Next Article in Journal
Virtual Biopsy for Diagnosis of Chemotherapy-Associated Liver Injuries and Steatohepatitis: A Combined Radiomic and Clinical Model in Patients with Colorectal Liver Metastases
Next Article in Special Issue
T-Cell Lymphoblastic Lymphoma Arising in the Setting of Myeloid/Lymphoid Neoplasms with Eosinophilia: LMO2 Immunohistochemistry as a Potentially Useful Diagnostic Marker
Previous Article in Journal
The Role of Extracellular Vesicles in Disease Progression and Detection of Hepatocellular Carcinoma
Previous Article in Special Issue
Crossing the Borders: An Integrated Approach to Myeloproliferative Neoplasms and Mastocytoses
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Cancers
Volume 13
Issue 12
10.3390/cancers13123078
cancers-logo
Submit to this Journal Review for this Journal Edit a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Systematic Review

The Prevalence of TET2 Gene Mutations in Patients with BCR-ABL-Negative Myeloproliferative Neoplasms (MPN): A Systematic Review and Meta-Analysis

by
Yuh Cai Chia
1,
Md Asiful Islam
1,*,
Phil Hider
2,
Peng Yeong Woon
3,
Muhammad Farid Johan
1,
Rosline Hassan
1 and
Marini Ramli
1,*
1
Department of Haematology, School of Medical Sciences, Universiti Sains Malaysia, Kubang Kerian 16150, Kelantan, Malaysia
2
Department of Population Health, University of Otago, Christchurch 8140, New Zealand
3
Department of Molecular Biology and Human Genetics, Tzu Chi University, Hualien 97004, Taiwan
*
Authors to whom correspondence should be addressed.
Cancers 2021, 13(12), 3078; https://doi.org/10.3390/cancers13123078
Received: 18 April 2021 / Revised: 13 June 2021 / Accepted: 17 June 2021 / Published: 20 June 2021
(This article belongs to the Special Issue Pathogenesis and Natural History of Myeloproliferative Neoplasms)
Browse Figures
Versions Notes

Abstract

:

Simple Summary

Many molecular biology techniques have been widely used to study the pathogenesis of different diseases, particularly haematologic malignancies which are generally caused by abnormalities in the genome. TET2 gene is one of the commonly found mutated genes in BCR-ABL-negative myeloproliferative neoplasms. However, the prevalence of TET2 gene mutations in the disease remains unclear. Therefore, this study aims to estimate the prevalence of TET2 gene mutations in myeloproliferative neoplasms. The findings may be helpful for future research, diagnoses and the identification of better therapeutic strategies to manage the diseases.

Abstract

Multiple recurrent somatic mutations have recently been identified in association with myeloproliferative neoplasms (MPN). This meta-analysis aims to assess the pooled prevalence of TET2 gene mutations among patients with MPN. Six databases (PubMed, Scopus, ScienceDirect, Google Scholar, Web of Science and Embase) were searched for relevant studies from inception till September 2020, without language restrictions. The eligibility criteria included BCR-ABL-negative MPN adults with TET2 gene mutations. A random-effects model was used to estimate the pooled prevalence with 95% confidence intervals (CIs). Subgroup analyses explored results among different continents and countries, WHO diagnostic criteria, screening methods and types of MF. Quality assessment was undertaken using the Joanna Briggs Institute critical appraisal tool. The study was registered with PROSPERO (CRD42020212223). Thirty-five studies were included (n = 5121, 47.1% female). Overall, the pooled prevalence of TET2 gene mutations in MPN patients was 15.5% (95% CI: 12.1–19.0%, I2 = 94%). Regional differences explained a substantial amount of heterogeneity. The prevalence of TET2 gene mutations among the three subtypes PV, ET and MF were 16.8%, 9.8% and 15.7%, respectively. The quality of the included studies was determined to be moderate–high among 83% of the included studies. Among patients with BCR-ABL-negative MPN, the overall prevalence of TET2 gene mutations was 15.5%.

1. Introduction

Myeloproliferative neoplasms (MPN) are a group of rare blood cancers characterised by the clonal expansion of a large number of abnormal haematopoietic stem cells. Classic Philadelphia-negative (BCR-ABL-negative) MPN can be divided into three categories: (i) polycythaemia vera (PV), (ii) essential thrombocythaemia (ET) and (iii) primary myelofibrosis (PMF). MPN can transform into acute myeloid leukaemia (AML) and may be associated with an elevated risk of thrombotic and haemorrhagic events [1,2]. Thrombosis and haemorrhage are the major causes of mortality and morbidity amongst patients with MPN and occur in about 34–39% of cases with PV, 10–29% with ET and 7.2–13.2% of patients with PMF [3].
Three main driver gene mutations, Janus kinase 2 (JAK2), Thrombopoietin receptor (MPL) and Calreticulin (CALR), have been identified in association with MPN and may have an important role in assisting the diagnosis of MPN [4]. In addition, epigenetic modification genes such as TET2, ASXL1, DNMT3A and EZH2 are also commonly mutated in cases of MPN with a frequency of 1–30% [5,6,7,8].
TET2 participates in one of the crucial steps in gene regulation, and mutations in this gene have been identified in 5–20% of people diagnosed with MPN [9]. Somatic missense mutations, somatic nonsense mutations and insertion–deletion mutations are detected in the TET2 gene among MPN patients. All of these mutations are loss-of-function mutations. Malfunction of TET2 protein may lead to the development of MPN and contributes to the disease progression [10,11]. However, some disagreement still exists about the relative significance of these TET2 gene mutations to MPN. Some researchers suggest that TET2 gene mutations are not important for MPN [12,13], whereas others have concluded that these mutations significantly contribute to their phenotype [14,15].
The prevalence of TET2 gene mutations among MPN has not yet been established. This meta-analysis aims to estimate the prevalence of TET2 gene mutations among all BCR-ABL-negative MPN and its three main subtypes.

2. Materials and Methods

PRISMA guidelines [16] were followed, and a study protocol was registered at the International Prospective Register of Systematic Reviews (PROSPERO), registration number CRD42020212223.

2.1. Data Sources and Searches

PubMed, Scopus, ScienceDirect, Google Scholar, Web of Science and Embase databases were searched from their inception till September 2020, without any language restrictions. Detailed search strategies are presented in Table S1. Any published studies or preprints with relevant data were included. Review articles, case reports and opinion articles were excluded. Data presented on websites or reported by press releases and news reports were not considered. Snowball searching was employed to review the references of included studies. Endnote X8 software was used to remove duplicate studies.

2.2. Study Selection

Study eligibility was determined by screening the title and abstract of the articles of interest. Two authors (Y.C.C. and M.A.I.) independently examined full-text reports of potentially relevant studies for inclusion. Any disagreements were resolved by consensus.

2.3. Extraction of Data

Data were independently extracted by two authors (Y.C.C. and M.A.I). The following data were obtained from each eligible study and inserted into a customised Excel spreadsheet: author surname, publication year, study design, study location, type of MPN, number of patients with MPN, demographic characteristics of patients including age and sex, clinical characteristics of the MPN patients including haemoglobin level, leucocyte and platelet counts, the total number of mutated ASXL1 and the screening method used to identify TET2 gene mutations and diagnostic criteria employed for MPN diagnoses.

2.4. Quality Assessment

A random-effects model was used to estimate the pooled prevalence of the TET2 gene mutations amongst patients with MPN, including 95% confidence intervals (Cis). Two authors (Y.C.C. and M.A.I.) independently assessed the quality of included studies using the Joanna Briggs Institute critical appraisal tools [13]. Study quality was categorised into three groups: low-quality or high risk of bias, moderate quality or moderate risk of bias, and high-quality or low risk of bias with overall scores of <50%, 50–69% and ≥70%, respectively [17].

2.5. Publication Bias

Funnel plots presenting estimates of prevalence plotted against standard error measures were used to assess the likelihood of publication bias. When a minimum of 10 studies were available, an Egger’s test was conducted to assess publication bias based on funnel plot asymmetry.

2.6. Data Synthesis and Sensitivity Analysis

The I2 statistic was used to gauge the heterogeneity between studies, with I2 > 75% indicating substantial heterogeneity. The statistical significance of study heterogeneity was also assessed using Cochran’s Q test; p < 0.05 was considered statistically heterogeneous. To help identify the outlier studies and the sources of heterogeneity, a Galbraith plot was constructed. Prevalence estimates were explored with sensitivity analyses. Three strategies were followed for these analyses: (i) studies with small sample sizes (<100) were excluded, (ii) low-quality studies were excluded and (iii) outlier studies were excluded. In each case, the results were then compared to the overall prevalence estimate. Metaprop codes in meta (version 4.15-1) and metaphor (version 2.4-0) packages of R (version 3.6.3) and RStudio (version 1.3.1093) were used for the analyses and graphs [18].

3. Results

3.1. Study Selection

The search generated 758 potentially relevant studies. After excluding 558 studies (duplicates n = 450; review articles n = 67; non-human studies n = 31; and case reports, n = 10), 200 full-text studies were examined and 35 studies met the inclusion criteria and were included in the review (Figure 1).

3.2. Characteristics of Included Studies

Table 1 presents the main characteristics of the 35 included studies. Overall, the meta-analysis includes data from 5121 patients with MPN (47.1% female). Study participants were located in four continents: Europe (n = 1758), Asia (n = 301), North America (n = 3019) and Australia (n = 43), and 12 countries (Australia, China, Denmark, France, Germany, Italy, Korea, Spain, Sweden, Switzerland, the United Kingdom and the United States of America). Most (27/35) studies used a version of the World Health Organization classification and diagnostic criteria (WHO 2016 7 studies, WHO 2008 17 studies and WHO 2001 3 studies) to determine MPN diagnoses. Many studies confirmed TET2 gene mutations with either next-generation sequencing (NGS) or Sanger sequencing, which have higher sensitivity in detecting mutations compared with other methods, such as high-resolution melting (HRM) analysis [19]. One study was published in Chinese Mandarin and was translated into English (Y.C.C.).

3.3. Meta-Analysis

The overall pooled prevalence of TET2 gene mutations in patients with MPN was 15.5% (95% CI: 12.1–19.0%, I2 = 94%, Figure 2A). The prevalence of TET2 gene mutations in PV, ET and MF patients was 16.8% (95% CI: 13.2–20.5%, I2 = 60%, Figure 2B), 9.8% (95% CI: 7.0–12.7%, I2 = 62%, Figure 2C) and 15.7% (95% CI: 11.2–20.2%, I2 = 89%, Figure 2D), respectively. In other subgroup analyses, the pooled prevalence of TET2 gene mutations was compared between four continents: Europe (13.0%; 95% CI: 8.8–17.2%, I2 = 92%), North America (17.4%; 95% CI: 14.0–20.9%, I2 = 74%), Asia (20.8%; 95% CI: 10.5–31.1%, I2 = 80%) and Australia (7.0%; 95% CI: 0.0–14.6%, I2 = NA). The prevalence of TET2 gene mutations were further analysed based on countries: China (23.9%; 95% CI: 9.6–38.1%, I2 = 82%), France (13.6%; 95% CI: 10.6–16.7%, I2 = 0%), Germany (14.2%; 95% CI: 9.2–19.1%, I2 = 61%), Italy (1.9%; 95% CI: 0.0–5.7%, I2 = 71%), Spain (10.7%; 95% CI: 0.0–23.2%, I2 = 82%) and the United States (17.4%; 95% CI: 14.0–20.9%, I2 = 74%). Assessments of PV, ET and MF prevalence across the four continents (Figure S1) and in relation to different countries were also examined (Figure S2). Three forms of WHO criteria were used and the prevalence of TET2 gene mutations was highest in the 2016 version (WHO 2001 criteria 12.9%, 95% CI: 10.2–15.5%, I2 = 0%, WHO 2008 criteria 14.5%, 95% CI: 9.7–19.3%, I2 = 95% and WHO 2016 criteria 20.1%, 95% CI: 14.7–25.4%, I2 = 61%) (Figure S3). A higher prevalence of TET2 gene mutations were observed while using NGS (17.2%, 95% CI: 14.0–20.4%, I2 = 80%) and Sanger sequencing (12.7%, 95% CI: 9.6–15.9%, I2 = 52%), but not in HRM analysis (7.7%, 95% CI: 0.0–16.6%, I2 = 88%) (Figure S4). The MF subgroup was further divided into two subgroups (PMF and SMF), and the prevalence of TET2 gene mutations were studied in both and found to be similar (PMF 16.7%, 95% CI: 13.6–19.8%, I2 = 24% and SMF 14.8%, 95% CI: 9.3–20.2%, I2 = 0%) (Figure S5). Various levels of heterogeneity were observed in the main analyses (ranging from 60% to 94%) (Figure 2) and subgroup analyses (ranging from 0% to 93%) (Table 2, Figures S1–S5).

3.4. Quality Assessment

Detailed quality assessments of the included studies are presented in Tables S2 and S3. Most studies were judged to be of high quality (68.6%), while the remainder were considered to be of either moderate (14.3%) or low quality (17.7%).

3.5. Publication Bias

The results from the funnel plots and Egger’s tests suggest that there is only a small likelihood of publication bias (Figure 3 and Figure S6).

3.6. Sensitivity Analyses

In the sensitivity analyses, only minor differences (ranging from 4.0% lower to 1.8% higher) were observed in the pooled prevalence estimates of TET2 gene mutations among cases of MPN compared to the main findings (Table 3 and Figure S7). A Galbraith plot was performed, and four outlier studies [29,30,37,39] were identified (Figure 4).

4. Discussion

The overall prevalence of TET2 gene mutations among BCR-ABL-negative MPN patients was estimated to be 15.5%. This estimate is similar to the occurrence of TET2 somatic mutations in patients with various myeloid cancers [11]. Compared with other myeloid malignancies, the prevalence of TET2 gene mutations among patients with BCR-ABL-negative MPN appears to be lower. Among patients with myelodysplastic syndromes (MDS), the prevalence of these mutations has been estimated to be 18–35% [11,53,54,55,56,57,58], 36–60% for those with chronic myelomonocytic leukaemia (CMML) [54,59,60,61], 24% in cases with AML, 22% in chronic myelogenous leukaemia (CML) [11], 20% in mastocytosis [62,63] and about 30% in patients with blastic plasmacytoid dendritic cell neoplasm [64,65].
The results appear to confirm the observation that epigenetic regulators like the TET2 gene have mutated more frequently among those patients with PV (p = 0.05), compared with those with either MF (p = 0.02) or ET (p = 0.023) [27]. According to a meta-analysis that analysed the frequency of three main genes (JAK2, MPL and CALR) in MPN from 2000 to 2018 [66], for the most common gene mutation JAK2 V617F, TET2 showed a lower prevalence as compared to JAK2 V617F in PV (46.7–100.0%), ET (31.3–72.1%) and MF (25.0–85.7%). For the MPL gene, our results displayed a higher proportion in PV (16.8% vs. 0%) but similar percentages in ET (9.8% vs. 0.9–12.5%) and MF (15.7% vs. 0–17.7%). Finally, in relation to the last common driver gene, CALR, TET2 manifested a higher prevalence in PV (16.8% vs. 0%), a lower prevalence in ET (9.8% vs. 12.6–50%) and a similar proportion in MF (15.7% vs. 10–100%). From these results, it appears that the TET2 gene mutations have distributed more evenly across MPN subcategories in contrast to the three main driver mutation genes [47].
This study has several notable strengths. To our knowledge, no meta-analysis has previously investigated the prevalence of TET2 gene mutations in patients with MPN. This meta-analysis included studies from six databases using robust search strategies without any language restrictions. All the sensitivity analyses produced similar results to the overall findings, suggesting that the main result is likely to be robust and credible.
Nevertheless, there are a few limitations. Several meta-analyses exhibited high heterogeneity, indicating considerable variability among the results from the included studies. After excluding the four outlier studies identified by the Galbraith plot, heterogeneity was reduced from 94% to 63% across all MPN studies, 60% to 54% for PV, 62% to 48% for ET and 89% to 18% for MF, suggesting that these four studies were an important source of heterogeneity. Several factors may further explain this heterogeneity. One of the outlier studies [29] recorded a very low prevalence of TET2 gene mutations (0.4%). This may be due to the use of a different method (HRM analysis) that may be less sensitive to identifying the mutations compared with most other studies. Notably, a similar result was also observed in one of the two other studies [35,44] that also employed the same analytical technique. Different etiological exposures might occur in different regions, resulting in differences in the prevalence estimates across the different studies [67]. In support of this hypothesis, a lower prevalence was recorded in Australia and Italy, whereas a higher result was identified in China and the United States. Variations in the use of different diagnostic guidelines may have also affected the estimates of prevalence and further contributed to the heterogeneity of results between studies. The discovery of the JAK2 gene mutations in 2005 and their subsequent inclusion in the diagnostic criteria [68] for MPN, PV and ET but not MF [2,69] may account for some of the differences observed among the studies. A stepwise increase in TET2 gene mutations in MPN was observed with subsequent versions of the WHO classification and diagnostic criteria among all cases of MPN (12.9% for WHO criteria 2001, 14.5% for WHO criteria 2008 and 20.1% for WHO criteria 2016), PV (13.7% for WHO criteria 2001, 16.9% for WHO criteria 2008 and 21.4% for WHO criteria 2016) and ET (5.3% for WHO criteria 2001, 9.4% for WHO criteria 2008 and 20.3% for WHO criteria 2016) but not in MF (17.5% for WHO criteria 2001, 14.4% for WHO criteria 2008 and 16.5% for WHO criteria 2016).
Another limitation of this meta-analysis is that the prevalence of MPN may be underestimated in some studies. Patients with MPN can be relatively symptom-free for many years so people, with little contact with health services, can remain undiagnosed for long periods [70]. Estimates of the prevalence of TET2 mutations in MPN may be underestimated, particularly in less-developed countries or among disadvantaged groups in well-developed countries.
Finally, the included studies largely focused on the allele frequencies of the main driver mutations (JAK2, MPL and CALR) and did not permit any analysis of the allelic frequencies of the TET2 mutant allele in MPN.

5. Conclusions

This meta-analysis provides the most comprehensive currently available estimate of the overall prevalence of TET2 gene mutations (15.5%) among patients with MPN. However, substantial heterogeneity was evident among the results included in this meta-analysis, likely related to factors such as regional differences in patients included in studies and variations in the diagnostic criteria employed by the studies. This heterogeneity suggests that caution should be employed with using the estimates of prevalence.

Supplementary Materials

The following are available online at https://www.mdpi.com/article/10.3390/cancers13123078/s1, Figure S1: prevalence of TET2 gene mutations based on continents, Figure S2: Prevalence of TET2 gene mutations based on countries, Figure S3: Prevalence of TET2 gene mutations based on the WHO classification and diagnostic criteria of MPN, Figure S4: Prevalence of TET2 gene mutations based on different methods used to detect TET2 gene mutations in MPN, Figure S5: Subgroup analysis. Prevalence of TET2 gene mutations in patients with (A) PMF and (B) SMF, Figure S6: Funnel plots estimating the prevalence of TET2 gene mutations in patients with (A) polycythaemia vera, (B) essential thrombocythaemia and (C) myelofibrosis, Figure S7: Sensitivity analyses, Table S1: Search strategies, Table S2: Quality assessment of the included cross-sectional studies, Table S3: Quality assessment of the included case–control studies.

Author Contributions

Conceptualisation, Y.C.C., M.A.I. and M.R.; methodology, Y.C.C., M.A.I. and P.H.; software, M.A.I.; validation, Y.C.C. and M.A.I.; formal analysis, Y.C.C. and M.A.I.; resources, Y.C.C. and M.A.I.; data curation, M.A.I., P.H., P.Y.W., M.F.J., R.H. and M.R.; writing—original draft preparation, Y.C.C.; writing—review and editing, M.A.I., P.H., P.Y.W., M.F.J., R.H. and M.R.; supervision, M.A.I., M.F.J., R.H. and M.R. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data presented in this study are available within the article and supplementary materials.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Campregher, P.V.; de Souza Santos, F.P.; Perini, G.F.; Hamerschlak, N. Molecular biology of Philadelphia-negative myeloproliferative neoplasms. Rev. Bras. Hematol. Hemoter. 2012, 34, 150–155. [Google Scholar] [CrossRef][Green Version]
  2. Tefferi, A.; Thiele, J.; Vardiman, J.W. The 2008 World Health Organization classification system for myeloproliferative neoplasms: Order out of chaos. Cancer 2009, 115, 3842–3847. [Google Scholar] [CrossRef]
  3. Tefferi, A.; Elliott, M. Thrombosis in myeloproliferative disorders: Prevalence, prognostic factors, and the role of leukocytes and JAK2V617F. In Seminars in Thrombosis and Hemostasis; Thieme Medical Publishers, Inc.: New York, NY, USA, 2007; pp. 313–320. [Google Scholar]
  4. 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. Somatic CALR mutations in myeloproliferative neoplasms with nonmutated JAK2. N. Engl. J. Med. 2013, 369, 2391–2405. [Google Scholar] [CrossRef] [PubMed][Green Version]
  5. Tenedini, E.; Bernardis, I.; Artusi, V.; Artuso, L.; Roncaglia, E.; Guglielmelli, P.; Pieri, L.; Bogani, C.; Biamonte, F.; Rotunno, G. Targeted cancer exome sequencing reveals recurrent mutations in myeloproliferative neoplasms. Leukemia 2014, 28, 1052–1059. [Google Scholar] [CrossRef][Green Version]
  6. Reinig, E.; Yang, F.; Traer, E.; Arora, R.; Brown, S.; Rattray, R.; Braziel, R.; Fan, G.; Press, R.; Dunlap, J. Targeted next-generation sequencing in myelodysplastic syndrome and chronic myelomonocytic leukemia aids diagnosis in challenging cases and identifies frequent spliceosome mutations in transformed acute myeloid leukemia. Am. J. Clin. Pathol. 2016, 145, 497–506. [Google Scholar] [CrossRef][Green Version]
  7. Vainchenker, W.; Kralovics, R. Genetic basis and molecular pathophysiology of classical myeloproliferative neoplasms. Blood 2017, 129, 667–679. [Google Scholar] [CrossRef] [PubMed][Green Version]
  8. Chia, Y.C.; Islam, M.A.; Woon, P.Y.; Johan, M.F.; Hassan, R.; Ramli, M. Molecular genetics of thrombotic myeloproliferative neoplasms: Implications in precision oncology. Genes Dis. 2021. [Google Scholar] [CrossRef]
  9. Scott, L.M.; Rebel, V.I. JAK2 and genomic instability in the myeloproliferative neoplasms: A case of the chicken or the egg? Am. J. Hematol. 2012, 87, 1028–1036. [Google Scholar] [CrossRef][Green Version]
  10. Kameda, T.; Shide, K.; Yamaji, T.; Kamiunten, A.; Sekine, M.; Taniguchi, Y.; Hidaka, T.; Kubuki, Y.; Shimoda, H.; Marutsuka, K.; et al. Loss of TET2 has dual roles in murine myeloproliferative neoplasms: Disease sustainer and disease accelerator. Blood 2015, 125, 304–315. [Google Scholar] [CrossRef]
  11. Delhommeau, F.; Dupont, S.; Della Valle, V.; James, C.; Trannoy, S.; Massé, A.; Kosmider, O.; Le Couedic, J.P.; Robert, F.; Alberdi, A.; et al. Mutation in TET2 in myeloid cancers. N. Engl. J. Med. 2009, 360, 2289–2301. [Google Scholar] [CrossRef]
  12. Swierczek, S.I.; Yoon, D.; Bellanné-Chantelot, C.; Kim, S.J.; Saint-Martin, C.; Delhommeau, F.; Najman, A.; Prchal, J.T. Extent of hematopoietic involvement by TET2 mutations in JAK2V617F polycythemia vera. Haematologica 2011, 96, 775–778. [Google Scholar] [CrossRef]
  13. Moola, S.; Munn, Z.; Tufanaru, C.; Aromataris, E.; Sears, K.; Sfetcu, R.; Currie, M.; Lisy, K.; Qureshi, R.; Mattis, P.; et al. Chapter 7: Systematic reviews of etiology and risk. In JBI Manual for Evidence Synthesis; Aromataris, E., Munn, Z., Eds.; JBI: Adelaide, Australia, 2020; Available online: https://synthesismanual.jbi.global (accessed on 12 December 2020).
  14. Ha, J.S.; Jeon, D.S.; Kim, J.R.; Ryoo, N.H.; Suh, J.S. Analysis of the ten-eleven translocation 2 (TET2) gene mutation in myeloproliferative neoplasms. Ann. Clin. Lab. Sci. 2014, 44, 173–179. [Google Scholar] [PubMed]
  15. Abdel-Wahab, O.; Levine, R.L. EZH2 mutations: Mutating the epigenetic machinery in myeloid malignancies. Cancer Cell 2010, 18, 105–107. [Google Scholar] [CrossRef][Green Version]
  16. Moher, D.; Liberati, A.; Tetzlaff, J.; Altman, D.G.; Group, P. Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. PLoS Med. 2009, 6, e1000097. [Google Scholar] [CrossRef][Green Version]
  17. Islam, M.A.; Alam, S.S.; Kundu, S.; Hossan, T.; Kamal, M.A.; Cavestro, C. Prevalence of Headache in Patients with Coronavirus Disease 2019 (COVID-19): A Systematic Review and Meta-Analysis of 14,275 Patients. Front. Neurol. 2020, 11, 562634. [Google Scholar] [CrossRef]
  18. Viechtbauer, W. Conducting meta-analyses in R with the metafor package. J. Stat. Softw. 2010, 36, 1–48. [Google Scholar] [CrossRef][Green Version]
  19. Ihle, M.A.; Fassunke, J.; König, K.; Grünewald, I.; Schlaak, M.; Kreuzberg, N.; Tietze, L.; Schildhaus, H.-U.; Büttner, R.; Merkelbach-Bruse, S. Comparison of high resolution melting analysis, pyrosequencing, next generation sequencing and immunohistochemistry to conventional Sanger sequencing for the detection of p. V600E and non-p. V600E BRAF mutations. BMC Cancer 2014, 14, 13. [Google Scholar] [CrossRef] [PubMed][Green Version]
  20. Andréasson, B.; Pettersson, H.; Wasslavik, C.; Johansson, P.; Palmqvist, L.; Asp, J. ASXL1 mutations, Pevious vascular complications and age at diagnosis predict survival in 85 WHO-defined polycythaemia vera patients. Br. J. Haematol. 2020, 189, 913–919. [Google Scholar] [CrossRef]
  21. Barraco, D.; Cerquozzi, S.; Gangat, N.; Patnaik, M.M.; Lasho, T.; Finke, C.; Hanson, C.A.; Ketterling, R.P.; Pardanani, A.; Tefferi, A. Monocytosis in polycythemia vera: Clinical and molecular correlates. Am. J. Hematol. 2017, 92, 640–645. [Google Scholar] [CrossRef] [PubMed][Green Version]
  22. 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 2019, 34, 1364–1372. [Google Scholar] [CrossRef]
  23. Brecqueville, M.; Rey, J.; Bertucci, F.; Coppin, E.; Finetti, P.; Carbuccia, N.; Cervera, N.; Gelsi-Boyer, V.; Arnoulet, C.; Gisserot, O.; et al. Mutation analysis of ASXL1, CBL, DNMT3A, IDH1, IDH2, JAK2, MPL, NF1, SF3B1, SUZ12, and TET2 in myeloproliferative neoplasms. Genes Chromosomes Cancer 2012, 51, 743–755. [Google Scholar] [CrossRef]
  24. Brecqueville, M.; Rey, J.; Devillier, R.; Guille, A.; Gillet, R.; Adélaide, J.; Gelsi-Boyer, V.; Arnoulet, C.; Chaffanet, M.; Mozziconacci, M.J.; et al. Array comparative genomic hybridization and sequencing of 23 genes in 80 patients with myelofibrosis at chronic or acute phase. Haematologica 2014, 99, 37–45. [Google Scholar] [CrossRef][Green Version]
  25. Carbuccia, N.; Murati, A.; Trouplin, V.; Brecqueville, M.; Adelaide, J.; Rey, J.; Vainchenker, W.; Bernard, O.; Chaffanet, M.; Vey, N. Mutations of ASXL1 gene in myeloproliferative neoplasms. Leukemia 2009, 23, 2183–2186. [Google Scholar] [CrossRef][Green Version]
  26. Cerquozzi, S.; Barraco, D.; Lasho, T.; Finke, C.; Hanson, C.A.; Ketterling, R.P.; Pardanani, A.; Gangat, N.; Tefferi, A. Risk factors for arterial versus venous thrombosis in polycythemia vera: A single center experience in 587 patients. Blood Cancer J. 2017, 7, 662. [Google Scholar] [CrossRef]
  27. Delic, S.; Rose, D.; Kern, W.; Nadarajah, N.; Haferlach, C.; Haferlach, T.; Meggendorfer, M. Application of an NGS-based 28-gene panel in myeloproliferative neoplasms reveals distinct mutation patterns in essential thrombocythaemia, primary myelofibrosis and polycythaemia vera. Br. J. Haematol. 2016, 175, 419–426. [Google Scholar] [CrossRef] [PubMed]
  28. Gill, H.; Ip, H.W.; Yim, R.; Tang, W.F.; Pang, H.H.; Lee, P.; Leung, G.M.K.; Li, J.; Tang, K.; So, J.C.C.; et al. Next-generation sequencing with a 54-gene panel identified unique mutational profile and prognostic markers in Chinese patients with myelofibrosis. Ann. Hematol. 2019, 98, 869–879. [Google Scholar] [CrossRef]
  29. Guglielmelli, P.; Biamonte, F.; Score, J.; Hidalgo-Curtis, C.; Cervantes, F.; Maffioli, M.; Fanelli, T.; Ernst, T.; Winkelman, N.; Jones, A.V.; et al. EZH2 mutational status predicts poor survival in myelofibrosis. Blood 2011, 118, 5227–5234. [Google Scholar] [CrossRef] [PubMed]
  30. Huang, X.; Wu, J.; Deng, X.; Xu, X.; Zhang, X.; Ma, W.; Hu, T.; Yang, J.; Guan, M.; Tang, G. Mutation profiles of classic myeloproliferative neoplasms detected by a customized next-generation sequencing-based 50-gene panel. J. Bio-X Res. 2020, 3, 13–20. [Google Scholar] [CrossRef]
  31. Hussein, K.; Abdel-Wahab, O.; Lasho, T.L.; Van Dyke, D.L.; Levine, R.L.; Hanson, C.A.; Pardanani, A.; Tefferi, A. Cytogenetic correlates of TET2 mutations in 199 patients with myeloproliferative neoplasms. Am. J. Hematol. 2010, 85, 81. [Google Scholar] [CrossRef][Green Version]
  32. Kröger, N.; Panagiota, V.; Badbaran, A.; Zabelina, T.; Triviai, I.; Araujo Cruz, M.M.; Shahswar, R.; Ayuk, F.; Gehlhaar, M.; Wolschke, C.; et al. Impact of Molecular Genetics on Outcome in Myelofibrosis Patients after Allogeneic Stem Cell Transplantation. Biol. Blood Marrow Transplant. 2017, 23, 1095–1101. [Google Scholar] [CrossRef] [PubMed][Green Version]
  33. Oppliger Leibundgut, E.; Haubitz, M.; Burington, B.; Ottmann, O.G.; Spitzer, G.; Odenike, O.; McDevitt, M.A.; Röth, A.; Snyder, D.S.; Baerlocher, G.M. Dynamics of mutations in patients with essential thrombocythemia treated with imetelstat. Haematologica 2020. [Google Scholar] [CrossRef] [PubMed]
  34. Magor, G.W.; Tallack, M.R.; Klose, N.M.; Taylor, D.; Korbie, D.; Mollee, P.; Trau, M.; Perkins, A.C. Rapid Molecular Profiling of Myeloproliferative Neoplasms Using Targeted Exon Resequencing of 86 Genes Involved in JAK-STAT Signaling and Epigenetic Regulation. J. Mol. Diagn. 2016, 18, 707–718. [Google Scholar] [CrossRef][Green Version]
  35. Martinez-Aviles, L.; Besses, C.; Alvarez-Larran, A.; Torres, E.; Serrano, S.; Bellosillo, B. TET2, ASXL1, IDH1, IDH2, And c-CBL genes in JAK2-and MPL-negative myeloproliferative neoplasms. Ann. Hematol. 2012, 91, 533–541. [Google Scholar] [CrossRef] [PubMed]
  36. Myrtue Nielsen, H.; Lykkegaard Andersen, C.; Westman, M.; Sommer Kristensen, L.; Asmar, F.; Arvid Kruse, T.; Thomassen, M.; Stauffer Larsen, T.; Skov, V.; Lotte Hansen, L.; et al. Epigenetic changes in myelofibrosis: Distinct methylation changes in the myeloid compartments and in cases with ASXL1 mutations. Sci. Rep. 2017, 7, 6774. [Google Scholar] [CrossRef][Green Version]
  37. Nischal, S.; Bhattacharyya, S.; Christopeit, M.; Yu, Y.; Zhou, L.; Bhagat, T.D.; Sohal, D.; Will, B.; Mo, Y.; Suzuki, M.; et al. Methylome profiling reveals distinct alterations in phenotypic and mutational subgroups of myeloproliferative neoplasms. Cancer Res. 2013, 73, 1076–1085. [Google Scholar] [CrossRef][Green Version]
  38. O’Sullivan, J.M.; Hamblin, A.; Yap, C.; Fox, S.; Boucher, R.; Panchal, A.; Alimam, S.; Dreau, H.; Howard, K.; Ware, P.; et al. The poor outcome in high molecular risk, hydroxycarbamide-resistant/intolerant ET is not ameliorated by ruxolitinib. Blood 2019, 134, 2107–2111. [Google Scholar] [CrossRef] [PubMed]
  39. Pardanani, A.; Lasho, T.; Finke, C.; Oh, S.T.; Gotlib, J.; Tefferi, A. LNK mutation studies in blast-phase myeloproliferative neoplasms, and in chronic-phase disease with TET2, IDH, JAK2 or MPL mutations. Leukemia 2010, 24, 1713–1718. [Google Scholar] [CrossRef] [PubMed]
  40. Patel, K.P.; Newberry, K.J.; Luthra, R.; Jabbour, E.; Pierce, S.; Cortes, J.; Singh, R.; Mehrotra, M.; Routbort, M.J.; Luthra, M.; et al. Correlation of mutation profile and response in patients with myelofibrosis treated with ruxolitinib. Blood 2015, 126, 790–797. [Google Scholar] [CrossRef][Green Version]
  41. Patriarca, A.; Colaizzo, D.; Tiscia, G.; Spadano, R.; Di Zacomo, S.; Spadano, A.; Villanova, I.; Margaglione, M.; Grandone, E.; Dragani, A. TET2 mutations in Ph-negative myeloproliferative neoplasms: Identification of three novel mutations and relationship with clinical and laboratory findings. BioMed Res. Int. 2013, 2013, 929840. [Google Scholar] [CrossRef]
  42. Saint-Martin, C.; Leroy, G.; Delhommeau, F.; Panelatti, G.; Dupont, S.; James, C.; Plo, I.; Bordessoule, D.; Chomienne, C.; Delannoy, A.; et al. Analysis of the Ten-Eleven Translocation 2 (TET2) gene in familial myeloproliferative neoplasms. Blood 2009, 114, 1628–1632. [Google Scholar] [CrossRef][Green Version]
  43. Schlenk, R.F.; Stegelmann, F.; Reiter, A.; Jost, E.; Gattermann, N.; Hebart, H.; Waller, C.; Hochhaus, A.; Platzbecker, U.; Schafhausen, P.; et al. Pomalidomide in myeloproliferative neoplasm-associated myelofibrosis. Leukemia 2017, 31, 889–895. [Google Scholar] [CrossRef] [PubMed]
  44. Schnittger, S.; Bacher, U.; Eder, C.; Dicker, F.; Alpermann, T.; Grossmann, V.; Kohlmann, A.; Kern, W.; Haferlach, C.; Haferlach, T. Molecular analyses of 15,542 patients with suspected BCR-ABL1-negative myeloproliferative disorders allow to develop a stepwise diagnostic workflow. Haematologica 2012, 97, 1582–1585. [Google Scholar] [CrossRef] [PubMed][Green Version]
  45. Segura-Díaz, A.; Stuckey, R.; Florido, Y.; González-Martín, J.M.; López-Rodríguez, J.F.; Sánchez-Sosa, S.; González-Pérez, E.; Perdomo, M.N.S.; del Mar Perera, M.; de la Iglesia, S.; et al. Thrombotic risk detection in patients with polycythemia vera: The predictive role of DNMT3A/TET2/ASXL1 mutations. Cancers 2020, 12, 934. [Google Scholar] [CrossRef]
  46. Song, J.; Hussaini, M.; Zhang, H.; Shao, H.; Qin, D.; Zhang, X.; Ma, Z.; Hussnain Naqvi, S.M.; Zhang, L.; Moscinski, L.C. Comparison of the Mutational Profiles of Primary Myelofibrosis, Polycythemia Vera, and Essential Thrombocytosis. Am. J. Clin. Pathol. 2017, 147, 444–452. [Google Scholar] [CrossRef][Green Version]
  47. Tefferi, A.; Pardanani, A.; Lim, K.H.; Abdel-Wahab, O.; Lasho, T.L.; Patel, J.; Gangat, N.; Finke, C.M.; Schwager, S.; Mullally, A.; et al. TET2 mutations and their clinical correlates in polycythemia vera, essential thrombocythemia and myelofibrosis. Leukemia 2009, 23, 905–911. [Google Scholar] [CrossRef][Green Version]
  48. Tefferi, A.; Lasho, T.L.; Abdel-Wahab, O.; Guglielmelli, P.; Patel, J.; Caramazza, D.; Pieri, L.; Finke, C.M.; Kilpivaara, O.; Wadleigh, M.; et al. IDH1 and IDH2 mutation studies in 1473 patients with chronic-, fibrotic- or blast-phase essential thrombocythemia, polycythemia vera or myelofibrosis. Leukemia 2010, 24, 1302–1309. [Google Scholar] [CrossRef] [PubMed]
  49. 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][Green Version]
  50. 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] [PubMed][Green Version]
  51. Verger, E.; Andreoli, A.; Chomienne, C.; Kiladjian, J.J.; Cassinat, B. TET2 gene sequencing may be helpful for myeloproliferative neoplasm diagnosis. Br. J. Haematol. 2014, 165, 416–419. [Google Scholar] [CrossRef][Green Version]
  52. Chunxia, Z.; Li, G.; Qianqian, J.; Zhenling, L.; Fanzhou, H.; Yayue, G.; Ming, G.; Shaohua, X.; Yin, T.; Yanrong, C.; et al. Symptom burden and its relationships with risk assessment and gene mutations in patients with primary myelofibrosis. J. Leuk. Lymphoma 2015, 24, 453–456. [Google Scholar]
  53. Tefferi, A.; Lim, K.H.; Abdel-Wahab, O.; Lasho, T.L.; Patel, J.; Patnaik, M.M.; Hanson, C.A.; Pardanani, A.; Gilliland, D.G.; Levine, R.L. Detection of mutant TET2 in myeloid malignancies other than myeloproliferative neoplasms: CMML, MDS, MDS/MPN and AML. Leukemia 2009, 23, 1343–1345. [Google Scholar] [CrossRef] [PubMed][Green Version]
  54. Shih, A.H.; Abdel-Wahab, O.; Patel, J.P.; Levine, R.L. The role of mutations in epigenetic regulators in myeloid malignancies. Nat. Rev. Cancer 2012, 12, 599–612. [Google Scholar] [CrossRef]
  55. Guo, Z.; Zhang, S.-K.; Zou, Z.; Fan, R.-H.; Lyu, X.-D. Prognostic significance of TET2 mutations in myelodysplastic syndromes: A meta-analysis. Leukemia Res. 2017, 58, 102–107. [Google Scholar] [CrossRef] [PubMed]
  56. Bejar, R.; Stevenson, K.; Abdel-Wahab, O.; Galili, N.; Nilsson, B.; Garcia-Manero, G.; Kantarjian, H.; Raza, A.; Levine, R.L.; Neuberg, D. Clinical effect of point mutations in myelodysplastic syndromes. N. Engl. J. Med. 2011, 364, 2496–2506. [Google Scholar] [CrossRef] [PubMed][Green Version]
  57. Kosmider, O.; Gelsi-Boyer, V.; Cheok, M.; Grabar, S.; Della-Valle, V.; Picard, F.; Viguié, F.; Quesnel, B.; Beyne-Rauzy, O.; Solary, E. TET2 mutation is an independent favorable prognostic factor in myelodysplastic syndromes (MDSs). Blood 2009, 114, 3285–3291. [Google Scholar] [CrossRef][Green Version]
  58. Itzykson, R.; Kosmider, O.; Cluzeau, T.; Mansat-De Mas, V.; Dreyfus, F.; Beyne-Rauzy, O.; Quesnel, B.; Vey, N.; Gelsi-Boyer, V.; Raynaud, S. Impact of TET2 mutations on response rate to azacitidine in myelodysplastic syndromes and low blast count acute myeloid leukemias. Leukemia 2011, 25, 1147–1152. [Google Scholar] [CrossRef] [PubMed][Green Version]
  59. Kohlmann, A.; Grossmann, V.; Klein, H.U.; Schindela, S.; Weiss, T.; Kazak, B.; Dicker, F.; Schnittger, S.; Dugas, M.; Kern, W.; et al. Next-generation sequencing technology reveals a characteristic pattern of molecular mutations in 72.8% of chronic myelomonocytic leukemia by detecting frequent alterations in TET2, CBL, RAS, and RUNX1. J. Clin. Oncol. 2010, 28, 3858–3865. [Google Scholar] [CrossRef]
  60. Itzykson, R.; Kosmider, O.; Renneville, A.; Gelsi-Boyer, V.; Meggendorfer, M.; Morabito, M.; Berthon, C.; Adès, L.; Fenaux, P.; Beyne-Rauzy, O. Prognostic score including gene mutations in chronic myelomonocytic leukemia. J. Clin. Oncol. 2013, 31, 2428–2436. [Google Scholar] [CrossRef]
  61. Grossmann, V.; Kohlmann, A.; Eder, C.; Haferlach, C.; Kern, W.; Cross, N.; Haferlach, T.; Schnittger, S. Molecular profiling of chronic myelomonocytic leukemia reveals diverse mutations in >80% of patients with TET2 and EZH2 being of high prognostic relevance. Leukemia 2011, 25, 877–879. [Google Scholar] [CrossRef]
  62. Tefferi, A.; Levine, R.L.; Lim, K.H.; Abdel-Wahab, O.; Lasho, T.L.; Patel, J.; Finke, C.M.; Mullally, A.; Li, C.Y.; Pardanani, A.; et al. Frequent TET2 mutations in systemic mastocytosis: Clinical, KITD816V and FIP1L1-PDGFRA correlates. Leukemia 2009, 23, 900–904. [Google Scholar] [CrossRef] [PubMed][Green Version]
  63. Soucie, E.; Hanssens, K.; Mercher, T.; Georgin-Lavialle, S.; Damaj, G.; Livideanu, C.; Chandesris, M.O.; Acin, Y.; Létard, S.; de Sepulveda, P. In aggressive forms of mastocytosis, TET2 loss cooperates with c-KITD816V to transform mast cells. Blood 2012, 120, 4846–4849. [Google Scholar] [CrossRef] [PubMed][Green Version]
  64. Jardin, F.; Ruminy, P.; Parmentier, F.; Troussard, X.; Vaida, I.; Stamatoullas, A.; Leprêtre, S.; Penther, D.; Duval, A.B.; Picquenot, J.M. TET2 and TP53 mutations are frequently observed in blastic plasmacytoid dendritic cell neoplasm. Br. J. Haematol. 2011, 153, 413–416. [Google Scholar] [CrossRef] [PubMed]
  65. Menezes, J.; Acquadro, F.; Wiseman, M.; Gomez-Lopez, G.; Salgado, R.; Talavera-Casanas, J.; Buno, I.; Cervera, J.; Montes-Moreno, S.; Hernandez-Rivas, J. Exome sequencing reveals novel and recurrent mutations with clinical impact in blastic plasmacytoid dendritic cell neoplasm. Leukemia 2014, 28, 823–829. [Google Scholar] [CrossRef] [PubMed]
  66. Mejía-Ochoa, M.; Toro, P.A.A.; Cardona-Arias, J.A. Systematization of analytical studies of polycythemia vera, essential thrombocythemia and primary myelofibrosis, and a meta-analysis of the frequency of JAK2, CALR and MPL mutations: 2000–2018. BMC Cancer 2019, 19, 590. [Google Scholar] [CrossRef] [PubMed]
  67. Anderson, L.A.; Duncombe, A.S.; Hughes, M.; Mills, M.E.; Wilson, J.C.; McMullin, M.F. Environmental, Lifestyle, And familial/ethnic factors associated with myeloproliferative neoplasms. Am. J. Hematol. 2012, 87, 175–182. [Google Scholar] [CrossRef] [PubMed]
  68. Levine, R.L.; Wadleigh, M.; Cools, J.; Ebert, B.L.; Wernig, G.; Huntly, B.J.; Boggon, T.J.; Wlodarska, I.; Clark, J.J.; Moore, S. Activating mutation in the tyrosine kinase JAK2 in polycythemia vera, essential thrombocythemia, and myeloid metaplasia with myelofibrosis. Cancer Cell 2005, 7, 387–397. [Google Scholar] [CrossRef] [PubMed][Green Version]
  69. Passamonti, F.; Maffioli, M. Update from the latest WHO classification of MPNs: A user’s manual. Hematology 2016, 2016, 534–542. [Google Scholar] [CrossRef][Green Version]
  70. Tefferi, A.; Vardiman, J.W. Classification and diagnosis of myeloproliferative neoplasms: The 2008 World Health Organization criteria and point-of-care diagnostic algorithms. Leukemia 2008, 22, 14–22. [Google Scholar] [CrossRef][Green Version]
Cancers 13 03078 g001 550
Figure 1. PRISMA flow diagram of study selection.
Figure 1. PRISMA flow diagram of study selection.
Cancers 13 03078 g001
Cancers 13 03078 g002a 550Cancers 13 03078 g002b 550Cancers 13 03078 g002c 550Cancers 13 03078 g002d 550
Figure 2. (A) Prevalence of TET2 gene mutations in patients with MPN (overall). (B) Prevalence of TET2 gene mutations in patients with PV. (C) Prevalence of TET2 gene mutations in patients with ET. (D) Prevalence of TET2 gene mutations in patients with MF.
Figure 2. (A) Prevalence of TET2 gene mutations in patients with MPN (overall). (B) Prevalence of TET2 gene mutations in patients with PV. (C) Prevalence of TET2 gene mutations in patients with ET. (D) Prevalence of TET2 gene mutations in patients with MF.
Cancers 13 03078 g002aCancers 13 03078 g002bCancers 13 03078 g002cCancers 13 03078 g002d
Cancers 13 03078 g003 550
Figure 3. Funnel plot estimating the prevalence of TET2 gene mutations in patients with MPN (overall).
Figure 3. Funnel plot estimating the prevalence of TET2 gene mutations in patients with MPN (overall).
Cancers 13 03078 g003
Cancers 13 03078 g004 550
Figure 4. Galbraith plot analysing MPN (overall) identified four outlier studies.
Figure 4. Galbraith plot analysing MPN (overall) identified four outlier studies.
Cancers 13 03078 g004
Table
Table 1. Major characteristics of the included studies.
Table 1. Major characteristics of the included studies.
NoStudy ID
[References]		Study
Design		CountryType of MPNTotal Number of MPN Patients (Female)Age (Years) [Mean ± SD/Median (IQR)/Range]Haemoglobin (g/dL)
[Mean ± SD/Median (IQR)/Range]		Leucocyte
Count (109/L)
[Mean ± SD/Range/Median (IQR)]		Platelet Count (109/L)
[Mean ± SD/Range/Median (IQR)]		Total Number of Mutated ASXL1 (%)Screening Method for TET2 Gene MutationsDiagnostic Criteria
1Andreasson 2020
[20]		Cross-sectionalSwedenPV85 (41)71.0
(37.0–94.0)		NRNRNR8.2NGS2008 WHO
2Barraco 2017
[21]		Cross-sectionalUSAPV267 (125)64.0
(17.0–94.0)		18.0
(14.8–24.3)		11.5
(4.3–59.3)		439.0
(37.0–2747.0)		8.1NR2016 WHO
3Bartels 2019
[22]		Case–controlGermanyMF104 (53)NRNRNRNR9.6NGS2016 WHO
4Brecqueville 2012
[23]		Cross-sectionalFrancePV, ET & MF127 (57)NR
(29.0–97.0)		NRNRNR11.0SS2008 WHO
5Brecqueville 2014
[24]		Cross-sectionalFranceMF68 (NR)69.0
(30.0–86.0)		11.4
(5.8–17.8)		8.9
(1.3–120.0)		256.0
(5.0–1188.0)		26.5SS2008 WHO
6Carbuccia 2009
[25]		Cross-sectionalFrancePV, ET & MFNRNRNRNRNR7.3SSNR
7Cerquozzi 2017
[26]		Cross-sectionalUSAPV587 (302)60.0
(17.0–94.0)		NRNR476.0
(41.0–2747.0)		10.5NGS2016 WHO, ELN
8Delhommeau 2009
[11]		Cross-sectionalFrancePV, ET & MF203 (41)NRNRNRNRNRSS, SNP array, CGH2001 WHO
9Delic 2016
[27]		Cross-sectionalGermanyPV, ET & MF100 (NR)69.0
(28.0–87.0)		NRNRNR21.0NGS2008 WHO
10Gill 2018
[28]		Cross-sectionalChinaMF101 (39)60.0
(26.0–89.0)		10.3
(3.0–18.5)		12.1
(1.5–177.4)		344.0
(19.0–1720.0)		30.7NGS2016 WHO, IWG-MRT
11Guglielmelli 2011
[29]		Cross-sectionalItalyMF518 (303)NRNRNRNR22.2HRM2008 WHO,
IWG-MRT
12Ha 2014
[14]		Cross-sectionalKoreaPV, ET & MF99 (50)63.7 ± 13.013.7 ± 3.816.5 ± 15.4825.4 ± 490.0NRSS, SNP array, CGH2008 WHO
13Huang 2020
[30]		Cross-sectionalChinaPV, ET & MF65 (32)62.0 (NR)NRNRNR10.8NGS2016 WHO
14Hussein 2010
[31]		Cross-sectionalUSAPV, ET & MF199 (96)58.0
(19.0–93.0)		NRNRNRNRNGS2001 WHO
15Kröger 2017
[32]		Cross-sectionalGermanyMF169 (73)58.0
(18.0–75.0)		NRNRNR29.0SSNR
16Leibundgut 2020
[33]		Cross-sectionalSwitzerlandET18 (10)59.5
(21.0–83.0)		NR7.8
(3.0–14.6)		788.0
(521.0–1359.0)		11.1NGS2016 WHO
17Magor 2016
[34]		Cross-sectionalAustraliaPV, ET & MF43 (16)61.0
(24.0–91.0)		NRNRNR9.3Targeted exon resequencing2008 WHO
18Martínez-Avilés 2012
[35]		Cross-sectionalSpainPV, ET & MF62 (43)NRNRNRNR4.8HRM, SS2008 WHO
19Nielsen 2017
[36]		Case–controlDenmarkMF16 (3)66.0
(52.0–80.0)		10.3
(7.9–13.4)		5.9
(2.3–64.4)		155.5
(56.0–357.0)		50.0PCR-DGGENR
20Nischal 2013
[37]		Cross-sectionalUSAPV, ET & MF25 (14)68.0
(54.0–72.0)		NRNRNR24.0SSNR
21O’Sullivan 2019
[38]		Cross-sectionalUKETNRNRNRNRNRNRNGSNR
22Pardanani 2010
[39]		Cross-sectionalUSAPV, ET & MF78 (34)64.0
(22.0–95.0)		NRNRNRNRNGS2008 WHO
23Patel 2015
[40]		Cross-sectionalUSAMF95 (44)66.0
(40.0–84.0)		10.7
(7.2–16.9)		25.0
(2.5–159.0)		339.0
(13.0–969.0)		21.1NGSIWG-MRT
24Patriarca 2013
[41]		Cross-sectionalItalyPV, ET & MF97 (44)NRNRNRNRNRNGS2008 WHO
25Saint-Martin 2009
[42]		Cross-sectionalFrancePV, ET & MFNRNRNRNRNRNRSS2008 WHO
26Schlenk 2016
[43]		Cross-sectionalGermanyMF96 (33)NRNRNRNR30.2SS2008 WHO, IWG-MRT
27Schnittger 2012
[44]		Cross-sectionalGermanyET & MFNRNRNRNRNRNRSS, HRMNR
28Segura-Díaz 2020
[45]		Cross-sectionalSpainPV, ET & MF68 (40)68.0
(43.0–90.0)		NRNRNR8.8NGS2016 WHO
29Song 2017
[46]		Cross-sectionalUSAPV, ET & MF135 (64)NRNRNRNR21.2NGS2008 WHO
30Tefferi 2009
[47]		Cross-sectionalUSAPV, ET & MF227 (111)NRNRNRNRNRNGS2001 WHO
31Tefferi 2010
[48]		Cross-sectionalUSAPV, ET & MF908 (487)NRNRNRNRNRNGS2008 WHO, IWG-MRT
32Tefferi 2016
[49]		Cross-sectionalUSAMF182 (64)63.0
(22.0–87.0)		10.1
(5.8–16.0)		10.5
(1.9–219.0)		224.0
(11.0–1493.0)		35.7NGS2008 WHO
33Tefferi 2016a
[50]		Cross-sectionalUSAPV & ET316 (177)NRNRNRNR11.4NGS2008 WHO
34Verger 2014
[51]		Cross-sectionalFrancePV, ET & MF27 (NR)NRNRNRNRNRSSNR
35Zhang 2015
[52]		Cross-sectionalChinaMF36 (15)65.0
(46.0–93.0)		10.9
(3.0–16.0)		22.3
(1.4–54.5)		215.0
(3.0–1157.0)		11.1WGS2008 WHO
aCGH: array-comparative genomic hybridisation; ASXL1: Additional sex combs-like 1; CGH: comparative genomic hybridisation; ELN: European Leukemia Net; ET: essential thrombocythaemia; HRM: high-resolution melting analysis; IQR: interquartile range; IWG-MRT: International Working Group for Myelofibrosis Research and Treatment; MF: myelofibrosis; MPN: myeloproliferative neoplasms; SS: Sanger sequencing; NGS: next-generation sequencing; NR: not reported; PCR-DGGE: polymerase chain reaction-denaturing gradient gel electrophoresis; PV: polycythaemia vera; SD: standard deviation; SNP: single nucleotide polymorphism; TET2: Ten–eleven translocation 2; UK: United Kingdom; USA: United States of America; WGS: whole-genome sequencing; WHO: World Health Organization.
Table
Table 2. The pooled prevalence of TET2 gene mutations in different subgroups of MPN.
Table 2. The pooled prevalence of TET2 gene mutations in different subgroups of MPN.
SubgroupsPrevalence
[95% CIs] (%)		Number of Studies AnalysedTotal Number of PatientsHeterogeneityPublication Bias, Egger’s Test (p-Value)
I2p-Value
Overall myeloproliferative neoplasms
Europe13.0 [8.8–17.2]19201092%<0.00010.004
North America17.4 [14.0–20.9]11197674%<0.00010.0005
Asia20.8 [10.5–31.1]429180%0.001NA
Australia7.0 [0.0–14.6]143NANANA
China23.9 [9.6–38.1]320082%0.003NA
France13.6 [10.6–16.7]54800%0.67NA
Germany14.2 [9.2–19.1]551061%0.03NA
Italy1.9 [0.0–5.7]260771%0.06NA
Spain10.7 [0.0–23.2]213082%0.01NA
USA17.4 [14.0–20.9]11197674%<0.00010.0005
WHO criteria reported15.7 [11.8–19.7]27378295%<0.00010.0002
WHO criteria not reported13.1 [8.9–17.3]853847%0.060.005
WHO 2001 criteria12.9 [10.2–15.5]36130%0.93NA
WHO 2008 criteria14.5 [9.7–19.3]17259495%<0.00010.0004
WHO 2016 criteria20.1 [14.7–25.4]757561%0.010.40
NGS method17.2 [14.0–20.4]18260480%<0.00010.0007
SS method12.7 [9.6–15.9]1196552%0.020.001
HRM method7.7 [0.0–16.6]362188%0.0002NA
Polycythaemia vera
Europe14.6 [8.0–21.1]1034363%0.010.58
North America18.2 [14.2–22.5]983957%0.01NA
Asia29.6 [14.1–45.2]23917%0.27NA
Australia0.0 [0.0–15.0]18NANANA
France12.5 [7.6–17.5]41720%0.90NA
Spain12.7 [0.0–37.2]22161%0.28NA
USA18.2 [14.0–22.5]983957%0.01NA
WHO 2001 criteria13.7 [9.6–17.9]32600%0.40NA
WHO 2008 criteria16.9 [11.3–22.6]1268569%0.00090.77
WHO 2016 criteria21.4 [15.6–27.3]425616%0.31NA
NGS method19.8 [15.1–24.6]1292267%0.00050.009
SS method13.0 [8.4–17.7]72030%0.71NA
HRM method0.0 [0.0–22.1]15NANANA
Essential thrombocythaemia
Europe8.8 [5.7–12.0]1253139%0.080.002
North America8.7 [3.8–13.6]750769%0.003NA
Asia25.1 [0.0–56.9]210093%0.0002NA
Australia6.2 [0.0–18.1]116NANANA
France9.7 [5.3–14.2]41660%0.44NA
Spain12.1 [0.0–21.2]24674%0.04NA
USA8.7 [3.8–13.6]750769%0.003NA
WHO 2001 criteria5.3 [1.1–9.6]318044%0.16NA
WHO 2008 criteria9.4 [6.1–12.6]1170049%0.030.06
WHO 2016 criteria20.3 [0.0–43.7]38189%<0.00010.41
NGS method10.2 [6.1–14.4]1278775%<0.00010.003
SS method10.4 [6.2–14.6]831631%0.18NA
HRM method14.9 [0.0–35.2]28283%0.01NA
Myelofibrosis
Europe13.7 [7.9–19.5]15112785%<0.00010.008
North America16.8 [12.3–23.7]964052%0.09NA
Asia17.4 [11.4–23.5]41520%0.82NA
Australia10.5 [0.0–24.3]119NANANA
China17.4 [11.2–23.6]31410%0.63NA
France17.6 [9.9–25.3]514220%0.51NA
Germany11.0 [8.0–14.0]54100%0.61NA
Italy0.4 [0.0–0.9]25270%0.50NA
Spain14.0 [0.0–39.4]23270%0.21NA
USA17.7 [13.8–21.6]863135%0.15NA
WHO 2001 criteria17.5 [11.9–23.3]31730%0.52NA
WHO 2008 criteria14.4 [8.1–20.7]15121090%<0.00010.04
WHO 2016 criteria16.5 [11.8–21.2]42380%0.390.20
NGS method16.5 [13.2–19.8]1389638%0.170.053
SS method13.3 [9.1–17.5]1144624%0.350.01
HRM method5.0 [0.0–18.2]353450%0.10NA
Different types of myelofibrosis
PMF16.7 [13.6–19.8]2085324%0.410.06
SMF14.8 [9.3–20.2]91580%0.95NA
CIs: confidence intervals; HRM: high-resolution melting analysis; NA: not applicable; NGS: next-generation sequencing; PMF: primary myelofibrosis; SMF: secondary myelofibrosis; SS: Sanger sequencing; WHO: World Health Organization.
Table
Table 3. Sensitivity analyses.
Table 3. Sensitivity analyses.
Strategies of Sensitivity AnalysesPrevalence
[95% CIs] (%)		Difference of Pooled Prevalence Compared to the Main ResultNumber of Studies AnalysedTotal Number of SubjectsHeterogeneity
I2p-Value
Myeloproliferative neoplasms (overall)
Excluding small studies13.6 [8.8–18.4]1.9% lower15311796%<0.0001
Excluding low- and moderate-quality studies15.4 [11.3–19.6]0.1% lower24348595%<0.0001
Excluding outlier studies13.9 [12.0–15.9]1.6% lower31363363%<0.0001
Polycythaemia vera
Excluding small studies15.6 [11.0–20.3]1.2% lower461459%0.06
Excluding low- and moderate-quality studies18.6 [14.4–22.7]1.8% higher1394659%0.003
Excluding outlier studies15.4 [12.0–18.7]1.4% lower19116154%0.01
Essential thrombocythaemia
Excluding small studies11.3 [5.9–16.8]1.5% higher347072%0.02
Excluding low- and moderate-quality studies11.1 [7.1–15.0]1.3% higher1283972%<0.0001
Excluding outlier studies8.4 [6.0–10.8]4.4% lower19109648%0.01
Myelofibrosis
Excluding small studies11.7 [4.0–19.5]4.0% lower6119195%<0.0001
Excluding low- and moderate-quality studies14.0 [8.9–19.1]1.7% lower18170091%<0.0001
Excluding outlier studies14.5 [12.4–16.7]1.2% lower25137718%0.46
CIs: Confidence intervals.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Chia, Y.C.; Islam, M.A.; Hider, P.; Woon, P.Y.; Johan, M.F.; Hassan, R.; Ramli, M. The Prevalence of TET2 Gene Mutations in Patients with BCR-ABL-Negative Myeloproliferative Neoplasms (MPN): A Systematic Review and Meta-Analysis. Cancers 2021, 13, 3078. https://doi.org/10.3390/cancers13123078

AMA Style

Chia YC, Islam MA, Hider P, Woon PY, Johan MF, Hassan R, Ramli M. The Prevalence of TET2 Gene Mutations in Patients with BCR-ABL-Negative Myeloproliferative Neoplasms (MPN): A Systematic Review and Meta-Analysis. Cancers. 2021; 13(12):3078. https://doi.org/10.3390/cancers13123078

Chicago/Turabian Style

Chia, Yuh Cai, Md Asiful Islam, Phil Hider, Peng Yeong Woon, Muhammad Farid Johan, Rosline Hassan, and Marini Ramli. 2021. "The Prevalence of TET2 Gene Mutations in Patients with BCR-ABL-Negative Myeloproliferative Neoplasms (MPN): A Systematic Review and Meta-Analysis" Cancers 13, no. 12: 3078. https://doi.org/10.3390/cancers13123078

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop