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Article

Clinical Features, Gene Alterations, and Outcomes in Prefibrotic and Overt Primary and Secondary Myelofibrotic Patients

by
Tong-Yoon Kim
1,2,
Daehun Kwag
1,
Jong-Hyuk Lee
1,
Joonyeop Lee
1,
Gi-June Min
1,3,
Sung-Soo Park
1,3,
Silvia Park
1,3,
Young-Woo Jeon
2,
Jae-Ho Yoon
1,3,
Seung-Hawn Shin
4,
Seung-Ah Yahng
5,
Byung-Sik Cho
1,3,
Ki-Seong Eom
1,3,
Yoo-Jin Kim
1,3,
Seok Lee
1,3,
Hee-Je Kim
1,3,
Chang-Ki Min
1,3,
Seok-Goo Cho
1,
Jong-Wook Lee
1,
Jong-Mi Lee
6,
Myungshin Kim
6 and
Sung-Eun Lee
1,3,*add Show full author list remove Hide full author list
1
Department of Hematology, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Korea
2
Department of Hematology, Yeouido St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Korea
3
Leukemia Research Institute, College of Medicine, The Catholic University of Korea, Seoul 06591, Korea
4
Department of Hematology, Eunpyeong St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Korea
5
Department of Hematology, Incheon St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Korea
6
Department of Laboratory Medicine, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Korea
*
Author to whom correspondence should be addressed.
Cancers 2022, 14(18), 4485; https://doi.org/10.3390/cancers14184485
Submission received: 7 August 2022 / Revised: 2 September 2022 / Accepted: 10 September 2022 / Published: 16 September 2022
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Abstract

:

Simple Summary

The final stages of myeloproliferative neoplasms (MPNs) are related to myelofibrosis (MF). This study aimed to compare secondary myelofibrosis (SMF), overt (PMF), and prefibrotic primary myelofibrosis (pre-PMF) and determine prognostic factors in clinical and genetic features. This study included 229 patients; 67 (29%), 122 (53%), and 40 (18%) cases were confirmed as SMF, overt PMF, and pre-PMF, respectively. We compare MFs, including pre-PMF, using six scoring stratifications associated with PMF and one scoring system with SMF. We also evaluated the impact of the genetic groups with clustering methods and previously reported genomic groups. We determined the clinical and genetic features associated with disease progression in SMF, overt PMF, and pre-PMF groups.

Abstract

The Philadelphia-negative myeloproliferative neoplasms (MPNs) are divided in three major groups: polycythemia vera (PV), essential thrombocythemia (ET), and primary myelofibrosis (PMF). The 2016 WHO classification incorporates also prefibrotic PMF (pre-PMF) and overt PMF. This study aimed to discriminate the clinical features, genetic alterations, and outcomes in patients with prefibrotic, overt PMF, and secondary MF (SMF). This study included 229 patients with diagnosed myelofibrosis (MF). Among 229 patients, 67 (29%), 122 (53%), and 40 (18%) were confirmed as SMF, overt PMF, and pre-PMF, respectively. The JAK2 V617F mutation was differentially distributed in SMF and PMF, contradictory to CALR and MPL mutations. Regarding nondriver mutations, the occurrence of ASXL1 mutations differed between PMF and SMF or pre-PMF. The three-year overall survival was 91.5%, 85.3%, and 94.8% in SMF, overt PMF, and pre-PMF groups. Various scoring systems could discriminate the overall survival in PMF but not in SMF and pre-PMF. Still, clinical features including anemia and thrombocytopenia were poor prognostic factors throughout the myelofibrosis, whereas mutations contributed differently. Molecular grouping by wild-type SF3B1 and SRSF2/RUNX1/U2AF1/ASXL1/TP53 mutations showed inferior progression-free survival (PFS) in PMF, SMF, and pre-PMF. We determined the clinical and genetic features related to poor prognosis in myelofibrosis.

Graphical Abstract

1. Introduction

Philadelphia-negative myeloproliferative neoplasms (MPNs) are clonal hematopoietic disorders characterized by the overproduction of differentiated hematopoietic cells, and are divided into three major subcategories, namely, polycythemia vera (PV), essential thrombocythemia (ET), and primary myelofibrosis (PMF). PV and ET can evolve to secondary forms of myelofibrosis (MF), known as post-polycythemia vera (PPV-MF) and post-essential thrombocythemia myelofibrosis (PET-MF).
The progression of myelofibrosis might lead to various pathological conditions, including thrombosis, infection, leukemic transformation, and eventually death [1]. However, the time to progression and median survival time vary between patients with MPN; thus, predicting prognosis or detecting the high-risk group for progression is essential [2]. The 2016 WHO classification has specified prefibrotic PMF (pre-PMF) and overt PMF as indicators for the early detection before profound progression in patients with PMF. A previous study has reported that pre-PMF accounted for approximately 10% of MPN cases, with a survival outcome inferior to ET or PV [3].
Excessive myeloproliferation in Philadelphia-negative MPNs are driven by mutations in JAK2, CALR, MPL, and uncommon variants. Recently, evidence for the acquisition of somatic mutations and information on factors influencing clonal outgrowth such as aging and the bone marrow microenvironment have been accumulating [4]. Approximately one third of patients with Philadelphia-negative MPN have additional mutations that alter DNA methylation (DNMT3A, TET2, and IDH1/2), chromatin modifications (ASXL1, EZH2, and IDH1/2), messenger RNA splicing (U2AF1, SF3B1, SRSF2, and ZRSR2), and DNA repair (TP53) [5]. Although there was a study on early MF [6], comparison studies between pre-PMF, overt PMF, and SMF data were limited. Patients with PMF possessing the CALR type 1/type 1-like mutation have a better prognosis than those with CALR type 2/type 2-like, JAK2V617F, or MPL W515 mutations [7,8,9], and triple-negative cancer [10]. Furthermore, a category of “high molecular risk” (HMR) patients has been defined in patients with PMF who harbor any mutation in ASXL1, EZH2, SRSF2, IDH1, and IDH2 [11,12], and U2AF1 Q157 [13]. As the studies of mutation order and acquisition accumulates, more interest is in correlation with the clinical course of myelofibrosis and genomic status. Due to clonal evolution during disease progression and various latencies among the genes, order of mutations was different among the patients. Furthermore, mutation order affects the presentation of clinical course and drug susceptibility [14,15,16]. However, knowledge about the molecular profile in secondary MF and current information about the differences between pre PMF and overt PMF are scarce. Here, we evaluated the clinical features, genetic alterations, and outcomes in patients with prefibrotic and overt PMF and SMF to assess the importance of distinguishing them in clinical practice.

2. Materials and Methods

2.1. Patient Selection

We included 229 patients who were diagnosed with myelofibrosis at Seoul St. Mary’s Hematology Hospital from December 2001 to August 2021 and provided DNA samples for next-generation sequencing (NGS) analysis. Pathologists confirmed the diagnosis based on the 2016 WHO classification [17].
Most DNA samples were from bone marrow (BM) aspirate samples at the diagnosis of myelofibrosis, regardless of pre-evolving myeloproliferative neoplasm (MPN) disease. This study was approved by the Institutional Review Board of Seoul St. Mary’s Hospital, The Catholic University of Korea, Seoul, Korea (KC22RISI0120) and was conducted in accordance with the tenets of the Declaration of Helsinki.

2.2. Molecular and Cytogenetic Studies

Conventional BM karyotyping was performed on G-banded metaphase chromosomes using routine techniques. Karyotypes were interpreted according to ISCN 2016 [18].
Regarding molecular analysis, NGS, was performed using a customized myeloid panel (“SM panel”), as described in our previous report [19]. The SM panel contains 87 genes frequently found mutated in patients with MPN (Supplementary Table S1). According to the manufacturer’s instructions, target-capturing sequencing was achieved using a customized target kit (3039061; Agilent Technologies (Santa Clara, CA, USA). DNA libraries were composed according to the protocol of the manufacturer, and sequencing was performed using the Illumina HiSeq4000 platform (San Diego, CA, USA). Variants with more than 20 reads and 5% variant allele frequencies (VAFs) were considered to be mutated. Mutations in JAK2, CALR, and MPL of less than 5% VAF were considered positive with a low allele burden. Using the Integrative Genomic Viewer, the detected variants were manually verified.

2.3. Definitions

Overall survival (OS) and progression-free survival (PFS) were calculated from the date of diagnosis. OS was defined as the time to death from any cause. PFS was defined as the time to progression to overt myelofibrosis or death in the pre-PMF groups; otherwise, progression to acute leukemia or death was considered the endpoint event.
The scoring system consisted of IPSS [1], DIPSS [20], DIPSS-plus [21], MIPSS70 [22], GIPSS [23], MIPSS70 + Ver2 [13], and MYSEC-PM [24], which were calculated as previously reported.

2.4. Statistical Analyses

Baseline: The clinical and molecular characteristics of patients classified into the three groups were compared: SMF, PMF, and pre-PMF. The chi-square or Fisher’s exact test were used for categorical variables, whereas the two-sample t-test or Mann–Whitney U test were used for continuous variables.
We used the decision tree method (R packages rpart 4.1-10) to discriminate between the MF groups using clinical and genetic variables. To emphasize the effect of the non-driver mutations, we included the other mutations except the JAK2, MPL, and CALR mutations in the clustering analysis. Binary distances were calculated and hierarchical clustering was performed using the Ward’s method. The best proposal cluster-number was calculated using Silhouette, Ratkowsky and Lance, and McClain and Rao indices (R packages Nbclust 3.0).
The OS and PFS were estimated using the Kaplan–Meier method, and groups were compared using the log-rank test. The Cox proportional hazards model was used for univariate and multivariate analyses of OS and PFS. Variables with p < 0.10 determined using univariate analysis were considered for multivariate analysis. Statistical analyses were performed using R statistical software (version 3.4.3; R Foundation for Statistical Computing, Vienna, Austria).

3. Results

3.1. Clinical and Cytogenetic Features of Patients with Prefibrotic PMF, Overt PMF, and Secondary MF

The clinical and cytogenetic characteristics of patients are summarized in Table 1. Among the patients, PMF was the most common (n = 122, 53.3%), followed by PET-MF (n = 46, 20.1%), pre-PMF (n = 40, 17.4%), and PPV-MF (n = 21, 9.2%). In particular, we found that pre-PMF cases had a lower median age (p = 0.003), higher hemoglobin levels (p < 0.001), higher platelet count (p < 0.001), lower peripheral blood blast proportion (p < 0.001), smaller spleen (p < 0.001), and fewer constitutional symptoms (p < 0.001) than other overt MF cases.
Based on the DIPSS scoring system, we did not identify any differences in unfavorable karyotype abnormalities among the three groups. However, we found that the MIPSS karyotype risk was higher in the PMF group than in the SMF and pre-PMF groups. In the pre-PMF group we did not identify any karyotypes including abnormalities of −7, i(17q), inv(3)/3q21, 12p-/12p11.2, 11q-/11q23, or any other autosomal trisomy not including +8, +9 and complex karyotype.

3.2. Genetic Features of Patients with Prefibrotic PMF, Overt PMF, and Secondary MF

We identified mutations in 92.1% of patients (n = 211). The mean number of mutations was 1.80 (range 0–6) in total, including 1.78 (range 1–5) in SMF, 1.91 (range 0–5) in PMF, and 1.65 (range 0–6) in pre-PMF. The genetic landscape in the three groups of MF is shown in Figure 1A, and a comparison of proportion among PMF, SMF, and pre-PMF is presented in Figure 1B. We found that in patients with PMF, mutations in JAK2 were the most common (n = 57, 46.7%), followed by those in ASXL1 (n = 43, 35.2%), CALR (n = 34, 27.9%), TET2 (n = 11, 9.0%), and U2AF1 (n = 8, 6.6%). Similarly, in the SMF group, mutations in JAK2 were the most common (n = 43, 64.2%), followed by those in ASXL1 (28.4%), CALR (26.9%), TET2 (13.4%), and SF3B1 (7.5%). In the pre-PMF group, mutations in JAK2 were the most common (50.0%), followed by those in CALR (17.5%), TET2 (17.5%), SF3B1 (10.0%), and DNMT3A (10.0%). However, it was found that the proportion of ASXL1 mutations in the pre-PMF group was lower than that in the SMF and PMF groups (Table 1).
The median variant allele frequency (VAF) of gene mutations was 0.37 overall, 0.36 in PMF, 0.39 in SMF, and 0.38 in pre-PMF. In PMF, SMF, and pre-PMF, 8.2%, 16.5%, and 10.5% of homozygous mutations had a higher VAF of 0.70. Except for JAK2 (0.54 vs. 0.62 vs. 0.34, p = 0.001), no differences were detected in VAFs of the examined genes, including CALR, MPL, ASXL1, TET2, SF3B1, DNMT3A, MPL, U2AF1, and RUNX1 (Figure 1C). Next, we analyzed the correlation of gene mutations detected in more than three patients (Supplementary Figure S1), and investigated co-occurrence driver mutations in JAK2, CALR, and MPL with other mutations. In the PMF group, mutated JAK2 and TP53 showed negative correlations (−0.19, p = 0.033). Additionally, CALR and TET2 were positively correlated (0.31, p < 0.001), In the pre-PMF group, we identified a negative correlation between mutated JAK2 and TET2 (−0.33, p = 0.038).

3.3. Correlation of Genetic and Clinical Features

We further analyzed the correlation of these gene mutations with clinical characteristics. In the PMF group, we found that the U2AF1 mutation was associated with thrombocytopenia. In the pre-PMF group, we noticed that higher leukocyte counts were negatively correlated with IDH2 and ASXL1 mutations. The peripheral blast was negatively correlated with the ASXL1 mutations. Anemia was positively correlated with the JAK2 and DNMT3A mutations. Thrombocytopenia was positively correlated with U2AF1 and MPL, whereas thrombocytopenia was negatively correlated with the TET2 mutations (Figure 1D).
To identify the factors in order of importance and to discriminate MF groups, we performed the decision-tree method using clinical and genetic variables. A representative clinical decision tree is shown in Figure 1E. According to the decision tree, variables such as thrombocytopenia, anemia, and peripheral blast counts were more dominant in the SMF and PMF groups than in the pre-PMF group. The distribution of the MF groups is shown relative to hemoglobin counts (X-axis) and platelet counts (Y-axis) (Supplementary Figure S1E). In addition, the SMF group was primarily associated with older age without the ASXL1 mutation. Among 65 patients that who did not have thrombocytopenia, anemia, peripheral blasts, and ASXL1 mutations, most of the patients were diagnosed with pre-PMF (30/65, 46.1%) (Figure 1E).

3.4. Distribution of Risk Categories, Outcomes, and Prognostic Effect of Risk Stratification Systems in Each Subgroup

The distribution of the risk stratification system in each subgroup is described in Table 2. We did not find statistical differences in the scores in IPSS, DIPSS, DIPSS-plus, MYSEC-PM, and MIPSS70 + Ver2 between SMF and PMF. Instead, these risk groups showed differences between the pre-PMF group and the other overt MF groups. Despite differences in the GIPSS score between the pre-PMF and PMF groups, no differences were observed between the SMF and pre-PMF groups. The effect of risk stratification systems on OS and PFS in each subgroup is described in Table 3. All scoring systems could discriminate overall survival in PMF, but not in the SMF and pre-PMF groups. In terms of PFS, we found that scores could predict the progression of pre-PMF to either overt-PMF or secondary acute myeloid leukemia (AML) in IPSS, DIPSS DIPSS-plus, MIPSS70, and MYSECPM, but not in GIPSS. These results implied that prognostic differences between pre-PMF and other MF depended more on the effect of clinical variables than that of genetic variables. The survival outcomes did not change when patients who underwent transplantation were censored at the time of their transplant.

3.5. Univariate and Multivariate Analyses for OS and PFS in Each Subgroup

After a median follow-up of 2.7 years (range, 0.2 to 19.9 years), we observed that the 3-year overall survival of patients was 85.3%, 91.5%, and 94.8% in the overt PMF, SMF, and pre-PMF groups (p = 0.026). We recorded 30 deaths among all patients (22 within PMF, 6 within SMF, and 2 within pre-PMF). Among all patients, 48 patients underwent allogeneic hematopoietic stem cell transplantation, including 31 in PMF, 15 in SMF, and 2 in pre-PMF. Among the patients who underwent transplantation, we recorded 10 deaths in the PMF group and 3 deaths in the SMF group.
The results of the univariate analysis are illustrated in Figure 2. In the pre-PMF group, we found that thrombocytopenia, ASXL1, MPL, U2AF1, SETBP1, and SRSF2 mutations were associated with inferior overall survival. In contrast, female sex, constitutional symptoms, anemia, peripheral blood blasts, and ASXL1 and SRSF2 mutations were correlated with inferior PFS. We then performed multivariate analysis using the clinical and genetic variables especially for the patients with pre-PMF (Table 4). In model I, we observed that ASXL1 and anemia were associated with poorer outcomes in PFS. In contrast, in model II, SRSF2 mutations were correlated with inferior PFS. These models highlighted the effect of genetic mutations on outcomes, such as inferior survival, leukemic transformation, and fibrotic progression. In patients with PMF, the multivariate analysis showed that the TP53 mutation (p = 0.020, HR = 9.29, [95% CI 1.42–60.7]) was associated with poor OS, whereas CARL type 1 mutation (p = 0.056, HR = 0.12, [95% CI 0.01–1.05]) correlated with favorable PFS.
In patients with SMF, the multivariate analysis showed that TP53 and female sex were associated with inferior OS. We also found that female sex, thrombocytopenia, and RUNX1, TP53, ZRSR2, and IDH1 mutations were associated with inferior outcome in PFS.

3.6. Genomic Subgroups in Myelofibrosis by Nondriver Mutations

To analyze the effect of nondriver mutations in MF, we performed hierarchical clustering of the 229 patients according to somatic mutations except the JAK2, MPL, and CARL driver mutations by five subgroups to calculate the specified mentioned index. Grouping nomenclature indicated the most distributed gene mutations in each group (Supplementary Table S2). We applied the clustering group to the estimation of overall survival. We accordingly found that 3-year OS was 93.0% 72.9%, 87.6%, 100%, and 100% in clusters 1–5, respectively (Figure 3A,B). Interestingly, the group dominated by U2AF1/TP53 showed the worst PFS in patients with the PMF and SMF groups compared with that in other groups (p = 0.258, p = 0.023) (Figure 3C,D). However, in the pre-PMF group, the SRSF2/ASXL1 dominant group showed the worst PFS (Figure 3E). This result indicated that each genetic clustering subgroup differently contributed to survival outcomes in each histologically distinct group. However, as TP53 mutations were not found in pre-PMF, the interpretation of these results requires caution.

3.7. Proposal of High-Risk Mutation Groups Predicting Survival Outcomes

First, we evaluated the effect of the genetic mutations, the ARCH (age related clonal hematopoiesis)/CHIP (Clonal Hematopoiesis of Indeterminate Potential) for fibrotic progression, which were previously reported by Bartels, et al. [25]. Among the ARCH/CHIP-associated mutations, the group of mutations was associated with later progression. that is, mutations in SRSF2, U2AF1, SF3B1, IDH1/2, and EZH2. These mutations were associated with poor OS in the pre-PMF group (p = 0.008) (Supplementary Figure S2) but not in the SMF and PMF groups in our study.
Next, we analyzed the effect of high-risk genetic mutations for the progression of myelodysplastic syndrome to AML [26], that is, wild type SF3B1 or mutations in SRSF2, RUNX1, U2AF1, ASXL1, and TP53, on the survival outcomes in the MF cohort (Figure 4). We observed that the high-risk mutation group discriminated the 3-year OS among all MF subtypes, including the PMF (90.8% vs. 51.1%, p < 0.001), SMF (92.2% vs. 83.3%, p = 0.009), and pre-PMF groups (100% vs. 50% p < 0.001). We also identified that this high-risk subgroup showed inferior PFS in the PMF, SMF, and pre-PMF groups (p < 0.001, p = 0.024, p = 0.026).

4. Discussion

Although several previous studies have compared pre-PMF versus ET [27], pre-PMF versus PMF [6,28], and PMF versus SMF [29], to date, no other study except the present study has compared the clinical and genetic characteristics of patients with pre-PMF versus overt PMF and secondary MF.
A comparison of the clinical features between pre-PMF and PMF showed that patients with pre-PMF were characterized by higher hemoglobin levels and platelet counts, whereas they were less frequently diagnosed with increased peripheral blood blasts, symptoms, and extensive splenomegaly. These findings are consistent with those of a previous study [6]. Similarly, with the exception of old age, a comparison of the clinical characteristics between pre-PMF versus SMF showed a similar trend between the groups. In clinical practice, pre-PMF is typically diagnosed when leukocytosis or thrombocytosis is detected, which is predicted to progress to myelofibrosis in the case of occurrence of cytopenic features such as anemia, thrombocytopenia, or other clinical symptoms during follow-up.
Comparison of the factors related to survival outcomes obtained in our study with those obtained from the literature revealed certain differences. Among the previously reported variables affecting OS in PET-MF [29] and SMF [24], thrombocytopenia was the only contributing factor to poorer OS in our cohort. Unlike the finding of Makarova et al., that is, 11% of patients with SMF underwent a leukemic transformation, we observed only 2.2% leukemic transformation. These discordances might have occurred because the previous studies were performed in the 1980s, whereas in the present study, MF was first diagnosed in 2001. MYSEC-PM showed a trend toward inferior PFS in intermediate-2 and high groups than the favorable and intermediate-1 group (p = 0.059), which is consistent with the results of previous studies.
We analyzed the effect of individual genes on MF by comparing different groups. In this study, ASXL1 mutations were relatively infrequent in the pre-PMF group compared with those in the overt MF group. This result is consistent with the previous reports that the presence of ASXL1 mutations also predicted progression [30,31] in the pre-PMF group. Furthermore, we found that the mean VAF in JAK2 was low in pre-PMF relative to that in overt MF. Based on these findings, homozygosity in the JAK2 mutations or higher VAF indicates higher vulnerability to progression, as shown in ET or PV cases [32].
Courtier et al. suggested the existence of different mutational landscapes in patients with SMF and PMF, owing to the order of occurrence of mutations. They suggested that RNA splicing genes such as SRSF2 and U2AF1 played a role in PMF, which had a higher incidence than that of SMF [33]. Among the RNA splicing genes, U2AF1 is vital for hematopoiesis and is associated with thrombocytopenia in PMF [34,35]. In the present study, the overt PMF and pre-PMF groups showed a positive correlation with U2AF1 mutations and thrombocytopenia; however, this correlation was not observed in the SMF group. SRSF2 mutations were associated with progression in pre-PMF and showed a trend in PMF; however, they were not associated with progression in SMF. However, ZRSR2 mutations were associated with progression in SMF; this mutation has a higher incidence in SMF than in PMF [33].
Our findings on the progression-related genes in PMF showed some discrepancies with those reported by Vannucchi et al. In their study, ASXL1, EZH2, SRSF2, or IDH1/2 were defined as molecular factors of high risk in PMF [11]. However, these high-molecular-risk-group genes were not associated with significant differences in survival. Instead, mutations in TP53 and U2AF1 were associated with poor OS. In addition, mutations in RUNX1, TP53, and U2AF1 were also correlated with inferior PFS. However, Grinfeld et al. reported that leukemic transformation is related to the occurrence of mutations in RUNX1, TP53, and U2AF1 in MPN disease, which are consistent with our results [36]. These differences might be caused by the different characteristics of the study cohort and the treatment strategies that we are actively implementing at HSCT for transplant candidates. The findings indicated that HSCT reduced progression-related death and that leukemic transformation was a notable contributing factor to survival at our center.
Patients had an average of 1.8 mutations, making it difficult to use a single gene as a prognostic factor. This finding was due to varying gene frequencies. The single effect of univariate analysis did not reflect the co-occurrence effect. We compensated this limitation by using a hierarchical clustering method of non-driver mutations. Each subgroup was further divided into sets based on the dominance of either chromatin modifications (ASXL1) or RNA splicing genes (U2AF1, SRSF2, and SF3B1). Interestingly, ASXL1, EZH2, SRSF2, IDH1, and IDH mutations were dominant in the third cluster, which was known to be a high-risk group in PMF. This finding indicated that mutations in different genes that frequently occur together need to be considered in addition to the single-gene effect. Within the SMF and pre-PMF groups, the second and third clusters, respectively, showed poor PFS. These findings implied that single genes and clusters of non-driver mutations affect MF survival differently among each group. It might be due to different genetic evolution timing and proportions in each histological MF subtype.
To identify the most influential mutation group in PMF in the literature, we applied the concept suggested by Courtier, et al. [33]. They suggested that mutations that could lead to CHIP and asymptomatic phase of MDS could have an impact different from that of other driver mutations. Therefore, to prove the prognostic effect of previously reported ARCH/CHIP for rapid fibrotic progression in pre-PMF [25] and for leukemic transformation in MDS [26] in the MF cohort, we analyzed an association between these mutations and outcomes (OS and PFS) in patients with prefibrotic, overt PMF, and SMF. Mutations pertaining to fibrotic progression [25] did not contribute to the differences in survival except in pre-PMF, whereas leukemic transformation high-risk mutations [26] showed differences in survival outcomes among all MF subgroups. These findings show that failure in bone marrow functioning contributes to the occurrence of MF.
Our study had several limitations. First, owing to the relatively small number of patients with pre-PMF, it is difficult to draw definite conclusions. Second, our study cohort was limited to a single center; thus, further studies are needed to verify the applicability of our findings to other groups. However, our study demonstrated the clinical and genetic characteristics of MF subtypes, which is important for discriminating secondary MF and prefibrotic MF. In this study, we showed that current risk stratifications are still important for predicting the outcome in patients with overt PMF. However, a widely known GIPSS in overt PMF could not correctly predict the outcome in SMF and pre-PMF.

5. Conclusions

In this study, we investigated distinct clinical and genetic characteristics of patients with prefibrotic PMF, overt PMF, and SMF. Our findings further suggested the mutation profiles that could be widely applied for the stratification of patients with MF, including those with pre-PMF. Using this molecular profiling, symptomatic patients with pre-PMF could be candidates for close monitoring and/or therapeutic application such as JAK2 inhibitor therapy [37]. Furthermore, our findings raise the possibility that inhibitors targeting SRSF2 mutations [38] or BET inhibitors [39] could be attempted in the high-risk MF group. Additional large cohort studies with a longer follow-up are needed to confirm the accurate disease-associated molecular characteristics in patients with prefibrotic, overt PMF, and SMF.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/cancers14184485/s1, Figure S1: Correlation between mutations in (A) overall cohort, (B) primary myelofibrosis (PMF), (C) secondary myelofibrosis (SMF), and (D) prefibrotic myelofibrosis (pre-PMF). (E) Partition of PMF, SMF, and pre-PMF by the decision tree method according to hemoglobin level (X-axis) and platelet counts (Y-axis); Figure S2: Estimation of overall survival (OS) in (A) primary myelofibrosis (PMF), (C) secondary myelofibrosis (SMF), and (E) prefibrotic myelofibrosis (pre-PMF), and progression-free survival (PFS) in (B) PMF, (D) SMF, and (F) pre-PMF following CHIP of the high-risk progression group; Table S1: List of target genes in the NGS panel; Table S2: Genetic distribution of non-driver mutations in five subgroups by hierarchical clustering methods; Table S3: p-value of OS and PFS in between clusters.

Author Contributions

Collecting and analyzing the data and writing the manuscript, T.-Y.K.; providing patients and materials, D.K., J.-H.L., J.-M.L., M.K., G.-J.M., S.-S.P., S.P. and J.-H.Y.; providing the materials and reviewing the manuscript, J.L., Y.-W.J., B.-S.C., K.-S.E., H.-J.K. and C.-K.M.; Reviewing the manuscript and analyzing the data, S.-H.S., S.-A.Y., Y.-J.K., S.L., S.-G.C. and J.-W.L.; Designing the study, providing patients and materials, analyzing the data, and supervising the writing, S.-E.L. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by the Catholic Medical Center Research Foundation (No. 2020-B0001-00003) and the Korean Government for the National Research Foundation of Korea grant (No. 2020R1F1A1065841).

Institutional Review Board Statement

This study was approved by the Institutional Review Board of Seoul St. Mary’s Hospital, The Catholic University of Korea, Seoul, Korea (KC22RISI0120) and was conducted in accordance with the tenets of the Declaration of Helsinki. The need for informed consent was waived due to the retrospective nature of the study.

Informed Consent Statement

Patient consent was not required because of the retrospective nature of the study.

Data Availability Statement

The data presented in this study are available on request from the corresponding author. The data are not publicly available owing to privacy and ethical reasons.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Cervantes, F.; Dupriez, B.; Pereira, A.; Passamonti, F.; Reilly, J.T.; Morra, E.; Vannucchi, A.M.; Mesa, R.A.; Demory, J.-L.; Barosi, G.; et al. New prognostic scoring system for primary myelofibrosis based on a study of the International Working Group for Myelofibrosis Research and Treatment. Blood 2009, 113, 2895–2901. [Google Scholar] [CrossRef] [PubMed]
  2. Tefferi, A. Myeloproliferative neoplasms: A decade of discoveries and treatment advances: Myeloproliferative neoplasms. Am. J. Hematol. 2016, 91, 50–58. [Google Scholar] [CrossRef] [PubMed]
  3. Song, I.C.; Yeon, S.H.; Lee, M.W.; Ryu, H.; Lee, H.J.; Yun, H.J.; Kim, S.Y.; Jo, D.Y. Thrombotic and hemorrhagic events in 2016 World Health Organization-defined Philadelphia-negative myeloproliferative neoplasm. Korean J. Intern. Med. 2021, 36, 1190–1203. [Google Scholar] [CrossRef] [PubMed]
  4. Nangalia, J.; Green, A.R. Myeloproliferative neoplasms: From origins to outcomes. Blood 2017, 130, 2475–2483. [Google Scholar] [CrossRef] [PubMed]
  5. Nangalia, J.; Massie, C.E.; Baxter, E.J.; Nice, F.L.; Gundem, G.; Wedge, D.C.; Avezov, E.; Li, J.; Kollmann, K.; Kent, D.G.; et al. Somatic CALR mutations in myeloproliferative neoplasms with nonmutated JAK2. N. Engl. J. Med. 2013, 369, 2391–2405. [Google Scholar] [CrossRef]
  6. Guglielmelli, P.; Pacilli, A.; Rotunno, G.; Rumi, E.; Rosti, V.; Delaini, F.; Maffioli, M.; Fanelli, T.; Pancrazzi, A.; Pietra, D.; et al. Presentation and outcome of patients with 2016 WHO diagnosis of prefibrotic and overt primary myelofibrosis. Blood 2017, 129, 3227–3236. [Google Scholar] [CrossRef]
  7. Tefferi, A.; Lasho, T.L.; Finke, C.; Belachew, A.A.; Wassie, E.A.; Ketterling, R.P.; Hanson, C.A.; Pardanani, A. Type 1 vs type 2 calreticulin mutations in primary myelofibrosis: Differences in phenotype and prognostic impact. Leukemia 2014, 28, 1568–1570. [Google Scholar] [CrossRef]
  8. Tefferi, A.; Lasho, T.L.; Finke, C.M.; Knudson, R.A.; Ketterling, R.; Hanson, C.H.; Maffioli, M.; Caramazza, D.; Passamonti, F.; Pardanani, A. CALR vs JAK2 vs MPL-mutated or triple-negative myelofibrosis: Clinical, cytogenetic and molecular comparisons. Leukemia 2014, 28, 1472–1477. [Google Scholar] [CrossRef]
  9. Guglielmelli, P.; Rotunno, G.; Fanelli, T.; Pacilli, A.; Brogi, G.; Calabresi, L.; Pancrazzi, A.; Vannucchi, A.M. Validation of the differential prognostic impact of type 1/type 1-like versus type 2/type 2-like CALR mutations in myelofibrosis. Blood Cancer J. 2015, 5, e360. [Google Scholar] [CrossRef]
  10. Rumi, E.; Pietra, D.; Pascutto, C.; Guglielmelli, P.; Martínez-Trillos, A.; Casetti, I.; Colomer, D.; Pieri, L.; Pratcorona, M.; Rotunno, G.; et al. Clinical effect of driver mutations of JAK2, CALR, or MPL in primary myelofibrosis. Blood 2014, 124, 1062–1069. [Google Scholar] [CrossRef] [Green Version]
  11. Vannucchi, A.M.; Lasho, T.L.; Guglielmelli, P.; Biamonte, F.; Pardanani, A.; Pereira, A.; Finke, C.; Score, J.; Gangat, N.; Mannarelli, C.; et al. Mutations and prognosis in primary myelofibrosis. Leukemia 2013, 27, 1861–1869. [Google Scholar] [CrossRef]
  12. Guglielmelli, P.; Biamonte, F.; Rotunno, G.; Artusi, V.; Artuso, L.; Bernardis, I.; Tenedini, E.; Pieri, L.; Paoli, C.; Mannarelli, C.; et al. Impact of mutational status on outcomes in myelofibrosis patients treated with ruxolitinib in the COMFORT-II study. Blood 2014, 123, 2157–2160. [Google Scholar] [CrossRef] [PubMed]
  13. Tefferi, A.; Paola, G.; Terra, L.L.; Naseema, G.; Rhett, P.K.; Animesh, P.; Alessandro, M.V. MIPSS70+ version 2.0: Mutation and karyotype-enhanced international prognostic scoring system for primary myelofibrosis. J. Clin. Oncol. 2018, 36, 1769–1770. [Google Scholar] [CrossRef] [PubMed]
  14. Ortmann, C.A.; Kent, D.G.; Nangalia, J.; Silber, Y.; Wedge, D.C.; Grinfeld, J.; Baxter, E.J.; Massie, C.E.; Papaemmanuil, E.; Menon, S.; et al. Effect of Mutation Order on Myeloproliferative Neoplasms. N. Engl. J. Med. 2015, 372, 601–612. [Google Scholar] [CrossRef] [PubMed]
  15. Mylonas, E.; Yoshida, K.; Frick, M.; Hoyer, K.; Christen, F.; Kaeda, J.; Obenaus, M.; Noerenberg, D.; Hennch, C.; Chan, W.; et al. Single-cell analysis based dissection of clonality in myelofibrosis. Nat. Commun. 2020, 11, 73. [Google Scholar] [CrossRef]
  16. Williams, N.; Lee, J.; Mitchell, E.; Moore, L.; Baxter, E.J.; Hewinson, J.; Dawson, K.J.; Menzies, A.; Godfrey, A.L.; Green, A.R.; et al. Life histories of myeloproliferative neoplasms inferred from phylogenies. Nature 2022, 602, 162–168. [Google Scholar] [CrossRef]
  17. Swerdlow, S.H.; Elias, C.; Stefano, A.P.; Nancy, L.H.; Harald, S.; Reiner, S.; Ranjana, A.; Michele, G.; Gilles, A.S.; Andrew, D.Z.; et al. The 2016 Revision of the World Health Organization Classification of Lymphoid Neoplasms. Blood 2016, 127, 2375–2390. [Google Scholar] [CrossRef]
  18. McGowan-Jordan, J.; Simons, A.; Schmid, M. An International System for Human Cytogenomic Nomenclature; Karger: Basel, Switzerland, 2016; ISBN 978-3-318-05857-4. [Google Scholar]
  19. Lee, J.M.; Kim, Y.J.; Park, S.S.; Han, E.; Kim, M.; Kim, Y. Simultaneous monitoring of mutation and chimerism using next-generation sequencing in myelodysplastic syndrome. J. Clin. Med. 2019, 8, 2077. [Google Scholar] [CrossRef]
  20. Passamonti, F.; Cervantes, F.; Vannucchi, A.M.; Morra, E.; Rumi, E.; Pereira, A.; Guglielmelli, P.; Pungolino, E.; Caramella, M.; Maffioli, M.; et al. A dynamic prognostic model to predict survival in primary myelofibrosis: A study by the IWG-MRT (International Working Group for Myeloproliferative Neoplasms Research and Treatment). Blood 2010, 115, 1703–1708. [Google Scholar] [CrossRef]
  21. Gangat, N.; Caramazza, D.; Vaidya, R.; George, G.; Begna, K.; Schwager, S.; Dyke, D.V.; Hanson, C.; Wu, W.; Pardanani, A.; et al. DIPSS plus: A refined Dynamic International Prognostic Scoring System for primary myelofibrosis that incorporates prognostic information from karyotype, platelet count, and transfusion status. J. Clin. Oncol. 2011, 29, 392–397. [Google Scholar] [CrossRef]
  22. Guglielmelli, P.; Lasho, T.L.; Rotunno, G.; Mudireddy, M.; Mannarelli, C.; Nicolosi, M.; Pacilli, A.; Pardanani, A.; Rumi, E.; Rosti, V.; et al. MIPSS70: Mutation-enhanced international prognostic score system for transplantation-age patients with primary myelofibrosis. J. Clin. Oncol. 2018, 36, 310–318. [Google Scholar] [CrossRef] [PubMed]
  23. Tefferi, A.; Guglielmelli, P.; Nicolosi, M.; Mannelli, F.; Mudireddy, M.; Bartalucci, N.; Finke, C.M.; Lasho, T.L.; Hanson, C.A.; Ketterling, R.P.; et al. GIPSS: Genetically inspired prognostic scoring system for primary myelofibrosis. Leukemia 2018, 32, 1631–1642. [Google Scholar] [CrossRef]
  24. Passamonti, F.; Giorgino, T.; Mora, B.; Guglielmelli, P.; Rumi, E.; Maffioli, M.; Rambaldi, A.; Caramella, M.; Komrokji, R.; Gotlib, J.; et al. A clinical-molecular prognostic model to predict survival in patients with post polycythemia vera and post essential thrombocythemia myelofibrosis. Leukemia 2017, 31, 2726–2731. [Google Scholar] [CrossRef] [PubMed]
  25. Bartels, S.; Faisal, M.; Büsche, G.; Schlue, J.; Hasemeier, B.; Schipper, E.; Vogtmann, J.; Westphal, L.; Lehmann, U.; Kreipe, H. Mutations associated with age-related clonal hematopoiesis in PMF patients with rapid progression to myelofibrosis. Leukemia 2020, 34, 1364–1372. [Google Scholar] [CrossRef] [PubMed]
  26. Greenberg, P.L.; Stone, R.M.; Al-Kali, A.; Barta, S.K.; Bejar, R.; Bennett, J.M.; Carraway, H.; De Castro, C.M.; Deeg, H.J.; DeZern, A.E.; et al. Myelodysplastic syndromes, version 2.2017, NCCN clinical practice guidelines in oncology. J. Natl. Compr. Cancer Netw. 2017, 15, 60–87. [Google Scholar] [CrossRef]
  27. Jeryczynski, G.; Thiele, J.; Gisslinger, B.; Wölfler, A.; Schalling, M.; Gleiß, A.; Burgstaller, S.; Buxhofer-Ausch, V.; Sliwa, T.; Schlögl, E.; et al. Pre-fibrotic/early primary myelofibrosis vs. WHO-defined essential thrombocythemia: The impact of minor clinical diagnostic criteria on the outcome of the disease. Am. J. Hematol. 2017, 92, 885–891. [Google Scholar] [CrossRef]
  28. Mudireddy, M.; Shah, S.; Lasho, T.; Barraco, D.; Hanson, C.A.; Ketterling, R.P.; Gangat, N.; Pardanani, A.; Tefferi, A. Prefibrotic versus overtly fibrotic primary myelofibrosis: Clinical, cytogenetic, molecular and prognostic comparisons. Br. J. Haematol. 2018, 182, 594–597. [Google Scholar] [CrossRef]
  29. Masarova, L.; Bose, P.; Daver, N.; Pemmaraju, N.; Newberry, K.J.; Manshouri, T.; Cortes, J.; Kantarjian, H.M.; Verstovsek, S. Patients with post-essential thrombocythemia and post-polycythemia vera differ from patients with primary myelofibrosis. Leuk. Res. 2017, 59, 110–116. [Google Scholar] [CrossRef]
  30. Triviai, I.; Zeschke, S.; Rentel, J.; Spanakis, M.; Scherer, T.; Gabdoulline, R.; Panagiota, V.; Thol, F.; Heuser, M.; Stocking, C.; et al. ASXL1/EZH2 mutations promote clonal expansion of neoplastic HSC and impair erythropoiesis in PMF. Leukemia 2019, 33, 99–109. [Google Scholar] [CrossRef]
  31. McNamara, C.J.; Panzarella, T.; Kennedy, J.A.; Arruda, A.; Claudio, J.O.; Daher-Reyes, G.; Ho, J.; Siddiq, N.; Devlin, R.; Tsui, H.; et al. The mutational landscape of accelerated- and blast-phase myeloproliferative neoplasms impacts patient outcomes. Blood Adv. 2018, 2, 2658–2671. [Google Scholar] [CrossRef] [Green Version]
  32. Vannucchi, A.M.; Antonioli, E.; Guglielmelli, P.; Rambaldi, A.; Barosi, G.; Marchioli, R.; Marfisi, R.M.; Finazzi, G.; Guerini, V.; Fabris, F.; et al. Clinical profile of homozygous JAK2 617V>F mutation in patients with polycythemia vera or essential thrombocythemia. Blood 2007, 110, 840–846. [Google Scholar] [CrossRef]
  33. Courtier, F.; Garnier, S.; Carbuccia, N.; Guille, A.; Adélaide, J.; Chaffanet, M.; Hirsch, P.; Paz, D.L.; Slama, B.; Vey, N.; et al. Targeted molecular characterization shows differences between primary and secondary myelofibrosis. Genes Chromosomes Cancer 2019, 59, 30–39. [Google Scholar] [CrossRef] [PubMed]
  34. Dutta, A.; Yang, Y.; Le, B.T.; Zhang, Y.; Abdel-Wahab, O.; Zang, C.; Mohi, G. U2AF1 is required for survival and function of hematopoietic stem/progenitor cells. Leukemia 2021, 35, 2382–2398. [Google Scholar] [CrossRef] [PubMed]
  35. Tefferi, A.; Finke, C.M.; Lasho, T.L.; Wassie, E.A.; Knudson, R.; Ketterling, R.P.; Hanson, C.A.; Pardanani, A. U2AF1 mutations in primary myelofibrosis are strongly associated with anemia and thrombocytopenia despite clustering with JAK2V617F and normal karyotype. Leukemia 2014, 28, 431–433. [Google Scholar] [CrossRef]
  36. Grinfeld, J.; Nangalia, J.; Baxter, E.J.; Wedge, D.C.; Angelopoulos, N.; Cantrill, R.; Godfrey, A.L.; Papaemmanuil, E.; Gundem, G.; MacLean, C.; et al. Classification and personalized prognosis in myeloproliferative neoplasms. N. Engl. J. Med. 2018, 379, 1416–1430. [Google Scholar] [CrossRef] [PubMed]
  37. Skov, V. Next Generation Sequencing in MPNs. Lessons from the Past and Prospects for Use as Predictors of Prognosis and Treatment Responses. Cancers 2020, 12, 2194. [Google Scholar] [CrossRef] [PubMed]
  38. Yoda, A.; Morishita, D.; Mizutani, A.; Satoh, Y.; Ochi, Y.; Nannya, Y.; Makishima, H.; Miyake, H.; Ogawa, S. CTX-712, a Novel Clk inhibitor targeting myeloid neoplasms with SRSF2 mutation. Blood 2019, 134, 404. [Google Scholar] [CrossRef]
  39. Zavidij, O.; Keller, P.; Cui, J.; Mertz, J.; Salama, M.; Olteanu, H.; Chiu, A.; Chen, D.; Hanson, C.A.; Chen, Z.; et al. MPN-343: The BET inhibitor pelabresib decreases inflammatory cytokines, improves bone marrow fibrosis and function, and demonstrates clinical response irrespective of mutation status in myelofibrosis patients in the phase 2 MANIFEST Trial. Clin. Lymphoma Myeloma Leuk. 2021, 21, S363–S364. [Google Scholar] [CrossRef]
Cancers 14 04485 g001 550
Figure 1. Mutational spectrum in overt primary myelofibrosis (PMF), secondary myelofibrosis (SMF), and prefibrotic myelofibrosis (pre-PMF). (A) Distribution of mutations according to the MIPSS karyotype and RNA splicing genes: SF3B1, SRSF2, ZRSR2, and U2AF1. (B) Proportion of genetic mutations. (C) Proportion of variant allele frequencies (VAF). (D) Chord diagram showing the correlation between clinical variables and mutations. The red bar indicates a positive correlation, and the blue bar indicates a negative correlation. The red and blue bars indicate positive and negative correlations, respectively. (E) Decision tree classifying myelofibrosis (MF) by clinical and mutational criteria.
Figure 1. Mutational spectrum in overt primary myelofibrosis (PMF), secondary myelofibrosis (SMF), and prefibrotic myelofibrosis (pre-PMF). (A) Distribution of mutations according to the MIPSS karyotype and RNA splicing genes: SF3B1, SRSF2, ZRSR2, and U2AF1. (B) Proportion of genetic mutations. (C) Proportion of variant allele frequencies (VAF). (D) Chord diagram showing the correlation between clinical variables and mutations. The red bar indicates a positive correlation, and the blue bar indicates a negative correlation. The red and blue bars indicate positive and negative correlations, respectively. (E) Decision tree classifying myelofibrosis (MF) by clinical and mutational criteria.
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Cancers 14 04485 g002 550
Figure 2. Univariate analysis of overall survival (OS) in the (A) primary myelofibrosis (PMF), (C) secondary myelofibrosis (SMF), and (E) prefibrotic myelofibrosis (pre-PMF) groups. Progression-free survival (PFS) in the (B) PMF, (D) SMF, and (F) pre-PMF groups.
Figure 2. Univariate analysis of overall survival (OS) in the (A) primary myelofibrosis (PMF), (C) secondary myelofibrosis (SMF), and (E) prefibrotic myelofibrosis (pre-PMF) groups. Progression-free survival (PFS) in the (B) PMF, (D) SMF, and (F) pre-PMF groups.
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Cancers 14 04485 g003 550
Figure 3. Hierarchical clustering in the overall cohort (A) applied to overall survival (OS) and (B) dendrogram. PFS by clusters in (C) primary myelofibrosis (PMF), (D) secondary myelofibrosis (SMF), and (E) prefibrotic myelofibrosis (pre-PMF). For detailed p-value between clusters is described in Supplementary Table S3.
Figure 3. Hierarchical clustering in the overall cohort (A) applied to overall survival (OS) and (B) dendrogram. PFS by clusters in (C) primary myelofibrosis (PMF), (D) secondary myelofibrosis (SMF), and (E) prefibrotic myelofibrosis (pre-PMF). For detailed p-value between clusters is described in Supplementary Table S3.
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Figure 4. Estimation of overall survival (OS) in (A) primary myelofibrosis (PMF), (C) secondary myelofibrosis (SMF), and (E) prefibrotic myelofibrosis (pre-PMF), and progression-free survival (PFS) in (B) PMF, (D) SMF, and (F) pre-PMF after consideration of the high-risk molecular group.
Figure 4. Estimation of overall survival (OS) in (A) primary myelofibrosis (PMF), (C) secondary myelofibrosis (SMF), and (E) prefibrotic myelofibrosis (pre-PMF), and progression-free survival (PFS) in (B) PMF, (D) SMF, and (F) pre-PMF after consideration of the high-risk molecular group.
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Table
Table 1. Baseline clinical and genetic characteristics of patients in the present study.
Table 1. Baseline clinical and genetic characteristics of patients in the present study.
VariableSMF
All (n = 229)PPV-MF
(n = 21)		PET-MF
(n = 46)		SMF (n = 67)PMF (n = 122)Pre-PMF
(n = 40)		SMF vs. PMF
p		SMF vs. Pre-PMF pPMF vs. Pre-PMF p
Age at diagnosis (years), mean ± SD56.8 ± 12.162.8 ± 10.159.6 ± 11.960.6 ± 11.455.9 ± 11.853.4 ± 12.60.0080.0030.250
Sex, male, n (%)119 (52.0)9 (42.9)20 (43.5)29 (43.3)73 (59.8)17 (42.5)0.042>0.9990.083
White blood cells (109/L), mean ± SD14.7 ± 2.623.0 ± 18.912.1 ± 9.215.5 ± 1.414.3 ± 33.614.4 ± 12.30.7210.6780.969
Hemoglobin (g/dL), mean ± SD10.6 ± 2.811.5 ± 3.49.9 ± 2.010.4 ± 2.610.0 ± 2.712.7 ± 2.30.306<0.001<0.001
Platelet (109/L), mean ± SD442.7 ± 357.8396.7 ± 232.4487.5 ± 243.2459.0 ± 241.8350.9 ± 345.8695.5 ± 431.60.0130.002<0.001
Peripheral blast proportion (%)1.0 ± 1.91.2 ± 2.21.2 ± 2.31.2 ± 2.21.2 ± 1.90.1 ± 0.50.8190.001<0.001
Spleen size (cm) mean ± SD15.6 ± 4.817.9 ± 5.513.7 ± 3.514.9 ± 4.616.7 ± 4.913.1 ± 3.20.0180.017<0.001
Constitutional symptoms, n (%)112 (48.9)11 (52.4)24 (52.2)35 (52.2)70 (57.4)7 (17.5)0.5980.001<0.001
Allogeneic HSCT, n (%)48 (21.0)3 (14.3)10 (21.7)13 (19.4)27 (22.1)8 (20.0)0.800>0.9990.895
Ruxolitinib exposure before HSCT, n (%)33 (14.4)2 (9.5)10 (21.7)12 (17.9)21 (17.2)0 (0.0)>0.9990.0120.017
Treatment of non-transplant patients, n (%)181 (79.0)18 (85.7)36 (78.3)54 (80.6)95 (77.9)32 (80.0)0.8>0.9990.895
 Ruxolitinib136 (59.6)17 (81.0)29 (63.0)46 (68.7)82 (67.8)8 (20.0)>0.999<0.001<0.001
 Androgens47 (20.6)3 (14.3)8 (17.4)11 (16.4)31 (25.6)5 (12.5)0.2050.7870.124
 Hydroxyurea91 (39.9)14 (66.7)26 (56.5)40 (59.7)31 (25.6)20 (50.0)<0.0010.437<0.001
 Anagrelide67 (29.4)4 (19.0)26 (56.5)30 (44.8)14 (11.6)23 (57.5)<0.0010.283<0.001
 Thalidomide1 (0.4)0 (0.0)0 (0.0)0 (0.0)1 (0.8)0 (0.0)>0.999-0.641
 Investigational agent13 (5.7)2 (9.5)2 (4.3)4 (6.0)5 (4.1)4 (10.0)0.8350.6990.379
Leukemic transformation, n (%)8 (3.5)0 (0.0)1 (2.2)1 (1.5)6 (4.9)1 (2.5)0.429>0.9990.838
DIPSS Karyotype, n (%)
 Unfavorable *17 (7.4)2 (9.5)4 (8.7)6 (9.0)11 (9.0)0 (0.0)>0.9990.1300.109
MIPSS Karyotype, n (%) 0.0350.1230.016
 Favorable †184 (80.3)18 (85.7)38 (82.6)56 (83.5)90 (73.8)38 (95.0)
 Intermediate33 (14.4)2 (9.5)3 (6.5)5 (7.5)26 (21.3)2 (5.0)
 Very High risk ††12 (5.2)1 (4.8)5 (10.9)6 (9.0)6 (4.9)0 (0.0)
Mutation, n (%)
 JAK2V617F117 (51.1)21 (100.0)22 (47.8)43 (64.2)54 (44.3)20 (50.0)0.0140.2150.653
 CALR60 (26.2)0 (0.0)18 (39.1)18 (26.9)35 (28.7)7 (17.5)0.9220.3830.233
  Type1/like39 (17.0)0 (0.0)9 (19.6)9 (13.4)25 (20.5)5 (12.5)0.3200.3770.534
  Type2/like16 (7.0)0 (0.0)8 (17.4)8 (11.9)7 (5.7)1 (2.5)
  Others5 (2.2)0 (0.0)1 (2.2)1 (1.5)3 (2.5)1 (2.5)
 MPL10 (4.4)0 (0.0)3 (6.5)3 (4.5)5 (4.1)2 (5.0)>0.999>0.999>0.999
 ASXL166 (28.8)3 (14.3)16 (34.8)19 (28.4)44 (36.1)3 (7.5)0.3610.0200.001
 CBL5 (2.2)1 (4.8)0 (0.0)1 (1.5)4 (3.3)0 (0.0)0.796>0.9990.567
 CUX14 (1.7)0 (0.0)0 (0.0)0 (0.0)4 (3.3)0 (0.0)0.332 0.567
 DNMT3A11 (4.8)3 (14.3)0 (0.0)3 (4.5)4 (3.3)4 (10.0)0.9880.4750.200
 EZH25 (2.2)1 (4.8)2 (4.3)3 (4.5)2 (1.6)0 (0.0)0.4910.452>0.999
 IDH14 (1.7)1 (4.8)1 (2.2)2 (3.0)2 (1.6)0 (0.0)0.9310.715>0.999
 IDH24 (1.7)1 (4.8)0 (0.0)1 (1.5)1 (0.8)2 (5.0)>0.9990.6470.305
 NOTCH13 (1.3)0 (0.0)1 (2.2)1 (1.5)1 (0.8)1 (2.5)>0.999>0.9990.992
 NRAS5 (2.2)0 (0.0)0 (0.0)0 (0.0)3 (2.5)2 (5.0)0.4930.2670.780
 RUNX18 (3.5)0 (0.0)3 (6.5)3 (4.5)4 (3.3)1 (2.5)0.988>0.999>0.999
 SETBP15 (2.2)0 (0.0)0 (0.0)0 (0.0)3 (2.5)2 (5.0)0.4930.2670.780
 SF3B117 (7.4)0 (0.0)5 (10.9)5 (7.5)8 (6.6)4 (10.0)>0.9990.9220.709
 SRSF25 (2.2)0 (0.0)0 (0.0)0 (0.0)3 (2.5)2 (5.0)0.4930.2670.780
 TET228 (12.2)4 (19.0)5 (10.9)9 (13.4)12 (9.8)7 (17.5)0.6100.7710.306
 TP538 (3.5)2 (9.5)1 (2.2)3 (4.5)5 (4.1)0 (0.0)>0.9990.4520.439
 U2AF111 (4.8)0 (0.0)1 (2.2)1 (1.5)8 (6.6)2 (5.0)0.2270.647>0.999
 U2AF1Q1573 (1.3)0 (0.0)0 (0.0)0 (0.0)3 (2.5)0 (0.0)0.493 0.745
 ZRSR24 (1.7)1 (4.8)1 (2.2)2 (3.0)1 (0.8)1 (2.5)0.595>0.9990.992
SMF, secondary myelofibrosis; PPV-MF, post-polycythemia vera myelofibrosis; PET-MF, post-essential thrombocythemia myelofibrosis; PMF, primary myelofibrosis; pre-PMF, prefibrotic, myelofibrosis; n, number; P, p-value; SD, standard deviation; HSCT, hematopoietic stem cell transplantation; DIPSS, Dynamic International Prognostic Scoring System; MIPSS70, Mutation-Enhanced International Prognostic Scoring System. * Unfavorable karyotype: Complex karyotype or single or tow abnormalities including +8, −7/7q-, i(17q), −5/5q-, 12p-, inv(3), or 11q23 rearrangements. † Favorable karyotype normal karyotype, sole abnormalities of 20q-, 13q-, +9, chromosome 1 translocation/duplication—sex chromosome abnormality including—Y; †† Very-High-Risk karyotype: single/multiple abnormalities of −7, i(17q), inv(3)/3q21, 12p-/12p11.2, 11q-/11q23, or other autosomal trisomies not including +8/ +9 (e.g., +21, +19).
Table
Table 2. Scores of risk stratification in patients with myelofibrosis.
Table 2. Scores of risk stratification in patients with myelofibrosis.
VariableAll (n = 229)SMF (n = 67)PMF (n = 122)pre-PMF (n = 40)SMF vs. PMF
p		SMF vs. pre-PMF pPMF vs. pre-PMF p
IPSS Score, mean ± SD1.8 ± 1.22.1 ± 1.22.0 ± 1.20.7 ± 1.00.574<0.001<0.001
IPSS Risk Group, n (%) 0.721<0.001<0.001
 Low44 (19.2)6 (9.0)15 (12.3)23 (57.5)
 Intermediate-154 (23.6)15 (22.4)30 (24.6)9 (22.5)
 Intermediate-262 (27.1)23 (34.3)33 (27.0)6 (15.0)
 High69 (30.1)23 (34.3)44 (36.1)2 (5.0)
DIPSS Score, mean ± SD2.3 ± 1.62.6 ± 1.52.5 ± 1.50.8 ± 1.20.734<0.001<0.001
DPSS Risk Group, n (%) 0.813<0.001<0.001
 Low44 (19.2)6 (9.0)15 (12.3)23 (57.5)
 Intermediate-177 (33.6)24 (35.8)42 (34.4)11 (27.5)
 Intermediate-292 (40.2)30 (44.8)56 (45.9)6 (15.0)
 High16 (7.0)7 (10.4)9 (7.4)0 (0.0)
DIPSS-plus Score, mean ± SD1.8 ± 1.41.9 ± 1.12.1 ± 1.40.7 ± 1.10.153<0.001<0.001
DIPSS-plus Risk Group, n (%) 0.051<0.001<0.001
 Low43 (18.8)5 (7.5)15 (12.3)23 (57.5)
 Intermediate-166 (28.8)23 (34.3)33 (27.0)10 (25.0)
 Intermediate-292 (40.2)35 (52.2)51 (41.8)6 (15.0)
 High28 (12.2)4 (6.0)23(18.9)1 (2.5)
MYSEC-PM Score, mean ± SD12.3 ± 3.013.1 ± 2.512.5 ± 3.010.4 ± 2.60.159<0.001<0.001
MYSEC-PM Risk Group, n (%) 0.336<0.0010.002
 Low68 (29.7)12 (17.9)35 (28.7)21 (52.5)
 Intermediate-184 (36.7)26 (38.8)42 (34.4)16 (40.0)
 Intermediate-253 (23.1)18 (26.9)32 (26.2)3 (7.5)
 High24 (10.5)11 (16.4)13 (10.7)0 (0.0)
MIPSS70 Score, mean ± SD3.8 ± 2.04.0 ± 1.64.4 ± 1.91.8 ± 1.70.164<0.001<0.001
MIPSS70 Risk Group, n (%) 0.025<0.001<0.001
 Low34 (14.8)1 (1.5)5 (4.1)28 (70.0)
 Intermediate111 (48.5)46 (68.7)59 (48.4)6 (15.0)
 High84 (36.7)20 (29.9)58 (47.5)6 (15.0)
MIPSS70 + Ver2 Score, mean ± SD5.0 ± 2.95.1 ± 2.65.6 ± 3.12.9 ± 2.00.260<0.001<0.001
MIPSS70 + Ver2 Risk Group, n (%) 0.418<0.001<0.001
 Very low7 (3.1)0 (0.0)4 (3.3)3 (7.5)
 Low54 (23.6)13 (19.4)17 (13.9)24 (60.0)
 Intermediate48 (21.0)15 (22.4)28 (23.0)5 (12.5)
 High87 (38.0)30 (44.8)50 (41.0)7 (17.5)
 Very high33 (14.4)9 (13.4)23 (18.9)1 (2.5)
GIPSS Score, mean ± SD1.4 ± 0.91.4 ± 0.81.5 ± 0.91.0 ± 0.60.4100.208<0.001
GIPSS Risk Group, n (%) 0.6970.1210.018
 Low21 (9.2)5 (7.5)11 (9.0)5 (12.5)
 Intermediate-1127 (55.5)38 (56.7)60 (49.2)29 (72.5)
 Intermediate-254 (23.6)17 (25.4)32 (26.2)5 (12.5)
 High27 (11.8)7 (10.4)19 (15.6)1 (2.5)
SMF, secondary myelofibrosis; PMF, primary myelofibrosis; pre-PMF, prefibrotic myelofibrosis; P, p-value; SD, standard deviation; IPSS, International Prognostic Scoring System; MYSEC-PM, Myelofibrosis Secondary to PV and ET-Prognostic Model; GIPSS, genetically inspired prognostic scoring system.
Table
Table 3. Application of risk stratification in myelofibrosis subgroups for predicting overall and progression-free survival.
Table 3. Application of risk stratification in myelofibrosis subgroups for predicting overall and progression-free survival.
VariableOverall SurvivalProgression-Free Survival *
HR95% CIpHR95% CIp
pre-PMF
Risk stratification
  IPSS, INT2 or high-->0.9999.692.03, 46.10.004
  DIPSS, INT2 or high-->0.99911.22.40, 52.20.002
  DIPSS plus, INT2 or high-->0.9995.441.20, 24.70.028
  MIPSS70, high-->0.9996.111.32, 28.30.021
  MIPSS70 +Ver2, high or very high-->0.9993.930.86, 18.00.078
  GIPSS, INT2 or high5.930.37, 94.80.23.370.63, 18.00.2
  MYSECPM, INT2 or high-->0.99916.72.71, 1030.002
PMF
Risk stratification
  IPSS, INT2 or high6.731.94, 23.30.0036.922.02, 23.70.002
  DIPSS, INT2 or high10.93.06, 38.7<0.0017.712.51, 23.7<0.001
  DIPSS plus, INT2 or high6.211.82, 21.30.0044.641.57, 13.70.006
  MIPSS70, high6.622.39, 18.4<0.0015.412.11, 13.9<0.001
  MIPSS70 +Ver2, high or very high9.052.10, 39.00.0039.722.27, 41.50.002
  GIPSS, INT2 or high2.681.12, 6.430.0273.11.32, 7.280.009
  MYSEC-PM, INT2 or high2.370.91, 6.180.0792.621.07, 6.370.034
SMF
Risk stratification
  IPSS, INT2 or high3.630.42, 31.40.24.150.49, 34.90.2
  DIPSS, INT2 or high4.590.54, 39.40.25.480.66, 45.60.12
  DIPSS plus, INT2 or high4.460.52, 38.30.25.250.63, 43.80.13
  MIPSS70, high5.040.92, 27.60.0623.330.74, 14.90.12
  MIPSS70 +Ver2, high or very high-->0.999- >0.999
  GIPSS, INT2 or high3.260.59, 17.90.24.180.81, 21.60.088
  MYSECPM, INT2 or high4.180.75, 23.50.14.980.94, 26.30.059
* Progression refers to leukemic transformation in PMF and SMF, and leukemic transformation and fibrosis progression in pre-PMF. SMF, secondary myelofibrosis; PMF, primary myelofibrosis; pre-PMF, prefibrotic, myelofibrosis; P, p-value; SD, standard deviation; HR, hazard ratio; CI, confidence interval; INT2, intermediate-2.
Table
Table 4. Estimation of progression-free survival using univariate and multivariate analyses of clinical and genetic factors in patients with pre-PMF.
Table 4. Estimation of progression-free survival using univariate and multivariate analyses of clinical and genetic factors in patients with pre-PMF.
VariableUnivariateMultivariate Model IMultivariate Model II
HR95% CIpHR95% CIpHR95% CIp
pre-PMF
Clinical variable
 Age at diagnosis (years)2.340.45, 12.10.3
 Sex, male vs. female0.230.04, 1.200.081
 White blood cells (109/L) > 251.710.20, 14.40.6
 Hemoglobin (g/dL) < 105.61.23, 25.40.0266.301.02, 39.10.048
 Platelet (109/L) < 1001.720.21, 14.30.6
 Peripheral blast (%) > 16.451.14, 36.50.035
 Splenomegaly (cm)0.940.18, 4.85>0.999
 Constitutional symptom, yes4.71.04, 21.30.044 4.470.75, 26.60.1
Genetic variable
 JAK2V617F0.470.09, 2.410.4
 CALR Type1/like0.940.11, 7.85>0.999
 MPL3.290.38, 28.40.3
 ASXL18.681.57, 48.10.01311.41.59, 81.50.0153.690.55, 24.80.2
 DNMT3A1.680.20, 14.10.6
 RUNX1-->0.999
 SETBP14.360.52, 36.40.2
 SF3B1-->0.999
 SRSF213.71.22, 1540.0342.230.10, 50.90.619.51.29, 2940.032
 TET21.940.38, 10.00.4
 U2AF12.860.34, 23.90.3
 ZRSR2-->0.999
pre-PMF, prefibrotic, myelofibrosis; P, p-value; HR, hazard ratio; CI, confidence interval.
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Kim, T.-Y.; Kwag, D.; Lee, J.-H.; Lee, J.; Min, G.-J.; Park, S.-S.; Park, S.; Jeon, Y.-W.; Yoon, J.-H.; Shin, S.-H.; et al. Clinical Features, Gene Alterations, and Outcomes in Prefibrotic and Overt Primary and Secondary Myelofibrotic Patients. Cancers 2022, 14, 4485. https://doi.org/10.3390/cancers14184485

AMA Style

Kim T-Y, Kwag D, Lee J-H, Lee J, Min G-J, Park S-S, Park S, Jeon Y-W, Yoon J-H, Shin S-H, et al. Clinical Features, Gene Alterations, and Outcomes in Prefibrotic and Overt Primary and Secondary Myelofibrotic Patients. Cancers. 2022; 14(18):4485. https://doi.org/10.3390/cancers14184485

Chicago/Turabian Style

Kim, Tong-Yoon, Daehun Kwag, Jong-Hyuk Lee, Joonyeop Lee, Gi-June Min, Sung-Soo Park, Silvia Park, Young-Woo Jeon, Jae-Ho Yoon, Seung-Hawn Shin, and et al. 2022. "Clinical Features, Gene Alterations, and Outcomes in Prefibrotic and Overt Primary and Secondary Myelofibrotic Patients" Cancers 14, no. 18: 4485. https://doi.org/10.3390/cancers14184485

APA Style

Kim, T. -Y., Kwag, D., Lee, J. -H., Lee, J., Min, G. -J., Park, S. -S., Park, S., Jeon, Y. -W., Yoon, J. -H., Shin, S. -H., Yahng, S. -A., Cho, B. -S., Eom, K. -S., Kim, Y. -J., Lee, S., Kim, H. -J., Min, C. -K., Cho, S. -G., Lee, J. -W., ... Lee, S. -E. (2022). Clinical Features, Gene Alterations, and Outcomes in Prefibrotic and Overt Primary and Secondary Myelofibrotic Patients. Cancers, 14(18), 4485. https://doi.org/10.3390/cancers14184485

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