Encyclopedia
1000/1000
Hot
Most Recent
Submitted Successfully!
To reward your contribution, here is a gift for you: A free trial for our video production service.
+1 point
Thank you for your contribution! You can also upload a video entry or images related to this topic.
Video Upload Options
Do you have a full video?
Confirm
Are you sure to Delete?
Cite
If you have any further questions, please contact Encyclopedia Editorial Office.
Fiorentino, V.; Tralongo, P.; Martini, M.; Betti, S.; Rossi, E.; Pierconti, F.; Stefano, V.D.; Larocca, L.M. Models of Philadelphia-negative Chronic Myeloproliferative Neoplasms. Encyclopedia. Available online: https://encyclopedia.pub/entry/25730 (accessed on 05 October 2024).
Fiorentino V, Tralongo P, Martini M, Betti S, Rossi E, Pierconti F, et al. Models of Philadelphia-negative Chronic Myeloproliferative Neoplasms. Encyclopedia. Available at: https://encyclopedia.pub/entry/25730. Accessed October 05, 2024.
Fiorentino, Vincenzo, Pietro Tralongo, Maurizio Martini, Silvia Betti, Elena Rossi, Francesco Pierconti, Valerio De Stefano, Luigi Maria Larocca. "Models of Philadelphia-negative Chronic Myeloproliferative Neoplasms" Encyclopedia, https://encyclopedia.pub/entry/25730 (accessed October 05, 2024).
Fiorentino, V., Tralongo, P., Martini, M., Betti, S., Rossi, E., Pierconti, F., Stefano, V.D., & Larocca, L.M. (2022, August 01). Models of Philadelphia-negative Chronic Myeloproliferative Neoplasms. In Encyclopedia. https://encyclopedia.pub/entry/25730
Fiorentino, Vincenzo, et al. "Models of Philadelphia-negative Chronic Myeloproliferative Neoplasms." Encyclopedia. Web. 01 August, 2022.
Copy Citation
Philadelphia-negative chronic myeloproliferative neoplasms (MPNs) represent a group of hematological disorders that are traditionally considered as indistinct slow progressing conditions. Many of the discoveries on the pathogenesis of MPNs are due to in vivo and in vitro models that have made it possible to reproduce this type of pathology more and more faithfully.
myeloproliferative neoplasms
polycythemia vera
idiopathic myelofibrosis
1. Introduction
The term Philadelphia-negative chronic myeloproliferative neoplasms (MPNs) refer to a heterogeneous group of hematological disorders which originate from the neoplastic transformation of a pluripotent stem cell and are associated with myeloproliferation, extramedullary hematopoiesis, splenomegaly and, in due course, bone marrow fibrosis (MF). According to the WHO 2016 classification, MPNs can be divided into Polycythemia Vera (PV), Essential Trombocythemia (ET) and idiopathic (primary) Myelofibrosis (IMF) in the prefibrotic and overt form [1]. For over two decades, MPNs have been considered as indistinctly slow-progressing conditions [2][3]. However, recent clinical evidence highlighted a subset of cases [4] with a rapid evolution towards myelofibrotic bone marrow failure, placing interest in developing personalized prognosticators and timely therapeutic strategies against this evolution, correlating latest advances in MPNs’ molecular profiling with differences in clinical outcomes [5][6].
In fact, alongside MPNs’ driver gene mutations (JAK2, CALR, MPL), molecular profiling identified other gene mutations, involving for example DNA methylation (TET2, DNMT3A, IDH1/2), histone modification (ASXL1, EZH2), RNA splicing (U2AF1, SRSF2, SF3B1), DNA repair (TP53) and signal transduction (NRAS, CBL). These mutations can coexist with or without driver gene mutations, affecting the evolution and prognosis of MPNs [5][6].
On this basis, different groups developed several prognostic scores, mainly based on clinical, laboratory and molecular parameters, with less emphasis on morphological and immunophenotypic data [7]. Given the improvements and advances in MPN molecular profiling, the newer models included JAK2, CALR and MPL mutation status in addition to the IPSS parameters, so that the prognostic prediction in IMF patients can be improved [4]. Furthermore, novel insights were supported by a deep analysis of genomic subsets with different clinical prognoses [5]. Recent publications have introduced new prognostic models for PMF, respectively MIPSS70 (mutation-enhanced international prognostic scoring system for transplant-age patients) [6], MIPSS70+ version 2.0 (karyotype-enhanced MIPSS70) and GIPSS (genetically-inspired prognostic scoring system) [8][9]. As the previous models, other ones have been recently introduced for both ET and PV, namely MIPSS-ET and MIPSS-PV, underlining the prognostic importance of spliceosome gene mutations [10].
In opposition to this, all these predictive models do not consider other parameters as morphological or phenotypical features, with the exception of BM fibrosis grade in the MIPPS70 model (Table 1).
Table 1. List of prognostic scores of MPNs from the oldest to the most recent ones and with their respective genetic and/or clinical variables, the subclassification in risk groups and the respective median survival.
| Prognostic Model and Risk Factors (Weight) | Risk Groups and Median Survival | |
|---|---|---|
| IPSS | ||
| Hemoglobin < 10 g/dL (1 point) | Low risk: 0 point (135 months) | |
| Leukocytes > 25 × 109/L (1 point) | Intermediate risk-1:1 point (95 months) | |
| Age > 65 years (1 point) | Intermediate risk-2:2 points (48 months) | |
| Circulating blast ≥ 1% (1 point) | High risk: ≥3 points (27 months) | |
| Constitutional symptoms (1 point) | ||
| DIPSS. Same variables as IPSS, apart from: | ||
| Hemoglobin < 10 g/dL (2 points) | ||
| Low risk: 0 point (not reached) | ||
| Intermediate risk-1:1–2 points (14.2 yrs) | ||
| Intermediate risk-2:3–4 points (4 yrs) | ||
| High risk: 5–6 points (1.5 yrs) | ||
| DIPSS+. Same variables of DIPSS, apart from: | ||
| Unfavorable karyotype (1 point) | Low risk: 0 point (185 months) | |
| Red cell transfusion need (1 point) | Intermediate risk-1:1 point (78 months) | |
| Hemoglobin < 10 g/dL (1 point) | Intermediate risk-2:2–3 points (35 months) | |
| Platelet < 100 × 109/L (1 point) | High risk: 4–6 points (16 months) | |
| Prognostic model and risk factors (weight) | Risk groups and median survival | |
| MIPSS70. Same variables as DIPSS+, apart from: | ||
| Genetic variables | Clinical variables | |
| One high molecular risk (HMR) mutation (1 point) | Marrow fibrosis grade ≥ 2 (1 point) | Low risk: 0–1 point (not reached) |
| ≥2 HMR mutations (2 points) | Leukocytes > 25 × 109/L (2 points) | Intermediate risk: 2–4 (6.3 yr) |
| Type 1/like CALR absent (1 point) | Platelet < 100 × 109/L (2 points) | High risk: ≥5 (3.1 yr) |
| Circulating blast ≥ 2% (1 point) | ||
| MIPSS70+ version 2.0 | ||
| Genetic variables | Clinical variables | |
| VHR karyotype (4 points) | Severe anemia (2 points) | Very low risk: 0 point (not reached) |
| Unfavorable karyotype (3 points) | Moderate anemia (1 point) | Low risk: 1–2 (16.4 yr) |
| ≥2 HMR mutations (3 points) | Circulating blasts ≥ 2% (1 point) | Intermediate-1 risk: 3–4 (7.7 yr) |
| One HMR mutation (2 points) | Constitutional symptoms (2 points) | High risk: 5–8 (4.1 yr) |
| Type 1/like CALR absent (2 points) | Very high risk: ≥9 (1.8 yr) | |
| GIPSS. Based on a genetic-only risk factors model. | ||
| VHR karyotype (2 points) | Low risk: 0 point (26.4 yr) | |
| Unfavorable karyotype (1 point) | Intermediate-1 risk: 1 point (8 yr) | |
| Type 1/like CALR absent (1 point) | Intermediate-2 risk: 2 points (4.2 yr) | |
| ASXL1 mutation (1 point) | High risk: ≥3 points (2 yr) | |
| SRSF2 mutation (1 point) | ||
| U2AF1Q157 mutation (1 point) | ||
2. MPNs’ Molecular Landscape, In Vivo and In Vitro Models and Possible Novel Therapeutic Strategies
Many of the discoveries on the pathogenesis of MPNs are due to in vivo and in vitro models that have made it possible to reproduce this type of pathology more and more faithfully. There are several animal models of myeloproliferative neoplasms, used to investigate the role of mutations in the development of MPNs, or the impact of additional factors in MPN phenotype modulation.
3. GATA-1 Low Models
The thrombopoietin-treated (TPO-high) model and the GATA-1 low model are two murine models of MPN used to evaluate the megakaryocyte lineage in the MPNs pathogenesis and evolution [11][14]. The first model develops a myeloproliferative disorder mimicking human myelofibrosis, characterized by leukocytosis, anemia, thrombocytosis, splenomegaly, extramedullary hematopoiesis, fibrosis and osteosclerosis. This model is very useful for evaluating some pathogenetic mechanisms associated with fibrosis development, such as the role of transforming growth factor-beta1 (TGF-β1). The second model consists of the virtual abolishment of GATA-1 expression in megakaryocytes, while the protein continues to be expressed in erythroid cells, although at significantly lower levels [14]. These mice develop a progressive myeloproliferative disorder that has many features of myelofibrosis after 1 year of life and reduced levels of GATA-1 have also been demonstrated in the megakaryocytes of patients with IMF [14][15]. Moreover, mice with a MK-specific deficiency of the transcription factor-encoding gene GATA1 show elevated numbers of immature MK in the BM.
4. Lysyl Oxidases Models
It was also demonstrated that GATA1 (low) mutation was associated with low ploidy megakaryocytes with an extensive matrix of fibers due to the overexpression of lysis oxidase (LOX) [16]. Lysyl oxidases (LOXs) have been demonstrated to be important in this process by cross-linking collagens and elastins through deamination of lysins and hydroxylysins, resulting in a stiffer extracellular matrix (ECM) consistency [17]. Lysyl oxidases are expressed in immature megakaryocytes and downregulated in mature megakaryocytes, but upregulated in MF patient megakaryocytes and in murine models of MF [16][18]. Lysyl oxidase inhibition has shown efficacy in Gata1low and JAK2V617F mouse models of MF [19][20]. However, a novel phase 2 study of simtuzumab, a monoclonal inhibitor of LOX2, did not reduce bone fibrosis in patients with MF [21]. It was also demonstrated that the inhibition of LOX via the administration of β-aminopropionitrile could stop the progression of the myelofibrosis [22]. This last model is particularly used as a preclinical model for drug testing.
References
- 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.
- Murphy, S. Diagnostic criteria and prognosis in polycythemia vera and essential thrombocythemia. Semin. Hematol. 1999, 36, 9–13.
- Georgii, A.; Buesche, G.; Kreft, A. The histopathology of chronic myeloproliferative diseases. Baillieres Clin. Haematol. 1998, 11, 721–749.
- Rumi, E.; Cazzola, M. Diagnosis, risk stratification, and response evaluation in classical myeloproliferative neoplasms. Blood 2017, 129, 680–692.
- 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.
- 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.
- Schino, M.; Fiorentino, V.; Rossi, E.; Betti, S.; Di Cecca, M.; Ranucci, V.; Chiusolo, P.; Martini, M.; De Stefano, V.; Larocca, L.M. Bone marrow megakaryocytic activation predicts fibrotic evolution of Philadelphia-negative myeloproliferative neoplasms. Hematologica 2021, 106, 3162.
- 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.
- 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.
- 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.
- Abbonante, V.; Di Buduo, C.A.; Gruppi, C.; Malara, A.; Gianelli, U.; Celesti, G.; Anselmo, A.; Laghi, L.; Vercellino, M.; Visai, L.; et al. Thrombopoietin/TGF-b1 Loop Regulates Megakaryocyte extracellular Matrix Component Synthesis. Stem Cells 2016, 34, 1123–1133.
- Woods, B.; Chen, W.; Chiu, S.; Marinaccio, C.; Fu, C.; Gu, L.; Bulic, M.; Yang, Q.; Zouak, A.; Jia, S.; et al. Activation of JAK/STAT signaling in megakaryocytes sustains myeloproliferation in vivo. Clin. Cancer Res. 2019, 25, 5901–5912.
- Ciurea, S.O.; Merchant, D.; Mahmud, N.; Ishii, T.; Zhao, Y.; Hu, W.; Bruno, E.; Barosi, G.; Xu, M.; Hoffman, R. Pivotal contributions of megakaryocytes to the biology of idiopathic myelofibrosis. Blood 2007, 110, 986–993.
- Jacquelin, S.; Kramer, F.; Mullally, A.; Lane, S.W. Murine Models of Myelofibrosis. Cancers 2020, 12, 2381.
- Centurione, L.; Di Baldassarre, A.; Zingariello, M.; Bosco, D.; Gatta, V.; Rana, R.A.; Langella, V.; Di Virgilio, A.; Vannucchi, A.M.; Migliaccio, A.R. Increased and pathologic emperipolesis of neutrophils within megakaryocytes associated with marrow fibrosis in GATA-1 (low) mice. Blood 2004, 104, 3573–3580.
- Abbonante, V.; Chitalia, V.; Rosti, V.; Leiva, O.; Matsuura, S.; Balduini, A.; Ravid, K. Upregulation of lysyl oxidase and adhesion to collagen of human megakaryocytes and platelets in primary myelofibrosis. Blood 2017, 130, 829–831.
- Lucero, H.A.; Kagan, H.M. Lysyl oxidase: An oxidative enzyme and effector of cell function. Cell Mol. Life Sci. 2006, 63, 2304–2316.
- Tadmor, T.; Bejar, J.; Attias, D.; Mischenko, E.; Sabo, E.; Neufeld, G.; Vadasz, Z. The expression of lysyl-oxidase gene family members in myeloproliferative neoplasms. Am. J. Hematol. 2013, 88, 355–358.
- Schilter, H.; Findlay, A.D.; Perryman, L.; Yow, T.T.; Moses, J.; Zahoor, A.; Turner, C.I.; Deodhar, M.; Foot, J.S.; Zhou, W.; et al. The lysyl oxidase like 2/3 enzymatic inhibitor, PXS-5153A, reduces crosslinks and ameliorates fibrosis. J. Cell Mol. Med. 2018, 23, 1759–1770.
- Leiva, O.; Ng, S.K.; Matsuura, S.; Chitalia, V.; Lucero, H.; Findlay, A.; Turner, C.; Jarolimek, W.; Ravid, K. Novel lysyl oxidase inhibitors attenuate hallmarks of primary myelofibrosis in mice. Int. J. Hematol. 2019, 110, 699–708.
- Verstovsek, S.; Savona, M.R.; Mesa, R.A.; Dong, H.; Maltzman, J.D.; Sharma, S.; Silverman, J.; Oh, S.T.; Gotlib, J. A phase 2 study of simtuzumab in patients with primary, post-polycythaemia vera or post-essential thrombocythaemia myelofibrosis. Brit. J. Haematol. 2017, 176, 939–949.
- Papadantonakis, N.; Matsuura, S.; Ravid, K. Megakaryocyte pathology and bone marrow fibrosis: The lysyl oxidase connection. Blood 2012, 120, 1774–1781.
More
Information
Subjects:
Hematology
Contributors
MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register
:
View Times:
428
Update Date:
03 Aug 2022
Table of Contents
