Threefold IPSS-M reclassification outperforms original stratification in predicting post-transplant outcomes for MDS patients

Abstract

The predictive performance of the Molecular International Prognostic Scoring System (IPSS-M) for high-risk myelodysplastic syndromes (MDS) patients undergoing transplantation remains uncertain. We retrospectively analyzed 86 MDS patients who underwent allogeneic hematopoietic stem cell transplantation (allo-HSCT) at our center from 2016 to 2023. According to IPSS-M, patients were classified as Low (n = 3), Moderate-Low (n = 9), Moderate-High (n = 15), High (n = 28), and Very-High risk (n = 31). The IPSS-M did not demonstrate good prognostic accuracy for overall survival (OS) (P = 0.227) and disease-free survival (DFS) (P = 0.095) in these 86 patients. We then divided the patients into three groups based on their IPSS-M scores: IPSS-M < 0.56 (n = 28), IPSS-M 0.56–1.75 (n = 30), and IPSS-M>1.75 (n = 28). There was a significant difference in the long-term OS (P = 0.010) and DFS among the three groups (P < 0.001). This indicates that, based on the original IPSS-M scores, we may be able to find a more precise risk stratification for high-risk MDS patients undergoing allo-HSCT. Compared with TP53 wild-type and TP53 monoallelic mutations, TP53 biallelic mutations have a significant negative impact on OS and DFS (P = 0.016, P = 0.006). It is crucial to identify TP53 allelic status at diagnosis to distinguish these patients and determine the need for early involvement in clinical trials.

Graphical Abstract

Keywords

myelodysplastic syndrome Molecular International Prognostic Scoring System allogeneic hematopoietic stem cell transplantation risk stratification

Introduction

Myelodysplastic syndromes (MDS) encompass a group of highly heterogeneous myeloid clonal disorders that range from nearly normal individuals to rapidly progressing to acute leukemia and even death in a short span, with survival after diagnosis ranging from months to years^1,2. Particularly, patients with high-risk MDS often have a life expectancy of less than one year³, which is closely related to abnormality in molecular biology^4,5. Only 40% to 60% of patients with high-risk MDS achieve complete remission (CR) after traditional chemotherapy, which is much lower than that of patients with de novo acute myeloid leukemia (AML)^6,7. Owing to the efficacy of high-dose chemotherapy and the graft-versus-leukemia (GVL) effect, allogeneic hematopoietic stem cell transplantation (allo-HSCT) represents the therapeutic modality with the highest potential for curing MDS^8,9. However, the decision to proceed with this treatment necessitates careful weighing between the risk of disease progression and transplantation-related mortality (TRM). Therefore, it is critically important to perform precise stratification of MDS patients to determine the most appropriate treatment at the optimal time.

Over decades, the development of molecular diagnostic techniques has elucidated the pivotal role that genetic mutations play in the pathogenesis, clinical manifestation, therapeutic response, and prognostication of MDS, underscoring the criticality of incorporating genetic mutation into prognostic evaluations to optimize patient management^10–12. In more recent years, genomic data have been incorporated into risk stratification to guide clinical treatment decisions^13–17. Among them, the Molecular International Prognostic Scoring System (IPSS-M) represents a novel prognostic model that expands on the IPSS-R by including a comprehensive panel of 31 somatic mutation genes¹⁸. IPSS-M has been validated as more accurate than IPSS-R in predicting overall survival (OS), leukemia-free survival, and leukemic transformation^19–21. However, due to the wide range of patients included in the original cohorts, the accuracy of IPSS-M for predicting outcomes in patients undergoing transplantation still needs further evaluation^22,23. Currently, there are limited studies to verify the prognostic performance of IPSS-M in MDS patients who received allo-HSCT^19,24,25.

Therefore, we carried out this retrospective study to validate that IPSS-M risk stratification may not be appropriate for transplant MDS patients and to seek risk stratification tailored for the transplant patients based on this model. In addition, we also analyzed the relationship between TP53 allele status and survival outcomes as well as prognostic factors affecting post-transplant outcomes in MDS patients.

Methods

Participants and study design

We retrospectively analyzed the MDS patients who underwent allo-HSCT in our center from 2016 to 2023. The inclusion criteria were as follows: (1) Diagnosis of primary MDS according to the 2016 World Health Organization (WHO) criteria²⁶; (2) Age ≥18 years at the time of transplantation; and (3) Availability of IPSS-M related information at the initial diagnosis. Patients affected with secondary MDS were excluded.

Based on the above criteria, we examined data from 113 consecutive subjects, of whom 27 patients were excluded due to incomplete information on gene mutations, resulting in a total of 86 patients included in this study. All patients were stratified according to both IPSS-R and IPSS-M. Cytogenetic data were derived from conventional G-banding and Fluorescence in situ hybridization (FISH) analysis, with karyotypes classified according to the International System for Human Cytogenetic Nomenclature. The IPSS-M score was calculated according to the original publication¹⁸. All investigations were conducted with the approval of the Ethics Committee of Union Hospital, Tongji Medical College, Huazhong University of Science and Technology and in accordance with the Declaration of Helsinki (approval number: [2023] No.0969).

Definition

Relapse was defined as the occurrence of more than 5% marrow blasts and/or reappearance of major myelodysplastic features associated with cytopenia (or worsening of previous cytopenia) and evidence of autologous reconstitution when chimerism was available²⁷.

The minimal residual disease (MRD) levels of bone marrow (BM) were assessed pre-transplantation in CR patients by multiparameter flow cytometry (MFC)²⁸.

Donor chimerism at 28 days and 100 days were categorized as complete (>95%), mixed (5%–95%), graft rejection (<5%), or death before assessment of donor chimerism using short tandem repeat (STR) amplification by fluorescence labeling polymerase chain reaction (PCR) combined with automatic capillary electrophoresis²⁹.

The primary endpoints were OS and disease-free survival (DFS). The secondary endpoints included Cumulative Incidence of Relapse (CIR), non-relapse mortality (NRM), acute Graft-versus-Host Disease (aGVHD), and chronic Graft-versus-Host Disease (cGVHD). OS was defined as the time from the date of transplantation to the date of death from any cause or the last follow-up for surviving patients. DFS was estimated from the date of transplantation to the development of MDS or death from any cause or the last follow-up for survivors. For NRM, any death in the absence of disease relapse was considered an event. AGVHD and cGVHD were classified based on previous studies^30,31.

Targeted gene sequencing

Genomic DNA was extracted from mononuclear cells of BM aspirate through next-generation sequencing (NGS) to acquire mutation data. Mutation sequencing was performed on 13 subjects using a 25-genes panel from January 2016 to December 2017. Major effect genes NPM1 and FLT3, as well as residual genes CEBPA, ETNK1, GATA2, GNB1, NF1, PHF6, PPM1D, PRPF8, PTPN11, STAG2, and WT1 in the IPSS-M model were not included in this panel. From January 2018 to December 2019, mutation sequencing was performed on 17 participants using a targeted sequencing panel of 96 genes, lacking the residual genes ETNK1, GNB1, PPM1D, and PRPF8. From January 2020 to January 2021, targeted sequencing for 10 participants was executed using a 127-genes panel. GNB1, PPM1D, and FLT3-ITD mutations, which were considered in the molecular model but were absent from the 121 genes analyzed. Mutation sequencing for 46 participants was carried out using a targeted gene sequencing panel of 214 genes from February 2021 to December 2023. GNB1, defined as a residual gene in the IPSS-M model, was not included in this combination. All patients were subjected to test for leukemia fusion gene including the MLL-PTD (Supplementary Tables S1–5). TP53 allelic state was determined as described^32–34. The biallelic TP53 mutations are defined as the presence of 2 or more distinct TP53 mutations (regardless of VAF) or a single TP53 mutation associated with either (1) a cytogenetic deletion involving the TP53 locus at 17p, (2) a VAF of > 50%, or (3) any complex karyotype.

Conditioning regimen

Patients ≥55 years or with a hematopoietic cell transplantation-specific comorbidity index (HCT-CI) ≥3 received the reduced-intensity conditioning (RIC) regimens based on fludarabine (Flu) with busulfan (BU) (FB, n = 18; AZA + FB, n = 3). Other patients received the myeloablative conditioning (MAC) regimen based on modified busulfan and cyclophosphamide (mBUCY, n = 17; Dec + mBUCY, n = 26; AZA + mBUCY, n = 10; Flu + mBUCY, n = 8) or decitabine (Dec) with fludarabine and busulfan (Dec + FB, n = 4; Supplementary Table S6).

GVHD prophylaxis

A regimen of cyclosporine (CsA) combined with short-term methotrexate (MTX) was employed to prevent GVHD for allo-HSCT from human leukocyte antigen (HLA)-matched related donors³⁵. For haploidentical transplants and unrelated donor matches, the regimen included tacrolimus (FK506), mycophenolate mofetil (MMF), basiliximab (CD25), short-term MTX, and anti-thymocyte globulin (ATG)³⁶. The conventional dose of ATG was 6–7.5 mg/kg.

Statistical analysis

Numerical variables between groups were tested by the Wilcoxon rank-sum test. Categorical co-variates were compared with the chi-squared test and Fisher’s exact test. The OS and DFS were estimated using the Kaplan–Meier method and CIR, NRM, and GVHD were evaluated using a competing risk model and Fine-Gray test. Cox proportional hazards models were utilized to predict the risk factors for OS and DFS. Factors with P < 0.1 in the univariate analysis were included in the multivariate analysis. Two-sided P values < 0.05 were considered of statistical significance. Data analysis was performed using SPSS 26.0 and R 4.2.2.

Results

Mutation topography

When considering only the mutations incorporated in the IPSS-M model, 63 patients (73.3%) had available mutation data, of which 40 patients (46.5%) carried ≥2 relevant mutations, with a median number of mutations being 2 (interquartile range [IQR] 1–3). A total of 77 (89.5%) patients presented at least one pathogenic molecular abnormality. 23 subjects (26.7%) had mutations only, 14 (16.3%) had abnormal cytogenetics only, and 40 (46.5%) had both. (Supplementary Table S7).

Patient characteristics and outcomes

Among the 86 patients, 53 (61.6%) were male and 33 (38.4%) were female. The median age at diagnosis was 43.0 years (IQR, 36.0–52.3), with the last follow-up date being February 4, 2024. The patients were classified according to the IPSS-M: 3 patients (3.5%) were classified as Low-risk, 9 patients (10.5%) as Moderate Low-risk, 15 patients (17.4%) as Moderate High-risk, and 26 patients (30.2%) and 33 patients (38.4%) as High and Very High-risk. (Supplementary Table S8).

As of the last follow-up, 28 (32.6%) patients had died, with the causes of death being relapse in 10 cases, GVHD in 3, infections in 11, graft failure in 3, and sudden cardiac death in 1. The median follow-up time for survivors was 22.9 months (IQR, 9.5–42.1), with 3-year OS and 3-year DFS of 62.6% (95% CI, 51.9%–75.4%) and 50.4% (95% CI, 38.9%–65.2%), respectively (Fig. 1a and b). The 3-year CIR and 3-year NRM were 25.4% (95% CI, 24.7%–26.1%) and 24.2% (95% CI, 23.7%–24.8%), respectively (Fig. 1c and d). The 100-day cumulative incidence of grades I–IV, II–IV, and III–IV aGVHD were 27.2% (95% CI, 26.7%–27.8%), 20.8% (95% CI, 20.4%–21.2%) and 8.9% (95% CI, 8.7%–9.1%), respectively. The 1-year cumulative incidence of mild, moderate and severe cGVHD was 38.2% (95% CI, 37.5%–38.9%), 24.7% (95% CI, 24.1%–25.3%), and 13.6% (95% CI, 13.2%–14.0%).

Figure 1.

OS, DFS, CIR, and NRM in 86 patients. (a) OS. (b) DFS. (c) CIR, (d) NRM.

Notably, in our cohort, employing the IPSS-M risk stratification did not demonstrate predictive value for the post-transplant survival of the 86 patients (Fig. 2a and b). In contrast, the IPSS-R risk stratification exhibited significant prognostic capability for OS (P = 0.022) and DFS (P = 0.001) (Fig. 2c and d).

Figure 2.

Kaplan–Meier probability estimates of OS (a and c) and DFS (b and d) for 86 patients with MDS stratified by IPSS-R and IPSS-M risk categories, respectively.

Patient characteristics after re-stratifying

The 86 patients were grouped based on their IPSS-M scores into three categories: IPSS-M < 0.56, IPSS-M 0.56–1.75, and IPSS-M >1.75, comprising 28, 30, and 28 cases, respectively. The median follow-up times for survivors in these groups were 18.4 (95% CI, 7.6–34.7) months, 29.8 (95% CI, 12.5–55.2) months, and 22.0 (95% CI, 7.0–42.5) months, respectively (P = 0.302). Patients in the IPSS-M < 0.56 were primarily categorized into the Intermediate-risk category with IPSS-R, with 15 cases (53.6%), while patients in the IPSS-M 0.56–1.75 and IPSS-M >1.75 were primarily categorized into the High and Very High-risk category, with 15 cases (50.0%) and 18 cases (64.3%), respectively. Regarding the IPSS-M, patients in the IPSS-M < 0.56 were primarily categorized into the Moderate High-risk category, with 15 cases (53.6%), followed by Moderate Low-risk with 9 cases (32.1%), Low risk with 3 cases (10.7%), and High-risk with 1 case (3.6%). Of the patients with IPSS-M 0.56–1.75, 25 cases (83.3%) were classified as High-risk for IPSS-M, and the remaining 5 cases (16.7%) were classified as Very High-risk. (Table 1).

Table 1.

Clinical and laboratory characteristics of patients (n = 86) with MDS after re-stratifying.

Characteristic	IPSS-M < 0.56	IPSS-M 0.56–1.75	IPSS-M >1.75	P value	P value
Number of patients, n (%)	28 (32.6)	30 (34.9)	28 (32.6)
Follow-up for survivors (months), median (IQR)	18.4 (7.6–34.7)	29.8 (12.5–55.2)	22.0 (7.0–42.5)	0.302	0.546
Age, median (IQR)	38.5 (34.0–48.5)	44.0 (34.5–50.3)	49.5 (37.0–58.0)	0.506	0.631
Gender				0.505
Male, n (%)	16 (57.1)	21 (70.0)	16 (57.1)		0.228
Female, n (%)	12 (42.9)	9 (30.0)	12 (42.9)
Hematologic Feature, median (IQR)
Hemoglobin, (g/dL)	90.0 (72.3–114.3)	66.0 (52.0–80.3)	65.0 (54.3–77.8)	0.955	0.459
Neutrophils, (×10⁹/L)	0.9 (0.5–2.2)	1.2 (0.7–2.0)	0.8 (0.4–2.6)	0.554
Platelets, (×10⁹/L)	63.0 (25.0–136.8)	36.5 (27.3–113.5)	41.0 (16.5–70.0)	0.307
Myeloblast percentage, median (IQR)	4.8 (2.5–7.4)	5.5 (3.4–10.3)	8.3 (4.4–12.5)	0.554	0.337
2016 WHO Category:				0.429
MDS-SLD, n (%)	1 (3.6)	0 (0)	0 (0)
MDS-MLD, n (%)	7 (25.0)	8 (26.7)	3 (10.7)
MDS-RS-SLD, n (%)	1 (3.6)	0 (0)	0 (0)
MDS-RS-MLD, n (%)	2 (7.1)	2 (6.7)	1 (3.6)		0.899
MDS-5q-, n (%)	0 (0)	0 (0)	0 (0)		0.024
MDS-EB1, n (%)	12 (42.9)	11 (36.7)	13 (46.4)
MDS-EB2, n (%)	4 (14.3)	9 (30.0)	11 (39.3)
MDS-U, n (%)	1 (3.6)	0 (0)	0 (0)		0.159
Cytogenetic risk according to IPSS-R criteria:				0.024
Very good, n (%)	0 (0)	0 (0)	0 (0)
Good, n (%)	13 (46.4)	13 (43.3)	8 (28.6)		0.946
Intermediate, n (%)	13 (46.4)	12 (40.0)	7 (25.0)		0.484
Poor, n (%)	2 (7.1)	2 (6.7)	5 (17.9)		0.797
Very poor, n (%)	0 (0)	3 (10.0)	8 (28.6)
IPSS-R category at diagnosis:				<0.001
Very low, n (%)	0 (0)	0 (0)	0 (0)
Low, n (%)	3 (10.7)	0 (0)	0 (0)
Intermediate, n (%)	15 (53.6)	7 (23.3)	2 (7.1)		0.007
High, n (%)	9 (32.1)	15 (50.0)	8 (28.6)
Very high, n (%)	1 (3.6)	8 (26.7)	18 (64.3)
Treatment before allo-HSCT:				0.155	0.822
Untreated, n (%)	10 (35.7)	13 (43.3)	15 (53.6)
Hypomethylating agents, n (%)	8 (28.6)	7 (23.3)	1 (1.2)
Chemotherapy, n (%)	10 (35.7)	10 (33.3)	12 (42.9)
Disease status:					0.252
NR, n (%)	15 (53.6)	16 (53.3)	20 (71.4)	0.282
CR, n (%)	13 (46.4)	14 (46.7)	8 (28.6)
MRD positive, n (%)	3 (23.1)	5 (35.7)	5 (62.5)	0.19
MRD negative, n (%)	10 (76.9)	9 (64.3)	3 (37.5)
Time from diagnosis to allo-HSCT (month), median (IQR)	3.1 (1.9–5.5)	3.2 (1.5–5.7)	2.7 (1.6–4.6)	0.866	0.7580.314
Stem cell source				0.402
PB, n (%)	25 (89.3)	29 (96.7)	27 (96.4)
BM + PB, n (%)	3 (10.7)	1 (3.3)	1 (3.6)
Donor type				0.898
HLA-identical sibling, n (%)	7 (25.0)	9 (30.0)	6 (21.4)		0.891
Haploidentical related, n (%)	19 (67.9)	19 (63.3)	21 (75.0)
Unrelated, n (%)	2 (7.1)	2 (6.7)	1 (3.6)
Gender relationship				0.857
Male to male, n (%)	10 (35.7)	15 (50.0)	9 (32.1)		0.314
Male to female, n (%)	9 (32.1)	6 (20.0)	8 (28.6)
Female to male, n (%)	6 (21.4)	6 (20.0)	7 (25.0)		0.081
Female to female, n (%)	3 (10.7)	3 (10.0)	4 (14.3)
Donor age, median (IQR)	26.5 (17.3–34.5)	30.0 (21.0–35.3)	34.0 (24.3–45.5)	0.227
ABO blood type				0.671	0.659
Matched, n (%)	14 (50.0)	16 (53.3)	13 (46.4)
Major, n (%)	2 (7.1)	6 (20.0)	5 (17.9)		0.081
Minor, n (%)	8 (28.6)	7 (23.3)	7 (25.0)
Bi-directional, n (%)	4 (14.3)	1 (3.3)	3 (10.7)
Conditioning regimen				0.173	0.387
Myeloablative, n (%)	24 (85.7)	23 (76.7)	18 (64.3)
Reduced-intensity, n (%)	4 (14.3)	7 (23.3)	10 (35.7)		0.573
Infused CD34⁺ (*10⁶/kg) cells median (IQR)	5.3 (4.6–7.2)	5.4 (3.7–7.5)	6.4 (4.7–8.1)	0.307
GVHD prophylaxis				0.275
CSA + MTX, n (%)	7 (25.0)	9 (30.0)	6 (21.4)		0.005
FK506 + MMF + CD25 + MTX + ATG, n (%)	21 (75.0)	21 (70.0)	22 (78.6)
Neutrophil engraftment				0.133
Yes, n (%)	28 (100.0)	27 (90.0)	24 (85.7)		0.752
No, n (%)	0 (0)	3 (10.0)	4 (14.3)
Time of neutrophil engraftment (days), median (IQR)	11.5 (10.0–14.0)	11.0 (10.0–13.0)	11.0 (10.0–13.0)	0.611
Platelet engraftment				0.381
Yes, n (%)	27 (96.4)	27 (90.0)	24 (85.7)
No, n (%)	1 (3.6)	3 (10.0)	4 (14.3)
Time of platelet engraftment (days), median (IQR)	11.0 (10.0–13.0)	11.0 (10.0–14.0)	11.5 (10.0–14.0)	0.779
Donor chimerism at +28 days				0.634
CDC, n (%)	28 (100.0)	27 (90.0)	24 (85.7)
MC, n (%)	0 (0)	1 (3.3)	1 (3.6)
GF, n (%)	0 (0)	1 (3.3)	1 (3.6)
Death before assessment, n (%)	0 (0)	1 (3.3)	2 (7.1)
Donor chimerism at +100 days				0.146
CDC, n (%)	28 (100.0)	27 (90.0)	21 (75.0)
MC, n (%)	0 (0)	1 (3.3)	3 (10.7)
GF, n (%)	0 (0)	0 (0)	1 (3.6)
Death before assessment, n (%)	0 (0)	2 (6.7)	3 (10.7)
CMV reactivation after allo-HSCT, n (%)				0.430
Yes, n (%)	12 (42.9)	13 (43.3)	8 (28.6)
No, n (%)	16 (57.1)	17 (56.7)	20 (71.4)

MDS: myelodysplastic syndromes; IQR: interquartile range; MDS-SLD: MDS with single-lineage dysplasia; MDS-MLD: MDS with multilineage dysplasia; MDS-RS-SLD: MDS with ring sideroblasts and single-lineage dysplasia; MDS-RS-MLD: MDS with ring sideroblasts and multilineage dysplasia; MDS-5q-: MDS with isolated deletion of long arm of chromosome 5; MDS-EB1: MDS with excess of blasts type 1; MDS-EB2: MDS with excess of blasts type 2; MDS-U: MDS unclassifiable; IPSS-R: Revised International Prognostic Scoring System; IPSS-M: Molecular International Prognostic Scoring System; allo-HSCT: allogeneic hematopoietic stem cell transplantation; NR: non-remission; CR: complete remission; MRD: minimal residual disease; PB: peripheral blood; BM: bone marrow; HLA: human leukocyte antigen; GVHD: graft-versus-host disease; CSA: cyclosporine; MTX: methotrexate; FK506: tacrolimus; MMF: mycophenolate mofetil; CD25: basiliximab; ATG: anti-thymocyte globulin; CDC: complete donor chimerism; MC: mixed chimerism; GF: graft failure; CMV: cytomegalovirus.

Engraftment

In this study, 28 (100.0%), 27 (90.0%), and 24 (85.7%) patients in the three groups successfully achieved neutrophil engraftment, and 27 (96.4%), 27 (90.0%), and 24 (85.7%) patients successfully achieved platelet engraftment, respectively. There was no statistical difference between the three groups in terms of neutrophil and platelet engraftment (P = 0.133 and P = 0.381, respectively). In the IPSS-M >1.75, one patient experienced sudden cardiac death following stem cell infusion, while the remaining cases of engraftment failure in three groups were due to not meeting hematological engraftment criteria within 28 days post-transplant. There were no significant differences in the status of donor chimerism at days +28 and +100 between the three groups (Table 1).

Relapse

As the IPSS-M score intervals increased, there was an upward trend in the number of relapses among the three groups, with 4 (14.3%) cases, 5 (16.7%) cases, and 9 (32.1%) cases, respectively, P = 0.202. The median time to relapse was 15.5 (9.0–50.8) months, 4.3 (2.0–19.3) months, and 3.8 (2.7–20.3) months, respectively, P = 0.320.

Outcomes (OS, DFS, CIR, NRM and GVHD)

By the last follow-up, 4 (14.3%) cases in the IPSS-M < 0.56, 10 (33.3%) cases in the IPSS-M 0.56–1.75, and 14 (50.0%) cases in the IPSS-M >1.75 had died. (Table 2).

Table 2.

Cause of death in three groups.

Cause of death	IPSS-M < 0.56	IPSS-M 0.56–1.75	IPSS-M >1.75
All causes	4	10	14
Relapse	1	3	6
GVHD	0	0	0
Acute	0	0	0
Chronic	0	2	1
Infection	3	5	3
Graft failure	0	0	3
Others	0	0	1^a

IPSS-M: Molecularnternationalrognostic Scoring System; GVHD::: graft-versus-host disease.

The patient suffered from sudden cardiac arrest while undergoing stem cell infusion.

There was a significant difference in the long-term OS and DFS among the three groups (P = 0.010, P < 0.001) (Fig. 3a and b). The 3-year OS and DFS for the three groups were 82.3% (95% CI, 64.7%–100.0%) vs. 62.4% (95% CI, 46.2%–86.4%) vs. 43.2% (95% CI, 27.0%–69.1%) (P = 0.007) and 73.0% (95% CI, 53.9%–98.7%) vs. 58.4% (95% CI, 41.9%–81.4%) vs. 19.0% (95% CI, 6.5%–55.4%; P < 0.001). There were no significant differences among the three groups in terms of CIR, NRM, aGVHD, and cGVHD (Table 3).

Figure 3.

Kaplan-Meier probability estimates of OS (a) and DFS (b) for 86 patients with MDS after re-stratifying.

Table 3.

Clinical outcomes after allo-HSCT.

Characteristic	IPSS-M < 0.56	IPSS-M 0.56–1.75	IPSS-M >1.75	P value
aGVHD at 100 days (%, [95% CI])
Grade I–IV	37.1 (35.3–38.9)	26.6 (25.3–28.0)	18.1 (17.0–19.2)	0.373
Grade II–IV	27.4 (25.7–29.0)	20.6 (19.5–21.8)	14.7 (13.7–15.6)	0.659
Grade III–IV	8.3 (7.7–9.0)	10.9 (10.2–11.6)	7.3 (6.8–7.8)	0.967
cGVHD at 1 years (%, [95% CI])
≥Mild	49.8 (47.3–52.3)	43.3 (41.4–45.2)	34.9 (32.8–37.1)	0.761
≥Moderate	34.5 (32.4–36.5)	21.6 (19.9–23.3)	28.5 (26.4–30.6)	0.613
≥Severe	23.8 (21.9–25.6)	11.8 (10.9–12.7)	9.0 (8.2–9.8)	0.435
OS at 1 years (%, [95% CI])	91.5 (80.8–100.0)	71.4 (56.4–90.4)	62.4 (46.4–84.1)	0.039
at 2 years (%, [95% CI])	91.5 (80.8–100.0)	62.4 (46.2–84.4)	43.2 (27.0–69.1)	0.003
at 3 years (%, [95% CI])	82.3 (64.7–100.0)	62.4 (46.2–84.4)	43.2 (27.0–69.1)	0.007
DFS at 1 years (%, [95% CI])	82.1 (67.6–99.7)	68.2 (52.9–87.8)	51.0 (34.8–74.5)	0.016
at 2 years (%, [95% CI])	82.1 (67.6–99.7)	58.4 (41.9–81.4)	35.7 (20.5–62.2)	0.002
at 3 years (%, [95% CI])	73.0 (53.9–98.7)	58.4 (41.9–81.4)	19.0 (6.5–55.4)	<0.001
NRM at 1 years (%, [95% CI])	8.5 (7.8–9.2)	21.5 (20.2–22.7)	25.4 (23.9–26.8)	0.133
at 2 years (%, [95% CI])	8.5 (7.8–9.2)	21.5 (20.2–22.7)	35.5 (33.5–37.6)
at 3 years (%, [95% CI])	17.6(15.4–19.8)	21.5 (20.2–22.7)	35.5 (33.5–37.6)
CIR at 1 years (%, [95% CI])	9.4 (8.6–10.2)	10.4 (9.7–11.1)	23.7 (22.2–25.2)	0.059
at 2 years (%, [95% CI])	9.4 (8.6–10.2)	20.1 (18.7–21.5)	28.8 (27.0–30.6)
at 3 years (%, [95% CI])	9.4 (8.6–10.2)	20.1 (18.7–21.5)	45.4 (41.8–49.0)

IPSS-M: Molecular International Prognostic Scoring System; aGVHD: acute graft-versus-host disease; cGVHD: chronic graft-versus-host disease; OS: overall survival; DFS: disease-free survival; NRM: non-relapse mortality; CIR: cumulative incidence of relapse.

Relationship between TP53 allele status and survival outcomes

Among our cohort, 13(15.1%) patients were identified to have TP53 mutations. Out of these, 8 patients (61.5%) had biallelic mutations, and 5 of them (62.5%) died. There were 5 patients (38.5%) identified with monoallelic mutation, and 2 of them (40%) died.

Patients with TP53 mutations had a poor prognosis for both OS and DFS, particularly in patients with TP53 biallelic mutations (P = 0.053, P = 0.023) (Fig. 4a and b). Furthermore, when combining TP53 wild-type and TP53 monoallelic mutations, the negative impact of TP53 biallelic mutations on OS and DFS becomes even more significant (P = 0.016, P = 0.006) (Fig. 4c and d).

Figure 4.

Kaplan-Meier probability estimates of OS (a and c) and DFS (b and d) for 86 patients with MDS stratified by TP53 allele status.

Univariate and multivariate analyses

Univariate analysis revealed that patients with IPSS-M >1.75 was identified as a risk factor for both OS and DFS with hazard ratios (HRs) of 4.79 (95% CI, 1.56–14.71, P = 0.006) and 4.86 (95% CI, 1.90–12.42, P = 0.001), respectively. Biallelic TP53 was a risk factor for OS and DFS with HRs of 3.18 (95% CI 1.19–8.52, P = 0.021) and 3.25 (95% CI 1.33–7.97, P = 0.010). The occurrence of cGVHD was associated with a lower risk of DFS (hazard ratio [HR] = 0.39, 95% CI 0.18–0.83, P = 0.015). Patients >55 years old at the time of transplantation or with poor and very poor cytogenetic categories had a higher risk of relapse (HR = 6.61, 95% CI 2.23–19.61, P = 0.001) (HR = 4.35, 95% CI 1.71–11.05, P = 0.002).

In the multivariate analysis, IPSS-M >1.75 was identified as a risk factor for OS and DFS with HRs of 3.88 (95% CI 1.17–12.85, P = 0.026) and 4.54 (95% CI 1.67–12.42, P = 0.003). CGVHD acted as a protective factor for DFS, with an HR of 0.37 (95% CI 0.18–0.78, P = 0.010). Age >55 years at the time of transplantation was a risk factor for relapse (HR = 3.59, 95% CI 1.06–12.16, P = 0.040) (Table 4).

Table 4.

Results of univariate and multivariate analyses.

Outcomes	Variables in the model	Univariate analysis			Multivariate analysis
Outcomes	Variables in the model	HR	95% CI	P value	HR	95% CI	P value
OS	IPSS-M < 0.56	1			1
	IPSS-M 0.56–1.75	2.48	0.78–7.92	0.124	2.38	0.74–7.70	0.146
	IPSS-M>1.75	4.79	1.56–14.71	0.006	3.88	1.17–12.85	0.026
	TP53 WT	1			1
	Monoallelic TP53	1.28	0.30–5.47	0.74	1.11	0.25–4.83	0.893
	Biallelic TP53	3.18	1.19–8.52	0.021	1.93	0.63–5.86	0.248
	cGVHD (Yes vs. No)	0.44	0.19–1.00	0.051	0.47	0.21–1.07	0.053
DFS	IPSS-M < 0.56	1			1
	IPSS-M 0.56–1.75	1.99	0.74–5.30	0.171	1.94	0.72–5.22	0.19
	IPSS-M>1.75	4.86	1.90–12.42	0.001	4.54	1.67–12.42	0.003
	TP53 WT	1			1
	Monoallelic TP53	1	0.23–4.20	0.996	0.89	0.21–3.83	0.877
	Biallelic TP53	3.25	1.33–7.97	0.01	1.56	0.58–4.24	0.382
	cGVHD (Yes vs. No)	0.39	0.18–0.83	0.015	0.37	0.18–0.78	0.01
RELAPSE	Age at allo-HSCT (>55 years vs. ≤55 years)	6.61	2.23–19.61	0.001	3.59	1.06–12.16	0.04
RELAPSE	Cytogenetic (good and intermediate vs. poor and very poor)	4.35	1.71–11.05	0.002	2.81	0.95–8.31	0.062

OS: overall survival; DFS: disease-free survival; IPSS-M: Molecular International Prognostic Scoring System; TP53 WT: TP53 wild-type; cGVHD: chronic graft-versus-host disease; HR: Hazard Ratio; CI: Confidence Interval.

Discussion

IPSS-R has become the most widely adopted tool for individual risk assessment and treatment decision-making for newly diagnosed MDS patients since its initial publication in 2012³⁷. Although IPSS-M has demonstrated higher accuracy and sensitivity than IPSS-R in overall clinical endpoints, its prognostic value in specific treatment cohorts remains uncertain, considering that only 30% of patients received disease-modifying treatment, and 9% underwent allo-HSCT in the original cohort¹⁸. Research by Sauta et al.¹⁹ indicated that IPSS-M failed to stratify individual responses to hypomethylating agents (HMAs), while Urrutia et al.³⁸ found it also lacked prognostic value after HMA failure. In our transplant cohort, IPSS-M failed to show superior prognostic capabilities compared to IPSS-R (Fig. 2). Moreover, in another retrospective study, IPSS-M did not show a predictive advantage for the survival of 17 patients who underwent allo-HSCT²⁵.

Molecular prognostic models can offer deeper insights into the impact of mutation alterations on the overall risk in MDS. However, similar to IPSS and IPSS-R, which, although once used to assess post-transplant survival, were originally intended to reflect the natural course of disease^39,40. Hence, they may lack precise prognoses for MDS patients undergoing transplantation^25,41. Several transplant-specific prognostic scoring systems have been developed for MDS by integrating patient-specific factors (e.g., Karnofsky Performance Status, KPS; Hematopoietic Cell Transplantation-Comorbidity Index, HCT-CI), disease characteristics (e.g., cytogenetic risk stratification), and transplant-related variables (e.g., conditioning intensity, donor type, graft source), including the MDS transplantation risk index (TRI)⁴², the Center for International Blood and Marrow Transplant Research (CIBMTR) scoring system⁴³, the MDS transplantation prognostic scoring system (MTPSS)⁴⁴, and the EBMT transplant-specific risk score⁴⁵, demonstrating reliability as predictors of post-transplant outcomes. Considering the non-transplant specificity of the original IPSS-M cohort and its poor prognosis in some transplant cohorts, a clinical-molecular model obtained by combining IPSS-M with transplant-specific parameters by Gurnari et al.²⁴ has demonstrated stronger prognostication performance than IPSS-M in 416 MDS patients who received allo-HSCT. In our research, a simple adjustment of risk stratification without adding variables to the IPSS-M model also resulted in a significant difference in survival between the three groups (Fig. 3). These attempts indicated that, based on the IPSS-M scores, we may be able to find a more accurate risk stratification for specific transplant MDS patients, thereby identifying patients who would benefit the most from allo-HSCT and maximizing the value of the model. Meanwhile, our multivariate analysis revealed adverse effects on OS in the highest-risk group, which has a median survival of 1.2 (1.0–1.4) years, better than that of the Very High-risk in the original IPSS-M cohort, possibly due to transplantation improving the OS in our cohort²². However, the survival benefit of allo-HSCT in the highest-risk group was limited compared with patients with IPSS-M < 1.75 (Supplementary Fig. S1), with 3-year OS and 3-year DFS 43.2% (27.0%–69.1%) versus 71.9% (59.6%–86.9%) and 19.0% (6.5%–55.4%) versus 65.4% (52.4%–81.6%) (P = 0.006, P < 0.001), respectively. Some new drugs, such as sabatolimab, maybe more promising in improving their survival⁴⁶.

Previous studies have repeatedly confirmed that MDS patients with TP53 mutations have a poor prognosis^32,47–49. However, recent research has found that biallelic TP53 is the true culprit behind the adverse outcomes caused by this gene mutation, and only patients with monoallelic TP53 or those without complex chromosomal abnormalities benefit from allo-HSCT^12,50. These findings solidify biallelic TP53 mutations as an independent prognostic risk factor, a concept recognized in the 2022 WHO classification, which recognized MDS-biTP53 as a separate category³⁴, highlighting the importance of identifying the TP53 allelic status at diagnosis to distinguish these patients and the necessity of early involvement in clinical trials to find more effective therapies.

Older age is often predictive of poor post-transplant outcomes. In our multivariate analysis, patients over the age of 55 had nearly a fourfold higher risk of relapse compared to those ≤55 years, which was consistent with previous studies^43,51. Moreover, this subset of patients receiving RIC regimens may increase the risk of relapse while reducing toxicity and improving tolerability⁵². Additionally, as previous studies have mentioned, the presence of cGVHD is beneficial for the long-term survival of patients, which is mainly attributed to the GVL it induces⁵³.

Our study has several limitations, including its retrospective design and small sample size, which may impact the external validity and statistical power of the risk stratification model. As a result, we cannot confidently assert its universal applicability. The primary value of this study lies in its aim to promote the development of more accurate prognostic assessment tools for transplant patients by enhancing the predictive dimensions of the IPSS-M scoring system, such as incorporating transplant-specific indicators. Additionally, incomplete molecular information for some patients and the absence of TP53 loss of heterozygosity (LOH) status in our routine tests have led to an underestimation of risk stratification. However, the main effect genes in the IPSS-M are mostly included in our sequencing panels, and the mutational incidences of some residual genes such as ETNK1, GNB1, NF1, PPM1D and PRPF8 were low (<3%) as reported by Bernard et al.¹⁸ In addition, Baer et al.⁵⁴ verified that the absence of residual genes may not affect the classification of some patients. Accordingly, most of the subjects in our study could be well categorized by IPSS-M.

Conclusion

In conclusion, although IPSS-M risk stratification failed to show superior prognostic performance for MDS patients undergoing allo-HSCT compared to IPSS-R, identifying new stratification based on IPSS-M scores could help pinpoint high-risk MDS patients who respond better to allo-HSCT. Patients with IPSS-M > 1.75 or with TP53 biallelic mutations had very poor survival post-transplantation, suggesting early consideration of clinical trials. Older patients were at higher risk of relapse after transplantation. The presence of non-life-threatening cGVHD was beneficial for long-term survival.

Supplemental Material

sj-docx-1-cll-10.1177_09636897251348406 – Supplemental material for Threefold IPSS-M reclassification outperforms original stratification in predicting post-transplant outcomes for MDS patients

Supplemental material, sj-docx-1-cll-10.1177_09636897251348406 for Threefold IPSS-M reclassification outperforms original stratification in predicting post-transplant outcomes for MDS patients by Hongru Chen, Shan Jiang, Ruowen Wei, Ao Zhang, Xiena Cao, Wei Shi and Linghui Xia in Cell Transplantation

Footnotes

ORCID iDs

Hongru Chen

Shan Jiang

Ruowen Wei

Ao Zhang

Xiena Cao

Wei Shi

Linghui Xia

Ethical considerations

The study was conducted in accordance with the Declaration of Helsinki and was approved by the Ethics Committee of Union Hospital, Tongji Medical College, Huazhong University of Science and Technology (approval number: [2023] No.0969), with the need for written informed consent waived.

Author contributions

Hongru Chen: Writing—original draft, Project administration, Methodology, Investigation, Conceptualization. Shan Jiang: Writing—original draft, Project administration, Methodology, Investigation, Conceptualization. Ruowen Wei: Writing—original draft, Data curation, Software, Formal analysis. Ao Zhang: Writing—original draft, Software, Formal analysis. Xiena Cao: Writing—review & editing, Formal analysis, Funding acquisition, Methodology, Resources. Wei Shi: Writing—review & editing, Funding acquisition, Project administration, Methodology, Conceptualization, Resources, Supervision. Linghui Xia: Writing—review & editing, Project administration, Methodology, Conceptualization, Resources, Supervision. All authors read and approved the final manuscript.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the National Natural Science Foundation of China (grant no. 82370220), Young Scientists Fund of the National Natural Science Foundation of China (grant no. 82100233), and the National Natural Science Foundation of Hubei Province (2023AFB723).

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Statement of human and animal rights

This article does not contain any studies with human or animal subjects.

Statement of informed consent

There are no human subjects in this article and informed consent is not applicable.

Data availability statement

The data that support the findings of our study are available from the corresponding author upon request.

Supplemental material

Supplemental material for this article is available online.

References

Garcia-Manero

. Myelodysplastic syndromes: 2023 update on diagnosis, risk-stratification, and management. Am J Hematol. 2023;98(8):1307–25. doi:10.1002/ajh.26984.

Sekeres

Taylor

. Diagnosis and treatment of myelodysplastic syndromes: a review. JAMA. 2022;328(9):872. doi:10.1001/jama.2022.14578.

Platzbecker

Kubasch

Homer-Bouthiette

Prebet

. Current challenges and unmet medical needs in myelodysplastic syndromes. Leukemia. 2021;35(8):2182–98. doi:10.1038/s41375-021-01265-7.

Cazzola

Della Porta

Malcovati

. The genetic basis of myelodysplasia and its clinical relevance. Blood. 2013;122(25):4021–34. doi:10.1182/blood-2013-09-381665.

Papaemmanuil

Gerstung

Malcovati

Tauro

Gundem

Van Loo

Yoon

Ellis

Wedge

Pellagatti

Shlien

, et al. Clinical and biological implications of driver mutations in myelodysplastic syndromes. Blood. 2013;122(22):3616–27. doi:10.1182/blood-2013-08-518886.

Kantarjian

Beran

Cortes

O’Brien

Giles

Pierce

Shan

Plunkett

Keating

Estey

. Long-term follow-up results of the combination of topotecan and cytarabine and other intensive chemotherapy regimens in myelodysplastic syndrome. Cancer. 2006;106(5):1099–109. doi:10.1002/cncr.21699.

Kolla

Parikh

. Uses and limitations of artificial intelligence for oncology. Cancer. 2024;130:2101–107. doi:10.1002/cncr.35307.

Jain

Elmariah

. BMT for myelodysplastic syndrome: when and where and how. Front Oncol. 2022;11:771614. doi:10.3389/fonc.2021.771614.

De Witte

Bowen

Robin

Malcovati

Niederwieser

Yakoub-Agha

Mufti

Fenaux

Sanz

Martino

Alessandrino

, et al. Allogeneic hematopoietic stem cell transplantation for MDS and CMML: recommendations from an international expert panel. Blood. 2017;129(13):1753–62. doi:10.1182/blood-2016-06-724500.

10.

Malcovati

Papaemmanuil

Ambaglio

Elena

Gallì

Della Porta

Travaglino

Pietra

Pascutto

Ubezio

Bono

, et al. Driver somatic mutations identify distinct disease entities within myeloid neoplasms with myelodysplasia. Blood. 2014;124(9):1513–21. doi:10.1182/blood-2014-03-560227.

11.

Adding molecular data to prognostic models can improve predictive power in treated patients with myelodysplastic syndromes. Leukemia. 2017;31(12):2848–50. doi:10.1038/leu.2017.266.

12.

Bernard

Nannya

Hasserjian

Devlin

Tuechler

Medina-Martinez

Yoshizato

Shiozawa

Saiki

Malcovati

Levine

, et al. Implications of TP53 allelic state for genome stability, clinical presentation and outcomes in myelodysplastic syndromes. Nat Med. 2020;26(10):1549–56. doi:10.1038/s41591-020-1008-z.

13.

Bejar

Stevenson

Caughey

Abdel-Wahab

Steensma

Galili

Raza

Kantarjian

Levine

Neuberg

Garcia-Manero

, et al. Validation of a prognostic model and the impact of mutations in patients with lower-risk myelodysplastic syndromes. JCO. 2012;30(27):3376–82. doi:10.1200/JCO.2011.40.7379.

14.

Haferlach

Nagata

Grossmann

Okuno

Bacher

Nagae

Schnittger

Sanada

Kon

Alpermann

Yoshida

, et al. Landscape of genetic lesions in 944 patients with myelodysplastic syndromes. Leukemia. 2014;28(2):241–47. doi:10.1038/leu.2013.336.

15.

Hou

Tsai

Lin

Chou

Kuo

Liu

Tseng

Peng

Liu

Liao

, et al. Incorporation of mutations in five genes in the revised International Prognostic Scoring System can improve risk stratification in the patients with myelodysplastic syndrome. Blood Cancer J. 2018;8(4):39. doi:10.1038/s41408-018-0074-7.

16.

Bersanelli

Travaglino

Meggendorfer

Matteuzzi

Sala

Mosca

Chiereghin

Di Nanni

Gnocchi

Zampini

Rossi

, et al. Classification and personalized prognostic assessment on the Basis of clinical and genomic features in myelodysplastic syndromes. JCO. 2021;39(11):1223–33. doi:10.1200/JCO.20.01659.

17.

Nazha

Komrokji

Meggendorfer

Jia

Radakovich

Shreve

Hilton

Nagata

Hamilton

Mukherjee

Al Ali

, et al. Personalized prediction model to risk stratify patients with myelodysplastic syndromes. JCO. 2021;39(33):3737–46. doi:10.1200/JCO.20.02810.

18.

Bernard

Tuechler

Greenberg

Hasserjian

Arango Ossa

Nannya

Devlin

Creignou

Pinel

Monnier

Gundem

, et al. Molecular International Prognostic Scoring System for myelodysplastic syndromes. NEJM Evidence. 2022;1(7):EVIDoa2200008. doi:10.1056/EVIDoa2200008.

19.

Sauta

Robin

Bersanelli

Travaglino

Meggendorfer

Zhao

Caballero Berrocal

Sala

Maggioni

Bernardi

Di Grazia

, et al. Real-world validation of Molecular International Prognostic Scoring System for myelodysplastic syndromes. JCO. 2023;41(15):2827–42. doi:10.1200/JCO.22.01784.

20.

Lee

Tsai

Tien

Tseng

Kuo

Liu

Yang

Chen

Tang

, et al. Validation of the Molecular International Prognostic Scoring System in patients with myelodysplastic syndromes defined by international consensus classification. Blood Cancer J. 2023;13(1):120. doi:10.1038/s41408-023-00894-8.

21.

Aguirre

Al Ali

Sallman

Ball

Jain

Chan

Tinsley-Vance

Kuykendall

Sweet

Lancet

Padron

, et al. Assessment and validation of the Molecular International Prognostic Scoring System for myelodysplastic syndromes. Leukemia. 2023;37(7):1530–39. doi:10.1038/s41375-023-01910-3.

22.

Vittayawacharin

Kongtim

Ciurea

. Allogeneic stem cell transplantation for patients with myelodysplastic syndromes. Am J Hematol. 2023;98(2):322–37. doi:10.1002/ajh.26763.

23.

Xie

Chen

Stahl

Zeidan

. Prognostication in myelodysplastic syndromes (neoplasms): molecular risk stratification finally coming of age. Blood Rev. 2023;59:101033. doi:10.1016/j.blre.2022.101033.

24.

Gurnari

Gagelmann

Badbaran

Awada

Dima

Pagliuca

D’Aveni-Piney

Attardi

Voso

Cerretti

Wolschke

, et al. Outcome prediction in myelodysplastic neoplasm undergoing hematopoietic cell transplant in the molecular era of IPSS-M. Leukemia. 2023;37(3):717–19. doi:10.1038/s41375-023-01820-4.

25.

Wei

Wang

Song

. Evaluation of new IPSS-Molecular model and comparison of different prognostic systems in patients with myelodysplastic syndrome. Blood Sci. 2023;5(3):187–95. doi:10.1097/BS9.0000000000000166.

26.

Arber

Orazi

Hasserjian

Thiele

Borowitz

Le Beau

Bloomfield

Cazzola

Vardiman

. The 2016 revision to the World Health Organization classification of myeloid neoplasms and acute leukemia. Blood. 2016;127(20):2391–2405. doi:10.1182/blood-2016-03-643544.

27.

Zeidan

Platzbecker

Bewersdorf

Stahl

Adès

Borate

Bowen

Buckstein

Brunner

Carraway

Daver

, et al. Consensus proposal for revised International Working Group response criteria for higher risk myelodysplastic syndromes. Blood. 2023;141:2047–61. doi:10.1182/blood.2022018604.

28.

Schulz

Aplan

Freeman

Pavletic

. Moving toward a conceptualization of measurable residual disease in myelodysplastic syndromes. Blood Advances. 2023;7(16):4381–94. doi:10.1182/bloodadvances.2023010098.

29.

Bernal

Diez-Campelo

Godoy

Rojas

Colado

Alcoceba

González

Vidriales

Sánchez-Guijo

López-Corral

Luño

, et al. Role of minimal residual disease and chimerism after reduced-intensity and myeloablative allo-transplantation in acute myeloid leukemia and high-risk myelodysplastic syndrome. Leukemia Research. 2014;38(5):551–56. doi:10.1016/j.leukres.2014.02.001.

30.

Harris

Young

Devine

Hogan

Ayuk

Bunworasate

Chanswangphuwana

Efebera

Holler

Litzow

Ordemann

, et al. International, multicenter standardization of acute graft-versus-host disease clinical data collection: a report from the Mount Sinai Acute GVHD International Consortium. Biol Blood Marrow Transplant. 2016;22(1):4–10. doi:10.1016/j.bbmt.2015.09.001.

31.

Jagasia

Greinix

Arora

Williams

Wolff

Cowen

Palmer

Weisdorf

Treister

Cheng

Kerr

, et al. National Institutes of Health consensus development project on criteria for clinical trials in chronic graft-versus-host disease: I. The 2014 diagnosis and staging working group report. Biol Blood Marrow Transplant. 2015;21(3):389–401. doi:10.1016/j.bbmt.2014.12.001.

32.

Grob

Al Hinai

ASA

Sanders

Kavelaars

Rijken

Gradowska

Biemond

Breems

Maertens

Van Marwijk Kooy

Pabst

, et al. Molecular characterization of mutant TP53 acute myeloid leukemia and high-risk myelodysplastic syndrome. Blood. 2022;139(15):2347–54. doi:10.1182/blood.2021014472.

33.

Arber

Orazi

Hasserjian

Borowitz

Calvo

Kvasnicka

Wang

Bagg

Barbui

Branford

Bueso-Ramos

, et al. International Consensus Classification of Myeloid Neoplasms and Acute Leukemias: integrating morphologic, clinical, and genomic data. Blood. 2022;140(11):1200–28. doi:10.1182/blood.2022015850.

34.

Khoury

Solary

Abla

Akkari

Alaggio

Apperley

Bejar

Berti

Busque

Chan

JKC

Chen

, et al. The 5th edition of the World Health Organization classification of haematolymphoid tumours: myeloid and histiocytic/dendritic neoplasms. Leukemia. 2022;36(7):1703–19. doi:10.1038/s41375-022-01613-1.

35.

Reshef

. Prevention of graft-versus-host disease. Clin Adv Hematol Oncol. 2012;10:663–65.

36.

Wang

Liu

Lin

Yang

Huang

. Optimizing antithymocyte globulin dosing in haploidentical hematopoietic cell transplantation: long-term follow-up of a multicenter, randomized controlled trial. Science Bulletin. 2021;66(24):2498–505. doi:10.1016/j.scib.2021.06.002.

37.

Greenberg

Tuechler

Schanz

Sanz

Garcia-Manero

Solé

Bennett

Bowen

Fenaux

Dreyfus

Kantarjian

, et al. Revised international prognostic scoring system for myelodysplastic syndromes. Blood. 2012;120(12):2454–65. doi:10.1182/blood-2012-03-420489.

38.

Urrutia

Chien

Bataller

Almanza

Sasaki

Montalban-Bravo

Short

Jabbour

Kadia

Ravandi

, et al. Performance of IPSS-M in patients with myelodysplastic syndrome after hypomethylating agent failure. Am J Hematol. 2023;98(10):E281–84. doi:10.1002/ajh.27043.

39.

Shin

Jeon

Yoon

Yahng

Lee

Cho

Eom

Lee

Kim

Min

Cho

, et al. Comparison of transplant-specific prognostic scoring systems in haploidentical transplantation for myelodysplastic syndrome. European J of Haematology. 2018;101(2):200–207. doi:10.1111/ejh.13095.

40.

Mishra

Corrales-Yepez

Ali

Kharfan-Dabaja

Padron

Zhang

Epling-Burnette

Pinilla-Ibarz

Lancet

List

Komrokji

. Validation of the revised International Prognostic Scoring System in treated patients with myelodysplastic syndromes. Am J Hematol. 2013;88(7):566–70. doi:10.1002/ajh.23454.

41.

Alzahrani

Power

Abou Mourad

Barnett

Broady

Forrest

Gerrie

Hogge

Nantel

Sanford

Song

, et al. Improving revised International Prognostic Scoring System pre-allogeneic stem cell transplantation does not translate into better post-transplantation outcomes for patients with myelodysplastic syndromes: a single-center experience. Biol Blood Marrow Transplant. 2018;24(6):1209–15. doi:10.1016/j.bbmt.2018.02.007.

42.

Della Porta

Alessandrino

Bacigalupo

Van Lint

Malcovati

Pascutto

Falda

Bernardi

Onida

Guidi

Iori

, et al. Predictive factors for the outcome of allogeneic transplantation in patients with MDS stratified according to the revised IPSS-R. Blood. 2014;123(15):2333–42. doi:10.1182/blood-2013-12-542720.

43.

Shaffer

Ahn

Nishihori

Malone

Valcárcel

Grunwald

Bacher

Hamilton

Kharfan-Dabaja

Saad

, et al. Scoring system prognostic of outcome in patients undergoing allogeneic hematopoietic cell transplantation for myelodysplastic syndrome. J Clin Oncol. 2016;34(16):1864–71. doi:10.1200/JCO.2015.65.0515.

44.

Yahng

Jeon

Yoon

Shin

Lee

Choi

Kim

Lee

Cho

Eom

Lee

, et al. Dynamic prognostic value of the revised international prognostic scoring system following pretransplant hypomethylating treatment in myelodysplastic syndrome. Bone Marrow Transplant. 2017;52(4):522–31. doi:10.1038/bmt.2016.295.

45.

Gagelmann

Eikema

Stelljes

Beelen

de Wreede

Mufti

Knelange

Niederwieser

Friis

Ehninger

Nagler

, et al. Optimized EBMT transplant-specific risk score in myelodysplastic syndromes after allogeneic stem-cell transplantation. Haematologica. 2019;104(5):929–36. doi:10.3324/haematol.2018.200808.

46.

Zeidan

Ando

Rauzy

Turgut

Wang

Cairoli

Hou

Kwong

Arnan

Meers

Pullarkat

, et al. Sabatolimab plus hypomethylating agents in previously untreated patients with higher-risk myelodysplastic syndromes (STIMULUS-MDS1): a randomised, double-blind, placebo-controlled, phase 2 trial. Lancet Haematol. 2024;11(1):e38–50. doi:10.1016/S2352-3026(23)00333-2.

47.

Kita-Sasai

Horiike

Misawa

Kaneko

Kobayashi

Nakao

Nakagawa

Fujii

Taniwaki

. International prognostic scoring system and TP53 mutations are independent prognostic indicators for patients with myelodysplastic syndrome. Br J Haematol. 2001;115(2):309–12. doi:10.1046/j.1365-2141.2001.03073.x.

48.

Loghavi

Al-Ibraheemi

Zuo

Garcia-Manero

Yabe

Wang

Kantarjian

Yin

Miranda

Luthra

Medeiros

, et al. TP53 overexpression is an independent adverse prognostic factor in de novo myelodysplastic syndromes with fibrosis. Br J Haematol. 2015;171(1):91–99. doi:10.1111/bjh.13529.

49.

Saft

Karimi

Ghaderi

Matolcsy

Mufti

Kulasekararaj

Göhring

Giagounidis

Selleslag

Muus

Sanz

, et al. p53 protein expression independently predicts outcome in patients with lower-risk myelodysplastic syndromes with del(5q). Haematologica. 2014;99(6):1041–49. doi:10.3324/haematol.2013.098103.

50.

Haase

Stevenson

Neuberg

, et al; for the International Working Group for MDS Molecular Prognostic Committee. TP53 mutation status divides myelodysplastic syndromes with complex karyotypes into distinct prognostic subgroups. Leukemia. 2019;33(7):1747–58. doi:10.1038/s41375-018-0351-2.

51.

Pereira

Remberger

Chen

Gerbitz

Kim

DDH

Kumar

Lam

Law

Lipton

Michelis

Novitzky-Basso

, et al. Choosing between older matched sibling donor and younger matched unrelated donor in allogeneic hematopoietic cell transplantation: comparison of clinical outcomes in acute myeloid leukemia and myelodysplastic syndrome. Transplant Cell Ther. 2023;29(11):697.e1–97. doi:10.1016/j.jtct.2023.08.009.

52.

Scott

Pasquini

Logan

Devine

Porter

Maziarz

Warlick

Fernandez

Alyea

Hamadani

, et al. Myeloablative versus reduced-intensity hematopoietic cell transplantation for acute myeloid leukemia and myelodysplastic syndromes. JCO. 2017;35(11):1154–61. doi:10.1200/JCO.2016.70.7091.

53.

Valcárcel

Martino

Caballero

Martin

Ferra

Nieto

Sampol

Bernal

Piñana

Vazquez

Ribera

, et al. Sustained remissions of high-risk acute myeloid leukemia and myelodysplastic syndrome after reduced-intensity conditioning allogeneic hematopoietic transplantation: chronic graft-versus-host disease is the strongest factor improving survival. JCO. 2008;26(4):577–84. doi:10.1200/JCO.2007.11.1641.

54.

Baer

Huber

Hutter

Meggendorfer

Nadarajah

Walter

Platzbecker

Götze

Kern

Haferlach

Hoermann

, et al. Risk prediction in MDS: independent validation of the IPSS-M—ready for routine? Leukemia. 2023;37(4):938–41. doi:10.1038/s41375-023-01831-1.

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.15 MB