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Review

Diagnosis- and Prognosis-Related Gene Alterations in BCR::ABL1-Negative Myeloproliferative Neoplasms

by
Soji Morishita
1,2,* and
Norio Komatsu
1,2,3
1
Development of Therapies against MPNs, Juntendo University Graduate School of Medicine, 2-1-1 Hongo, Bunkyo-ku, Tokyo 113-8421, Japan
2
Advanced Hematology, Juntendo University Graduate School of Medicine, 2-1-1 Hongo, Bunkuo-ku, Tokyo 113-8421, Japan
3
PharmaEssentia Japan, Akasaka Center Building 12 Fl, 1-3-13 Motoakasaka, Minato-ku, Tokyo 107-0051, Japan
*
Author to whom correspondence should be addressed.
Int. J. Mol. Sci. 2023, 24(16), 13008; https://doi.org/10.3390/ijms241613008
Submission received: 28 July 2023 / Revised: 15 August 2023 / Accepted: 17 August 2023 / Published: 21 August 2023
(This article belongs to the Special Issue Molecular Research on Myeloproliferative Disorders)
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Abstract

:
BCR::ABL1-negative myeloproliferative neoplasms (MPNs) are a group of hematopoietic malignancies in which somatic mutations are acquired in hematopoietic stem/progenitor cells, resulting in an abnormal increase in blood cells in peripheral blood and fibrosis in bone marrow. Mutations in JAK2, MPL, and CALR are frequently found in BCR::ABL1-negative MPNs, and detecting typical mutations in these three genes has become essential for the diagnosis of BCR::ABL1-negative MPNs. Furthermore, comprehensive gene mutation and expression analyses performed using massively parallel sequencing have identified gene mutations associated with the prognosis of BCR::ABL1-negative MPNs such as ASXL1, EZH2, IDH1/2, SRSF2, and U2AF1. Furthermore, single-cell analyses have partially elucidated the effect of the order of mutation acquisition on the phenotype of BCR::ABL1-negative MPNs and the mechanism of the pathogenesis of BCR::ABL1-negative MPNs. Recently, specific CREB3L1 overexpression has been identified in megakaryocytes and platelets in BCR::ABL1-negative MPNs, which may be promising for the development of diagnostic applications. In this review, we describe the genetic mutations found in BCR::ABL1-negative MPNs, including the results of analyses conducted by our group.

1. Introduction

Myeloproliferative neoplasms (MPNs) are characterized as a clonal proliferation of hematopoietic stem/progenitor cells, which cause an increase in one or more mature myeloid lineage cells. MPNs consist of multiple subgroups: chronic myeloid leukemia (CML); polycythemia vera (PV); essential thrombocythemia (ET); prefibrotic primary myelofibrosis (PMF); overt PMF; chronic neutrophilic leukemia (CNL); chronic eosinophilic leukemia (CEL); and unclassifiable MPN, not otherwise specified (Figure 1) [1,2]. In general, CML involves a typical driver gene alteration, the BCR::ABL1 fusion gene, and more rare diseases, namely CNL, CEL, and unclassifiable MPN, not otherwise specified, have been treated as independent diseases. In contrast, patients with BCR::ABL1-negative MPNs (PV, ET, prefibrotic PMF, and overt PMF) transform to other subgroups with a 10–15% frequency and share common gene mutations, namely JAK2 mutations (V617F and exon 12), MPLW515L/K, and CALR exon 9 frameshift mutations, in a mutually exclusive manner [3,4,5,6,7,8,9]. The frequencies of the three gene mutations in our cohort are shown in Figure 2A. Since these mutations exhibit oncogenic properties, they are defined as driver mutations of BCR::ABL1-negative MPNs (Figure 2B). Ahead of the MPL and CALR mutations, JAK2 mutations were listed as one of the major criteria in the 2008 WHO classification [10]. After identifying MPL and CALR mutations, the three driver genes are now available for a revised 2022 WHO classification, which highlights the importance of genetic testing in BCR::ABL1-negative MPN, and have become essential for the definitive diagnosis of BCR::ABL1-negative MPNs [1,2]. However, a portion of patients with ET and PMFs (approximately 10–15%) exhibit negativity for all these driver mutations, which are considered to be triple-negative (TN). As for the diagnosis of TN, an exclusion of the possibility of nonneoplastic blood cell mass elevation should be carefully conducted in addition to a bone marrow (BM) biopsy which is mandatory to confirm that the histopathological characteristics of the BM morphology match the diagnostic criteria of BCR::ABL1-negative MPNs regardless of the presence of driver gene mutations, because no diagnostic markers have been identified [11]. To find novel gene alterations, genome-wide approaches targeting TN cases have been performed, and although several novel driver mutations in BCR::ABL1-negative MPNs have been found, the functional role of these mutations in the pathogenesis of BCR::ABL1-negative MPNs remains unclear [12]. Recently, CREB3L1 overexpression in RNA from the platelets of MPN patients was identified, which may be a comprehensive diagnostic marker for BCR::ABL1-negative MPNs [13]. In addition, several mutations on the genes functioning as epigenetic modifiers and splicing factors were identified not only in leukemias/myelodysplastic syndromes but also in BCR::ABL1-negative MPNs, and the association of these mutations with the prognosis of BCR::ABL1-negative MPNs has been analyzed (Figure 2C) [14,15,16,17]. Furthermore, the influence of genetic background on the predisposition to BCR::ABL1-negative MPNs has been suggested in studies with large cohorts and familial BCR::ABL1-negative MPN pedigrees [18,19]. This article describes genetic abnormalities identified in BCR::ABL1-negative MPNs and their associations with the development or prognosis of BCR::ABL1-negative MPNs.

2. JAK2 Mutations

The JAK2 mutations considered as driver mutations of BCR::ABL1-negative MPNs are V617F substitution and complex mutations, including missense and in-frame deletions/insertions at exon 12 [3,4,5,6]. These mutations concentrate around the JH2 domain, which suppresses the kinase activity of the JH1 domain in JAK2 under a static state. JAK2 mutations decrease the suppression of kinase activity by the JH2 domain, resulting in the constitutive activation of JAK2. The JAK2V617F mutation is a single nucleotide alteration from guanine to thymine at nucleotide position 1849, which causes an amino acid change from V (valine, GTC) to F (phenylalanine, TTC) at codon 617. In addition, the JAK2V617F mutation was initially identified from the three driver gene mutations of BCR::ABL1-negative MPNs [3,4,5] and has been most frequently identified among the patients with BCR::ABL1-negative MPNs, and the positivity is approximately 97% in PV and approximately 50% in ET and PMF. In contrast, JAK2 exon 12 mutations are specific for PV, with 3% positivity (Figure 1A), and a variety of mutations have been identified at JAK2 exon 12 (Supplementary Table S1, according to the Catalogue Of Somatic Mutations In Cancer (COSMIC) database, as of June 2023) [20,21]. Clinically, patients with PV harboring the JAK2V617F mutation exhibit pancytosis, including leukocytosis, thrombocytosis, and erythrocytosis, whereas those harboring the JAK2 exon 12 mutation show only an aggressive increase in red cell mass. As for the relationship between the prognosis and JAK2 mutations, no significant differences between JAK2V617F-mutated and JAK2 exon 12-mutated patients with PV have been observed [22]; nevertheless, JAK2V617F is a well-known risk factor of thrombosis [23,24]. Thrombosis promoted through the increased neutrophil extracellular trap formation was observed in the JAK2V617F-mutated murine model [25]. JAK2V617F is also useful for monitoring the efficacy of treatments or predicting the outcome of patients. Pegylated interferon-α, for example, is one of the recently developed drugs against MPNs and decreases the JAK2V617F allele burden in patients [26,27,28]. To date, pegylated interferon-α is the only agent that specifically affects the hematopoietic stem/progenitor cells of BCR::ABL1-negative MPNs, indicating the need for methodologies that quantify the mutant burden to monitor the efficacy of such agents. Therefore, a unique quantitative technique for assessing the JAK2V617F allele burden, alternately named binding-probe competitive PCR (ABC-PCR), has been developed [29]. The JAK2V617F mutant allele load (allele burden) in Japanese patients with BCR::ABL1-negative MPNs quantified by utilizing ABC-PCR showed the tendency of distribution of JAK2V617F allele burden between the subtypes of BCR::ABL1-negative MPNs (Figure 3A). Furthermore, ABC-PCR enables precise quantification of the JAK2V617F allele burden by correlating it with fluorescence intensity and may be as effective as massively parallel sequencing (MPS, Figure 3B). By utilizing ABC-PCR, the relationship between the allele burden and clinical significance was clarified; the increase in mutant burden during the follow-up is associated with the transformation to secondary myelofibrosis [30]. Furthermore, more studies have shown that thrombotic risks increase in patients bearing high allele burden [31] and more frequent cooccurrence of chronic kidney disease and tendency for disease progression [32], implicating the relationship between the clinical relevance and mutant allele burdens. The series of results obtained by groups studying Western patients and our group studying Japanese patients demonstrate that the quantification of the JAK2V617F allele burden may be used as an indicator of drug efficacy or to predict the adverse prognosis of BCR::ABL1-negative MPNs. Moreover, the highly sensitive detection of JAK2V617F is desired for the early diagnosis of MPNs. Melting curve analysis after T allele enrichment (MelCaTle) detects the JAK2V617F allele at a single-copy level by eliminating the JAK2 wild-type allele using a peptide nucleic acid probe and BstXI restriction enzyme [33]. The combination of ABC-PCR and MelCaTle enables precise detection of the JAK2V617F mutation at a single-molecule level and accurately quantify the JAK2V617F burden in patients with MPNs.

3. MPL Mutations

The majority of MPL mutations involve the substitution of W (tryptophane, c.1542-1544TGG) at codon 515 with other nucleic acids, causing W515L/K/A/R (leucine/lysine/alanine/arginine) mutations [7,34]. In addition to these W515 mutations, the substitution of S (serine) at codon 505 with N/C (asparagine/cysteine) has been identified [35,36]. These mutations are located at the membrane-spanning segment of MPL and are considered to involve conformation changes that trigger constitutive activation of the downstream molecules. Although the frequency of MPL mutations in BCR::ABL1-negative MPNs is low (5% at most in ET, <10% in PMF), considering that mutant CALR binds to MPL and activates downstream signals [37], signal activation through MPL may play a key role in the pathogenesis of BCR::ABL1-negative MPNs [38]. Patients harboring MPL mutations are also relatively rare, but a meta-analysis unifying seven studies clarified that patients with ET harboring an MPL mutation showed higher risks for thrombosis than those harboring a JAK2V617F mutation [39]. In line with this study, Japanese patients with ET harboring MPL mutations demonstrated a higher risk for thrombosis [40]. Therefore, MPL mutations may be one of the adverse factors of thrombosis.

4. CALR Exon 9 Frameshift Mutations

The CALR mutation is the most recently discovered among the driver gene mutations in BCR::ABL1-negative MPNs and is found in approximately 20–30% of patients with ET and PMF [8,9]. This mutation is characterized by the presence of a deletion or insertion at the end of exon 9, the final exon of the CALR gene. Over 100 variations have been found (Supplementary Tables S2 and S3, according to the COSMIC database, as of January 2023), all of which cause the same frameshift and produce a common amino acid sequence at the C-terminus when translated into protein. Among them, deletions of 52 bases (type 1, p.L367fs*46) and insertions of 5 bases (type 2, p.K385fs*47) are the major mutations, accounting for approximately 85% of the observed variations (Figure 4). Mutant CALR activates downstream signaling by forming homomultimeric complexes through a new amino acid sequence generated by the mutation, changing the structure of the CALR protein and allowing it to bind with MPL [37,41,42]. Patients with overt PMF harboring a CALR mutation have a better prognosis than those with other driver gene mutations [43]. Regarding the CALR mutations in overt PMF, CALR type 1 mutations are dominant, and patients harboring the type 2 mutation exhibit a poorer prognosis than those with the type 1 mutation [44,45].

5. Triple-Negative BCR::ABL1-Negative MPNs

A portion of patients with BCR::ABL1-negative MPNs (none or rare in PV, 10–15% in ET, and ~10% in PMF) have none of the driver mutations, referred to as TN cases. Noncanonical somatic mutations at driver genes of BCR::ABL1-negative MPNs (e.g., JAK2G571S and MPLS204F/P) have been identified in TN cases; however, it should be considered that these mutations do not account for all the remaining cases (Supplementary Table S4) [12,46] and no evidence of cytokine-independent cell growth has been reported for these mutations. Whole exome sequencing and analysis of TN-ET have shown that approximately half of the patients exhibited polyclonal cell differentiation, which implies that some cases of thrombocytosis in TN-ET may be caused by nonneoplastic diseases [12,47]. In addition, several cases of nonneoplastic erythrocytosis (NNE) showing low EPO levels (<4.2 IU/mL, low EPO NNE) were identified in our analysis. BM biopsy samples derived from low EPO NNE patients were diagnosed by histopathologists, resulting in the denial of PV in all cases [48]. Careful histopathological diagnosis of promising diagnostic markers for BCR::ABL1-negative MPNs is mandatory for such puzzling cases.

6. CREB3L1 as a Novel Diagnostic Marker of BCR::ABL1-Negative MPNs

To diagnose TN cases in practice, a histopathological diagnosis of the BM biopsy is required. However, the discrimination of TN from reactive cases is challenging because the pathological diagnosis of BCR::ABL1-negative MPNs is not always reproducible, even for expert hematopathologists. By focusing on the fact that typical clinical presentations (i.e., thrombocytosis) of ET are similar regardless of the presence and type of driver gene mutations, and that the downstream RNA expression may be common and different from that in reactive cases, differential expression analysis utilizing RNA from platelet-rich plasma (PRP) obtained from ET and reactive thrombocytosis patients was conducted. As a result, CREB3L1 was found to be specifically overexpressed among ET patients [13]. CREB3L1 is a transcription factor that localizes in the endoplasmic reticulum (ER), migrates into the nucleus in response to ER stress, and induces the expression of various genes [49]. Although the role of CREB3L1 in the pathogenesis of BCR::ABL1-negative MPNs remains unclear, the IRE1a/XBP1 pathway, which is an ER stress-responsible pathway other than CREB3L1, is activated by the CALR type 1 mutation and drives BCR::ABL1-negative MPNs [50]. In breast and bladder cancer, CREB3L1 is highly methylated, and CREB3L1 expression is inversely correlated with tumor grade, indicating that it acts in a tumor-suppressive manner [51,52].
Expansion of the testing of CREB3L1 overexpression for other subtypes of BCR::ABL1-negative MPNs harboring one of the driver gene mutations in the validation analysis by employing quantitative PCR revealed that CREB3L1 was overexpressed widely among BCR::ABL1-negative MPNs compared with those of reactive cases and healthy volunteers (Figure 5). The area under the ROC curve showed that the sensitivity and specificity were both 1.0000, indicating that the CREB3L1 overexpression in PRP discriminates driver gene-mutated BCR::ABL1-negative MPNs from reactive cases [13]. Further investigations are required to determine whether CREB3L1 expression is a diagnostic marker for BCR::ABL1-negative MPNs, including TN cases. In our cohort, 20 cases without any driver mutations were definitively diagnosed with TN-ET according to the pathological characteristics. Among them, eight cases did not express CREB3L1 mRNA. Based on these findings, TN-ET cases were stratified into two groups (CREB3L1-positive TN-ET and CREB3L1-negative TN-ET) and the clinical parameters of the two groups were monitored. As a result, the platelet counts of two patients with CREB3L1-negative TN-ET decreased to normal levels during observation. In both cases, the BM biopsies at the time of initial diagnosis were consistent with ET, but the BM examinations performed at the time of spontaneous regression were negative for ET [13]. Therefore, aggressive treatments, such as anticancer drugs, should be avoided, and careful follow-up observation is recommended in cases with CREB3L1-negative TN-ET.

7. Nondriver Mutations and Their Association with the Prognosis of BCR::ABL1-Negative MPNs

Comprehensive genome analyses, such as MPS, have identified some mutations relating to epigenetic modification and RNA-splicing among patients with BCR::ABL1-negative MPNs at low frequency [14,15]. MPS-based comprehensive target resequencing methodology focusing on these nondriver mutations also revealed that the mutations highly accumulated in patients with BCR::ABL1-negative MPNs were correlated with a poor prognosis. Based on these findings, mutation-enhanced prognostic scoring systems based on the positivity of nondriver mutations have been proposed and are widely used to estimate the prognostic risks of patients with BCR::ABL1-negative MPNs. For example, the mutation-enhanced international prognostic scoring system (MIPSS) 70+ v2.0, which was originally designed for patients with PMF aged 70 years or younger and eligible for transplantation, uses the CALR, ASXL1, EZH2, IDH1/2, SRSF2, and U2AF1Q157 mutations [53]. MIPSS-ET and -PV are available for classifying the prognostic risks of patients with ET and PV, using the SF3B1, SRSF2, TP53, and U2AF1 mutations for ET and the SRSF2 mutation for PV [54].
Similar to the results obtained by other groups, patients with PMF harboring ASXL1, EZH2, and/or SRSF2 mutations exhibited significantly shorter 5-year overall survival, and these gene mutations are also the poor prognostic factors of PMF that were demonstrated in the Japanese cohort [16]. Furthermore, regarding ET and PV, the frequencies of ASXL1 and EZH2 mutations increase as the diseases progresses from ET or PV to prefibrotic PMF and overt PMF, whereas the frequencies of DNMT3A and TET2 mutations are unrelated to disease type. This implies that ASXL1 and EZH2 mutations are related to disease progression, whereas DNMT3A and TET2 mutations may trigger the disease. Logistic regression analysis showed that ASXL1 mutation-positive ET/PV patients had a high rate of progression to leukemia and myelofibrosis [17]. Nonetheless, the prognosis of BCR::ABL1-negative MPNs may be affected by the timing and order of the acquisition of these mutations. Although the effect of timing remains unclarified, the effect of acquisition order on the phenotype of BCR::ABL1-negative MPNs has been studied. Ortmann and colleagues cultured mononuclear cells isolated from the peripheral blood of BCR::ABL1-negative MPN patients who were positive for both JAK2V617F and TET2 mutations in methylcellulose medium and examined the positivity of JAK2 and TET2 mutations in the BFU-E colonies. As a result, patients with JAK2V617F-first colonies had stronger clinical symptoms and were at higher risk of developing thrombosis and PV than those with the TET2-first colonies. The authors proposed a model in which the respective clinical symptoms differ according to the acquisition order of JAK2 and TET2 mutations [55]. Moreover, a colony assay targeting a patient with myelodysplastic syndrome (MDS)/MPN-RS-T harboring both JAK2 exon 12 (p.H538_K539delinsL) and SF3B1E622D mutations demonstrated that SF3B1-mutated clones existed in isolation but all clones with JAK2 exon 12 mutations had accompanying SF3B1 mutations. This indicates that the SF3B1 mutation triggers pancytopenia in the cells and then the JAK2 mutation is acquired as a second-hit mutation, causing thrombocytosis in the patient to rescue the pancytopenia to some extent [56]. The above two studies show that the first-hit gene mutations characterize the basal phenotype of the disease, and the second-hit mutations contribute additional clinical presentations of the disease; therefore, the order in which the gene mutations are acquired may explain the development and progression of the disease. More recently, comprehensive RNA expression analysis with single-cell resolution has been employed to estimate cell profiling in patients. Tong and colleagues found prominent megakaryocyte lineage priming and elevated interferon signaling in hematopoietic stem cells (HSCs) in JAK2V617F-mutated ET patients, and the pathogenesis and therapeutic responses were dependent on the JAK2V617F heterogeneity of HSCs [57]. A more precise association between the disease development and traits, including single nucleotide polymorphisms (SNPs) or the differentiation and proliferation of neoplastic cells in BCR::ABL1-negative MPNs, may be revealed by novel approaches such as the network genome-wide association studies (networkGWAS) and RNA velocity-based algorithms (e.g., CellRank) [58,59,60].

8. Genetic Background Enhancing the Risk of Developing BCR::ABL1-Negative MPNs

The accumulation of mutations is highly dependent on age, and elderly individuals sometimes develop clonal hematopoiesis because of acquired mutations [61,62]. Such individuals are diagnosed with clonal hematopoiesis of intermediate potential (CHIP) or age-related clonal hematopoiesis (ARCH) [63,64]. Regarding the development of CHIP/ARCH, it has been experimentally demonstrated using the zebrafish model that mutant clones increase by developing clonal fitness, which is driven by enhanced resistance to inflammatory signals [65]. Notably, JAK2V617F has been identified with a frequency of approximately 0.1% in an analysis of 49,488 individuals, and 7 patients harboring JAK2V617F (48 individuals were removed for originally having MPNs) were then considered as CHIP/ARCH in the final cohort. Furthermore, the JAK2V617F mutant burden in CHIP/ARCH increases by 0.55% per year in the study, implying that the JAK2V617F-mutated cells acquire a mild growth advantage, and therefore the development of BCR::ABL1-negative MPNs may progress over time [66]. This implication is supported by another investigation, which clarified that JAK2V617F mutations occur decades before BCR::ABL1-negative MPN diagnosis, increase the fitness of HSCs, and induce a megakaryocyte–erythroid differentiation bias [67]. CHIP/ARCH individuals also have an increased risk of developing hematologic malignancies or cardiovascular diseases in patients with additional mutations [68].
Some studies have investigated the impact of genetic background on the risk of developing BCR::ABL1-negative MPNs. For example, the JAK2 46/1 haplotype concomitant with JAK2V617F, RBBP6, SH2B3, and TERT mutations was shown to increase the risk of developing BCR::ABL1-negative MPNs [69,70,71,72,73,74]. A GWAS analysis of 888,503 individuals, including 2949 patients with BCR::ABL1-negative MPNs, identified 17 loci, including JAK2 and TERT [18]. SNPs located at the identified loci, such as rs17879961 (located at CHEK2 exon 5) and rs534137 (located at the promoter region of GFI1B), were considered to induce the instability of HSCs homeostasis and may predispose patients to BCR::ABL1-negative MPNs. In addition, a germline frameshift mutation at Carbohydrate Sulfotransferase 15 (CHST15) has been identified among some familial BCR::ABL1-negative MPN pedigrees with the same geographical origin [19]. Mutant CHST15 reduces expression levels of CHST15 and its target genes, which induces a chronic inflammatory response, and this may also predispose patients to BCR::ABL1-negative MPNs. The above mutations may trigger genetic instability in the cells, induce chronic inflammation, and impose cell fitness toward a predisposition to BCR::ABL1-negative MPNs.
In addition to SNPs that confer higher susceptibility to develop BCR::ABL1-negative MPNs, SNPs that affect the phenotype, prognosis, and response to therapies of BCR::ABL1-negative MPNs have been reported and summarized [75]. Although allelic frequencies are not rare (0.114822 for rs6198, 0.279039 for rs1024611, and 0.444187 for rs2431697 by gnomAD, respectively), a poorer prognosis was observed among patients with PMF harboring both JAK2V617F and homozygous mutations of rs6198 locating at NR3C1 than those bearing wild-type NR3C1 [76], rs1024611 at CCL2 strongly correlated to the CCL2 expression and the myelofibrosis grade [77], and homozygous rs2431697 at miR-146a was associated with myelofibrosis progression [78]. Furthermore, IL28B rs12979860 homozygous phenotype showed hematologic response in patients with PV treated with interferon-α [79]. These SNPs would be useful to consider as a therapeutic strategy for BCR::ABL1-negative MPNs.

9. Conclusions

This review focused on the gene alterations involved in the pathogenesis or development of BCR::ABL1-negative MPNs. Notably, the number of mutations associated with BCR::ABL1-negative MPNs is smaller than those of acute leukemias and solid tumors, and the genetic mutation analyses play a significant role in confirming diagnosis and prognosis. On top of this, quantitation of mutant burden in the patients can provide important clinical information such as transformation to myelofibrosis and drug responses in the patients with BCR::ABL1-negative MPNs.

10. Future Perspective

The function of each mutation found in BCR::ABL1-negative MPNs is relatively easy to analyze; therefore, clarifying the pathogenesis of BCR::ABL1-negative MPNs may provide a model for other hematopoietic malignancies and solid cancers. The latest comprehensive gene expression technologies and subsequent statistical analyses help to elucidate the cell behavior with single-cell resolution. Moreover, deep machine learning approaches using artificial intelligence may assist in developing novel diagnostic/prognosis markers and help elucidate the pathogenesis of BCR::ABL1-negative MPNs, potentially leading to personalized therapies [80].

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/ijms241613008/s1.

Funding

This research received no external funding.

Institutional Review Board Statement

Samples treated by our group in this manuscript were collected in accordance with the 1975 Declaration of Helsinki. The experiments using human specimen samples by our group were approved by the ethics committee of Juntendo University (IRB #M12-0895, approved on 19 November 2012).

Informed Consent Statement

Written informed consent was obtained from all participants prior to sample collection.

Data Availability Statement

No new data was created in this review article.

Acknowledgments

We thank all members of the Laboratory for the development of therapies against MPN, the Department of Advanced Hematology, and the Department of Hematology at Juntendo University, Juntendo Urayasu Hospital, Juntendo Shizuoka Hospital, and Juntendo Nerima Hospital for the collection of samples.

Conflicts of Interest

Norio Komatsu has received a salary from PharmaEssentia Japan, where he is a board member.

References

  1. Arber, D.A.; Orazi, A.; Hasserjian, R.P.; Borowitz, M.J.; Calvo, K.R.; Kvasnicka, H.M.; Wang, S.A.; Bagg, A.; Barbui, T.; Branford, S.; et al. International Consensus Classification of Myeloid Neoplasms and Acute Leukemias: Integrating morphologic, clinical, and genomic data. Blood 2022, 140, 1200–1228. [Google Scholar] [CrossRef]
  2. Khoury, J.D.; Solary, E.; Abla, O.; Akkari, Y.; Alaggio, R.; Apperley, J.F.; Bejar, R.; Berti, E.; Busque, L.; Chan, J.K.C.; et al. The 5th edition of the World Health Organization Classification of Haematolymphoid Tumours: Myeloid and Histiocytic/Dendritic Neoplasms. Leukemia 2022, 36, 1703–1719. [Google Scholar] [CrossRef] [PubMed]
  3. Kralovics, R.; Passamonti, F.; Buser, A.S.; Teo, S.S.; Tiedt, R.; Passweg, J.R.; Tichelli, A.; Cazzola, M.; Skoda, R.C. A gain-of-function mutation of JAK2 in myeloproliferative disorders. N. Engl. J. Med. 2005, 352, 1779–1790. [Google Scholar] [CrossRef] [PubMed]
  4. Baxter, E.J.; Scott, L.M.; Campbell, P.J.; East, C.; Fourouclas, N.; Swanton, S.; Vassiliou, G.S.; Bench, A.J.; Boyd, E.M.; Curtin, N.; et al. Acquired mutation of the tyrosine kinase JAK2 in human myeloproliferative disorders. Lancet 2005, 365, 1054–1061. [Google Scholar] [CrossRef]
  5. James, C.; Ugo, V.; Le Couedic, J.P.; Staerk, J.; Delhommeau, F.; Lacout, C.; Garcon, L.; Raslova, H.; Berger, R.; Bennaceur-Griscelli, A.; et al. A unique clonal JAK2 mutation leading to constitutive signalling causes polycythaemia vera. Nature 2005, 434, 1144–1148. [Google Scholar] [CrossRef] [PubMed]
  6. Scott, L.M.; Tong, W.; Levine, R.L.; Scott, M.A.; Beer, P.A.; Stratton, M.R.; Futreal, P.A.; Erber, W.N.; McMullin, M.F.; Harrison, C.N.; et al. JAK2 exon 12 mutations in polycythemia vera and idiopathic erythrocytosis. N. Engl. J. Med. 2007, 356, 459–468. [Google Scholar] [CrossRef]
  7. Pardanani, A.D.; Levine, R.L.; Lasho, T.; Pikman, Y.; Mesa, R.A.; Wadleigh, M.; Steensma, D.P.; Elliott, M.A.; Wolanskyj, A.P.; Hogan, W.J.; et al. MPL515 mutations in myeloproliferative and other myeloid disorders: A study of 1182 patients. Blood 2006, 108, 3472–3476. [Google Scholar] [CrossRef]
  8. 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]
  9. Klampfl, T.; Gisslinger, H.; Harutyunyan, A.S.; Nivarthi, H.; Rumi, E.; Milosevic, J.D.; Them, N.C.; Berg, T.; Gisslinger, B.; Pietra, D.; et al. Somatic mutations of calreticulin in myeloproliferative neoplasms. N. Engl. J. Med. 2013, 369, 2379–2390. [Google Scholar] [CrossRef]
  10. Vardiman, J.W.; Thiele, J.; Arber, D.A.; Brunning, R.D.; Borowitz, M.J.; Porwit, A.; Harris, N.L.; Le Beau, M.M.; Hellstrom-Lindberg, E.; Tefferi, A.; et al. The 2008 revision of the World Health Organization (WHO) classification of myeloid neoplasms and acute leukemia: Rationale and important changes. Blood 2009, 114, 937–951. [Google Scholar] [CrossRef]
  11. Barbui, T.; Thiele, J.; Gisslinger, H.; Kvasnicka, H.M.; Vannucchi, A.M.; Guglielmelli, P.; Orazi, A.; Tefferi, A. The 2016 WHO classification and diagnostic criteria for myeloproliferative neoplasms: Document summary and in-depth discussion. Blood Cancer J. 2018, 8, 15. [Google Scholar] [CrossRef]
  12. Milosevic Feenstra, J.D.; Nivarthi, H.; Gisslinger, H.; Leroy, E.; Rumi, E.; Chachoua, I.; Bagienski, K.; Kubesova, B.; Pietra, D.; Gisslinger, B.; et al. Whole-exome sequencing identifies novel MPL and JAK2 mutations in triple-negative myeloproliferative neoplasms. Blood 2016, 127, 325–332. [Google Scholar] [CrossRef]
  13. Morishita, S.; Yasuda, H.; Yamawaki, S.; Kawaji, H.; Itoh, M.; Edahiro, Y.; Imai, M.; Kogo, Y.; Tsuneda, S.; Ohsaka, A.; et al. CREB3L1 overexpression as a potential diagnostic marker of Philadelphia chromosome-negative myeloproliferative neoplasms. Cancer Sci. 2021, 112, 884–892. [Google Scholar] [CrossRef]
  14. Tefferi, A.; Lasho, T.L.; Finke, C.M.; Elala, Y.; Hanson, C.A.; Ketterling, R.P.; Gangat, N.; Pardanani, A. Targeted deep sequencing in primary myelofibrosis. Blood Adv. 2016, 1, 105–111. [Google Scholar] [CrossRef]
  15. Tefferi, A.; Lasho, T.L.; Guglielmelli, P.; Finke, C.M.; Rotunno, G.; Elala, Y.; Pacilli, A.; Hanson, C.A.; Pancrazzi, A.; Ketterling, R.P.; et al. Targeted deep sequencing in polycythemia vera and essential thrombocythemia. Blood Adv. 2016, 1, 21–30. [Google Scholar] [CrossRef]
  16. Morishita, S.; Ochiai, T.; Misawa, K.; Osaga, S.; Inano, T.; Fukuda, Y.; Edahiro, Y.; Ohsaka, A.; Araki, M.; Komatsu, N. Clinical impacts of the mutational spectrum in Japanese patients with primary myelofibrosis. Int. J. Hematol. 2021, 113, 500–507. [Google Scholar] [CrossRef]
  17. Morishita, S.; Hashimoto, Y.; Furuya, C.; Edahiro, Y.; Ochiai, T.; Shirane, S.; Inano, T.; Yasuda, H.; Ando, M.; Araki, M.; et al. Non-driver gene mutation analysis in a large cohort of polycythemia vera and essential thrombocythemia. Eur. J. Haematol. 2023, 110, 131–136. [Google Scholar] [CrossRef] [PubMed]
  18. Bao, E.L.; Nandakumar, S.K.; Liao, X.; Bick, A.G.; Karjalainen, J.; Tabaka, M.; Gan, O.I.; Havulinna, A.S.; Kiiskinen, T.T.J.; Lareau, C.A.; et al. Inherited myeloproliferative neoplasm risk affects haematopoietic stem cells. Nature 2020, 586, 769–775. [Google Scholar] [CrossRef] [PubMed]
  19. Chen, Y.; Zhang, Y.; Wang, Z.; Wang, Y.; Luo, Y.; Sun, N.; Zheng, S.; Yan, W.; Xiao, X.; Liu, S.; et al. CHST15 gene germline mutation is associated with the development of familial myeloproliferative neoplasms and higher transformation risk. Cell Death Dis. 2022, 13, 586. [Google Scholar] [CrossRef] [PubMed]
  20. Percy, M.J.; Scott, L.M.; Erber, W.N.; Harrison, C.N.; Reilly, J.T.; Jones, F.G.; Green, A.R.; McMullin, M.F. The frequency of JAK2 exon 12 mutations in idiopathic erythrocytosis patients with low serum erythropoietin levels. Haematologica 2007, 92, 1607–1614. [Google Scholar] [CrossRef]
  21. Scott, L.M.; Beer, P.A.; Bench, A.J.; Erber, W.N.; Green, A.R. Prevalance of JAK2 V617F and exon 12 mutations in polycythaemia vera. Br. J. Haematol. 2007, 139, 511–512. [Google Scholar] [CrossRef] [PubMed]
  22. Tondeur, S.; Paul, F.; Riou, J.; Mansier, O.; Ranta, D.; Le Clech, L.; Lippert, E.; Tavitian, S.; Chaoui, D.; Mercier, M.; et al. Long-term follow-up of JAK2 exon 12 polycythemia vera: A French Intergroup of Myeloproliferative Neoplasms (FIM) study. Leukemia 2020, 35, 871–875. [Google Scholar] [CrossRef] [PubMed]
  23. Barbui, T.; Finazzi, G.; Carobbio, A.; Thiele, J.; Passamonti, F.; Rumi, E.; Ruggeri, M.; Rodeghiero, F.; Randi, M.L.; Bertozzi, I.; et al. Development and validation of an International Prognostic Score of thrombosis in World Health Organization-essential thrombocythemia (IPSET-thrombosis). Blood 2012, 120, 5128–5133. [Google Scholar] [CrossRef]
  24. Barbui, T.; Vannucchi, A.M.; Buxhofer-Ausch, V.; De Stefano, V.; Betti, S.; Rambaldi, A.; Rumi, E.; Ruggeri, M.; Rodeghiero, F.; Randi, M.L.; et al. Practice-relevant revision of IPSET-thrombosis based on 1019 patients with WHO-defined essential thrombocythemia. Blood Cancer J. 2015, 5, e369. [Google Scholar] [CrossRef] [PubMed]
  25. Wolach, O.; Sellar, R.S.; Martinod, K.; Cherpokova, D.; McConkey, M.; Chappell, R.J.; Silver, A.J.; Adams, D.; Castellano, C.A.; Schneider, R.K.; et al. Increased neutrophil extracellular trap formation promotes thrombosis in myeloproliferative neoplasms. Sci. Transl. Med. 2018, 10, eaan8292. [Google Scholar] [CrossRef]
  26. Verstovsek, S.; Komatsu, N.; Gill, H.; Jin, J.; Lee, S.E.; Hou, H.A.; Sato, T.; Qin, A.; Urbanski, R.; Shih, W.; et al. SURPASS-ET: Phase III study of ropeginterferon alfa-2b versus anagrelide as second-line therapy in essential thrombocythemia. Future Oncol. 2022, 18, 2999–3009. [Google Scholar] [CrossRef]
  27. Mesa, R.; Komatsu, N.; Gill, H.; Jin, J.; Lee, S.E.; Hou, H.A.; Sato, T.; Qin, A.; Urbanski, R.; Shih, W.; et al. MPN-545 Surpass-ET: Ropeginterferon Alfa-2b (P1101) vs. Anagrelide as Second Line Therapy in Essential Thrombocythemia. Clin. Lymphoma Myeloma Leuk. 2022, 22 (Suppl. S2), S342. [Google Scholar]
  28. Kiladjian, J.J.; Klade, C.; Georgiev, P.; Krochmalczyk, D.; Gercheva-Kyuchukova, L.; Egyed, M.; Dulicek, P.; Illes, A.; Pylypenko, H.; Sivcheva, L.; et al. Long-term outcomes of polycythemia vera patients treated with ropeginterferon Alfa-2b. Leukemia 2022, 36, 1408–1411. [Google Scholar] [CrossRef]
  29. Morishita, S.; Komatsu, N.; Kirito, K.; Koda, A.H.; Sekiguchi, Y.; Tsuneda, S.; Noda, N. Alternately binding probe competitive PCR as a simple, cost-effective, and accurate quantification method for JAK2V617F allele burden in myeloproliferative neoplasms. Leuk. Res. 2011, 35, 1632–1636. [Google Scholar] [CrossRef]
  30. Shirane, S.; Araki, M.; Morishita, S.; Edahiro, Y.; Sunami, Y.; Hironaka, Y.; Noguchi, M.; Koike, M.; Sato, E.; Ohsaka, A.; et al. Consequences of the JAK2V617F allele burden for the prediction of transformation into myelofibrosis from polycythemia vera and essential thrombocythemia. Int. J. Hematol. 2015, 101, 148–153. [Google Scholar] [CrossRef]
  31. Passamonti, F.; Rumi, E. Clinical relevance of JAK2 (V617F) mutant allele burden. Haematologica 2009, 94, 7–10. [Google Scholar] [CrossRef] [PubMed]
  32. Moliterno, A.R.; Kaizer, H.; Reeves, B.N. JAK2(V617F) allele burden in polycythemia vera: Burden of proof. Blood 2023, 141, 1934–1942. [Google Scholar] [PubMed]
  33. Morishita, S.; Takahashi, K.; Araki, M.; Hironaka, Y.; Sunami, Y.; Edahiro, Y.; Tsutsui, M.; Ohsaka, A.; Tsuneda, S.; Komatsu, N. Melting curve analysis after T allele enrichment (MelcaTle) as a highly sensitive and reliable method for detecting the JAK2V617F mutation. PLoS ONE 2015, 10, e0122003. [Google Scholar] [CrossRef]
  34. Boyd, E.M.; Bench, A.J.; Goday-Fernandez, A.; Anand, S.; Vaghela, K.J.; Beer, P.; Scott, M.A.; Bareford, D.; Green, A.R.; Huntly, B.; et al. Clinical utility of routine MPL exon 10 analysis in the diagnosis of essential thrombocythaemia and primary myelofibrosis. Br. J. Haematol. 2010, 149, 250–257. [Google Scholar] [CrossRef]
  35. Pietra, D.; Brisci, A.; Rumi, E.; Boggi, S.; Elena, C.; Pietrelli, A.; Bordoni, R.; Ferrari, M.; Passamonti, F.; De Bellis, G.; et al. Deep sequencing reveals double mutations in cis of MPL exon 10 in myeloproliferative neoplasms. Haematologica 2011, 96, 607–611. [Google Scholar] [CrossRef]
  36. Beer, P.A.; Campbell, P.J.; Scott, L.M.; Bench, A.J.; Erber, W.N.; Bareford, D.; Wilkins, B.S.; Reilly, J.T.; Hasselbalch, H.C.; Bowman, R.; et al. MPL mutations in myeloproliferative disorders: Analysis of the PT-1 cohort. Blood 2008, 112, 141–149. [Google Scholar] [CrossRef]
  37. Araki, M.; Yang, Y.; Masubuchi, N.; Hironaka, Y.; Takei, H.; Morishita, S.; Mizukami, Y.; Kan, S.; Shirane, S.; Edahiro, Y.; et al. Activation of the thrombopoietin receptor by mutant calreticulin in CALR-mutant myeloproliferative neoplasms. Blood 2016, 127, 1307–1316. [Google Scholar] [CrossRef] [PubMed]
  38. Cui, L.; Moraga, I.; Lerbs, T.; Van Neste, C.; Wilmes, S.; Tsutsumi, N.; Trotman-Grant, A.C.; Gakovic, M.; Andrews, S.; Gotlib, J.; et al. Tuning MPL signaling to influence hematopoietic stem cell differentiation and inhibit essential thrombocythemia progenitors. Proc. Natl. Acad. Sci. USA 2021, 118, e2017849118. [Google Scholar] [CrossRef] [PubMed]
  39. Yang, E.; Wang, M.; Wang, Z.; Li, Y.; Wang, X.; Ming, J.; Xiao, H.; Quan, R.; Liu, W.; Hu, X. Comparison of the effects between MPL and JAK2V617F on thrombosis and peripheral blood cell counts in patients with essential thrombocythemia: A meta-analysis. Ann. Hematol. 2021, 100, 2699–2706. [Google Scholar] [CrossRef]
  40. Furuya, C.; Hashimoto, Y.; Morishita, S.; Inano, T.; Ochiai, T.; Shirane, S.; Edahiro, Y.; Araki, M.; Ando, M.; Komatsu, N. MPL gene mutation is a possible risk factor for thrombosis in patients with essential thrombocythemia in Japan. Hematology 2023, 28, 2229131. [Google Scholar] [CrossRef]
  41. Araki, M.; Yang, Y.; Imai, M.; Mizukami, Y.; Kihara, Y.; Sunami, Y.; Masubuchi, N.; Edahiro, Y.; Hironaka, Y.; Osaga, S.; et al. Homomultimerization of mutant calreticulin is a prerequisite for MPL binding and activation. Leukemia 2019, 33, 122–131. [Google Scholar] [CrossRef]
  42. Masubuchi, N.; Araki, M.; Yang, Y.; Hayashi, E.; Imai, M.; Edahiro, Y.; Hironaka, Y.; Mizukami, Y.; Kihara, Y.; Takei, H.; et al. Mutant calreticulin interacts with MPL in the secretion pathway for activation on the cell surface. Leukemia 2020, 34, 499–509. [Google Scholar] [CrossRef]
  43. Rumi, E.; Pietra, D.; Pascutto, C.; Guglielmelli, P.; Martinez-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]
  44. Tefferi, A.; Lasho, T.L.; Tischer, A.; Wassie, E.A.; Finke, C.M.; Belachew, A.A.; Ketterling, R.P.; Hanson, C.A.; Pardanani, A.D. The prognostic advantage of calreticulin mutations in myelofibrosis might be confined to type 1 or type 1-like CALR variants. Blood 2014, 124, 2465–2466. [Google Scholar] [CrossRef] [PubMed]
  45. 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]
  46. Ma, W.; Kantarjian, H.; Zhang, X.; Yeh, C.H.; Zhang, Z.J.; Verstovsek, S.; Albitar, M. Mutation profile of JAK2 transcripts in patients with chronic myeloproliferative neoplasias. J. Mol. Diagn. 2009, 11, 49–53. [Google Scholar] [CrossRef] [PubMed]
  47. Cabagnols, X.; Favale, F.; Pasquier, F.; Messaoudi, K.; Defour, J.P.; Ianotto, J.C.; Marzac, C.; Le Couedic, J.P.; Droin, N.; Chachoua, I.; et al. Presence of atypical thrombopoietin receptor (MPL) mutations in triple-negative essential thrombocythemia patients. Blood 2016, 127, 333–342. [Google Scholar] [CrossRef] [PubMed]
  48. Mori, Y.; Araki, M.; Morishita, S.; Imai, M.; Edahiro, Y.; Ito, M.; Ochiai, T.; Shirane, S.; Hashimoto, Y.; Yasuda, H.; et al. Clinical features of acquired erythrocytosis: Low levels of serum erythropoietin in a subset of non-neoplastic erythrocytosis patients. Cancer Med. 2023, 12, 1079–1089. [Google Scholar] [CrossRef]
  49. Sampieri, L.; Di Giusto, P.; Alvarez, C. CREB3 Transcription Factors: ER-Golgi Stress Transducers as Hubs for Cellular Homeostasis. Front. Cell Dev. Biol. 2019, 7, 123. [Google Scholar] [CrossRef]
  50. Ibarra, J.; Elbanna, Y.A.; Kurylowicz, K.; Ciboddo, M.; Greenbaum, H.S.; Arellano, N.S.; Rodriguez, D.; Evers, M.; Bock-Hughes, A.; Liu, C.; et al. Type I but Not Type II Calreticulin Mutations Activate the IRE1alpha/XBP1 Pathway of the Unfolded Protein Response to Drive Myeloproliferative Neoplasms. Blood Cancer Discov. 2022, 3, 298–315. [Google Scholar] [CrossRef] [PubMed]
  51. Ward, A.K.; Mellor, P.; Smith, S.E.; Kendall, S.; Just, N.A.; Vizeacoumar, F.S.; Sarker, S.; Phillips, Z.; Alvi, R.; Saxena, A.; et al. Epigenetic silencing of CREB3L1 by DNA methylation is associated with high-grade metastatic breast cancers with poor prognosis and is prevalent in triple negative breast cancers. Breast Cancer Res. 2016, 18, 12. [Google Scholar] [CrossRef]
  52. Denard, B.; Jiang, S.; Peng, Y.; Ye, J. CREB3L1 as a potential biomarker predicting response of triple negative breast cancer to doxorubicin-based chemotherapy. BMC Cancer 2018, 18, 813. [Google Scholar] [CrossRef] [PubMed]
  53. Tefferi, A.; Guglielmelli, P.; Lasho, T.L.; Gangat, N.; Ketterling, R.P.; Pardanani, A.; Vannucchi, A.M. 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]
  54. Tefferi, A.; Guglielmelli, P.; Lasho, T.L.; Coltro, G.; Finke, C.M.; Loscocco, G.G.; Sordi, B.; Szuber, N.; Rotunno, G.; Pacilli, A.; et al. Mutation-enhanced international prognostic systems for essential thrombocythaemia and polycythaemia vera. Br. J. Haematol. 2020, 189, 291–302. [Google Scholar] [CrossRef]
  55. 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]
  56. Inano, T.; Araki, M.; Morishita, S.; Imai, M.; Yasuda, H.; Nitta, H.; Ito, M.; Edahiro, Y.; Ochiai, T.; Misawa, K.; et al. JAK2 exon 12 mutation in myelodysplastic/myeloproliferative neoplasm with ring sideroblasts and thrombocytosis: Not an exclusive mutation to polycythaemia vera. Br. J. Haematol. 2019, 187, e27–e31. [Google Scholar] [CrossRef] [PubMed]
  57. Tong, J.; Sun, T.; Ma, S.; Zhao, Y.; Ju, M.; Gao, Y.; Zhu, P.; Tan, P.; Fu, R.; Zhang, A.; et al. Hematopoietic Stem Cell Heterogeneity Is Linked to the Initiation and Therapeutic Response of Myeloproliferative Neoplasms. Cell Stem Cell 2021, 28, 502–513.e6. [Google Scholar] [CrossRef]
  58. Muzio, G.; O’Bray, L.; Meng-Papaxanthos, L.; Klatt, J.; Fischer, K.; Borgwardt, K. networkGWAS: A network-based approach to discover genetic associations. Bioinformatics 2023, 39, btad370. [Google Scholar] [CrossRef]
  59. La Manno, G.; Soldatov, R.; Zeisel, A.; Braun, E.; Hochgerner, H.; Petukhov, V.; Lidschreiber, K.; Kastriti, M.E.; Lonnerberg, P.; Furlan, A.; et al. RNA velocity of single cells. Nature 2018, 560, 494–498. [Google Scholar] [CrossRef]
  60. Lange, M.; Bergen, V.; Klein, M.; Setty, M.; Reuter, B.; Bakhti, M.; Lickert, H.; Ansari, M.; Schniering, J.; Schiller, H.B.; et al. CellRank for directed single-cell fate mapping. Nat. Methods 2022, 19, 159–170. [Google Scholar] [CrossRef] [PubMed]
  61. Kessler, M.D.; Damask, A.; O’Keeffe, S.; Banerjee, N.; Li, D.; Watanabe, K.; Marketta, A.; Van Meter, M.; Semrau, S.; Horowitz, J.; et al. Common and rare variant associations with clonal haematopoiesis phenotypes. Nature 2022, 612, 301–309. [Google Scholar] [CrossRef]
  62. Kar, S.P.; Quiros, P.M.; Gu, M.; Jiang, T.; Mitchell, J.; Langdon, R.; Iyer, V.; Barcena, C.; Vijayabaskar, M.S.; Fabre, M.A.; et al. Genome-wide analyses of 200,453 individuals yield new insights into the causes and consequences of clonal hematopoiesis. Nat. Genet. 2022, 54, 1155–1166. [Google Scholar] [CrossRef]
  63. Genovese, G.; Kahler, A.K.; Handsaker, R.E.; Lindberg, J.; Rose, S.A.; Bakhoum, S.F.; Chambert, K.; Mick, E.; Neale, B.M.; Fromer, M.; et al. Clonal hematopoiesis and blood-cancer risk inferred from blood DNA sequence. N. Engl. J. Med. 2014, 371, 2477–2487. [Google Scholar] [CrossRef] [PubMed]
  64. Jaiswal, S.; Fontanillas, P.; Flannick, J.; Manning, A.; Grauman, P.V.; Mar, B.G.; Lindsley, R.C.; Mermel, C.H.; Burtt, N.; Chavez, A.; et al. Age-related clonal hematopoiesis associated with adverse outcomes. N. Engl. J. Med. 2014, 371, 2488–2498. [Google Scholar] [CrossRef]
  65. Avagyan, S.; Henninger, J.E.; Mannherz, W.P.; Mistry, M.; Yoon, J.; Yang, S.; Weber, M.C.; Moore, J.L.; Zon, L.I. Resistance to inflammation underlies enhanced fitness in clonal hematopoiesis. Science 2021, 374, 768–772. [Google Scholar] [CrossRef] [PubMed]
  66. Nielsen, C.; Bojesen, S.E.; Nordestgaard, B.G.; Kofoed, K.F.; Birgens, H.S. JAK2V617F somatic mutation in the general population: Myeloproliferative neoplasm development and progression rate. Haematologica 2014, 99, 1448–1455. [Google Scholar] [CrossRef] [PubMed]
  67. Van Egeren, D.; Escabi, J.; Nguyen, M.; Liu, S.; Reilly, C.R.; Patel, S.; Kamaz, B.; Kalyva, M.; DeAngelo, D.J.; Galinsky, I.; et al. Reconstructing the Lineage Histories and Differentiation Trajectories of Individual Cancer Cells in Myeloproliferative Neoplasms. Cell Stem Cell 2021, 28, 514–523.e9. [Google Scholar] [CrossRef] [PubMed]
  68. Jaiswal, S.; Ebert, B.L. Clonal hematopoiesis in human aging and disease. Science 2019, 366, eaan4673. [Google Scholar] [CrossRef]
  69. Jones, A.V.; Chase, A.; Silver, R.T.; Oscier, D.; Zoi, K.; Wang, Y.L.; Cario, H.; Pahl, H.L.; Collins, A.; Reiter, A.; et al. JAK2 haplotype is a major risk factor for the development of myeloproliferative neoplasms. Nat. Genet. 2009, 41, 446–449. [Google Scholar] [CrossRef]
  70. Jager, R.; Harutyunyan, A.S.; Rumi, E.; Pietra, D.; Berg, T.; Olcaydu, D.; Houlston, R.S.; Cazzola, M.; Kralovics, R. Common germline variation at the TERT locus contributes to familial clustering of myeloproliferative neoplasms. Am. J. Hematol. 2014, 89, 1107–1110. [Google Scholar] [CrossRef]
  71. Tapper, W.; Jones, A.V.; Kralovics, R.; Harutyunyan, A.S.; Zoi, K.; Leung, W.; Godfrey, A.L.; Guglielmelli, P.; Callaway, A.; Ward, D.; et al. Genetic variation at MECOM, TERT, JAK2 and HBS1L-MYB predisposes to myeloproliferative neoplasms. Nat. Commun. 2015, 6, 6691. [Google Scholar] [CrossRef]
  72. Trifa, A.P.; Banescu, C.; Tevet, M.; Bojan, A.; Dima, D.; Urian, L.; Torok-Vistai, T.; Popov, V.M.; Zdrenghea, M.; Petrov, L.; et al. TERT rs2736100 A>C SNP and JAK2 46/1 haplotype significantly contribute to the occurrence of JAK2 V617F and CALR mutated myeloproliferative neoplasms—A multicentric study on 529 patients. Br. J. Haematol. 2016, 174, 218–226. [Google Scholar] [CrossRef] [PubMed]
  73. Harutyunyan, A.S.; Giambruno, R.; Krendl, C.; Stukalov, A.; Klampfl, T.; Berg, T.; Chen, D.; Milosevic Feenstra, J.D.; Jager, R.; Gisslinger, B.; et al. Germline RBBP6 mutations in familial myeloproliferative neoplasms. Blood 2016, 127, 362–365. [Google Scholar] [CrossRef] [PubMed]
  74. Rumi, E.; Harutyunyan, A.S.; Pietra, D.; Feenstra, J.D.; Cavalloni, C.; Roncoroni, E.; Casetti, I.; Bellini, M.; Milanesi, C.; Renna, M.C.; et al. LNK mutations in familial myeloproliferative neoplasms. Blood 2016, 128, 144–145. [Google Scholar] [CrossRef]
  75. Masselli, E.; Pozzi, G.; Carubbi, C.; Vitale, M. The Genetic Makeup of Myeloproliferative Neoplasms: Role of Germline Variants in Defining Disease Risk, Phenotypic Diversity and Outcome. Cells 2021, 10, 2597. [Google Scholar] [CrossRef] [PubMed]
  76. Poletto, V.; Rosti, V.; Villani, L.; Catarsi, P.; Carolei, A.; Campanelli, R.; Massa, M.; Martinetti, M.; Viarengo, G.; Malovini, A.; et al. A3669G polymorphism of glucocorticoid receptor is a susceptibility allele for primary myelofibrosis and contributes to phenotypic diversity and blast transformation. Blood 2012, 120, 3112–3117. [Google Scholar] [CrossRef]
  77. Hodeib, H.; Hai, D.A.; Tawfik, M.A.; Allam, A.A.; Selim, A.; Elsawy, A.A.; Youssef, A. CCL2 rs1024611Gene Polymorphism in Philadelphia-Negative Myeloproliferative Neoplasms. Genes 2022, 13, 492. [Google Scholar] [CrossRef]
  78. Ferrer-Marin, F.; Arroyo, A.B.; Bellosillo, B.; Cuenca, E.J.; Zamora, L.; Hernandez-Rivas, J.M.; Hernandez-Boluda, J.C.; Fernandez-Rodriguez, C.; Luno, E.; Hernandez, C.G.; et al. miR-146a rs2431697 identifies myeloproliferative neoplasm patients with higher secondary myelofibrosis progression risk. Leukemia 2020, 34, 2648–2659. [Google Scholar] [CrossRef] [PubMed]
  79. Lindgren, M.; Samuelsson, J.; Nilsson, L.; Knutsen, H.; Ghanima, W.; Westin, J.; Johansson, P.L.; Andreasson, B. Genetic variation in IL28B (IFNL3) and response to interferon-alpha treatment in myeloproliferative neoplasms. Eur. J. Haematol. 2018, 100, 419–425. [Google Scholar] [CrossRef]
  80. 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]
Ijms 24 13008 g001
Figure 1. Schematical illustration describing the subtypes of myeloproliferative neoplasms (MPNs). Cell types are depicted as light gray squares. MPP: multipotent progenitor; CMP: common myeloid progenitor; GMP: granulocyte-monocyte progenitor; MEP: megakaryocyte–erythrocyte progenitor. Subtypes of MPNs are depicted as white rounded rectangles. Chronic myeloid leukemia (CML) exhibits BCR::ABL1 gene (BCR::ABL1(+)). Other subtypes are stratified as BCR::ABL1-negative (−) MPNs. CNL: chronic neutrophilic leukemia; CEL: chronic eosinophilic leukemia; PV: polycythemia vera; ET: essential thrombocythemia; PMF: primary myelofibrosis.
Figure 1. Schematical illustration describing the subtypes of myeloproliferative neoplasms (MPNs). Cell types are depicted as light gray squares. MPP: multipotent progenitor; CMP: common myeloid progenitor; GMP: granulocyte-monocyte progenitor; MEP: megakaryocyte–erythrocyte progenitor. Subtypes of MPNs are depicted as white rounded rectangles. Chronic myeloid leukemia (CML) exhibits BCR::ABL1 gene (BCR::ABL1(+)). Other subtypes are stratified as BCR::ABL1-negative (−) MPNs. CNL: chronic neutrophilic leukemia; CEL: chronic eosinophilic leukemia; PV: polycythemia vera; ET: essential thrombocythemia; PMF: primary myelofibrosis.
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Figure 2. (A): Distribution of driver gene mutations for BCR::ABL1-negative MPNs in our Japanese cohort. Triple-negative: TN. (B): Driver gene mutations in BCR::ABL1-negative MPNs. Cell proliferation signal is regulated through the binding of cytokines (erythropoietin: EPO or thrombopoietin: TPO) to the receptors (erythropoietin receptor: EPOR or thrombopoietin receptor: MPL, left panel). However, in the driver gene-mutated BCR::ABL1-negative MPNs, downstream signal cascades constitutively activate owing to the mutant proteins without the binding of cytokines. JAK2 exon 12 mutations activate strong signals, especially for erythrocytosis, whereas JAK2V617F activates trilineage signals (erythrocytosis, leukocytosis, and thrombocytosis). MPLW515L/K and mutant CALR activate MPL signaling and trigger thrombocytosis (right panels). (C): Typical nondriver gene transcripts identified in BCR::ABL1-negative MPNs. Mutations that occur in these genes disrupt the gene regulations and my affect the prognosis of BCR::ABL1-negative MPNs. IDH1/2 generates a-ketoglutaric acids (a-KG) from isocitrate. Mutant IDH1/2 generates 2-hydroxyglutaric acid (2HG) from a-KG, resulting in the suppression of TET2. DNMT3A and TET2 act as DNA methylation and demethylation enzymes by methylating cytosine to 5-methylcytosine (5mC) and oxidizing 5mC to 5-hydroxymethylcytosine (5hmC), respectively. ASXL1 and EZH2 regulate the transcription through the trimethylation of histone 3 lysine 27. SF3B1, SRSF2, and U2AF1 function as splicing factors. TP53 is a well-known guardian gene of carcinogenesis.
Figure 2. (A): Distribution of driver gene mutations for BCR::ABL1-negative MPNs in our Japanese cohort. Triple-negative: TN. (B): Driver gene mutations in BCR::ABL1-negative MPNs. Cell proliferation signal is regulated through the binding of cytokines (erythropoietin: EPO or thrombopoietin: TPO) to the receptors (erythropoietin receptor: EPOR or thrombopoietin receptor: MPL, left panel). However, in the driver gene-mutated BCR::ABL1-negative MPNs, downstream signal cascades constitutively activate owing to the mutant proteins without the binding of cytokines. JAK2 exon 12 mutations activate strong signals, especially for erythrocytosis, whereas JAK2V617F activates trilineage signals (erythrocytosis, leukocytosis, and thrombocytosis). MPLW515L/K and mutant CALR activate MPL signaling and trigger thrombocytosis (right panels). (C): Typical nondriver gene transcripts identified in BCR::ABL1-negative MPNs. Mutations that occur in these genes disrupt the gene regulations and my affect the prognosis of BCR::ABL1-negative MPNs. IDH1/2 generates a-ketoglutaric acids (a-KG) from isocitrate. Mutant IDH1/2 generates 2-hydroxyglutaric acid (2HG) from a-KG, resulting in the suppression of TET2. DNMT3A and TET2 act as DNA methylation and demethylation enzymes by methylating cytosine to 5-methylcytosine (5mC) and oxidizing 5mC to 5-hydroxymethylcytosine (5hmC), respectively. ASXL1 and EZH2 regulate the transcription through the trimethylation of histone 3 lysine 27. SF3B1, SRSF2, and U2AF1 function as splicing factors. TP53 is a well-known guardian gene of carcinogenesis.
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Figure 3. (A): Scatter plot showing the relationship between JAK2V617F allele burden measured using massively parallel sequencing (MiSeq, x-axis) and those measured using ABC-PCR (y-axis). The correlation coefficient of R2 was calculated as 0.991; (B): box plot showing the JAK2V617F allele burden among patients with PV (red), ET (green), prefibrotic PMF (blue), and overt PMF (purple) harboring the JAK2V617F mutation (>1.0%). The median/mean JAK2V617F allele burden is 72.3/66.8% in PV, 27.3/30.2% in ET, 46.3/50.2% in prefibrotic PMF, and 43.7/49.3% in overt PMF.
Figure 3. (A): Scatter plot showing the relationship between JAK2V617F allele burden measured using massively parallel sequencing (MiSeq, x-axis) and those measured using ABC-PCR (y-axis). The correlation coefficient of R2 was calculated as 0.991; (B): box plot showing the JAK2V617F allele burden among patients with PV (red), ET (green), prefibrotic PMF (blue), and overt PMF (purple) harboring the JAK2V617F mutation (>1.0%). The median/mean JAK2V617F allele burden is 72.3/66.8% in PV, 27.3/30.2% in ET, 46.3/50.2% in prefibrotic PMF, and 43.7/49.3% in overt PMF.
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Figure 4. Distribution of CALR frameshift mutations analyzed in our data. ET (left, n = 179), prefibrotic PMF (middle, n = 13), and overt PMF (right, n = 31).
Figure 4. Distribution of CALR frameshift mutations analyzed in our data. ET (left, n = 179), prefibrotic PMF (middle, n = 13), and overt PMF (right, n = 31).
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Figure 5. Violin plot showing the expression levels of CREB3L1 measured using reverse transcription quantitative PCR. Dots represent the CREB3L1 levels of the individuals, which are expressed as the value relative to the mean expression levels among healthy volunteers. B2M was used as an internal control.
Figure 5. Violin plot showing the expression levels of CREB3L1 measured using reverse transcription quantitative PCR. Dots represent the CREB3L1 levels of the individuals, which are expressed as the value relative to the mean expression levels among healthy volunteers. B2M was used as an internal control.
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Morishita, S.; Komatsu, N. Diagnosis- and Prognosis-Related Gene Alterations in BCR::ABL1-Negative Myeloproliferative Neoplasms. Int. J. Mol. Sci. 2023, 24, 13008. https://doi.org/10.3390/ijms241613008

AMA Style

Morishita S, Komatsu N. Diagnosis- and Prognosis-Related Gene Alterations in BCR::ABL1-Negative Myeloproliferative Neoplasms. International Journal of Molecular Sciences. 2023; 24(16):13008. https://doi.org/10.3390/ijms241613008

Chicago/Turabian Style

Morishita, Soji, and Norio Komatsu. 2023. "Diagnosis- and Prognosis-Related Gene Alterations in BCR::ABL1-Negative Myeloproliferative Neoplasms" International Journal of Molecular Sciences 24, no. 16: 13008. https://doi.org/10.3390/ijms241613008

APA Style

Morishita, S., & Komatsu, N. (2023). Diagnosis- and Prognosis-Related Gene Alterations in BCR::ABL1-Negative Myeloproliferative Neoplasms. International Journal of Molecular Sciences, 24(16), 13008. https://doi.org/10.3390/ijms241613008

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