competing risk events. Logistic regression was used to identify risk factors for early mortality. Factors with P values <0.05 from univariate Cox regression were selected for inclusion in multivariate Cox analysis.

Results

Incidence of AEL

From 2000 to 2021, the age-adjusted incidence of AEL showed a significant decline, with an annual percentage change (APC) of −2.50% (95% CI: −4.34 to −0.63, P<0.05) as depicted in Figure 2A. Specifically, the incidence rate decreased by −2.13 per 100,000 (95% CI: −4.39 to 0.19, P>0.05) in men and −3.19 per 100,000 (95% CI: −5.11 to −1.24, P<0.05) in women (Figure 2B). In patients <60 years, the age-adjusted incidence was −2.82 per 100,000 (95% CI: −5.44 to 0.12, P<0.05). In the 60–69 years age group, the incidence was −1.75 per 100,000 (95% CI: −3.98 to 0.53, P>0.05), while for those ≥70 years, it was −2.39/100,000 (95% CI: −4.74 to 0.02, P>0.05) (Figure 2C). The age distribution of AEL incidence was unimodal, peaking at 73–77 years, followed by 63–67, 68–72, and 78–82 years (Figure 2D).

Figure 2 Annual percent change in acute erythroid leukemia in the overall cohort (A), male and female (B), <60, 60–69 and ≥70 years (C) from 2000 to 2021. (D) Bar chart represents the age distribution of patients.

Clinical features

This study used data from the SEER Incidence Research Database, covering 17 registries with the November 2023 submissions (2000–2021). A total of 919 patients diagnosed with AEL were included, with a mean age of 66.91 years [standard deviation (SD) =14.33]. The median follow-up period was 4 months. Approximately 72.1% of patients were over 60 years (N=663), more than half were male (N=584, 63.5%), a large proportion was white (N=753, 81.9%), and 58.9% (N=541) were married. Of the patients, 71.1% (N=653) had annual incomes of over $70,000, while 33.7% (N=310) and 62.5% (N=574) had SMN and chemotherapy, respectively (Table 1).

Risk factors for early mortality

The incidence of early mortality was 296 (32.21%) patients. Multivariate logistic regression identified the year of diagnosis, age, and chemotherapy as significant risk factors for early mortality in patients with AEL. Specifically, compared to a diagnosis made between 2010 and 2013, a diagnosis made between 2014 and 2021 was associated with a higher risk of early mortality [odds ratio (OR) =1.518, 95% confidence interval (CI): 1.020–2.260, P=0.04]. Conversely, using an age of <70 years as the reference, patients aged 71–80 years (OR =2.221, 95% CI: 1.419–3.478, P<0.001) and those aged 81–90+ years (OR =2.096, 95% CI: 1.249–3.516, P=0.005), showed elevated risk. Compared to patients who received chemotherapy, not receiving chemotherapy was strongly associated with increased early mortality risk (OR =4.651, 95% CI: 3.331–6.494; P<0.001, Table 2).

Prognostic factors

Univariate analysis identified the year of diagnosis, age, marital status, chemotherapy, and SMN as common risk factors for OS and CSS (Table 3). Multivariate Cox regression analysis revealed that year of diagnosis, age, and chemotherapy were independent prognostic factors for OS. For CSS, independent prognostic factors included year of diagnosis, age, race, and chemotherapy (Table 4), with age over 60 years and absence of chemotherapy emerging as risk factors for AEL.

Survival analysis

Kaplan-Meier analysis showed that patients diagnosed between 2007 and 2013 had superior 5-year OS and CSS rates compared to other periods (5-year OS: 2000–2006 vs. 2007–2013 vs. 2014–2021: 8.1% vs. 10.8% vs. 8.6%, P=0.007; 5-year CSS: 2000–2006 vs. 2007–2013 vs. 2014–2021: 11.3% vs. 14.8% vs. 13.2%, P=0.02) (Figure 3A,3B). Furthermore, patients aged 18–60 years demonstrated significantly better OS and CSS than those aged 60 years and older (5-year OS: 18–60 vs. 61–70 vs. 71–80 vs. 81–90+ years: 21.4% vs. 7.6% vs. 2.8% vs. 2.6%, P<0.001; 5-year CSS: 18–60 vs. 61–70 vs. 71–80 vs. 81–90+ years: 26.1% vs. 12.1% vs. 5.4% vs. 3.5%, P<0.001, Figure 3C,3D). Chemotherapy treatment also led to better OS and CSS (5-year OS: 12.8% with chemotherapy vs. without; 5-year CSS: 16.8% vs. 3.2%, P<0.001, Figure 3E,3F).

Figure 3 Kaplan-Meier OS and CSS survival analysis in patients with year of diagnosis (A,B), age (C,D), and chemotherapy (E,F). CSS, cancer-specific survival; OS, overall survival.

Discussion

AEL is a rare, aggressive subtype of AML with a generally poor prognosis. Our analysis of 919 patients diagnosed with AEL identified APC of −2.50%, with the incidence notably higher in males than in females. Furthermore, the incidence rate was elevated among individuals aged 60–69 years compared to those under 60 years and those aged 70 years and older. The distribution of incidence across age groups exhibited a unimodal pattern, with a peak observed in individuals aged 73–77 years. Year of diagnosis, age, and chemotherapy were independent risk factors of early mortality and survival outcomes, with chemotherapy showing a potential impact on 5-year survival, though its overall efficacy remains unclear.

In this population-based study, we observed a general decline in AEL incidence over the past two decades, possibly due to the continuous updating of diagnostic criteria, increased health awareness, precision of diagnostic techniques, and improved treatment methods. The incidence is higher in males, which is consistent with prior studies (15,16). For example, Zhang et al. (17) noted higher rates of hematological malignancies in males, with male-to-female age-standardized incidence rate ratios of 1.3:1 for leukemia, 1.4:1 for multiple myeloma, 1.6:1 for non-Hodgkin’s lymphoma, and 1.5:1 for Hodgkin’s lymphoma. These disparities may be attributed to variations in hormonal and genetic factors between men and women (18), as well as differences in environmental exposures and lifestyle factors, which may increase the risk of these malignancies in men compared to women (19). Previous research has indicated that the peak incidence of AEL occurs at the age of 67 years, with AEL being more prevalent among the elderly (20). However, this study demonstrated a unimodal distribution in the incidence of AEL, with a peak occurring between 73 and 77 years, which may contrast with the findings of previous research. Some studies have reported a bimodal age distribution for AEL diagnosis, characterized by a minor peak in individuals in their 20s and a more pronounced second peak in the early 70s (2,21). These variations may be attributable to differences in the inclusion period and the ethnic composition of the study population.

In the evaluation of early mortality and adverse prognostic factors, patients with AEL aged >60 years were identified as having a poor prognosis, consistent with the findings of most previous studies (22). A recent retrospective study conducted by Gera et al. (11) in 2023 demonstrated an inverse relationship between patient age and OS in AEL, with median OS rates of 69, 18, 8, 3, and 1 months for the <18, 18–49, 50–64, 65–79, and 80+ age groups, respectively. Age is widely recognized as a poor prognostic indicator of leukemia, which is potentially attributable to factors such as alterations in tumor biology, reduced treatment tolerance, and an increased incidence of complications.

Our study identified that an AEL diagnosis in the later period (2014–2021) was associated with a higher risk of early death compared to the earlier cohort (2010–2013). This finding appears paradoxical against the backdrop of reported increasing incidence rates of AML overall, particularly among older adults, in the US and globally (23,24). This discrepancy may be significantly influenced by the evolving landscape of diagnostic classification. The revised World Health Organization (2016) and ICC criteria have shifted the diagnostic paradigm for AML, including AEL, away from a primary reliance on morphology (identification of proerythroblasts) towards an integrated approach emphasizing genetic markers and a stricter blast percentage threshold (25,26). Consequently, a reclassification of cases likely occurred over our study period. Patients who might have been classified as AEL under older, more morphology-centric criteria may now be categorized as MDS with erythroid hyperplasia or other AML subtypes under the newer systems. This diagnostic migration could result in the cohort diagnosed as AEL in the later period representing a more refined, and potentially biologically more aggressive, subset of patients, thereby contributing to the observed poorer survival.

Furthermore, the lower survival in the more recent cohort may also reflect differential efficacy of contemporary treatment paradigms. The period from 2014 onwards saw the increasing adoption of novel therapies, particularly hypomethylating agents (HMAs) combined with the BCL-2 inhibitor venetoclax, for older or unfit patients with AML. However, emerging evidence suggests that AML with erythroid or megakaryocytic differentiation, including AEL, may exhibit intrinsic resistance to venetoclax-based regimens. Preclinical studies indicate that these leukemias are characterized by a dependency on the anti-apoptotic protein BCL-XL rather than BCL-2, conferring resistance to selective BCL-2 inhibition (27). Therefore, the shift in treatment patterns towards therapies that may be less effective for this specific biologic subtype could be another critical factor explaining the increased early mortality in the later diagnostic period. This highlights the necessity of considering the unique molecular pathophysiology of AEL when selecting therapeutic strategies and underscores the potential need for alternative targeted approaches, such as BCL-XL inhibition.

Chemotherapy also emerged as an important prognostic indicator. The current consensus recommends intensive chemotherapy (ICT), HMAs, and allogeneic bone marrow transplantation (AlloBMT) for treatment. If chemotherapy is tolerated, ICT should be the first line of treatment for AEL. Almeida et al. (22) studied 217 patients with AEL; 88 patients were treated with HMAs and 122 with ICT. ICT led to a higher overall response rate (complete or partial) than first-line HMA (72% vs. 46.2%, respectively, P<0.001) but similar progression-free survival (8.0 vs. 9.4 months, P=0.342). In 2018, hypomethylated drugs (azacytidine and decitabine) were approved for patients with AML who were unsuitable for ICTs. Reichard et al. (21) reported 41 patients with AEL, including those treated with a HMA alone (n=5), HMA + venetoclax (n=12), ICT (n=4), and supportive therapy (n=8). All patients died at a median duration of 1.8 months (range, 0.2–9.3 months). There was no significant difference in the survival time between the groups (P=0.4). While it has been documented that HMA can elicit a favorable initial response in patients with AEL, resistance to this treatment invariably develops in nearly all cases (28). Consequently, a more extensive multi-center study is warranted to thoroughly assess the efficacy of HMA therapy for AEL. Allo-BMT remains the sole curative option for the disease; however, it necessitates achieving profound disease remission. Previous studies have indicated that the median survival duration of patients with AEL undergoing alloBMT is 66 months post-transplantation (29). In a specific study, 28 patients, representing 71% of the AEL cohort received AlloBMT, with only 1% achieving a complete response. Therefore, using AlloBMT as a consolidation therapy may improve outcomes for patients with AEL; however, it is crucial that the initial treatment achieves deep remission, a target that remains highly challenging in clinical practice. While consolidation therapies such as allo-BMT may help maintain long-term remission, there is an urgent need to develop new pharmacological agents and combinations that can induce remission, necessitating a substantial research focus.

The pathogenesis of AEL is complex, primarily involving genetic, cellular, and cytokine/transcription factor abnormalities. In terms of genetic inheritance, AEL often shows complex karyotypes, with frequent abnormalities on chromosomes 5 and 7, and occasional translocations like t(8;21), t(15;17), and inv[16] (30). Although specific mutated genes have not been identified in AEL, the high-frequency mutation in TP53 suggests a significant role in its pathogenesis (31). For abnormal cell biology, disrupted progenitor erythrocyte differentiation in cases of pure erythrocyte leukemia leads to prolonged survival and unchecked proliferation. This phenomenon is primarily attributed to the dysregulation of various cytokines, transcription factors, chromosomal modifications, and microRNAs involved in erythropoiesis (32,33). Numerous targeted therapies, including BCL-XL (27), JAK2 (34), and HDAC7 inhibitors (35), are currently undergoing clinical trials. Future research should focus on developing targeted treatments for AEL by enhancing our understanding of the molecular biology of the disease and validating the findings using patient-derived samples.

Interpreting the results of this study necessitates acknowledging some limitations. First, the database lacked details on treatment modalities, such as specific treatment regimens and genetic mutations, thereby impeding further stratification of the dataset based on these variables. Second, the presence of missing data within the sample introduced a potential selection bias. Therefore, future prospective multicenter large-scale clinical studies are critical to better explore the prognostic factors of AEL and future treatment strategies.

Conclusions

In conclusion, AEL is an aggressive and rare malignancy, characterized by unfavorable clinical outcomes. Despite declining AEL incidence, survival rates have not markedly improved. Key factors influencing early mortality and survival include year of diagnosis, patient age, and chemotherapy regimen. These insights may help clinicians identify high-risk patients and tailor treatment approaches.

Acknowledgments

We sincerely thank the Surveillance, Epidemiology, and End Results (SEER) program for providing researchers with open resources.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2026-0452/rc

Peer Review File: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2026-0452/prf

Funding: This study was supported by the Medical Double-High Project of Fujian Province (No. ETK2024007), Fujian Children’s Hospital Cultivation Project (No. YP202401), and Joint Funds for the Innovation of Science and Technology, Fujian Province (No. 2024Y9554).

Conflicts of Interest: Both authors have completed the ICMJE uniform disclosure form (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2026-0452/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

References

1. Arber DA, Orazi A, Hasserjian R, et al. The 2016 revision to the World Health Or-ganization classification of myeloid neoplasms and acute leukemia. Blood 2016;127:2391-405. [Crossref] [PubMed]
2. Zuo Z, Polski JM, Kasyan A, et al. Acute erythroid leukemia. Arch Pathol Lab Med 2010;134:1261-70. [Crossref] [PubMed]
3. Montalban-Bravo G, Benton CB, Wang SA, et al. More than 1 TP53 abnormality is a dominant characteristic of pure erythroid leukemia. Blood 2017;129:2584-7. [Crossref] [PubMed]
4. Tashakori M, Wang W, Kadia TM, et al. Differential characteristics of TP53 altera-tions in pure erythroid leukemia arising after exposure to cytotoxic therapy. Leuk Res 2022;118:106860. [Crossref] [PubMed]
5. Cervera N, Lhoumeau AC, Adélaïde J, et al. Acute erythroid leukemias have a distinct molecular hierarchy from non-erythroid acute myeloid leukemias. Haematologica 2020;105:e340-2. [Crossref] [PubMed]
6. Ping N, Sun A, Song Y, et al. Exome sequencing identifies highly recurrent somatic GATA2 and CEBPA mutations in acute erythroid leukemia. Leukemia 2017;31:195-202. [Crossref] [PubMed]
7. Thoene S, Mandal T, Vegi NM, et al. The ParaHox gene Cdx4 induces acute erythroid leukemia in mice. Blood Adv 2019;3:3729-39. [Crossref] [PubMed]
8. Xu K, Mellios Z, Hyun J, et al. Diagnosis and management of acute erythroid leu-kemia (AEL). Leuk Lymphoma 2025;66:2553-7. [Crossref] [PubMed]
9. Khoury JD, Solary E, Abla O, et al. The 5th edition of the World Health Organization Classification of Haematolymphoid Tumours: Myeloid and Histiocytic/Dendritic Neo-plasms. Leukemia 2022;36:1703-19.
10. Qiu S, Jiang E, Wei H, et al. An analysis of 97 previously diagnosed de novo adult acute erythroid leukemia patients following the 2016 revision to World Health Organi-zation classification. BMC Cancer 2017;17:534. [Crossref] [PubMed]
11. Gera K, Martir D, Xue W, et al. Survival after Pure (Acute) Erythroid Leukemia in the United States: A SEER-Based Study. Cancers (Basel) 2023;15:3941. [Crossref] [PubMed]
12. Wei JX, Shastri A, Sica RA, et al. Impact of race and ethnicity on early mortality in multiple myeloma: a SEER analysis. Haematologica 2024;109:1480-6. [PubMed]
13. Bhatt VR, Shostrom V, Giri S, et al. Early mortality and overall survival of acute myeloid leukemia based on facility type. Am J Hematol 2017;92:764-71. [Crossref] [PubMed]
14. Derolf Å, Juliusson G, Benson L, et al. Decreasing early mortality in acute myeloid leukaemia in Sweden 1997-2014: improving performance status is a major contributing factor. Br J Haematol 2020;188:187-91. [Crossref] [PubMed]
15. Hemminki K, Hemminki J, Försti A, et al. Survival trends in hematological malig-nancies in the Nordic countries through 50 years. Blood Cancer J 2022;12:150. [Crossref] [PubMed]
16. Kasyan A, Medeiros LJ, Zuo Z, et al. Acute erythroid leukemia as defined in the World Health Organization classification is a rare and pathogenetically heterogeneous disease. Mod Pathol 2010;23:1113-26. [Crossref] [PubMed]
17. Zhang N, Wu J, Wang Q, et al. Global burden of hematologic malignancies and evolution patterns over the past 30 years. Blood Cancer J 2023;13:82. [Crossref] [PubMed]
18. Martinez A, Delpierre C, Grosclaude P, et al. Integrating gender into cancer research. Lancet 2024;403:1631. [Crossref] [PubMed]
19. Irigaray P, Newby JA, Clapp R, et al. Lifestyle-related factors and environmental agents causing cancer: an overview. Biomed Pharmacother 2007;61:640-58. [Crossref] [PubMed]
20. Khwaja A, Bjorkholm M, Gale RE, et al. Acute myeloid leukaemia. Nat Rev Dis Primers 2016;2:16010. [Crossref] [PubMed]
21. Reichard KK, Tefferi A, Abdelmagid M, et al. Pure (acute) erythroid leukemia: morphology, immunophenotype, cytogenetics, mutations, treatment details, and survival data among 41 Mayo Clinic cases. Blood Cancer J 2022;12:147. [Crossref] [PubMed]
22. Almeida AM, Prebet T, Itzykson R, et al. Clinical Outcomes of 217 Patients with Acute Erythroleukemia According to Treatment Type and Line: A Retrospective Multi-national Study. Int J Mol Sci 2017;18:837. [Crossref] [PubMed]
23. Sasaki K, Ravandi F, Kadia TM, et al. De novo acute myeloid leukemia: A popula-tion-based study of outcome in the United States based on the Surveillance, Epidemiol-ogy, and End Results (SEER) database, 1980 to 2017. Cancer 2021;127:2049-61. [Crossref] [PubMed]
24. Jani CT, Ahmed A, Singh H, et al. Burden of AML, 1990-2019: Estimates From the Global Burden of Disease Study. JCO Glob Oncol 2023;9:e2300229. [Crossref] [PubMed]
25. Arber DA. The 2016 WHO classification of acute myeloid leukemia: What the practicing clinician needs to know. Semin Hematol 2019;56:90-5. [Crossref] [PubMed]
26. Weinberg OK, Porwit A, Orazi A, et al. The International Consensus Classification of acute myeloid leukemia. Virchows Arch 2023;482:27-37. [Crossref] [PubMed]
27. Kuusanmäki H, Dufva O, Vähä-Koskela M, et al. Erythroid/megakaryocytic dif-ferentiation confers BCL-XL dependency and venetoclax resistance in acute myeloid leukemia. Blood 2023;141:1610-25. [Crossref] [PubMed]
28. Stomper J, Rotondo JC, Greve G, et al. Hypomethylating agents (HMA) for the treatment of acute myeloid leukemia and myelodysplastic syndromes: mechanisms of resistance and novel HMA-based therapies. Leukemia 2021;35:1873-89. [Crossref] [PubMed]
29. Fernandes P, Waldron N, Chatzilygeroudi T, et al. Acute Erythroid Leukemia: From Molecular Biology to Clinical Outcomes. Int J Mol Sci 2024;25:6256. [Crossref] [PubMed]
30. Hasserjian RP, Zuo Z, Garcia C, et al. Acute erythroid leukemia: a reassessment using criteria refined in the 2008 WHO classification. Blood 2010;115:1985-92. [Crossref] [PubMed]
31. Grossmann V, Bacher U, Haferlach C, et al. Acute erythroid leukemia (AEL) can be separated into distinct prognostic subsets based on cytogenetic and molecular genetic characteristics. Leukemia 2013;27:1940-3. [Crossref] [PubMed]
32. Di Genua C, Valletta S, Buono M, et al. C/EBPα and GATA-2 Mutations Induce Bilineage Acute Erythroid Leukemia through Transformation of a Neomorphic Neutro-phil-Erythroid Progenitor. Cancer Cell 2020;37:690-704.e8. [Crossref] [PubMed]
33. Zhan M, Miller CP, Papayannopoulou T, et al. MicroRNA expression dynamics during murine and human erythroid differentiation. Exp Hematol 2007;35:1015-25. [Crossref] [PubMed]
34. Li B, An W, Wang H, et al. BMP2/SMAD pathway activation in JAK2/p53-mutant megakaryocyte/erythroid progenitors promotes leukemic transformation. Blood 2022;139:3630-46. [Crossref] [PubMed]
35. Zhang W, Yamamoto K, Chang YH, et al. HDAC7 is a potential therapeutic target in acute erythroid leukemia. Leukemia 2024;38:2614-27. [Crossref] [PubMed]