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Hussein, S.E.;  Loghavi, S. Molecular Landscape of Myelodysplastic Neoplasms. Encyclopedia. Available online: https://encyclopedia.pub/entry/37673 (accessed on 05 December 2025).
Hussein SE,  Loghavi S. Molecular Landscape of Myelodysplastic Neoplasms. Encyclopedia. Available at: https://encyclopedia.pub/entry/37673. Accessed December 05, 2025.
Hussein, Siba El, Sanam Loghavi. "Molecular Landscape of Myelodysplastic Neoplasms" Encyclopedia, https://encyclopedia.pub/entry/37673 (accessed December 05, 2025).
Hussein, S.E., & Loghavi, S. (2022, December 01). Molecular Landscape of Myelodysplastic Neoplasms. In Encyclopedia. https://encyclopedia.pub/entry/37673
Hussein, Siba El and Sanam Loghavi. "Molecular Landscape of Myelodysplastic Neoplasms." Encyclopedia. Web. 01 December, 2022.
Molecular Landscape of Myelodysplastic Neoplasms
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This entry is adapted from the peer-reviewed paper 10.3390/cancers14225690

Conventional prognostication of myelodysplastic neoplasms (MDS) was performed using the revised International Prognostic Scoring System (IPSS-R), with additional adverse prognoses conferred by select mutations. Nonetheless, the clonal diversity and dynamics of coexisting mutations have been shown to alter the prognosis and treatment response in patients with MDS. Often in the process of clonal evolution, various initial hits are preferentially followed by a specific spectrum of secondary alterations, shaping the phenotypic and biologic features of MDS. The researchers' ability to recapitulate the clonal ontology of MDS is a necessary step toward personalized therapy and the conceptualization of a better classification system, which ideally would take into consideration all genomic aberrations and their inferred clonal architecture in individual cases.

myelodysplastic syndrome myelodysplastic neoplasm MDS

1. Introduction

Myelodysplastic syndromes (MDS) are now collectively referred to as myelodysplastic neoplasms by the WHO 5th edition, with the abbreviation “MDS” preserved [1] to emphasize the definition of MDS as clonal hematopoietic stem cell neoplasms and harmonize the terminology with myeloproliferative neoplasms (MPN). The pathogenesis of MDS includes initial ancestral lesions, often in the form of early CHIP (clonal hematopoiesis with indeterminate potential)-type or CHOP (clonal hematopoiesis with oncogenic potential)-type alterations followed by the acquisition of additional genetic aberrations (late and/or transforming events) that result in MDS, often with a highly diverse clonal hierarchy. The clonal sweeping of existing subclones by driver mutations play a critical role in MDS disease progression, refuting the previously accepted theory of linear evolution [2][3]. Deciphering mutational clonal trajectories uncovers key events that drive leukemic evolution in myeloid neoplasms. In fact, mutational hierarchical configuration can potentially delineate the fate of myeloid neoplasms and further allow people to predict the prognosis and clinical course of a disease.
Deciphering the clonal evolution of MDS up to its cell of origin is not feasible in most cases in the clinical setting; however, several investigations have provided valuable insights into the role of driver mutations in determining the trajectory of MDS development. Clonal hierarchies are best characterized using single-cell sequencing methods, preferably on longitudinal samples. A precise reconstruction of clonal ontology could be achieved by cross-sectional analysis of clonal architecture through single-cell sequencing [4][5]. Categorizing genetic aberrations into dominant versus secondary classes may enhance the researchers' understanding of MDS pathogenesis, as different driver mutations have distinct effects on disease progression and phenotypic manifestations in MDS as shown in multiple studies that combined large genotyping data sets [3][6][7][8], whole exome sequencing (WES), and targeted sequencing data [9].
Until recently, conventional prognostication of MDS was performed using the Revised International Prognostic Scoring System (IPSS-R), with adverse prognosis conferred by select mutations (i.e., ASXL1, EZH2, TP53). The recent introduction of the Molecular International Prognostic Scoring System (IPSS-M) for MDS [10] is a major step forward toward personalized care. Yet, established prognostic scoring systems are designed to be most informative and accurate when applied at diagnosis and prior to the initiation of therapy. Considering the numberof patients with MDS who receive immunomodulatory or hypomethylating agent therapy, the need for a more dynamic prognostic system, applicable at any time point in the course of disease and irrespective of therapy, is an unmet need. Furthermore, criteria for MDS complete remission (CR) in the bone marrow depend upon morphologic evaluation that includes the presence of <5% myeloblasts in the marrow with normal trilineage maturation [11]. These criteria have inherent limitations, as they depend on sample quality and morphologists’ experience, affecting their reproducibility and predictive value. In addition, they don’t consider underlying molecular events that may not translate into obvious morphologic abnormalities and/or increased blasts.
In this research, the researchers provide an overview for the role of co-mutational patterns, clonal size, and hierarchy in determining clinical phenotype, prognosis, and response to therapy in patients with MDS. In addition, the researchers provide a brief account of current and emerging promising tools to help them better refine their diagnostic and prognostic abilities in MDS.

2. Clonal Heterogeneity in MDS

Mutational catalogs in MDS represent a historical record of alterations that have accumulated during life. The heterogeneity among neoplastic cells can often be used to infer the temporal order of these events [12]. Alterations identified in every sequenced neoplastic cell can be considered to form the trunk of somatic mutations’ evolutionary tree, while subclonal mutations, present in only a subset of neoplastic cells, make up the branches [12]. A neoplastic clone emerges from a single cell that has acquired one or several somatic alterations. Additional driver events that occur in individual daughter cells generate subclones, each endowed with specific functional properties and fitness [12]. Nevertheless, this intraclonal genetic diversity may not explain the entire spectrum of functional heterogeneity among individual cells within a tumor clone. Table 1 provides a glossary of genetic terms discussed below pertaining to clonal evolution in MDS.
Table 1. Glossary of major concepts pertaining to clonal evolution in myelodysplastic neoplasms.
Term Definition
Clonal Hierarchy The order in which mutations are acquired. “Founder” (ancestral) mutations, typically involving epigenetic modifiers and splicing factors in MDS, occur early and are followed by “successor” alterations, which typically involve the signaling cascades, transcription factors, and copy number alterations
Clonal Burden Size of a population with a specific genetic aberrancy (for mutations, typically inferred using variant allele frequency “VAF” by next-generation sequencing analysis)
Clonal Sweeping The process by which a neoplastic population develops new advantageous genetic lesions, allowing it to expand and dominate the neoplasm (“selective sweeps” or “positive selection”)
Neutral Growth Egalitarian propagation of neoplastic populations harboring genetic aberrations that do not affect cellular fitness
Clonal Heterogeneity Genetic diversity among neoplastic populations resulting from an interplay between clonal sweeping and/or neutral growth
Mutational Combination Pre-determined (in contrast to random) co-occurrence of certain mutations resulting in a specific disease phenotype
Epistasis The conditional relationship between two genes that can determine a single phenotype (an allele of one gene hides or masks the phenotype of another gene)
A plethora of bioinformatic tools have been developed to help decipher the temporal order of mutations and clonality (clonal vs subclonal) in neoplastic conditions. Additionally, orthogonal tools to dissect heterogeneity using data from single-cell sequencing methods have also been developed more recently. The prevalence of subclonal mutations in different cell populations can be used to infer the clonal hierarchy of MDS. Accumulating evidence suggests that certain drivers are more likely to be subclonal than others [12]. Such differences may reflect the importance of epistasis in cancer evolution and agree with findings that co-occurrence and mutual exclusivity relationships between driver alterations can vary extensively in different cancer types.
Furthermore, neoplastic cells are subject to selection, and the genetic variation between these cells, influenced by endogenous and exogenous processes, provides the fuel for selection to act. Although heterogeneity is required for neoplastic clones to evolve, positive selection does not necessarily lead to heterogeneity, as pressure imposed by therapy among other epigenetic factors can follow the laws of neutral growth [13]. The relationship between the number of subclonal mutations and their frequency could be consistent with a neutral growth pattern rather than subclonal expansions, leading to heterogeneity detected in MDS. Thus, both selection and neutral growth may cooperate. However, this dynamic may change over the course of time, and diversity may lead to a selection of aggressive subclones, irrespective of therapeutic pressures.
Enrichment of subclonal mutations in MDS suggests that positive selection for certain mutations is present throughout its evolutionary timeline, and that the prediction of distinct clinical behaviors depending on the presence of certain mutations at a certain point is subject to ongoing investigation, even though the occurrence of neutral evolution and drift may limit the ability to predict patterns of growth. Clonal heterogeneity and mutational diversity as assessed by whole exome sequencing (WES) seem to be lowest in low-risk MDS and increased in myelodysplastic/myeloproliferative neoplasms (MDS/MPN), high-risk MDS, and secondary AML (sAML), where it tends to be highest [9]. The limitations of using WES to decipher disease-specific genomic alterations should be noted, as the exome comprises only ~1.2% of the whole genome, and the vast majority of single nucleotide pleomorphism (~94%) occurs within non-coding genomic regions, limiting the researchers' understanding of phenotypic manifestations and therapeutic responses to treatment.

3. Recurrent Cytogenetic and Molecular Alterations in MDS

Copy number alterations (CNAs), defined as gains or losses of chromosome material, comprise the majority of unbalanced chromosomal aberrations in MDS [14], with s del(7q) and del(5q), followed by trisomy 8, dup(1q), del(20q), del(11q), del(12p)/t(12p), del(17p)/ iso(17q) and del(13q) representing the most common forms.: [15]. Complex karyotypes (CKs), defined as harboring >3 chromosomal aberrations, are frequently associated with TP53 alterations(s), conferring a dismal clinical outcome [16][17][18]. Furthermore, it has been shown in a large cohort of MDS patients that the allelic state of TP53 (mono-allelic versus bi-allelic alterations) is critical for prognostication in MDS, as multi-hit (bi-allelic) TP53 lesions, which are found in approximately 2/3 of MDS with TP53 aberrations, may predict risk of death and leukemic transformation independently of the IPSS-R, whereas MDS with monoallelic TP53 alterations seems to behave similarly to MDS with wild-type TP53 with respect to hematologic parameters and outcomes [19]. Accordingly, both the WHO 5th edition [1] and the International Consensus Classification (ICC) [20] have introduced specific MDS subclassifications accounting for cases with bi-allelic loss of TP53 (further discussed below) (Figure 1).
Figure 1. Multi-hit TP53 defining alterations.
A large proportion of MDS patients harbor one or more recurrent mutation(s) in association with CNAs [8][21]. Involved genetic pathways include epigenetic regulation via DNA methylation (TET2, DNMT3A, and IDH1/IDH2) [22][23][24], chromatin/histone modification (KMT2D, EZH2, ARID2 and ASXL1) [25][26], and RNA splicing (SF3B1, SRSF2, U2AF1, U2AF2, and ZRSR2) [21][27][28]. Several other molecules and pathways may be involved, including TP53 and the DNA repair machinery (PPM1D, BRCC3, FANCL, and ATM) [8][21], cohesion complex and associated proteins (STAG2, RAD21, SMC1A, SMC3, NIPBL, PDS5B, and CTCF) [29], transcription factors (RUNX1, ETV6, GATA2, and IRF1), the RAS pathway (NRAS, KRAS, PTPN11, NF1, and CBL) [30], and other signaling molecules (JAK2, KIT, MPL, GNB1, and less commonly FLT3) [8][21]. Table 2 summarizes key genetic aberrations in MDS.
Table 2. Key genetic landscape in myelodysplastic neoplasms (MDS). Recurrent abnormalities can be broadly divided into mutations and cytogenetic aberrations. The most common cytogenetic alterations include del(7q), del(5q) & trisomy 8. Recurrent mutations in MDS affect several cellular pathways and functions, most commonly DNA methylation, chromatin/histone modification, RNA splicing, cohesion complex, the RAS pathway, DNA repair machinery, and several signaling molecules. Recurrent mutations can be further divided into early “CHIP (clonal hematopoiesis with indeterminate potential)-type” affecting genes commonly involved in clonal hematopoiesis, “CHOP (clonal hematopoiesis with oncogenic potential) -type,” and late and/or transforming events that affect genes not classically associated with clonal hematopoiesis. Three genetic events are MDS class-defining according to the 5th edition of the World Health Organization (WHO) classification and the International Consensus Classification (ICC), these include isolated del(5q), SF3B1 mutations and multi-hit/bi-allelic TP53 alterations. The International Prognostic Scoring System-Molecular (IPSS-M) identifies multi-hit TP53 alterations, FLT3 mutations, and KMT2A partial tandem duplication as the top three genetic predictors of leukemic transformation and adverse outcomes in patients with MDS.
Recurrent Cytogenetic Abnormalities
Most common: del(7q), del(5q) & + 8
Others: dup(1q), del(20q), del(11q), del(12p)/t(12p), del(17p)/ i(17q), del(18q), 12q gains, del(13q), der(1;7)(q10;p10)
Recurrent Mutations
DNA methylation:TET2, DNMT3A & IDH1/IDH2
Chromatin/histone modification:KMT2D, EZH2, ARID2 & ASXL1
RNA splicing:SF3B1, SRSF2, U2AF1, U2AF2 & ZXRSR2
Cohesin complex:STAG2, RAD21, SMC1A, SMC3, NIPBL, PDS5b & CTCF
Transcription:RUNX1, ETV6, GATA2 & IRF1
RAS pathway:NRAS, KRAS, PTPN11, NF1 & CBL
DNA repair machinery:TP53, PPM1D, BRCC3, FANCL & ATM
Signaling molecules:JAK2, KIT, MPL, GNB1 & FLT3
Early CHIP (clonal hematopoiesis with indeterminate potential)-type
DNMT3A, TET2, ASXL1, IDH2
CHOP (clonal hematopoiesis with oncogenic potential)-type
U2AF1, SRSF2, ZRSR2, SF3B1
Late/ Transforming mutations
ASXL1, RUNX1, TP53, EZH2, SETBP1, STAG2, NPM1, FLT3, PTPN11, N/KRAS, CBL, WT1, PHF6
MDS-Defining Genetic Abnormalities Per WHO & ICC
Isolated del(5q), SF3B1, Multi-hit/bi-allelic TP53 mutations
IPSS-M Top Predictors of Adverse Outcomes
Multi-hit TP53 alterations, FLT3 mutation, KMT2A partial tandem duplication

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