Abstract
This study aimed at evaluating prognostic factors for survival and treatment response in patients with myelodysplastic syndromes (MDS) transforming to acute myeloid leukemia (AML). This retrospective study included 182 MDS patients treated at our hospital between January 2018 and January 2023, with 52 patients transforming to AML. Patients were categorized into good and poor prognosis groups based on survival beyond 12 months. Data on baseline demographics, clinical parameters at MDS diagnosis and AML transformation, treatment response, and survival outcomes were analyzed. Multivariate Cox regression was used to identify prognostic factors. Of the 52 patients who transformed into AML, 20 were in the good prognosis group and 32 in the poor prognosis group. The mean age was 64.5 ± 10.2 years, with no significant age difference between the groups (P = .15). Gender distribution was 57.7% male and 42.3% female. The good prognosis group had significantly lower Eastern Cooperative Oncology Group (ECOG) performance status scores (P = .02). At MDS diagnosis, the poor prognosis group had worse International Prognostic Scoring System scores, higher bone marrow blast percentages, poorer cytogenetic risk, and shorter transformation time (P < .05). At AML transformation, the poor prognosis group had higher white blood cell counts, bone marrow blast percentages, and TP53 mutation rates (P < .05). Multivariate analysis identified ECOG score ≥ 2 (HR = 2.91, P = .02), higher IPSS score (HR = 2.56, P = .04), RAEB-1/RAEB-2 subtypes (HR = 4.73, P = .003), higher bone marrow blast percentage (HR = 1.38, P = .02), TP53 mutation (HR = 4.92, P = .01), and high-risk cytogenetic abnormalities (HR = 6.32, P < .001) as independent poor prognosis factors. Treatment response analysis showed that patients in complete remission had significantly longer overall survival than those with partial remission or no response (P < .001). The prognosis of MDS transforming into AML is influenced by factors such as ECOG performance status, IPSS score, bone marrow blast percentage, TP53 mutations, and cytogenetic risk. These findings stress the importance of early identification of high-risk patients for treatment strategies, including intensive therapies or hematopoietic stem cell transplantation. Achieving complete remission significantly improves survival outcomes, highlighting the need for optimal early treatment.
Keywords: acute myeloid leukemia, myelodysplastic syndromes, prognosis, retrospective study, TP53 mutation
1. Introduction
Myelodysplastic syndromes (MDS) are a group of diseases characterized by abnormal hematopoietic function in the bone marrow, primarily manifesting as cytopenia and morphological abnormalities of blood cells.[1,2] The clinical presentation of MDS is often diverse, with patients potentially exhibiting symptoms such as anemia, leukopenia, or thrombocytopenia.[3] MDS is commonly seen in elderly individuals, and its incidence increases with age. While the early course of MDS may be relatively stable, some patients will experience disease progression and ultimately transform into acute myeloid leukemia (AML).[4] The transformation of MDS into AML is a multi-stage and complex process, usually accompanied by a significant worsening of prognosis. AML is a highly malignant hematologic tumor with a high mortality rate and recurrence rate.[5,6] Therefore, exploring the mechanisms and prognostic factors related to the transformation of MDS into AML has become a crucial research direction in the field of hematology.
In recent years, with the continuous development of molecular biology and genetics, many molecular markers and clinical characteristics related to the transformation of MDS to AML have been gradually revealed.[7] For example, gene mutations such as TP53, ASXL1, and RUNX1 have been shown to be closely related to the transformation process of MDS.[8,9] Additionally, the International Prognostic Scoring System (IPSS) and the revised IPSS (IPSS-R) scoring systems are widely used for prognostic assessment in MDS patients. These prognostic scoring systems help clinicians evaluate the development trend and treatment plan of MDS patients by considering factors such as bone marrow blast percentage, cytogenetic risk, and hematological indicators.[10–12] However, despite numerous studies revealing clinical and molecular factors associated with the transformation of MDS into AML, there is still a lack of in-depth analysis and multi-dimensional research on the long-term survival prognosis of patients who transform from MDS to AML.
Currently, most studies focus on the initial clinical diagnosis and treatment response of MDS patients. However, research on the survival, relapse, and prognostic factors during the transformation of MDS to AML is relatively limited.[13] In particular, predicting which MDS patients are likely to transform into AML at an early stage and reasonably estimating their treatment outcomes and survival remains a clinical challenge that needs to be addressed. Therefore, this study aims to explore the prognostic factors influencing the transformation of MDS to AML by retrospectively analyzing clinical, genetic, and treatment response data from patients, providing a valuable reference for clinical treatment.
By analyzing detailed clinical data of a group of MDS patients who transformed into AML, this study not only helps further understand the prognostic patterns of MDS transformation to AML but also provides new prognostic evaluation tools for clinical practice, improving individualized treatment strategies for MDS patients and enhancing their survival rates. Thus, this study fills a gap in the existing research and holds significant clinical and scientific value.
2. Materials and methods
2.1. Study design
This study was approved by the Ethics Committee of Huangshi Central Hospital. This study is a retrospective analysis that included 182 patients diagnosed and treated for myelodysplastic syndromes (MDS) at the Department of Hematology or Oncology of our hospital between January 2018 and January 2023, who were clinically followed up. Among these, 52 patients transformed into AML, referred to as MDS-AML. The patients were divided into 2 groups: a good prognosis group and a poor prognosis group, based on whether their survival exceeded 12 months.
2.1.1. Inclusion criteria
Initial diagnosis of MDS, followed by a confirmed diagnosis of AML; complete MDS diagnostic data, evidence of AML transformation, and follow-up records; regular follow-up records from the time of MDS diagnosis to AML transformation; treatment and outcome data after the confirmation of AML (e.g., remission status, survival, relapse, etc); consent to participate in research analysis.
2.1.2. Exclusion criteria
Patients diagnosed with AML or other types of leukemia at the initial diagnosis; patients with a history of other malignant tumors or concurrent hematologic malignancies; inability to confirm the diagnostic process of MDS transforming into AML (unclear diagnostic criteria or time points); missing clinical data, insufficient follow-up time, or loss to follow-up; patients under 18 years old; individuals with severe organ dysfunction that hinders prognostic assessment (such as end-stage liver or kidney failure).
2.1.3. Data collection
2.1.3.1. Baseline clinical data
Basic demographic information was collected for all patients, including gender, age, body mass index (BMI), smoking history, alcohol consumption history, and comorbidities (such as hypertension, diabetes, etc). Clinical assessment was performed using the Eastern Cooperative Oncology Group (ECOG) performance status score to reflect the overall health status of the patients.
At the time of MDS diagnosis, the following clinical parameters were recorded: IPSS score, MDS subtype, revised IPSS (IPSS-R) score, bone marrow blast percentage, hemoglobin level, platelet count, and cytogenetic risk classification. All data were collected and entered by clinicians during the initial diagnosis.
2.1.3.2. Clinical parameters at AML transformation
The clinical parameters at the time of AML transformation included white blood cell count, bone marrow blast percentage, and genetic mutation analysis at the time of AML diagnosis. White blood cell count and bone marrow blast percentage were obtained through routine hematological tests and bone marrow smear examination. Genetic mutation analysis was performed using high-throughput genomic sequencing (NGS) or polymerase chain reaction technology, focusing on mutations in TP53, ASXL1, RUNX1, and other related genes.
2.1.3.3. Treatment response and survival outcomes
In this study, treatment response was assessed based on international standards. Specifically, treatment responses were categorized into 3 groups: complete remission (CR), partial remission (PR), and no response (NR). Complete remission (CR) was defined as the complete resolution of clinical symptoms, no abnormal white blood cells detected in peripheral blood, bone marrow myeloid cell count ≤ 5%, and no abnormalities found in cytogenetic testing. Additionally, hematological indicators were restored to normal ranges, including hemoglobin, white blood cells, and platelet levels. PR referred to an improvement in clinical symptoms, with peripheral blood and bone marrow examinations showing myeloid cell counts > 5%, but not meeting the criteria for CR, and with some improvement in hematological indicators. NR indicated that the patient’s clinical symptoms did not improve or worsened, with no significant changes in peripheral blood and bone marrow examination results, and hematological indicators showing no improvement or further deterioration.
Survival data included overall survival (OS) and progression-free survival (PFS). Overall survival (OS) was defined as the time from MDS diagnosis or the start of treatment until the patient’s death or the last follow-up. PFS was the time from diagnosis or initiation of treatment until disease progression or death. All survival data were regularly collected during the follow-up period after treatment. Follow-up methods included outpatient visits, telephone follow-ups, or other effective contact methods to ensure the completeness and accuracy of the data.
2.2. Statistical analysis
All data were analyzed using SPSS statistical software (version 22.0, IBM Corp., Armonk). Continuous variables were expressed as mean ± standard deviation (Mean ± SD), and independent samples t-test was used for group comparisons. For continuous variables with non-normal distribution, median and interquartile range (IQR) were used for description, and Mann–Whitney U test was applied for group comparisons. Categorical variables were presented as frequency (n) and percentage (%), and comparisons between groups were made using Chi-square test or Fisher exact test.
To evaluate the impact of different variables on survival, multivariate Cox proportional hazards regression analysis was performed. Based on the results of the univariate analysis, clinically significant variables (such as ECOG performance status, IPSS score, bone marrow blast percentage, cytogenetic risk, etc) were selected for multivariate regression analysis. Hazard ratios (HR) and their 95% confidence intervals (95% CI) were used to describe the influence of each variable. All statistical tests were 2-sided, and a P-value < .05 was considered statistically significant. Survival analysis was performed using the Log-rank test for inter-group comparisons. All results were considered significant at P < .05. Missing data were minimal, accounting for <5% of the total dataset. Multiple imputation was used to handle missing values, ensuring robust and unbiased statistical analysis.
3. Results
3.1. Baseline demographic and clinical characteristics
A total of 52 patients who transformed from MDS to AML were included in this study, with 20 patients in the good prognosis group and 32 patients in the poor prognosis group. There were no significant differences between the 2 groups in baseline characteristics such as gender, age, and BMI. In the overall patient cohort, 57.7% were male and 42.3% were female. The mean age was 64.5 ± 10.2 years, with ages of 61.3 ± 9.5 years in the good prognosis group and 66.8 ± 10.8 years in the poor prognosis group, which was not statistically significant (P = .15). There was also no significant difference in BMI between the 2 groups (P = .42). ECOG performance status scores showed a significant difference between the groups (P = .02). In the good prognosis group, 85.0% of patients had an ECOG score of 0 to 1, while in the poor prognosis group, it was 56.3%. Smoking history and alcohol consumption history did not differ significantly between the 2 groups (P = .18 and P = .13, respectively). There were also no significant differences in comorbidities such as hypertension and diabetes between the 2 groups (P > .05) (Table 1).
Table 1.
Baseline demographic and clinical characteristics of patients.
| Variable | Total (n = 52) | Favorable prognosis (n = 20) | Poor prognosis (n = 32) | P-value |
|---|---|---|---|---|
| Gender (male/female) | 30/22 | 12/8 | 18/14 | .81 |
| Age (year, Mean ± SD) | 64.5 ± 10.2 | 61.3 ± 9.5 | 66.8 ± 10.8 | .15 |
| BMI, Mean ± SD | 23.6 ± 3.2 | 24.1 ± 3.0 | 23.2 ± 3.3 | .42 |
| ECOG performance status (0–1/≥2) | 35/17 | 17/3 | 18/14 | .02 |
| Smoking history (yes/no) | 24/28 | 7/13 | 17/15 | .18 |
| Alcohol consumption (yes/no) | 22/30 | 6/14 | 16/16 | .13 |
| History of comorbidities (n, %) | ||||
| Hypertension | 18 (34.6%) | 6 (30.0%) | 12 (37.5%) | .58 |
| Diabetes mellitus | 10 (19.2%) | 3 (15.0%) | 7 (21.9%) | .49 |
| Coronary heart disease | 8 (15.4%) | 3 (15.0%) | 5 (15.6%) | .95 |
| COPD | 5 (9.6%) | 1 (5.0%) | 4 (12.5%) | .38 |
| CKD stage 3–5 | 6 (11.5%) | 1 (5.0%) | 5 (15.6%) | .22 |
BMI = body mass index, CKD = chronic kidney disease, COPD = chronic obstructive pulmonary disease, ECOG = Eastern Cooperative Oncology Group, SD = standard deviation.
3.2. Clinical parameters at initial MDS diagnosis
At the time of MDS diagnosis, there were significant differences between the good prognosis group and the poor prognosis group in terms of IPSS score, MDS subtype, IPSS-R score, bone marrow blast percentage, hemoglobin, platelet count, and cytogenetic risk classification, as shown in Table 2. Most patients in the poor prognosis group had intermediate or high-risk IPSS scores (P = .02), while the good prognosis group had more low-risk patients. Additionally, regarding MDS subtype distribution, the proportion of RAEB-2 type patients was significantly higher in the poor prognosis group than in the good prognosis group (P = .01). In terms of IPSS-R scores, the proportion of high-risk and very high-risk patients in the poor prognosis group was significantly higher (P = .01). The bone marrow blast percentage was significantly higher in the poor prognosis group compared to the good prognosis group (7.0% vs 5.1%, P = .03). The difference in platelet count was also significant, with the poor prognosis group having lower platelet counts (P = .04). Regarding cytogenetic risk, the proportion of high-risk patients was significantly higher in the poor prognosis group (P < .001). Furthermore, the time to transformation to AML was longer in the good prognosis group (22.1 months vs 15.3 months, P = .008).
Table 2.
Clinical parameters at initial MDS diagnosis and AML transformation.
| Variable | Total (n = 52) | Favorable prognosis (n = 20) | Poor prognosis (n = 32) | P-value |
|---|---|---|---|---|
| At initial MDS diagnosis | ||||
| IPSS score (low/intermediate/high) | 8/25/19 | 6/10/4 | 2/15/15 | .02 |
| MDS subtype (RA/RARS/RCMD/RAEB-1/RAEB-2) | 5/5/8/13/21 | 4/3/5/5/3 | 1/2/3/8/18 | .01 |
| IPSS-R score (very low/low/intermediate/high/very high) | 4/12/18/10/8 | 4/8/6/2/0 | 0/4/12/8/8 | .01 |
| Bone marrow blasts (%) (Mean ± SD) | 6.2 ± 2.3 | 5.1 ± 1.8 | 7.0 ± 2.5 | .03 |
| Hemoglobin (g/L, Mean ± SD) | 98.2 ± 14.3 | 102.5 ± 12.8 | 95.6 ± 15.2 | .12 |
| Platelet count (×10⁹/L, Mean ± SD) | 104.8 ± 35.2 | 118.4 ± 30.5 | 95.2 ± 37.1 | .04 |
| Cytogenetic risk classification (favorable/intermediate/unfavorable) | 15/20/17 | 2012/6/2 | 3/14/15 | <.001 |
| Time to AML transformation (months, median [IQR]) | 18.5 (12.2–24.8) | 22.1 (17.3–28.4) | 15.3 (9.1–19.5) | .008 |
| At AML diagnosis | ||||
| AML classification (M0/M1/M2/M4/M5/M6/M7) | 6/9/19/7/5/4/2 | 2/3/7/3/2/2/1 | 4/6/12/4/3/2/1 | .28 |
| White blood cell count (×10⁹/L, Mean ± SD) | 14.5 ± 6.8 | 11.2 ± 5.3 | 16.7 ± 7.1 | .02 |
| Bone marrow blasts (%) (Mean ± SD) | 38.6 ± 8.9 | 34.2 ± 7.6 | 41.2 ± 9.4 | .04 |
| Genetic mutations (n, %) | ||||
| TP53 mutation | 14 (26.9%) | 2 (10.0%) | 12 (37.5%) | .01 |
| ASXL1 mutation | 10 (19.2%) | 3 (15.0%) | 7 (21.9%) | .49 |
| RUNX1 mutation | 8 (15.4%) | 1 (5.0%) | 7 (21.9%) | .08 |
| Treatment modalities | ||||
| Chemotherapy (n, %) | 48 (92.3%) | 18 (90.0%) | 30 (93.8%) | .67 |
| Hematopoietic stem cell transplantation (n, %) | 18 (34.6%) | 10 (50.0%) | 8 (25.0%) | .06 |
AML = acute myeloid leukemia, IPSS = International Prognostic Scoring System, IQR = interquartile range, MDS = myelodysplastic syndromes, SD = standard deviation.
3.3. Clinical parameters at AML transformation
At the time of AML transformation diagnosis, there were differences in white blood cell count, bone marrow blast percentage, and the distribution of genetic mutations between the good prognosis group and the poor prognosis group, as shown in Table 2. The white blood cell count in the poor prognosis group was significantly higher than in the good prognosis group (16.7 × 10⁹/L vs 11.2 × 10⁹/L, P = .02). Additionally, the bone marrow blast percentage was higher in the poor prognosis group (41.2% vs 34.2%, P = .04). Genetic mutation analysis revealed that TP53 mutations were more common in the poor prognosis group (37.5% vs 10.0%, P = .01), while there was no significant difference in ASXL1 mutations between the 2 groups (P = .49). Regarding treatment modalities, chemotherapy was used at similar rates in both groups (P = .67), while hematopoietic stem cell transplantation (HSCT) was more commonly applied in the good prognosis group (50.0% vs 25.0%, P = .06) (Table 2).
3.4. Multivariate Cox regression analysis of prognostic factors
Multivariate Cox regression analysis showed that patients with an ECOG performance status score ≥ 2 had a significantly increased risk of death (HR = 2.91, P = .02). Higher IPSS scores (intermediate-risk group vs low-risk group) and IPSS-R high-risk/very high-risk scores (HR = 2.56, P = .04; HR = 5.18, P = .002) were also associated with poorer prognosis. Patients with RAEB-1/RAEB-2 subtypes had a significantly higher risk of death (HR = 4.73, P = .003). For each 1% increase in bone marrow blast percentage, the risk of death increased (HR = 1.38, P = .02), while for each 10 × 10⁹/L increase in platelet count, the risk of death decreased (HR = 0.97, P = .04). High-risk cytogenetic groups (HR = 6.32, P < .001) and TP53 mutations (HR = 4.92, P = .01) were both associated with poorer prognosis. Patients with a longer transformation time to AML had a lower risk of death (HR = 0.91, P = .02), while for each 10 × 10⁹/L increase in white blood cell count, the risk of death increased (HR = 1.06, P = .01) (Table 3).
Table 3.
Multivariate Cox regression analysis of prognostic factors for survival.
| Variable | HR | 95% CI | P-value |
|---|---|---|---|
| ECOG performance status (0–1 vs ≥2) | 2.91 | 1.20–7.04 | .02 |
| IPSS score (intermediate vs low) | 2.56 | 1.03–6.34 | .04 |
| MDS subtype (RAEB-1/RAEB-2 vs RA/RARS/RCMD) | 4.73 | 1.75–12.71 | .003 |
| IPSS-R score (high/very high vs low/very low) | 5.18 | 1.90–14.05 | .002 |
| Bone marrow blasts (%) at initial diagnosis | 1.38 | 1.07–1.79 | .02 |
| Platelet count (per 10 × 10⁹/L increase) | 0.97 | 0.95–0.99 | .04 |
| Cytogenetic risk (unfavorable vs favorable/intermediate) | 6.32 | 2.38–16.84 | <.001 |
| Time to AML transformation (per 1-mo increase) | 0.91 | 0.84–0.98 | .02 |
| White blood cell count (per 10 × 10⁹/L increase) | 1.06 | 1.02–1.11 | .01 |
| TP53 mutation (yes vs no) | 4.92 | 1.40–17.33 | .01 |
All variables were independently included in the multivariate regression model, and basic variables such as gender and age were adjusted for.
AML = acute myeloid leukemia, CI = confidence interval, ECOG = Eastern Cooperative Oncology Group, HR = hazard ratio, IPSS = International Prognostic Scoring System, MDS = myelodysplastic syndromes.
3.5. Relapse and treatment response analysis
Regarding initial treatment response, 80.0% of patients in the good prognosis group achieved CR, significantly higher than the 31.3% in the poor prognosis group (P < .001). There was no significant difference in the proportion of PR between the 2 groups (good prognosis group: 15.0%, poor prognosis group: 25.0%, P = .34). However, the proportion of NR was significantly higher in the poor prognosis group compared to the good prognosis group (43.8% vs 5.0%, P < .001) (Table 4).
Table 4.
Relapse and treatment response analysis.
| Variable | Favorable prognosis group (n = 20) | Poor prognosis group (n = 32) | P-value |
|---|---|---|---|
| Initial treatment response (n, %) | |||
| CR | 16 (80.0%) | 10 (31.3%) | <.001 |
| PR | 3 (15.0%) | 8 (25.0%) | .34 |
| NR | 1 (5.0%) | 14 (43.8%) | <.001 |
| Relapse rate (n, %) | 8 (40.0%) | 22 (68.8%) | .03 |
| Time to relapse (months, after CR) | 14.2 (9.8–18.3) | 7.4 (4.2–11.5) | .01 |
CR = complete remission, NR = no response, PR = partial remission.
Regarding relapse rate, the relapse rate in the good prognosis group was 40.0%, significantly lower than the 68.8% in the poor prognosis group (P = .03). Additionally, the time to relapse after CR was longer in the good prognosis group, with a median relapse time of 14.2 months (IQR: 9.8–18.3), compared to 7.4 months (IQR: 4.2–11.5) in the poor prognosis group, and this difference was statistically significant (P = .01).
3.6. Treatment response and survival outcomes
Treatment response had a significant impact on survival outcomes. Among the good prognosis group, 61.5% of patients achieved CR, compared to only 38.5% in the poor prognosis group (P < .001). There were also significant differences in the distribution of PR and NR patients between the 2 groups, with the NR rate in the good prognosis group being only 6.7%, much lower than the 93.3% in the poor prognosis group (P < .001). Patients in CR had better survival, with a median overall survival (OS) of 18.3 months (95% CI: 14.2–24.1) in the CR group, compared to 10.5 months (95% CI: 7.8–13.6) in the PR group, and only 4.2 months (95% CI: 2.5–6.0) in the NR group, with a significant difference (P < .001). One-year survival rates were 72.7%, 35.7%, and 6.3% in the CR, PR, and NR groups, respectively (P < .001) (Table 5).
Table 5.
Treatment response and survival outcomes.
| Variable | CR (n = 26)* | PR (n = 11) | NR (n = 15) | P-value |
|---|---|---|---|---|
| Prognostic group (n, %) | ||||
| Favorable (n = 20) | 16 (61.5%) | 3 (27.3%) | 1 (6.7%) | <.001 |
| Poor (n = 32) | 10 (38.5%) | 8 (72.7%) | 14 (93.3%) | <.001 |
| Median OS (months, 95% CI) | 18.3 (14.2–24.1) | 10.5 (7.8–13.6) | 4.2 (2.5–6.0) | <.001 |
| 1-yr survival rate (%) | 72.70% | 35.70% | 6.30% | <.001 |
| Baseline predictors of CR | ||||
| IPSS-R low/intermediate (n, %) | 19 (73.1%) | 5 (45.5%) | 3 (20.0%) | .002 |
| Non-TP53 mutation (n, %) | 23 (88.5%) | 8 (72.7%) | 8 (53.3%) | .01 |
| HSCT received (n, %) | 12 (46.2%) | 4 (36.4%) | 2 (13.3%) | .02 |
CR = complete remission, HSCT = hematopoietic stem cell transplantation, IPSS = International Prognostic Scoring System, OS = overall survival.
Additionally, baseline factors significantly predicted CR. The CR rate in the IPSS-R low/medium-risk group was significantly higher than in the high-risk group (73.1% vs 20.0%, P = .002). The CR rate was also higher in patients without TP53 mutations (88.5% vs 53.3%, P = .01). Patients who received HSCT also showed an advantage in CR rates (46.2% vs 13.3%, P = .02).
4. Discussion
This study, through retrospective analysis of the clinical characteristics, molecular markers, and treatment responses of patients with myelodysplastic syndromes (MDS) transforming into AML, explored key factors influencing the prognosis of MDS transformation. Our results indicate that ECOG performance status, IPSS score, bone marrow blast percentage, TP53 mutations, and cytogenetic risk are closely associated with patient prognosis. These findings not only further reinforce existing prognostic evaluation standards but also provide new reference points for clinical decision-making.
Firstly, the ECOG performance status score is an important clinical indicator reflecting the overall health status of patients.[14] We found that patients with an ECOG score ≥2 had a significantly increased risk of death, which is consistent with conclusions in the existing literature. Poor performance status reflects a worse general condition, which may impact treatment tolerance and reduce survival chances.[14,15] Additionally, IPSS and IPSS-R scores are classic tools for assessing the prognosis of MDS patients. Our study showed that higher IPSS scores and IPSS-R high-risk/very high-risk scores were closely associated with poorer prognosis. This result further underscores the importance of IPSS and IPSS-R in the prognostic evaluation of MDS patients, particularly in predicting the risk of MDS transformation to AML. The IPSS scoring system, by assessing the patient’s hematological, marrow findings, and cytogenetic risks, helps identify high-risk patients early, allowing for timely intervention.[16,17]
Moreover, we found that an increase in bone marrow blast percentage is an important marker for the transformation of MDS to AML. As the bone marrow blast percentage rises, the patient’s risk of death significantly increases, a phenomenon that has been validated in multiple studies. Bone marrow blasts are a typical feature of AML, and a higher blast percentage suggests the acceleration of the disease’s acute progression, indicating greater treatment difficulty and faster transformation.[18] Therefore, regular monitoring of bone marrow blast percentage may help assess the risk of MDS patients transforming into AML.
At the molecular level, TP53 mutations are closely associated with poor prognosis. Our study found that the incidence of TP53 mutations was significantly higher in the poor prognosis group than in the good prognosis group, further confirming the importance of TP53 as a prognostic marker for AML. TP53 gene mutations have been widely recognized as driver mutations in various malignancies and are closely associated with chemotherapy resistance, relapse, and poor survival prognosis.[19,20] Although mutations in genes such as ASXL1 and RUNX1 have also been mentioned in relation to AML, the relationship between these mutations and prognosis in our study was more complex, likely influenced by the patient’s specific clinical background and treatment response.
Patients in the high-risk cytogenetic risk group have a worse prognosis after transformation to AML, further proving the central role of cytogenetic risk in the management of MDS patients. Our data indicated that patients in the adverse cytogenetic risk group had a higher risk of death. Cytogenetic abnormalities, particularly those involving chromosome deletions and complex abnormalities, have been confirmed as important predictors of prognosis in AML patients.[21,22] Therefore, cytogenetic testing should be part of the routine early evaluation of MDS patients to help identify high-risk patients and guide treatment.
In terms of treatment response, patients achieving CR had significantly better survival than those with PR or NR. Our study found that the CR group had a lower relapse rate and longer relapse-free time, suggesting that CR is a key prognostic factor and that patients who achieve CR are more likely to have better long-term survival. This finding provides important clinical reference, indicating that patients who achieve CR early should receive aggressive intervention and long-term follow-up.[23] Additionally, patients who underwent HSCT showed advantages in both CR rates and survival outcomes, supporting HSCT as an important therapeutic option for patients whose MDS transforms into AML.
However, this study also has several limitations. First, as a retrospective study, it may be subject to selection bias, and it is not possible to fully exclude the impact of differences in clinical treatment approaches on the results. Second, the sample size is relatively small. Although it includes patient data from 2018 to 2023, further large-scale prospective studies are needed to validate our findings. Additionally, this study only focused on a subset of clinical and molecular parameters and did not cover all potential prognostic factors. Future research should further explore other potential biomarkers and their impact on prognosis.
In conclusion, this study identified several prognostic factors related to the transformation of MDS to AML, including clinical indicators, molecular markers, and treatment responses. These factors can help clinicians more accurately assess the risk of transformation and prognosis and develop individualized treatment plans. Despite its limitations, this study provides valuable reference for the management and treatment of MDS patients and has important clinical significance.
Author contributions
Conceptualization: Yufang Wang.
Data curation: Yufang Wang, Fang Hu.
Formal analysis: Yufang Wang, Fang Hu, Jinyong Ke.
Investigation: Jinyong Ke.
Methodology: Jinyong Ke.
Supervision: Jinyong Ke.
Validation: Yufang Wang, Fang Hu, Jinyong Ke.
Visualization: Yufang Wang, Fang Hu, Jinyong Ke.
Writing – original draft: Yufang Wang, Fang Hu, Jinyong Ke.
Writing – review & editing: Yufang Wang.
Abbreviations:
- AML
- acute myeloid leukemia
- CR
- complete remission
- IPSS
- International Prognostic Scoring System
- IPSS-R
- revised IPSS
- MDS
- myelodysplastic syndromes
- NR
- no response
- OS
- overall survival
- PFS
- progression-free survival
- PR
- partial remission
The authors have no funding and conflicts of interest to disclose.
The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.
How to cite this article: Wang Y, Hu F, Ke J. Prognosis of the transformation of myelodysplastic syndromes to acute myeloid leukemia: A retrospective study. Medicine 2025;104:35(e43783).
YW and FH contributed to this article equally.
Contributor Information
Yufang Wang, Email: 59615225@qq.com.
Fang Hu, Email: funci20002000@163.com.
References
- [1].Sekeres MA, Taylor J. Diagnosis and treatment of myelodysplastic syndromes: a review. JAMA. 2022;328:872–80. [DOI] [PubMed] [Google Scholar]
- [2].Tanaka TN, Bejar R. MDS overlap disorders and diagnostic boundaries. Blood. 2019;133:1086–95. [DOI] [PubMed] [Google Scholar]
- [3].Chamseddine AN, Jabbour E, Kantarjian HM, Bohannan ZS, Garcia-Manero G. Unraveling myelodysplastic syndromes: current knowledge and future directions. Curr Oncol Rep. 2016;18:4. [DOI] [PubMed] [Google Scholar]
- [4].Li H, Hu F, Gale RP, Sekeres MA, Liang Y. Myelodysplastic syndromes. Nat Rev Dis Primers. 2022;8:74. [DOI] [PubMed] [Google Scholar]
- [5].Kota V, Ogbonnaya A, Farrelly E, et al. Clinical impact of transformation to acute myeloid leukemia in patients with higher-risk myelodysplastic syndromes. Future Oncol. 2022;18:4017–29. [DOI] [PubMed] [Google Scholar]
- [6].Carraway HE, Pollyea DA, Stein EM. Recent progress in acute leukemia and myelodysplasia. Best Pract Res Clin Haematol. 2022;35:101415. [DOI] [PubMed] [Google Scholar]
- [7].Menssen AJ, Walter MJ. Genetics of progression from MDS to secondary leukemia. Blood. 2020;136:50–60. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [8].Sugimoto K, Hirano N, Toyoshima H, et al. Mutations of the p53 gene in myelodysplastic syndrome (MDS) and MDS-derived leukemia. Blood. 1993;81:3022–6. [PubMed] [Google Scholar]
- [9].Yun S, Geyer SM, Komrokji RS, et al. Prognostic significance of serial molecular annotation in myelodysplastic syndromes (MDS) and secondary acute myeloid leukemia (sAML). Leukemia. 2021;35:1145–55. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [10].Welch JS, Petti AA, Miller CA, et al. TP53 and decitabine in acute myeloid leukemia and myelodysplastic syndromes. N Engl J Med. 2016;375:2023–36. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [11].Ambinder AJ, DeZern AE. Navigating the contested borders between myelodysplastic syndrome and acute myeloid leukemia. Front Oncol. 2022;12:1033534. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [12].Pedersen-Bjergaard J, Andersen MK, Andersen MT, Christiansen DH. Genetics of therapy-related myelodysplasia and acute myeloid leukemia. Leukemia. 2008;22:240–8. [DOI] [PubMed] [Google Scholar]
- [13].Gao L, Saeed A, Golem S, et al. High-level MYC expression associates with poor survival in patients with acute myeloid leukemia and collaborates with overexpressed p53 in leukemic transformation in patients with myelodysplastic syndrome. Int J Lab Hematol. 2021;43:99–109. [DOI] [PubMed] [Google Scholar]
- [14].Thépot S, Itzykson R, Seegers V, et al. ; Groupe Francophone des Myélodysplasies (GFM), Acute Leukemia French Association (ALFA); Groupe Ouest-Est des Leucémies Aiguës; Maladies du Sang (GOELAMS). Azacitidine in untreated acute myeloid leukemia: a report on 149 patients. Am J Hematol. 2014;89:410–6. [DOI] [PubMed] [Google Scholar]
- [15].Lee YJ, Park SW, Lee IH, et al. Report on outcomes of hypomethylating therapy for analyzing prognostic value of Revised International Prognostic Scoring System for patients with lower-risk myelodysplastic syndromes. Ann Hematol. 2016;95:1795–804. [DOI] [PubMed] [Google Scholar]
- [16].Cluzeau T, Mounier N, Karsenti JM, et al. Monosomal karyotype improves IPSS-R stratification in MDS and AML patients treated with Azacitidine. Am J Hematol. 2013;88:780–3. [DOI] [PubMed] [Google Scholar]
- [17].Della Porta MG, Alessandrino EP, Bacigalupo A, et al. ; Gruppo Italiano Trapianto di Midollo Osseo. Predictive factors for the outcome of allogeneic transplantation in patients with MDS stratified according to the revised IPSS-R. Blood. 2014;123:2333–42. [DOI] [PubMed] [Google Scholar]
- [18].Bacher U, Kern W, Alpermann T, et al. Prognosis in patients with MDS or AML and bone marrow blasts between 10% and 30% is not associated with blast counts but depends on cytogenetic and molecular genetic characteristics. Leukemia. 2011;25:1361–4. [DOI] [PubMed] [Google Scholar]
- [19].Daver NG, Maiti A, Kadia TM, et al. TP53-Mutated myelodysplastic syndrome and acute myeloid leukemia: biology, current therapy, and future directions [published correction appears in Cancer Discov. 2022 Dec 2;12(12):2954. doi: 10.1158/2159-8290.CD-22-1192.]. Cancer Discov. 2022;12:2516–29. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [20].Weinberg OK, Siddon A, Madanat YF, et al. TP53 mutation defines a unique subgroup within complex karyotype de novo and therapy-related MDS/AML. Blood Adv. 2022;6:2847–53. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [21].Stengel A, Meggendorfer M, Walter W, et al. Interplay of TP53 allelic state, blast count, and complex karyotype on survival of patients with AML and MDS. Blood Adv. 2023;7:5540–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [22].Harada Y, Harada H. Molecular pathways mediating MDS/AML with focus on AML1/RUNX1 point mutations. J Cell Physiol. 2009;220:16–20. [DOI] [PubMed] [Google Scholar]
- [23].Brunner AM, Gavralidis A, Ali NA, et al. Evaluating complete remission with partial hematologic recovery (CRh) as a response criterion in myelodysplastic syndromes (MDS). Blood Cancer J. 2022;12:153. [DOI] [PMC free article] [PubMed] [Google Scholar]