Effect of RAS Pathway Gene Mutations on Survival in Myelodysplastic Syndrome: A Systematic Review and Meta-Analysis

Abstract

Introduction

Myelodysplastic syndromes (MDS) are clonal hematopoietic disorders characterized by ineffective hematopoiesis, cytopenia, and risk of progression to acute myeloid leukemia. Somatic mutations in RAS pathway, including NRAS, KRAS, and PTPN11, are known contributors to leukemogenesis, yet their prognostic significance in MDS remains incompletely defined. This systematic review and meta-analysis assesses the impact of RAS pathway genes mutation on survival outcomes in adult patients with MDS.

Methods

PubMed, Embase, Scopus, Web of Science, and Gene Expression Omnibus were systematically searched on January 2025. This review included English-language studies involving adults with MDS that examined the impact of RAS pathway mutations on survival, including either hazard ratios or Kaplan-Meier data. Studies were excluded if they included only specific treatments, narrow subgroups, secondary MDS, or were not original research. Sixteen papers eventually met the inclusion criteria. Data extraction and quality assessment were independently performed by multiple reviewers. The methodological quality of each study was assessed using the MASTER scale. Hazard ratios were pooled using a random-effects model.

Results

Sixteen retrospective cohort studies involving 7969 patients tested for RAS pathway mutations were included. KRAS mutations were associated with poorer overall survival when compared to patients without the mutation (HR 1.66, 95% CI 1.32-2.08, P < 0.001). NRAS mutations were linked to worse overall survival (HR 1.73, 95% CI 1.46-2.04, P < 0.001) and leukemia-free survival (HR 2.48, 95% CI 1.47-4.18, P < 0.001) in comparison to those without the mutation. PTPN11 mutations were also associated with decreased overall survival (HR 1.36, 95% CI 1.01-1.85, P = 0.046) compared to individuals without the mutation.

Conclusion

Mutations in the RAS pathway, particularly NRAS, KRAS, and PTPN11, are associated with inferior survival outcomes in adult patients with MDS. These findings underscore the prognostic relevance of RAS mutations and highlight their potential utility in refining current risk stratification models such as IPSS-M, WPSS, and MDAS.

Keywords

MDS RAS NRAS KRAS PTPN11 survival

Introduction

Myelodysplastic neoplasms or myelodysplastic syndromes (MDS)¹ are a group of myeloid neoplasms marked by clonal proliferation of hematopoietic stem cells, genetic mutations, morphologic dysplasia, dysfunctional hematopoiesis leading to low counts of circulating blood cells and an increased risk of progression to acute myeloid leukemia (AML).^2,3 In the United States, MDS affects around 3 to 4 individuals per 100,000 people; however, among those aged 60 and older, the prevalence rises to 7 to 35 per 100,000.⁴

The World Health Organization (WHO) most recent classification of Hematolymphoid Tumors in 2022 classified MDS into two categories: MDS with defining genetic abnormalities and morphologically defined MDS. The first category includes the following: MDS with low blasts and isolated 5q deletion (MDS-5q), MDS with low blasts and splicing factor 3B subunit 1 (SF3B1) mutation (MDS-SF3B1), and MDS with biallelic tumor protein P53 (TP53) inactivation (MDS-biTP53). The second category included the following: MDS with low blasts (MDS-LB), hypoplastic MDS (MDS-h), and MDS with increased blasts (MDS-IB) which included MDS-IB1, MDS-IB2, and MDS with fibrosis (MDS-f).³

In 2022, the International Consensus Classification (ICC) of Myeloid Neoplasms and Acute Leukemias classified MDS into seven categories: MDS with mutated SF3B1 (MDS-SF3B1), MDS with del(5q), MDS - not otherwise specified (NOS) without dysplasia, MDS - NOS with single lineage dysplasia, MDS - NOS with multilineage dysplasia, MDS with excess blasts (MDS-EB), and MDS/AML.⁵ Although the WHO-2022 and the ICC-2022 classifications share several common terminologies, some disagreements remain. For example, the ICC reclassifies cases with 10-19 % blasts as MDS/AML, whereas the WHO categorizes the same cases as MDS-IB2.⁶

Several systems have been proposed for risk assessment in patients with MDS, with the most widely used being the International Prognostic Scoring System (IPSS), which was introduced in 1997.⁷ In 2012, it was revised and updated as the Revised International Prognostic Scoring System (IPSS-R).⁸ Both systems stratified risk and guided treatment decisions based on cytogenetic and hematologic features but do not incorporate somatic mutations. Most recently, in 2022, the Molecular International Prognostic Scoring System (IPSS-M) was developed to address this limitation. It integrates hematologic and cytogenetic data along with somatic mutations in 31 genes. Based on this score, patients are classified into one of six risk categories: very low, low, moderate, moderate-high, high, or very high.⁹

Nearly 50% of MDS patients exhibit cytogenetic abnormalities, with −7/del(7q) and −5/del(5q) being the most common. These abnormalities often occur within complex karyotypes (CKs), defined by the involvement of three or more chromosomes or chromosomal segments. Notably, TP53 mutations frequently co-occur with CKs, further worsening clinical prognosis.¹⁰

Over the past 15 years, advancements in sequencing techniques have significantly expanded our understanding of somatic mutations in MDS. The most frequently affected genes are those involved in DNA methylation, chromatin/histone modification, and RNA splicing.¹⁰

Mutations in Rat sarcoma (RAS) pathway genes—such as NRAS, KRAS, protein tyrosine phosphatase non-receptor type 11 (PTPN11), neurofibromin 1 gene (NF1), and Casitas B-lineage lymphoma proto-oncogene (CBL)—are also observed in MDS.¹⁰ Among these, NRAS and KRAS mutations occur in approximately 2-3% of adult MDS cases. Notably, RAS mutations typically arise as a late event, contributing to AML transformation. They are also associated with more severe disease, higher IPSS-M risk, and poorer prognosis, including lower event-free survival and overall survival.¹¹ However, their precise prognostic impact in MDS remains unclear, highlighting the need for this systematic review.

This systematic review aims to assess the impact of RAS mutations (NRAS, KRAS, PTPN11) on overall survival (OS), leukemia-free survival (LFS), and leukemia transformation in adult patients with MDS.

Methods

This systematic review and meta-analysis was registered on INPLASY under the number INPLASY202610049.

Search Strategy

The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)¹² guidelines were followed in this study.

On January 10, 2025, a systematic search was conducted across PubMed, Embase, Scopus, Web of Science, and the Gene Expression Omnibus (GEO), covering all records from the inception of each database up to the search date. The search terms included “MDS,” “Myelodysplastic Syndromes,” “RAS mutations,” “Prognosis,” “Overall survival,” and “Event-free survival.” The search strategy yielded 1997 records. Then, 677 duplicates were removed using Endnote, 1320 records were included and imported into Rayyan¹³ for title/abstract and full-text screening.

Eligibility Criteria

To be included in this systematic review, studies had to meet all of the following criteria: (1) involve adult participants (18 years and above); (2) assess the impact of a RAS pathway mutation (NRAS, KRAS, or PTPN11) on survival outcomes in MDS patients compared to those without the mutation; (3) report a hazard ratio (HR) or provide a Kaplan-Meier curve for overall survival (OS), leukemia-free survival (LFS) or leukemia transformation, (4) be published in English; (5) be a primary, original research study.

For studies that only provided Kaplan-Meier curves, individual patient data (IPD) were reconstructed using the method described by Liu et al.¹⁴ and these data were then used to estimate the HR.

Studies were excluded if they: (1) included only patients receiving a specific treatment (e.g., bone marrow transplant), (2) included only a highly specific subset of MDS patients (e.g., those with a 5q deletion), (3) examined only secondary MDS, or (4) were review articles, case reports, case series, or conference abstracts.

Screening Process

Four researchers (Y.E., A.S., A.O.S., E.A.) independently screened the titles and abstracts of the retrieved studies. Any disagreements were resolved by the senior investigator (S.F.). This screening resulted in the exclusion of 1197 records. The remaining 123 studies that met the inclusion criteria were imported for full-text review.

During the full-text review, a total of 108 reports were excluded for the following reasons: 60 studies did not address the population, exposure, or outcome of interest; 20 studies did not included hazard ratios or Kaplan-Meier curves; 12 articles were not in English; 9 were review articles, case reports, or comments; 5 had no full-text available online; and 1 was excluded because HR estimation from IPD was not possible.

Following the final selection of 16 eligible studies as shown in PRISM flow chart (Figure 1), data extraction was conducted using a standardized Excel sheet.

Figure 1.

PRISMA flow chart

Data Extraction

Data extraction was performed by the same authors who conducted the initial screening. If a study had implausible or important missing data, the corresponding author was contacted via the email address provided in the paper.

The extracted data included the first author’s name, year of publication, country of origin, sample size, median age, number of male and female participants, and the type of molecular test used.

Survival data comprised HRs for OS, LFS, and leukemia transformation. Whenever available, HRs from multivariable analyses were preferred; otherwise, HRs from univariable analyses were used.

OS was defined as the time from diagnosis to death from any cause or the date the patient was last known to be alive. LFS was defined as the time from MDS diagnosis to either AML progression or death, whichever occurred first. Leukemia transformation was defined as the time from MDS diagnosis to progression to AML.

Quality Assessment

The quality of the full manuscripts was assessed by two reviewers (R.M. and A.A.) using the Methodological Standards for Epidemiological Research (MASTER) scale¹⁵ which provides a standardized criteria to assess the quality of epidemiological studies.

Statistical Analysis

The association between various mutations and clinical outcomes (OS, LFS and leukemia transformation) was investigated and summarized as HRs and their corresponding 95% confidence intervals (CIs), comparing outcomes between patients with and without the mutations. A random-effects meta-analysis was performed using the restricted maximum likelihood (REML) method to estimate the between-study variance. P-values less than 0.05 were considered statistically significant. Statistical analysis was conducted using Stata version 17.

Ethic Statement

As this study is a meta-analysis of published data and did not involve human participants or unpublished individual data, ethical approval was not required.

Results

Ultimately, 16 studies^9,16-30 met the predefined inclusion criteria and were included in the meta-analysis, comprising a total of 9021 participants. Of these, 7969 individuals were tested for at least one mutation in the RAS pathway (NRAS, KRAS, PTPN11). The remaining 1052 patients did not undergo any mutation testing and hence were not included in the meta-analysis.

Studies were from multiple countries, including Germany, the USA, China, Taiwan, Australia, India, and Korea, with one multicenter cohort spanning institutions in both the USA and Germany (Table 1). The studies were published between 1993 and 2022, reflecting a mix of pre- and post-NGS-era molecular diagnostics. All the included studies were retrospective cohort studies (n = 16).^9,16-30 The proportion of male participants ranged from 54.6% to 68.8% across studies, with pooled female-to-male ratio of approximately 0.63. The reported median age was between 49 and 75.3 years, reflecting a predominantly older adult population in line with the typical age distribution of MDS.

Table 1.

Characteristics of Included Studies

Study	Country	Study design	Data collection years	Number of participants	Male (%)	Age, year (mean/median)	Tumor types
Paquette, 1993²⁵	USA	Retrospective cohort	1985-1986	234	63.2	NA	MDS
Dicker, 2010¹⁸	Germany	Retrospective cohort	2005-2008	269	NA	NA	MDS, sAML, CMML
Haferlach, 2013¹⁹	Germany	Retrospective cohort	2005-2011	611	61.5	73	MDS
Chen, 2014¹⁷	Taiwan	Retrospective cohort	1995- 2011	466	66.1	66	De novo MDS
Badar, 2015¹⁶	USA	Retrospective cohort	2004-2014	102	NA	65	MDS
Xu, 2017²⁸	China	Retrospective cohort	2008-2014	125	66.4	49	MDS
Singhal, 2019²⁷	Australia	Retrospective cohort	NA	108	65.7	75.3	Primary MDS
Hung, 2020²⁰	Taiwan	Retrospective cohort	NA	176	68.8	68.65	Primary MDS
Jiang, 2020³⁰	China	Retrospective cohort	2009-2019	382	63.1	58	De novo MDS
Kim, 2021²¹	Korea	Retrospective cohort	NA	266	62.8	50	MDS, CMML
Nazha, 2021²⁴	USA, Germany	Retrospective cohort	2001-2018	1471	67.0	71	MDS
Yan, 2021²⁹	China	Retrospective cohort	2008-2018	634	58.2	57	Primary MDS
Bernard, 2022⁹	Multicenter	Retrospective cohort	1999-2015	2957	60.1	72	MDS, MDS/MPN
Lee, 2022²²	Taiwan	Retrospective cohort	NA	698	63.3	66.5	Primary MDS
Maurya, 2022²³	India	Retrospective cohort	NA	152	54.6	55	Primary MDS
Ren, 2022²⁶	China	Retrospective cohort	2016-2020	370	66.8	60	MDS

Abbreviations: CMML, chronic myelomonocytic leukemia; MDS, myelodysplastic syndrome; MPN, myeloproliferative neoplasm; NGS, next-generation sequencing; PCR, polymerase chain reaction; sAML, secondary acute myeloid leukemia; NA, not available in the original paper.

Most studies included patients with primary de novo MDS, though some also enrolled individuals with MDS/myeloproliferative neoplasm (MDS/MPN) overlap syndromes, and a smaller proportion included cases that progressed to secondary AML. All included cohorts were based on bone marrow-confirmed diagnoses, using WHO or FAB classification systems appropriate to their respective publication periods.

Molecular diagnostics were most commonly performed using next-generation sequencing (NGS) and polymerase chain reaction (PCR). A few older studies used Sanger sequencing or other targeted techniques. Several studies included data on co-mutations, notably ASXL1, TET2, TP53, and RUNX1, although reporting was inconsistent.

Fourteen studies^9,16-30 reported HRs for OS; however, the HRs from one study³⁰ were excluded from NRAS and PTPN11 analyses due to implausible confidence interval values. Three studies^9,21,22 reported HRs for LFS, while four studies^9,18,24,29 reported HRs for leukemia transformation. An additional three studies^25,26,31 did not report HRs but presented KM curves instead. IPD were successfully reconstructed for two of these studies^25,26; however, reconstruction was not feasible for the third.³¹

All of the included studies demonstrated acceptable methodological quality based on the MASTER scale due to clearly defined endpoints, consistent use of diagnostic criteria, and appropriate statistical adjustments for. confounders. Total scores ranged from 23 to 31. Most studies scored highly in the Equal Recruitment safeguard. However, the Equal Ascertainment and Equal Prognosis safeguards received comparatively lower scores across studies, likely due to the retrospective observational design.

KRAS Mutation

Six studies^{9,19,20,23,24,30} evaluated the association between KRAS mutations and OS, yielding a pooled HR of 1.66 (95% CI: 1.32-2.08, P < 0.001) (Figure 2). Heterogeneity between the studies was minimal. Two studies^9,24 evaluated the association between KRAS mutation and leukemia transformation with a pooled HR of 1.41 (95% CI: 0.90-2.20, P = 0.13) (eFigure 1). Minimal heterogeneity was observed between the studies. Only Bernard et al.⁹ evaluated LFS (HR 1.42, 95% CI: 1.05-1.93, P = 0.02). Therefore, meta-analysis was not conducted for this outcome.

Figure 2.

Association of KRAS mutation and overall survival (OS)

NRAS Mutation

Twelve studies^9,17-29 examined the association between NRAS mutations and OS. The pooled HR was 1.73 (95% CI: 1.46-2.04, P < 0.001) and the heterogeneity was low (I² = 5.3%) (Figure 3). Four studies^9,18,24,29 investigated NRAS mutations in relation to leukemia transformation, with a pooled HR of 1.64 (95% CI: 1.17-2.30, P < 0.001) and minimal heterogeneity (I² = 0%) (Figure 4). Four studies^9,21,22,25 investigated NRAS mutations in relation to LFS, producing a pooled HR of 2.48 (95% CI: 1.47-4.18, P < 0.001) but between-study heterogeneity was substantial (I² = 71.5%) (Figure 5).

Figure 3.

Association of NRAS mutation and overall survival (OS)

Figure 4.

Association of NRAS mutation and leukemia transformation

Figure 5.

Association of NRAS mutation and leukemia-free survival (LFS)

PTPN11 Mutation

Two studies^9,24 assessed the association between PTPN11 mutations and OS, producing a pooled HR of 1.36 (95% CI: 1.01-1.85, P = 0.046) with minimal heterogeneity (I² = 0%) (eFigure 2). Moreover, the same two studies evaluated the association between PTPN11 mutation and leukemia transformation, resulting in a pooled HR of 2.11 (95% CI: 1.41-3.16, P < 0.001) (eFigure 3), with minimal heterogeneity. Only Bernard et al.⁹ evaluated LFS (HR 1.5, 95% CI: 1.02-2.23, P = 0.04). Therefore, meta-analysis was not conducted for this outcome.

RAS Mutation

Three^16,26,28 studies assessed the association between RAS mutations and OS. The pooled HR was 2.23 (95% CI: 1.51-3.29, P < 0.001), indicating a significant association between RAS mutations and worse OS. Heterogeneity between the studies was moderate (I² = 37.5%) (eFigure 4).

Discussion

MDS represent a biologically and clinically heterogeneous group of hematologic malignancies characterized by ineffective hematopoiesis, cytopenia, and a variable risk of progression to AML.^32,33 While age, gender, and treatment-related exposures remain important epidemiological factors,³² increasing emphasis has been placed on understanding the molecular drivers that underpin disease progression and prognosis. Among the numerous genetic lesions described in MDS, mutations in genes regulating epigenetics (e.g., TET2, DNMT3A, ASXL1), RNA splicing (e.g., SF3B1, SRSF2, U2AF1),³⁴ and signal transduction pathways (e.g., NRAS, KRAS, PTPN11)³⁵ have emerged as critical determinants of disease biology.

This systematic review and meta-analysis specifically focused on RAS pathway mutations, a relatively underexplored but increasingly recognized class of alterations in MDS. Our study synthesizes data from 16 retrospective cohort studies involving 9021 MDS patients, of whom 7969 underwent testing for RAS mutations. This represents one of the biggest and most comprehensive analyses of RAS pathway alterations in MDS to date.

The pooled hazard ratio (HR) for overall survival (OS) in patients harboring any RAS mutation was 2.23 (95% CI, 1.51-3.29), indicating a more than twofold increased risk of death compared to mutation-negative individuals.

Among the RAS genes, NRAS mutations showed the strongest prognostic effect (HR 1.73; 95% CI, 1.46-2.04), followed by KRAS (HR, 1.66; 95% CI, 1.32-2.08), and PTPN11 (HR, 1.36; 95% CI, 1.01-1.85).

These results support previous biological observations that RAS mutations promote cellular proliferation, survival, and leukemic transformation through sustained activation of downstream pathways such as PI3K/AKT/mTOR and MAPK/ERK.³⁶ In fact, leukemic transformation was significantly associated with NRAS (HR, 1.64) and PTPN11 (HR, 2.11) mutations, while KRAS showed a trend in the same direction (HR, 1.41). LFS was also negatively impacted, particularly in NRAS-mutated MDS (HR, 2.48), reinforcing the role of RAS activation in disease progression.

Our findings highlight important epidemiological and demographic patterns observed in MDS. The median patient age across studies ranged from 49 to 75.3 years, consistent with the known age dependency of MDS. A male predominance was noted (pooled female-to-male ratio ∼0.63), and across 13 studies reporting death rates, the average mortality was 34.6%, though follow-up times varied. Importantly, the geographic diversity of the data, involving the USA, China, Korea, India, Germany, Australia, and Taiwan—strengthens the generalizability of these findings. Moreover, the analysis focused on studies that are generalizable, systematically excluding those that involved very specific populations or treatments.

The differential clinical impact of NRAS vs KRAS mutations is particularly noteworthy. Although both belong to the same gene family and share high sequence homology (∼85%), they demonstrate distinct codon mutation profiles and biological behavior. NRAS mutations in MDS are distributed relatively evenly across codons G12, G13, and Q61, whereas KRAS mutations more frequently involve non-canonical codons such as K117 and Q146. These variations may contribute to differences in downstream signaling strength, clonal fitness, and transformation risk, aligning with the “sweet spot” hypothesis, which suggest that mutation-specific biochemical effects drive selection pressure in disease evolution.³⁷

Interestingly, the prognostic impact of RAS mutations appears to be modulated by both mutational burden and clonal architecture. High variant allele frequencies (VAF) in genes like NRAS and TET2 have been associated with increased disease aggressiveness, while co-mutation patterns involving genes such as EZH2 and TP53 may further influence outcomes. This supports the evolving classification of MDS-associated mutations into functional groups: type 1 mutations (e.g., NRAS, FLT3, IDH1/2, PTPN11) being more leukemia-prone and type 2 mutations (e.g., TP53, ASXL1, RUNX1) being more commonly seen in advanced but preleukemic disease states.³⁸

From a clinical standpoint, these findings underscore the importance of integrating RAS mutation testing into the standard diagnostic and prognostic workup for MDS patients. With the advent of models like IPSS-M that incorporate molecular features, RAS mutations should be considered not only for prognostication but potentially for risk-adapted treatment approaches. For example, patients with NRAS or PTPN11 mutations may benefit from earlier therapeutic intervention or enrollment in trials exploring MEK inhibitors or synthetic lethality-based strategies.

We acknowledge some limitations in this study. First, the retrospective nature of the included studies introduces potential biases, such as selection bias and reporting bias. Although the studies met predefined inclusion criteria and used rigorous molecular testing, the observational design of the studies limits the ability to establish causal relationships. Additionally, variability in treatment regimen, patient populations, and follow-up times could influence the generalizability and consistency of the results. Furthermore, while most studies employed next-generation sequencing for mutation detection, older studies used less sensitive methods like Sanger sequencing, which may have led to underreporting of certain mutations. The presence of missing data in some studies also impacted the completeness of survival analyses, though this was mitigated by reconstructing individual patient data (IPD) from Kaplan-Meier plots. Moreover, the inherent limitations of retrospective studies, such as variations in diagnostic criteria and the absence of uniform reporting standards for co-mutations, should be considered when interpreting the findings. Lastly, this study did not explore the effect of RAS pathway gene mutations on different subtypes of MDS which could be an area to explore in future research.

Conclusion

This meta-analysis provides robust evidence that RAS pathway mutations, particularly NRAS and PTPN11, are associated with inferior survival and increased leukemic transformation in MDS. The inclusion of these mutations in molecular scoring systems and therapeutic decision-making frameworks is warranted and may improve outcomes through earlier recognition of high-risk disease.

Supplemental Material

Supplemental Material - Effect of RAS Pathway Gene Mutations on Survival in Myelodysplastic Syndrome: A Systematic Review and Meta-Analysis

Supplemental Material for Effect of RAS Pathway Gene Mutations on Survival in Myelodysplastic Syndrome: A Systematic Review and Meta-Analysis by Yasmin Eslalakawi, Mohamed Omar Saad, Amin S Sanosi, Rowan Mesilhy, Abdulrahman F Al-Mashdali, Nabil E. Omar, Ahmed O Saleh, Elmustafa Abdalla, Amal Elfatih, Shehab F Mohamed in Cancer Control.

Footnotes

ORCID iDs

Yasmin Eslalakawi

Nabil E. Omar

Ethical Considerations

As this study is a meta-analysis of published data and did not involve human participants or unpublished individual data, ethical approval was not required.

Author Contributions

Conceptualization: S.F.

Data curation: Y.E., A.S.

Formal Analysis: M.S.

Investigation: Y.E., A.S., A.O.S., E.A.

Methodology: Y.E., R.M., A.A., A.E.

Project administration: Y.E., S.F.

Supervision: S.F.

Visualization: M.S., Y.E.

Writing – original draft: Y.E., M.S., N.O., S.F.

Writing – review & editing: All authors.

Funding

The authors received no financial support for the research, or authorship of this article.

Declaration of Conflicting Interests

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

Data Availability Statement

This study is a meta-analysis of published data which are accessible online. Upon request, the corresponding author can provide the data extraction forms.*

Study Protocol

INPLASY registration number: INPLASY202610049

Supplemental Material

Supplemental material for this article is available online.

Appendix

References

Sekeres

Taylor

. Diagnosis and treatment of myelodysplastic syndromes: a review. JAMA. 2022;328(9):872-880. doi:10.1001/jama.2022.14578

Cazzola

. Myelodysplastic syndromes. N Engl J Med. 2020;383(14):1358-1374. doi:10.1056/NEJMra1904794

Khoury

Solary

Abla

, et al. The 5th edition of the world health organization classification of haematolymphoid tumours: myeloid and histiocytic/dendritic neoplasms. Leukemia. 2022;36(7):1703-1719. doi:10.1038/s41375-022-01613-1

Garcia-Manero

. Myelodysplastic syndromes: 2023 update on diagnosis, risk-stratification, and management. Am J Hematol. 2023;98(8):1307-1325. doi:10.1002/ajh.26984

Arber

Orazi

Hasserjian

, et al. International consensus classification of myeloid neoplasms and acute leukemias: integrating morphologic, clinical, and genomic data. Blood. 2022;140(11):1200-1228. doi:10.1182/blood.2022015850

Lee

Lin

Tsai

, et al. Comparison of the 2022 world health organization classification and international consensus classification in myelodysplastic syndromes/neoplasms. Blood Cancer J. 2024;14(1):57. doi:10.1038/s41408-024-01031-9

Greenberg

Cox

LeBeau

, et al. International scoring system for evaluating prognosis in myelodysplastic syndromes. Blood. 1997;89(6):2079-2088.

Greenberg

Tuechler

Schanz

, et al. Revised international prognostic scoring system for myelodysplastic syndromes. Blood. 2012;120(12):2454-2465. doi:10.1182/blood-2012-03-420489

Bernard

Tuechler

Greenberg

, et al. Molecular international prognostic scoring system for myelodysplastic syndromes. NEJM Evid. 2022;1(7):EVIDoa2200008. doi:10.1056/EVIDoa2200008

10.

Ogawa

. Genetics of MDS. Blood. 2019;133(10):1049-1059. doi:10.1182/blood-2018-10-844621

11.

Alawieh

Cysique-Foinlan

Willekens

Renneville

. RAS mutations in myeloid malignancies: revisiting old questions with novel insights and therapeutic perspectives. Blood Cancer J. 2024;14(1):72. doi:10.1038/s41408-024-01054-2

12.

Page

McKenzie

Bossuyt

, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021;372:n71. doi:10.1136/bmj.n71

13.

Ouzzani

Hammady

Fedorowicz

Elmagarmid

. Rayyan-a web and mobile app for systematic reviews. Syst Rev. 2016;5(1):210. doi:10.1186/s13643-016-0384-4

14.

Liu

Zhou

Lee

. IPDfromKM: reconstruct individual patient data from published Kaplan-Meier survival curves. BMC Med Res Methodol. 2021;21(1):111. doi:10.1186/s12874-021-01308-8

15.

Ahmed

Kaleem

Elshoeibi

, et al. MASTER scale for methodological quality assessment: reliability assessment and update. J Evid Base Med. 2024;17(2):263-266. doi:10.1111/jebm.12618

16.

Badar

Patel

Thompson

, et al. Detectable FLT3-ITD or RAS mutation at the time of transformation from MDS to AML predicts for very poor outcomes. Leuk Res. 2015;39(12):1367-1374. doi:10.1016/j.leukres.2015.10.005

17.

Chen

Hou

Chou

, et al. Dynamics of ASXL1 mutation and other associated genetic alterations during disease progression in patients with primary myelodysplastic syndrome. Blood Cancer J. 2014;4(1):e177. doi:10.1038/bcj.2013.74

18.

Dicker

Haferlach

Sundermann

, et al. Mutation analysis for RUNX1, MLL-PTD, FLT3-ITD, NPM1 and NRAS in 269 patients with MDS or secondary AML. Leukemia. 2010;24(8):1528-1532. doi:10.1038/leu.2010.124

19.

Haferlach

Nagata

Grossmann

, et al. Landscape of genetic lesions in 944 patients with myelodysplastic syndromes. Leukemia. 2013;28(2):241-247. doi:10.1038/leu.2013.336

20.

Hung

Lin

Hsu

, et al. The expression levels of long non-coding RNA KIAA0125 are associated with distinct clinical and biological features in myelodysplastic syndromes. Br J Haematol. 2020;192(3):589-598. doi:10.1111/bjh.17231

21.

Kim

Jung

Hur

, et al. Clinical implications of copy number alteration detection using panel-based next-generation sequencing data in myelodysplastic syndrome. Leuk Res. 2021;103:106540. doi:10.1016/j.leukres.2021.106540

22.

Lee

Lin

Tsai

, et al. Effect of mutation allele frequency on the risk stratification of myelodysplastic syndrome patients. Am J Hematol. 2022;97(12):1589-1598. doi:10.1002/ajh.26734

23.

Maurya

Mohanty

Dhangar

, et al. Comprehensive analysis of genetic factors predicting overall survival in myelodysplastic syndromes. Sci Rep. 2022;12(1):5925. doi:10.1038/s41598-022-09864-9

24.

Nazha

Komrokji

Meggendorfer

, et al. Personalized prediction model to risk stratify patients with myelodysplastic syndromes. J Clin Oncol. 2021;39(33):3737-3746. doi:10.1200/JCO.20.02810

25.

Paquette

Landaw

Pierre

, et al. N-ras mutations are associated with poor prognosis and increased risk of leukemia in myelodysplastic syndrome. Blood. 1993;82(2):590-599.

26.

Ren

Lang

Mei

, et al. Co-mutation landscape and clinical significance of RAS pathway related gene mutations in patients with myelodysplastic syndrome. Hematol Oncol. 2022;41(1):159-166. doi:10.1002/hon.3099

27.

Singhal

Wee

LYA

Kutyna

, et al. The mutational burden of therapy-related myeloid neoplasms is similar to primary myelodysplastic syndrome but has a distinctive distribution. Leukemia. 2019;33(12):2842-2853. doi:10.1038/s41375-019-0479-8

28.

, et al. Implications of mutational spectrum in myelodysplastic syndromes based on targeted next-generation sequencing. Oncotarget. 2017;8(47):82475-82490. doi:10.18632/oncotarget.19628

29.

Yan

Wang

Jiang

, et al. Clinical significance of cytogenetic and molecular genetic abnormalities in 634 Chinese patients with myelodysplastic syndromes. Cancer Med. 2021;10(5):1759-1771. doi:10.1002/cam4.3786

30.

Jiang

Wang

Shen

, et al. Impact of mutational variant allele frequency on prognosis in myelodysplastic syndromes. Am J Cancer Res. 2020;10(12):4476-4487.

31.

Al-Kali

Quintás-Cardama

Luthra

, et al. Prognostic impact of RAS mutations in patients with myelodysplastic syndrome. Am J Hematol. 2013;88(5):365-369. doi:10.1002/ajh.23410

32.

Rollison

Howlader

Smith

, et al. Epidemiology of myelodysplastic syndromes and chronic myeloproliferative disorders in the United States, 2001-2004, using data from the NAACCR and SEER programs. Blood. 2008;112(1):45-52. doi:10.1182/blood-2008-01-134858

33.

Papaemmanuil

Gerstung

Malcovati

, et al. Clinical and biological implications of driver mutations in myelodysplastic syndromes. Blood. 2013;122(22):3616-3627. doi:10.1182/blood-2013-08-518886

34.

Gallì

Todisco

Catamo

, et al. Relationship between clone metrics and clinical outcome in clonal cytopenia. Blood. 2021;138(11):965-976. doi:10.1182/blood.2021011323

35.

Schwind

Jentzsch

Kubasch

Metzeler

Platzbecker

. Myelodysplastic syndromes: biological and therapeutic consequences of the evolving molecular aberrations landscape. Neoplasia. 2021;23(11):1101-1109. doi:10.1016/j.neo.2021.09.002

36.

Haigis

Kendall

Wang

, et al. Differential effects of oncogenic K-Ras and N-Ras on proliferation, differentiation and tumor progression in the colon. Nat Genet. 2008;40(5):600-608. doi:10.1038/ng.115

37.

Balmain

Counter

. A model for RAS mutation patterns in cancers: finding the sweet spot. Nat Rev Cancer. 2018;18(12):767-777. doi:10.1038/s41568-018-0076-6

38.

Pellagatti

Roy

Di Genua

, et al. Targeted resequencing analysis of 31 genes commonly mutated in myeloid disorders in serial samples from myelodysplastic syndrome patients showing disease progression. Leukemia. 2016;30(1):247-250. doi:10.1038/leu.2015.129

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