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Increased risk of leukaemia in children with Down syndrome: a somatic evolutionary view

Published online by Cambridge University Press:  27 April 2021

K. A. L. Hasaart
Affiliation:
Princess Máxima Center for Pediatric Oncology, Heidelberglaan 25, 3584 CSUtrecht, the Netherlands Oncode, Utrecht, the Netherlands
E. J. M. Bertrums
Affiliation:
Princess Máxima Center for Pediatric Oncology, Heidelberglaan 25, 3584 CSUtrecht, the Netherlands Oncode, Utrecht, the Netherlands Department of Pediatric Oncology/Hematology, Erasmus Medical Center, Doctor Molewaterplein 40, 3015 GD, Rotterdam, the Netherlands
F. Manders
Affiliation:
Princess Máxima Center for Pediatric Oncology, Heidelberglaan 25, 3584 CSUtrecht, the Netherlands Oncode, Utrecht, the Netherlands
B. F. Goemans
Affiliation:
Oncode, Utrecht, the Netherlands
R. van Boxtel*
Affiliation:
Princess Máxima Center for Pediatric Oncology, Heidelberglaan 25, 3584 CSUtrecht, the Netherlands Oncode, Utrecht, the Netherlands
*
Author for correspondence: R. van Boxtel, E-mail: R.vanBoxtel@prinsesmaximacentrum.nl
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Abstract

Children show a higher incidence of leukaemia compared with young adolescents, yet their cells are less damaged because of their young age. Children with Down syndrome (DS) have an even higher risk of developing leukaemia during the first years of life. The presence of a constitutive trisomy of chromosome 21 (T21) in DS acts as a genetic driver for leukaemia development, however, additional oncogenic mutations are required. Therefore, T21 provides the opportunity to better understand leukaemogenesis in children. Here, we describe the increased risk of leukaemia in DS during childhood from a somatic evolutionary view. According to this idea, cancer is caused by a variation in inheritable phenotypes within cell populations that are subjected to selective forces within the tissue context. We propose a model in which the increased risk of leukaemia in DS children derives from higher rates of mutation accumulation, already present during fetal development, which is further enhanced by changes in selection dynamics within the fetal liver niche. This model could possibly be used to understand the rate-limiting steps of leukaemogenesis early in life.

Type
Review
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0), which permits unrestricted re- use, distribution and reproduction, provided the original article is properly cited.
Copyright
Copyright © The Author(s), 2021. Published by Cambridge University Press

Introduction

Down syndrome (DS) is the most common chromosomal disorder and has a prevalence of approximately one in every 700 live births worldwide (Refs Reference Parker1, Reference Antonarakis2). DS is caused by a full trisomy of chromosome 21 (T21) in 90% of the cases (Ref. Reference Ross3). Nevertheless, other chromosome 21 abnormalities also cause DS, such as partial T21, translocations involving chromosome 21 and mosaic T21 (Refs Reference Ross3, Reference Papavassiliou4). The DS-related clinical phenotypes involve multiple body systems, in particular the neurological, cardiovascular and musculoskeletal systems (Ref. Reference Antonarakis5). These typical DS-phenotypes are believed to result directly from the extra copy of human chromosome 21 (Ref. Reference Banno6). Studies performed in DS individuals with partial T21 suggest that a critical region of this chromosome (DS critical region; DSCR) is responsible for these phenotypes (Ref. Reference Banno6).

DS individuals show a unique cancer distribution pattern during life (Refs Reference Ross3, Reference Lee7, Reference Hasle8). They have an increased risk of developing leukaemia during the first years of life and a marginal increased risk of developing germ-cell tumours, but show a decreased risk of solid tumours throughout life (Refs Reference Hasle8Reference Hasle, Clemmensen and Mikkelsen10). DS children have a 500-fold increased risk of developing myeloid leukaemia of DS (ML-DS), a subtype of acute megakaryoblastic leukaemia (AMKL) (Ref. Reference Zipursky9). ML-DS is often preceded by transient abnormal myelopoiesis (TAM) (Ref. Reference Vardiman11), which occurs in 5–30% of all neonates with DS, depending on the diagnostic criteria that are used (Refs Reference Roberts and Izraeli12, Reference Zipursky13). TAM is characterised by circulating myeloid leukaemic blasts in peripheral blood, harbouring mutations in GATA1 (Refs Reference Roberts and Izraeli12, Reference Zipursky13). TAM spontaneously disappears within the first 3 months after birth; however, 20% of these patients subsequently develop ML-DS before the age of five years (Refs Reference Zipursky13, Reference Sas14). ML-DS in these children is characterised by the same unique GATA1 mutation that occurred in the (pre)leukaemic TAM blasts as well as additional oncogenic mutations (Ref. Reference Alford15). In addition, the risk of acute lymphoblastic leukaemia (ALL) during the first years of life is increased 7–20 times compared with non-DS children (Refs Reference Ross3, Reference Zipursky9, Reference Roberts and Izraeli12). These leukaemias consist almost exclusively of B-cell precursor ALL (BCP-ALL) (Refs Reference Ross3, Reference Zipursky9, Reference Roberts and Izraeli12). Both DS acute lymphoblastic leukemia (DS-ALL) and ML-DS present with different genetic aberrations compared with their non-DS counterpart (Refs Reference Mullighan16Reference Buitenkamp18). The genetically and clinically distinct characteristics of TAM, ML-DS and DS-ALL compared with other paediatric leukaemias raise the question of why children with DS have an increased risk of developing leukaemia during their first years of life.

In this review, we provide a comprehensive overview of available literature and describe the increased risk of leukaemia in DS as a process of Darwinian evolution occurring among cell populations (Ref. Reference Stratton, Campbell and Futreal19). In this model, inheritable phenotypic diversity between cells is the substrate of selective forces driven by specific ecological features present within tissues (Ref. Reference Stratton, Campbell and Futreal19). Somatic mutations that accumulate throughout life as well as epigenetic changes can drive this phenotypic diversity. Ultimately, oncogenic mutations can provide a cell with a growth advantage, which in the correct tissue context results in clonal expansion and eventually cancer (Ref. Reference Stratton, Campbell and Futreal19). We hypothesise that the phenotypic diversity of cells, which is already present during fetal development (Refs Reference Hasaart20Reference Osorio22), initiates leukaemic development in DS. Subsequent progression towards full-blown leukaemia is then fuelled by changes in selection dynamics of the niche as the site of haematopoiesis migrates from the liver to the bone marrow. Studies have shown that non-DS paediatric leukaemia often has an in utero origin as well, since mutations driving paediatric leukaemia have been detected in neonatal bloodspots (Ref. Reference Greaves23). If this model can indeed be used to explain the high risk of leukaemia in DS, then this could potentially be extended to understand the rate-limiting steps of leukaemogenesis in general early in life.

Heritable phenotypic diversity in haematopoietic stem cell pools

Cell-intrinsic mutational processes and exogenous mutagenic exposures cause DNA damage, which will result in somatic mutations if the damage is not efficiently or incorrectly repaired (Ref. Reference Chatterjee and Walker24). Although DNA repair pathways are highly efficient, a part of the damage escapes these mechanisms, resulting in a gradual accumulation of somatic mutations throughout life (Refs Reference Osorio22, Reference Blokzijl25). This accumulation of mutations with age may explain why ageing is the biggest risk factor for developing cancer (Ref. Reference Vogelstein26). Indeed, the higher the number of somatic mutations in the genome of a cell, the higher the chance that one of these genetic alterations may serve as an oncogenic event. In DS children, T21 serves as the first genetic driver for leukaemic development (Refs Reference Roberts and Izraeli12, Reference Laurent, Kotecha and Malinge27). Importantly, T21, either complete or partially, is one of the most frequent chromosomal alterations found in paediatric B-ALL and is often seen in non-DS AMKL (Refs Reference Laurent, Kotecha and Malinge27, Reference Hama28). Moreover, two percent of all paediatric B-ALLs show an intra-chromosomal amplification of chromosome 21 (iAMP21), which overlaps largely with the DSCR. This overlap stresses the importance of T21 and the DSCR as an initiation event in leukaemogenesis (Ref. Reference Laurent, Kotecha and Malinge27). However, additional drivers are required for the initiation of leukaemogenesis (Ref. Reference Roberts and Izraeli12). Besides oncogenic mutations, also epigenetic changes, such as alterations in DNA methylation and chromatin modifications, are known to be associated with DS-associated leukemogenesis (Ref. Reference Letourneau29).

Genetic drivers of progression towards TAM and ML-DS

T21 in combination with a somatic truncating mutation in GATA1, acquired prenatally, is required and sufficient for the development of TAM (Refs Reference Yoshida30Reference Taub32). GATA1 is located on the X-chromosome and encodes a haematopoietic transcription factor with essential functions for the differentiation of haematopoietic stem cells (HSCs) towards the erythroid and megakaryocytic lineages (Ref. Reference Hitzler33). Most mutations are found in exon 2 of GATA1 and result in an N-terminally truncated protein isoform GATA1s because of the utilisation of an alternative start codon located in exon 3 (Refs Reference Antonarakis2, Reference Alford15, Reference Taub32, Reference Garnett, Cruz Hernandez and Vyas34). GATA1s lacks the amino-terminal activation domain and has a reduced transactivation potential, which causes decreased expression of GATA1 target genes (Ref. Reference Sas14). Although the DNA binding domain of GATA1s is intact, GATA1s binding is impaired at specific erythroid regulatory regions and the MYC promoter, whereas binding to megakaryocytic and myeloid target genes is normal (Refs Reference Klusmann35Reference Byrska-Bishop38). Also, studies have shown that GATA1s is not able to bind to RUNX1, an important protein for megakaryocyte differentiation; however, evidence is inconsistent (Ref. Reference Vyas and Crispino39). As a result, GATA1s in T21 causes aberrant differentiation and proliferation of megakaryocytes, which results in the production of megakaryoblasts (Refs Reference Banno6, Reference Li40). In addition, the expression of GATA1s is increased by the extra copy of the 4 megabase DSCR containing RUNX1, ETS2 and ERG, which further promotes the proliferation of megakaryoblasts (Refs Reference Banno6, Reference Li40).

Upon progression to ML-DS later in life, the same GATA1 mutations are present, originating from a dominant or minor TAM clone, which indicates that ML-DS evolves from a persistent TAM clone during clinical remission (Refs Reference Alford15, Reference Hitzler33, Reference Labuhn41). Nonetheless, additional driver mutations are required for progression towards ML-DS (Refs Reference Alford15, Reference Yoshida30). Thus far identified additional drivers include mutations in cohesins (53%) or CTCF (20%), EZH2, KANSL1 and other epigenetic regulators (45%) and common signalling pathways including JAK family kinases, MPL, SH2B3, CSFR2B and multiple RAS pathway genes (47%), which all provide the cell with a growth advantage (Refs Reference Yoshida30, Reference Nikolaev31, Reference Labuhn41). Cohesins are essential for DNA replication and repair and are thought to have a tumour-suppressor function in cancer (Refs Reference Viny and Levine42, Reference Cuartero, Innes and Merkenschlager43). Models of dose-specific cohesin loss have shown HSC expansion and impairments in differentiation as a result of an open-chromatin state, which causes increased transcription of genes involved in self-renewal (Refs Reference Viny and Levine42, Reference Cuartero, Innes and Merkenschlager43). Mutations in epigenetic regulators and CTCF, which directly interacts with cohesins, are known to disrupt the expression of genes involved in HSC renewal and differentiation by chromatin modifications (Refs Reference Fisher44, Reference Ernst45). Both the JAK-STAT pathway and RAS signalling pathway are involved in the regulation of cell proliferation, survival and differentiation (Refs Reference Vainchenker and Constantinescu46, Reference Mercher and Schwaller47). Mutations in these genes have been identified in different haematological malignancies and have been shown to contribute to the proliferation of leukaemic blasts (Refs Reference Vainchenker and Constantinescu46, Reference Mercher and Schwaller47). In conclusion, mutations in these genes could all result in increased cell survival, cell proliferation and impairments in the differentiation of the oncogenic clone (Refs Reference Viny and Levine42, Reference Vainchenker and Constantinescu46, Reference Mercher and Schwaller47).

Mutations in cohesins, CTCF and EZH2 showed a similar variant allele frequency (VAF) to GATA1 mutations, which indicates that they are the oncogenic mutations driving the progression towards ML-DS (Ref. Reference Yoshida30). On the other hand, the RAS pathway, other tyrosine kinases and cytokine receptor mutations had a lower VAF compared with GATA1 mutations, indicating they originate from a later timepoint (Ref. Reference Yoshida30). These varying VAFs point towards the subclonal development of ML-DS (Ref. Reference Yoshida30). In rare cases, some of the driver mutations identified in ML-DS were present at a low VAF in the (pre)leukaemic TAM blasts (Ref. Reference Labuhn41). This observation supports the hypothesis that in some cases the ML-DS clone has been already present early in life, possibly prenatal. This hypothesis is further supported by a study of monozygotic twins who share the same GATA1 mutation and somatic translocation involving CUX1, identified at the time of simultaneous ML-DS development (Ref. Reference Bache48).

The mutations driving ML-DS have, at lower frequencies, also been identified in non-DS AMKL (Refs Reference Yoshida30, Reference de Rooij49), indicating that ML-DS has a different mutational landscape compared with paediatric non-DS AMKL. Non-DS AMKL frequently presents with specific fusion genes as driving aberrations, such as CBFA2T3-GLIS2 and RBM15-MKL1, which both have not been detected in TAM and ML-DS (Refs Reference Yoshida30, Reference de Rooij49). On the other hand, somatic T21 is frequently seen in children with non-DS AMKL, underlining the role of an extra chromosome 21 in leukaemic development (Ref. Reference Hama28). Interestingly, these AMKL blasts frequently carry a somatic GATA1 mutation and the same spectrum of additional aberrations as seen in ML-DS (Ref. Reference Lopez50).

Genetic drivers of progression towards ALL

DS-ALL patients show a different spectrum of cancer driver gene mutations compared with non-DS ALL patients (Refs Reference Mullighan16Reference Buitenkamp18, Reference Hertzberg51). High hyperdiploidy, ETV6-RUNX1 and BCR-ABL1 are less common in DS-ALL compared with non-DS ALL (Ref. Reference Buitenkamp18). Up to 62% of all DS-ALL cases show upregulation of CRLF2, caused by rearrangements (i.e. IGH-CRLF2, P2RY8-CRLF2) or mutations in CRLF2, compared with 5–12% of non-DS ALL cases (Refs Reference Mullighan16, Reference Nikolaev17, Reference Hertzberg51, Reference Potter52). Other cancer initiation events in DS-ALL are mutations in chromatin remodelers, classic tumour suppressors and B-lymphocyte differentiation factors (Ref. Reference Nikolaev17). Moreover, DS-ALL displays an increased incidence of activating JAK2 mutations, which are only found in CRLF2 overexpressing cases (Refs Reference Mullighan16, Reference Nikolaev17, Reference Hertzberg51, Reference Bercovich53). This connection suggests a cooperating effect of these genes in leukaemogenesis and CRLF2 upregulation as a potential first event, before additional genetic aberrations in JAK2 (Ref. Reference Hertzberg51). CRLF2 is an atypical type I cytokine receptor and a weak activator of JAK2, and thus the JAK-STAT pathway (Ref. Reference Hertzberg51). Hence positive regulation of CRLF2 by JAK2 may partially explain the identified relation (Ref. Reference Hertzberg51). CRLF2 binds the ligand TSLPR, which promotes early B-cell development (Refs Reference Levin54, Reference Yoda55). Upregulation of CRLF2 likely causes increased proliferation of early B-cells. Gain-of-function mutations have also been identified in IL7R, which forms a heterodimeric complex with CRLF2 (Refs Reference Yoda55, Reference Shochat56). IL7R is required for normal lymphoid development and part of the JAK-STAT signalling pathway, which further stresses the importance of the JAK-STAT pathway in DS-ALL (Refs Reference Nikolaev17, Reference Yoda55, Reference Shochat56). DS-ALL patients more often harbour KRAS and NRAS oncogenic mutations compared with non-DS ALL patients, which are mutually exclusive with JAK2 mutations (Ref. Reference Nikolaev17). Interestingly, a complete or partial gain of chromosome 21 is often seen in non-DS paediatric B-ALL cases, predominantly in hyperdiploid ALL and iAMP21 ALL (Refs Reference Laurent, Kotecha and Malinge27, Reference Paulsson57). Similar to in DS-ALL, NRAS and KRAS mutations are often found in these patients, suggesting that T21 selects for these specific mutations (Refs Reference Paulsson58, Reference Malinowska-Ozdowy59). However, the mechanism behind this cooperation is yet unknown.

Somatic mutation accumulation during fetal development

As explained before, besides T21, additional oncogenic mutations are needed for leukaemogenesis in DS (Ref. Reference Nikolaev31). Since TAM is already present in DS newborns, mutation accumulation has to occur before birth (Ref. Reference Bhatnagar60). In some TAM cases, multiple GATA1 mutated clones have been detected, characterised by distinct GATA1 mutations (Refs Reference Yoshida30, Reference Labuhn41, Reference Ahmed61). Indeed, it has been suggested that aneuploidy can promote genomic instability, which has been proven in yeast (Refs Reference Matzke62, Reference Sheltzer63). These observations suggest an increased mutation rate of haematopoietic cells of DS fetuses, which could contribute to their higher risk of leukaemia. We have recently tested this hypothesis by whole genome sequencing (WGS) of clonally expanded single haematopoietic stem and progenitor cells (HSPCs) of non-DS and DS human fetuses (Ref. Reference Hasaart20). We have shown that HSPCs of DS fetuses have 34 extra somatic mutations compared with HSPCs of non-DS fetuses (Ref. Reference Hasaart20). This might not seem a lot, however, non-DS fetal HSPCs have already shown a somatic mutation rate of 100 base substitutions per year, which is 5.8 times higher compared with the mutation rate of adult HSPCs (Ref. Reference Hasaart20). This relatively high prenatal mutation rate may contribute to the increased leukaemia risk in children compared with young adults by increasing the chance of acquiring an oncogenic mutation (Ref. Reference Greaves64). The even higher mutation load in HSPCs of DS fetuses could further increase this chance.

Underlying causes of the increased mutation load in DS fetal cells

The driving mechanisms behind the increased somatic mutation load observed in fetal DS stem cells are still unknown (Ref. Reference Hasaart20). Increased reactive oxygen species (ROS) production, caused by oxidative stress, has been found in almost all cancer types and is thought to be an important driving factor for tumour development (Refs Reference Sabharwal and Schumacker65, Reference Schieber and Chandel66). Deficiencies in mitochondrial functioning can result in increased ROS production (Ref. Reference Zorov, Juhaszova and Sollott67). Several cell types of DS individuals show mitochondrial dysfunction and increased levels of ROS (Refs Reference Piccoli68Reference Busciglio73). Moreover, several genes involved in mitochondrial function are located on chromosome 21 and overexpressed in DS, which further supports deregulation of mitochondrial function (Refs Reference Vilardell74Reference Izzo76). However, knowledge on an association between increased ROS levels and DS-leukaemogenesis is lacking.

Oxidative stress can damage DNA, which can result in mutations if left unrepaired (Ref. Reference Burrows and Muller77). ROS are mainly mutagenic by inducing 8-oxoguanine lesions, which increase mispairing with adenine instead of cytosine (Ref. Reference Burrows and Muller77). G:C > T:A mutations were identified as the predominant base substitution type causing mutations in GATA1 (Fig. 1b) (Ref. Reference Cabelof78). These predominant G:C > T:A mutations could indicate that ROS production induces GATA1 mutations in DS. Mutational processes, such as oxidative stress, are known to generate a characteristic pattern of mutations, which is called a mutational signature (Ref. Reference Alexandrov79). These signatures are classified in the Catalogue of Somatic Mutations in Cancer (COSMIC) database (Ref. Reference Alexandrov80). COSMIC signature 18, which has previously been associated with oxidative stress mutagenesis (Ref. Reference Viel81), was identified in a subset of HSPCs isolated from DS fetuses (Ref. Reference Hasaart20). However, this signature did not contribute to the somatic mutations observed in the (pre)leukaemic blasts of TAM patients (Ref. Reference Hasaart20) or to the single base substitutions causing GATA1s in TAM (Fig. 1b). Instead, insertions, deletions and duplications are more often the cause of GATA1s in TAM (Fig. 1a). The indel profile of the identified GATA1 mutations in TAM (pre)leukaemic blasts shows that 5 + base pair insertions are the most frequently observed type of indels causing GATA1s (Fig. 1c). The GATA1 indel profile does not show similarity to a known small indel COSMIC signature (Ref. Reference Alexandrov80). Therefore, the mutational process causing this specific indel profile is not yet known and needs further investigation (Fig. 1c). Altogether, there is evidence of mitochondrial dysfunction and increased levels of ROS in DS. However, DNA damage caused by ROS is not necessarily needed to induce the somatic mutations that are necessary for the development of TAM (Ref. Reference Hasaart20).

Fig. 1. GATA1 aberrations in transient abnormal myelopoiesis (TAM). Analysis of all GATA1 mutations identified in TAM, which are annotated in the Catalogue of Somatic Mutations in Cancer (COSMIC) Cancer Gene Census database and GATA1 mutations from the recently published study of Labuhn et al. (Refs Reference Yoshida30, Reference Nikolaev31, Reference Labuhn41, Reference Ahmed61, Reference Cabelof78, Reference Xu138Reference McElwaine149). (a) Pie chart showing the different types of GATA1 mutations observed in TAM (pre)leukaemic blasts. (b) 96-trinucleotide spectrum of all single nucleotide variants (SNVs) observed in GATA1. (c) The spectrum of insertions and deletions (indels) within GATA1.

Besides mitochondrial dysfunction, deficiencies in DNA damage repair may also explain the increased somatic mutation load observed in DS fetal cells (Refs Reference Hasaart20, Reference Morawiec82). Multiple studies have shown a defect in DNA repair in DS cells (Refs Reference Cabelof78, Reference Morawiec82). Also, in the fetal liver, lower base excision repair activity was detected in DS-tissues compared with non-DS tissues (Ref. Reference Cabelof78). These findings support the hypothesis that, already in the prenatal setting, ineffective DNA damage repair in DS could, together with mitochondrial dysfunction, lead to increased mutagenesis and potentially to phenotypic diversity.

Epigenetic and transcriptional alterations in DS haematopoiesis

Besides genetic aberrations, epigenetic changes can contribute to the inheritable phenotypic diversity within cell populations by changing transcription levels. Many studies have shown genome-wide epigenetic alterations in cells of DS fetuses prior to leukaemia development, not solely restricted to chromosome 21 (Refs Reference Letourneau29, Reference Eckmann-Scholz83Reference Malinge85). Transcriptome analysis of fetal fibroblasts isolated from monozygotic twins, discordant for T21, showed that differential expression is organised in chromosomal domains that vary in size and contain up to 507 genes (Ref. Reference Letourneau29). These well-defined domains that consist of neighbouring genes sharing differential expression profiles are called gene expression dysregulation domains (GEDDs) (Ref. Reference Letourneau29). Syntenic blocks along the mouse chromosomes showed that these GEDDs were conserved in induced pluripotent stem cells (IPSCs) derived from fetal fibroblasts and fibroblasts from the Ts65Dn DS mouse model in which DS is modelled through a partial trisomy comprised of a distal portion of mouse chromosome 16 and a centromeric portion of mouse chromosome 17 (Ref. Reference Letourneau29). This finding indicates a consistent influence of T21 on the transcriptome (Ref. Reference Letourneau29). The identified GEDD pattern in mice and human IPSCs is shown to be associated with chromatin modifications in T21 cells, since the altered H3K4me3 profile observed in these cells followed the GEDD pattern (Ref. Reference Letourneau29). H3K4me3 marks are positively correlated with these gene expression levels, suggesting that the transcriptomic alterations in DS are a result of chromatin modifications (Ref. Reference Letourneau29). Several genes encoding chromatin modifiers, such as HMGN1, DYRK1A, BRWD1 and RUNX1, are located on chromosome 21 and may explain the altered H3K4me3 profile (Ref. Reference Letourneau29). In line with this, B-cells from HMGN1 overexpressing mice show a global increase in hyperacetylation of histone H3K27 (Ref. Reference Mowery86). This hyperacetylation also causes transcriptional changes, which recapitulates the transcriptional changes observed in pro-B-cells of the Ts1Rhr DS mice model in which DS is modelled by triplication of 31 genes on a mouse chromosome orthologous to the DSCR (Ref. Reference Mowery86). Overexpression of several genes that encode chromatin modifiers, located on chromosome 21, may be responsible for global transcriptional changes observed in DS cells.

Characterisation of DNA methylation profiles of DS fetuses exhibited inconsistent results, however, these all demonstrated an aberrant methylation pattern in DS newborns (Refs Reference Eckmann-Scholz83Reference Malinge85, Reference Sailani87Reference Henneman89). Further research is warranted to clarify the role of these changes in the development of leukaemia in DS.

These findings indicate that besides genetic aberrations, epigenetic alterations can be found in DS cells, which contribute to the increased inheritable phenotypic diversity. However, additional oncogenic mutations and/or epigenetic alterations are needed to drive leukaemia in DS. The chance to acquire an oncogenic mutation causing leukaemia in DS may be increased by the enlarged phenotypic diversity that is already present early during development.

Epigenetic and transcriptional differences in DS-associated leukaemias

RNA expression profiles of TAM and ML-DS have shown to be similar to each other, and distinct from non-DS AMKL (Refs Reference Yoshida30, Reference Malinge85, Reference Bourquin90). However, RNA expression levels of recurrently mutated genes in ML-DS, such as RAD21 and EZH2, appeared to be similar in ML-DS and non-DS AMKL patients (Ref. Reference Yoshida30). Yet, there are some exceptions. Genome-wide DNA methylation analysis demonstrated that mononuclear cells of both TAM and ML-DS are epigenetically deregulated (Ref. Reference Malinge85). Malinge et al. showed that mutations in GATA1 in TAM led to focal DNA hypermethylation of specific target genes, whereas the DSCR remained hypomethylated (Ref. Reference Malinge85). The hypermethylated target genes are related to haematological development and regulation of key cellular processes, such as proliferation, growth, cell cycle regulation and cell death (Ref. Reference Malinge85). In addition, ML-DS and TAM have a similar DNA methylation pattern (Ref. Reference Malinge85). The strong similarities in gene expression and DNA methylation patterns between TAM and ML-DS suggest that the transient nature of TAM and the later development of ML-DS are unlikely to be driven by changes in epigenetic factors. However, mononuclear cells from ML-DS patients were hypomethylated compared with mononuclear cells from non-DS AMKL patients, indicating that ML-DS is epigenetically different from non-DS AMKL (Ref. Reference Malinge85).

RNA expression profiles of DS-ALL cases are very heterogeneous. Some DS-ALL cases cluster together, whereas other cases are more similar to non-DS ALL subtypes, such as the BCR-ABL1 translocated type (Refs Reference Hertzberg51, Reference Kubota91). In line with this, there are no genes on chromosome 21 that show a significantly higher expression in DS-ALL when compared with non-DS ALL (Ref. Reference Kubota91). Cases with increased CRLF2 expression are found to have a Philadelphia-like expression signature (Ref. Reference Kubota91). Nevertheless, the fusion created by the Philadelphia chromosome, BCR-ABL1, is uncommon in DS-ALL (Refs Reference Kubota91, Reference Bernt and Hunger92). The heterogeneity of DS-ALL patients is further supported by the DNA methylation profile, which shows clustering per subtype as opposed to DS-ALL clustering together (Ref. Reference Kubota91). However, in most subtypes, the RUNX1 promoter is hypermethylated in DS-ALL compared with non-DS ALL (with exception of ETV6-RUNX1 non-DS ALL) (Ref. Reference Kubota91). Also, in DS individuals without leukaemia, this promoter is, already congenitally, found to be hypermethylated (Ref. Reference Kubota91). The fact that RUNX1 is essential for B-cell differentiation suggests that RUNX1 hypermethylation is a predisposing factor for BCP-ALL in DS individuals (Ref. Reference Kubota91). Strikingly, another study demonstrated that DS-ALL and non-DS ALL are distinguishable by the overexpression of genes, marked with H3K27me3 (Ref. Reference Lane93). This study showed that H3K27me3 marks are globally suppressed in B-cells of the TsRhr1 DS mice model, which is possibly explained by overexpression of HMGN1 (Ref. Reference Lane93). This suppression, in combination with H3K4me3, leads to overexpression of genes that contribute to B-cell proliferation in Tshr1, pointing out the importance of epigenetic changes in DS-ALL (Ref. Reference Lane93).

An extra chromosome 21 in DS individuals serves as the basis of a broad variety of genetic and epigenetic changes. As mentioned before, we have previously demonstrated that fetal HSPCs of DS fetuses have an increased somatic mutation load compared with HSPCs of non-DS fetuses (Ref. Reference Hasaart20). Using WGS data of fetal HSPCs, we have constructed a model to determine the mutation rate during fetal haematopoeisis (Ref. Reference Hasaart20). For example, HSPCs of a 14 WG (weeks of gestation) DS fetus have 58 somatic mutations (95% confidence interval 40.16–75.39). When we use our model to extrapolate the mutation load for a non-DS fetus of the same gestational age, these HSPCs would have 24 (95% confidence interval 11.88–34.8) somatic mutations. This suggests that HSPCs of a 14 WG DS fetus have a 2.4-fold increase in somatic mutations (Ref. Reference Hasaart20). This increased mutation load combined with the 3.5 times higher number of HSPCs (Ref. Reference Roy94) indicates that DS fetal HSPCs have a higher risk to acquire an oncogenic mutation during fetal haematopoiesis compared with non-DS fetal HSPCs. However, the specific time-based higher incidence of AML and BCP-ALL in these children, with a lower risk of solid tumours (Ref. Reference Hasle, Clemmensen and Mikkelsen10), suggests that other factors may play a role. Additional epigenetic aberrations on top of the increased mutation load further increase the phenotypic diversity, but still do not completely explain the increased incidence of leukaemia in DS children. In conclusion, these findings support our hypothesis that the increased risk of leukaemia in DS children is not solely explained by phenotypic diversity.

Selection dynamics

According to the somatic evolutionary model, oncogenic clones can be positively selected depending on the tissue context (Ref. Reference Rozhok and DeGregori95). Alterations in the haematopoietic microenvironment, such as changes in the cellular composition of the niche as well as the immune system, which are observed in DS, are likely to affect selection dynamics contributing to the progression of leukaemogenesis.

The fetal liver niche in DS

During fetal development, the liver is the dominant site of haematopoiesis, which transfers to the bone marrow after birth (Ref. Reference Ivanovs96). As discussed before, the first oncogenic mutations in DS are thought to be acquired during fetal development (Ref. Reference Antonarakis2). These observations suggest that the fetal liver microenvironment, according to the somatic evolutionary model, may have a crucial role in the selection and growth of (pre)leukaemic clones that arise during fetal development in DS. Indeed, it has been shown that megakaryocyte progenitors isolated from the yolk sac or fetal liver of GATA1s mice show a hyperproliferative phenotype (Ref. Reference Li40). Notably, this effect was transient, indicating that GATA1s has a developmental stage-dependent effect (Ref. Reference Li40). Little is known about which cells within the human fetal liver support the expansion and differentiation of HSPCs during fetal haematopoiesis. Mice studies have shown that fetal liver stromal cells and Nestin + NG2 + pericytes support the proliferation of HSPCs (Refs Reference Khan97Reference Chou and Lodish99). Moreover, fetal liver stromal cells support the expansion of megakaryocyte committed progenitors (Ref. Reference Brouard100).

Both supportive cell types express insulin growth factor-2 (IGF2), which is known to promote the expansion of HSPCs during fetal haematopoiesis (Refs Reference Khan97Reference Chou and Lodish99). More importantly, the proliferation of mice fetal liver megakaryocyte progenitors is reliant on IGF signalling, whereas proliferation of adult megakaryocyte progenitors isolated from bone marrow is not (Ref. Reference Klusmann35). Several studies have demonstrated that IGF signalling is increased in DS (Refs Reference Klusmann35, Reference Chiang101). Recently, it has been shown that increased IGF signalling is associated with the overproduction of CD43+ haematopoietic progenitors derived from T21 IPSCs (Ref. Reference Chiang101). CD43 is the earliest marker of full haematopoietic commitment after endothelial to haematopoietic transition, which suggests that T21 promotes this transition (Ref. Reference Chiang101). Furthermore, T21 CD43+ haematopoietic progenitors are more sensitive to inhibition of IGF signalling compared with CD43+ haematopoietic progenitors in which the extra chromosome 21 is silenced by site-directed insertion of the X-inactive inactive specific transcript (XIST) gene in one copy of chromosome 21 (Ref. Reference Chiang101). Altogether, these findings suggest that T21 itself increases IGF signalling (Ref. Reference Chiang101).

IGF signalling seems to have a preserved role in the proliferation of TAM and ML-DS (pre)leukaemic blasts (Ref. Reference Klusmann35). Klusmann et al. (Ref. Reference Klusmann35) showed that the IGF receptor 1 (IGFR1) is expressed higher in primary ML-DS leukaemic blasts and ML-DS cell lines, compared with non-DS AMKL leukaemic blasts and cell lines. The same study demonstrated that inhibition of IGF signalling results in impaired proliferation of TAM and ML-DS (pre)leukaemic blasts (Ref. Reference Klusmann35). Moreover, overexpression of IGF2 in GATA1s liver mice megakaryocyte progenitors resulted in increased proliferation, whereas a negligible effect was observed in GATA1 wild-type liver megakaryocyte progenitors and GATA1s bone marrow megakaryocyte progenitors (Ref. Reference Klusmann35). These findings suggest that there might be an interaction between IGF signalling and GATA1 in the fetal liver. It has been shown that the IGF signalling pathway activates E2F target genes through activation of mTOR signalling and upregulation of MYC (Ref. Reference Klusmann35). E2F target genes play a role in the regulation of cell proliferation (Ref. Reference Chen, Tsai and Leone102). Normally, GATA1 represses the expression of E2F target genes by direct interaction with E2F transcription factors and via repression of MYC, this will result in a decrease in cell proliferation (Ref. Reference Klusmann35). However, GATA1s is not able to interact with E2F transcription factors and thereby cannot regulate the expression of E2F target genes, which results in overactive IGF signalling and hyperproliferation (Refs Reference Klusmann35, Reference Maroz36). Together, these findings support that increased IGF signalling in DS has a role in the selection and proliferation of (pre)leukaemic TAM clones within the fetal liver. The role of IGF signalling in the development of DS-ALL has, to our knowledge, not been studied yet. Intriguingly, in non-DS paediatric BCP-ALL, it has been demonstrated that IGF signalling promotes the expansion of leukaemic blasts and therefore IGF signalling might also have a role in the development of DS-ALL (Refs Reference Yamada103, Reference Vorwerk104). Another study found granulocyte-macrophage colony-stimulating factor (GM-CSF), secreted by fetal liver stromal cells, as the main growth factor supporting TAM (pre)leukaemic blasts (Ref. Reference Miyauchi and Kawaguchi105). Here, IGF signalling was not identified to have a supportive role in the growth of TAM (pre)leukaemic blasts (Ref. Reference Miyauchi and Kawaguchi105).

Altogether, these studies indicate that the fetal liver niche cells and IGF signalling pathway in DS contribute to the proliferation and selection of pre(leukaemic) blasts. This supports the hypothesis that alterations in the fetal liver microenvironment in DS can change selection dynamics in such a way that GATA1-mutated clones have a growth advantage, whereas the bone marrow does not provide this advantage. This model may explain why TAM spontaneously disappears when haematopoiesis migrates to the bone marrow after birth (Refs Reference Zipursky13, Reference Sas14). In turn, the additional oncogenic mutations observed in ML-DS leukaemic blasts may subsequently increase the phenotypic diversity among cells (Refs Reference Yoshida30, Reference Labuhn41). This increased phenotypic diversity will then result in the selection of the leukaemic clones and progression to ML-DS.

Chronic inflammation and interferon (IFN) signalling in DS

According to the immune surveillance theory, the immune system acts as a barrier to prevent cancer development (Refs Reference Palucka and Coussens106, Reference Dunn, Old and Schreiber107). However, sequencing studies have demonstrated a neutral drift for most missense and nonsense mutations in normal and cancer tissues, suggesting that selection or removal of these mutations is a stochastic process (Ref. Reference Martincorena108). Of note, not all missense mutations result in a neoantigen. This finding suggests that immune cells may not have a crucial role in the first steps of cancer initiation (Ref. Reference Martincorena108). Several studies have demonstrated that inflammation changes the selection pressure on oncogenic clones (Refs Reference Vermeulen109Reference de Visser, Eichten and Coussens111). Not surprisingly, chronic inflammation is a hallmark of cancer (Ref. Reference Coussens and Werb112). In addition, chronic inflammation can result in genomic instability (Ref. Reference Kawanishi113), which increases the chance to acquire oncogenic driver mutations.

Abnormalities in immune function are a common characteristic of DS (Ref. Reference Ram and Chinen114). DS individuals have an increased incidence of autoimmune diseases and infections (Ref. Reference Ram and Chinen114). This observation suggests that tissues of DS individuals show signs of chronic inflammation, which may contribute to the increased leukaemia risk. Indeed, inflammatory factors related to chronic inflammation are upregulated in blood cells and plasma of children and adults with DS (Refs Reference Sullivan115Reference Sullivan118). NK-cells and T-cells are hyperactivated, whereas cells of the myeloid compartment show signs of inflammation and increased cytotoxic potential (Refs Reference Waugh117, Reference Bloemers119). Moreover, several studies have indicated that the blood cell composition is perturbed in DS, which further supports the deregulation of the immune system (Refs Reference Roy94, Reference Tunstall-Pedoe120, Reference Chou121). The number of HSCs and megakaryocyte and erythrocyte progenitors are increased in the DS fetal liver, whereas the number of granulocyte–macrophage progenitors, B-cells, pre–pro-B cells and pro-B cell progenitors are decreased (Refs Reference Roy94, Reference Tunstall-Pedoe120, Reference Chou121). The observed depletion of B-cells, pre–pro-B cells and pro-B cells suggests a maturation defect of B-cells during DS fetal haematopoiesis. Studies in adults have shown that this depletion was preserved over time (Refs Reference Waugh117, Reference de Hingh122).

Altogether, these findings suggest that the haematopoietic system of both children and adults with DS is altered and shows signs of chronic inflammation. However, if an altered immune system and chronic inflammation contribute to the increased incidence of leukaemia in children with DS, it remains unclear why this risk decreases tremendously during adolescence, since alterations of the immune system are preserved during adulthood. Moreover, it remains an open question why this increased risk is limited to the haematopoietic system because chronic inflammation is associated with increased cancer risk in general and DS individuals do not experience an increased risk of other malignancies besides paediatric leukaemia (Refs Reference Hasle, Clemmensen and Mikkelsen10, Reference Coussens and Werb112).

Over the last years, it has become evident that hyperactive immune signalling can induce changes in the haematopoietic system, such as the increased proliferation of HSPCs and a biased differentiation towards the myeloid lineage (Ref. Reference Mirantes, Passegué and Pietras123). Overactive IFN signalling is associated with increased proliferation of fetal and adult HSCs and myeloid skewing in adults (Refs Reference Essers124Reference Li128). Four of the six IFN receptors are located on chromosome 21 and proteomics and RNA sequencing approaches have shown consistent activation of IFN signalling in immune cells of adults with DS (Refs Reference Sullivan115, Reference Waugh117, Reference Sullivan118). These findings suggest that overactive IFN signalling may contribute to the observed changes in DS haematopoiesis. On the contrary, it has been shown that IFN signalling has an antiproliferative effect on megakaryocyte progenitors, which are enriched in DS compared with karyotypically normal newborns (Refs Reference Woo129, Reference Wang130). IFN signalling is increased in adult bone marrow megakaryocyte progenitors isolated from wild-type mice compared with megakaryocyte progenitors isolated from the fetal liver (Ref. Reference Woo129). Indeed, both bone marrow GATA1s and GATA1 wild-type megakaryocyte progenitors, isolated from IFNAR1−/− mice, show a hyperproliferative phenotype (Ref. Reference Woo129). However, hyperproliferation was more pronounced in GATA1s IFNAR1−/− mice (Ref. Reference Woo129). In line with this, IFN-alpha stimulation resulted in a more pronounced decrease in proliferation of GATA1s megakaryocyte progenitors as compared with GATA1 wild-type megakaryocyte progenitors (Ref. Reference Woo129). Taken together, these findings imply that there is an interplay between GATA1s and IFN signalling, which may contribute to the proliferation and selection of (pre)leukaemic blasts in the fetal liver and the spontaneous remission of TAM (Ref. Reference Woo129). Indeed, (pre)leukaemic blasts from TAM patients show a higher expression of IFN signalling genes, which is, considering the anti-proliferative effect, consistent with the spontaneous remission of TAM within three months after birth (Ref. Reference Woo129). Nonetheless, leukaemic blasts from ML-DS patients also highly express IFN signalling genes, indicating that additional acquired oncogenic mutations in ML-DS are needed for leukaemic progression (Ref. Reference Woo129).

Discussion

Children with DS show an increased incidence of AMKL and BCP-ALL during the first years of life (Ref. Reference Zipursky9). However, the reason for this remains unknown. It is well accepted that the presence of a constitutive trisomy of chromosome 21 is sufficient to perturb (fetal) haematopoiesis (Refs Reference Banno6, Reference Roy94, Reference Chiang101, Reference Tunstall-Pedoe120, Reference Chou121). Interestingly, complete or partial gains of chromosome 21 are frequently seen in non-DS paediatric B-ALL cases and non-DS AMKL, but are rarely observed in adult leukaemias (Refs Reference Laurent, Kotecha and Malinge27, Reference Paulsson57, Reference Roberts131). Nonetheless, besides T21, additional oncogenic mutations in somatic T21 and DS-associated leukaemias are needed to drive leukaemogenesis (Figs. 2 and 3). These observations suggest that trisomy of chromosome 21 may prime the haematopoietic system for cancer and that DS-associated leukaemia could be used to study paediatric leukaemia in general (Figs. 2 and 3).

Fig. 2. Development of transient abnormal myelopoiesis (TAM) and myeloid leukaemia of Down syndrome (ML-DS). Model for the development of TAM and ML-DS in Down syndrome individuals. Schematic representation, indicating the initiation of TAM before birth when trisomy 21 (T21) fetal haematopoietic stem and progenitor cells (HSPCs) acquire a GATA1 mutation. The proliferation of (pre)leukaemic TAM blasts in the fetal liver is supported by secretion of granulocyte-macrophage colony-stimulating factor (GM-CSF) in combination with increased insulin growth factor-2 (IGF) signalling and lower levels of IFN signalling. TAM resolves when haematopoiesis migrates to the bone marrow after birth and interferon (IFN) signalling increases. Additional oncogenic mutations in an existing GATA1 mutated clone provide these cells with an additional growth advantage and are required for progression towards ML-DS.

Fig. 3. Development of DS acute lymphoblastic leukemia (DS-ALL). Model for the development of acute lymphoblastic leukemia (ALL) in Down syndrome individuals. Trisomy 21 (T21) haematopoietic stem and progenitor cells (HSPCs) show a B-cell maturation defect. An extra copy of genes encoding chromatin modifiers on chromosome 21 causes a decrease in H3k27me3 marks and an increase in H3K27ac and H3K4me3 marks on genes involved in B-cell proliferation and B-ALL development, which will result in overexpression of these genes. The acquisition of cancer driver mutations and additional oncogenic mutations is sufficient for the development of DS-ALL.

In adult cancers, the acquisition of oncogenic mutations is thought to be rate-limiting (Ref. Reference Tomasetti, Li and Vogelstein132). This may explain why aging is the biggest risk factor for developing cancer, since somatic mutations gradually increase throughout life (Refs Reference Hasaart20, Reference Osorio22, Reference Blokzijl25, Reference Tomasetti, Li and Vogelstein132). However, the incidence of leukaemia in young children compared with (young) adults is higher even though their cells harbour less somatic mutations (Ref. Reference Rozhok, Salstrom and DeGregori133). Remarkably, DS children show an even higher incidence (Refs Reference Ross3, Reference Lee7, Reference Hasle8). Together, this suggests that paediatric leukaemia, in general, has a different aetiology compared with adult leukaemia.

It has been shown that genetic drivers of childhood leukaemia are acquired prenatally (Ref. Reference Greaves64). The increased somatic mutation rate of fetal HSPCs and the even higher mutation load of DS fetal HSPCs compared with the rate of adult HSPCs may partially explain the higher incidence of leukaemia in (DS) children (Refs Reference Hasaart20, Reference Osorio22). This increased mutation rate promotes the phenotypic diversity of fetal HSPCs and the chance to acquire an oncogenic mutation (Refs Reference Hasaart20, Reference Osorio22). Of note, the postnatal somatic mutation rate of DS-HSPCs has not been characterised yet, whereas additional mutations required for ML-DS and possibly also for DS-ALL are acquired after birth. Since the risk of leukaemia in DS decreases tremendously during life (Ref. Reference Hasle8), we suggest that other factors, such as selection, play an additional role in the development of paediatric leukaemia and DS-associated leukaemogenesis.

Several findings suggest that, besides the observed increased phenotypic diversity, changes in selection dynamics in the fetal liver and bone marrow may have a role in leukaemic development in non-DS and DS children. First of all, the (pre)leukaemic TAM blasts spontaneously disappear when haematopoietic cells migrate to the bone marrow, suggesting that the selection dynamics in the bone marrow are different from the fetal liver (Refs Reference Zipursky13, Reference Sas14). Second, the mutational landscape of childhood leukaemia, in general, is different from adult leukaemia (Refs Reference Roberts131, Reference Bolouri134). Also, cancer driver mutations found in DS-associated leukaemia are less frequently found in non-DS-associated leukaemia (Refs Reference Mullighan16, Reference Nikolaev17, Reference Yoshida30, Reference de Rooij49, Reference Bercovich53). These observations imply that different selection forces act on oncogenic clones (Refs Reference Roberts131, Reference Bolouri134). GATA1 mutations are only observed in non-DS AMKL patients with a somatic T21, suggesting that T21 is needed to select these (pre)leukaemic clones (Refs Reference de Rooij49, Reference Hara135, Reference Reinhardt136). Remarkably, germline GATA1 mutations without a T21 background are associated with many rare red cell disorders (Ref. Reference Ling and Crispino137), which points towards a specific aetiology of DS and T21-associated leukaemogenesis. This distinct aetiology and selection raise the question if a model based on DS leukaemogenesis would be applicable to study paediatric leukaemia in general.

In conclusion, we suggest, in line with a somatic evolutionary point of view, that leukaemogenesis in both non-DS and DS-associated leukaemia is driven by phenotypic diversity and changes in selection dynamics. However, both phenotypic diversity and selection dynamics are likely different in non-T21 associated leukaemia and DS-associated leukaemia. These differences suggest that non-T21 associated leukaemia has a different aetiology. Here, we propose a model in which leukaemic development in DS is driven by an increase in inheritable phenotypic diversity of HSPCs, caused by epigenetic changes and an increased somatic mutation rate during fetal development, which increases the chance to acquire an oncogenic hit. In turn, leukaemogenesis is further promoted by the selection of (pre)leukaemic clones in the fetal liver and paediatric bone marrow niche. This model would mean that treatment of DS-leukaemogenesis should possibly not only focus on genetic and epigenetic changes, but also on the microenvironment that supports the selection of leukaemic clones. This offers opportunities for future therapy development.

Financial support

This study was financially supported by a VIDI grant from the Netherlands Organisation for Scientific Research (NWO no. 016.Vidi.171.023) and a grant from the Dutch Cancer Society (KWF no. 11307) to R.v.B.

Conflict of interest

The authors declare that there are no conflicts of interest.

Footnotes

*

These authors contributed equally.

References

Parker, SE et al. (2010) Updated national birth prevalence estimates for selected birth defects in the United States, 2004–2006. Birth Defects Research. Part A, Clinical and Molecular Teratology 88, 10081016.CrossRefGoogle ScholarPubMed
Antonarakis, SE (2017) Down syndrome and the complexity of genome dosage imbalance. Nature Reviews Genetics 18, 147163.CrossRefGoogle ScholarPubMed
Ross, JA et al. (2005) Epidemiology of leukemia in children with Down syndrome. Pediatric Blood & Cancer 44, 812.CrossRefGoogle ScholarPubMed
Papavassiliou, P et al. (2015) Mosaicism for trisomy 21: a review. American Journal of Medical Genetics. Part A 167a, 2639.CrossRefGoogle ScholarPubMed
Antonarakis, SE et al. (2020) Down syndrome. Nature Reviews Disease Primers 6, 9.CrossRefGoogle ScholarPubMed
Banno, K et al. (2016) Systematic cellular disease models reveal synergistic interaction of trisomy 21 and GATA1 mutations in hematopoietic abnormalities. Cell Reports 15, 12281241.CrossRefGoogle ScholarPubMed
Lee, P et al. (2016) The biology, pathogenesis and clinical aspects of acute lymphoblastic leukemia in children with Down syndrome. Leukemia 30, 18161823.CrossRefGoogle ScholarPubMed
Hasle, H (2001) Pattern of malignant disorders in individuals with Down's syndrome. The Lancet. Oncology 2, 429436.CrossRefGoogle ScholarPubMed
Zipursky, A (2000) Susceptibility to leukemia and resistance to solid tumors in Down syndrome. Pediatric Research 47, 704.CrossRefGoogle ScholarPubMed
Hasle, H, Clemmensen, IH and Mikkelsen, M (2000) Risks of leukaemia and solid tumours in individuals with Down's syndrome. Lancet 355, 165169.CrossRefGoogle ScholarPubMed
Vardiman, JW et al. (2009) The 2008 revision of the World Health Organization (WHO) classification of myeloid neoplasms and acute leukemia: rationale and important changes. Blood 114, 937951.CrossRefGoogle ScholarPubMed
Roberts, I and Izraeli, S (2014) Haematopoietic development and leukaemia in Down syndrome. British Journal of Haematology 167, 587599.CrossRefGoogle ScholarPubMed
Zipursky, A (2003) Transient leukaemia—a benign form of leukaemia in newborn infants with trisomy 21. British Journal of Haematology 120, 930938.CrossRefGoogle ScholarPubMed
Sas, V et al. (2019) Transient leukemia of Down syndrome. Critical Reviews in Clinical Laboratory Sciences 56, 247259.CrossRefGoogle ScholarPubMed
Alford, KA et al. (2011) Analysis of GATA1 mutations in Down syndrome transient myeloproliferative disorder and myeloid leukemia. Blood 118, 22222238.CrossRefGoogle ScholarPubMed
Mullighan, CG et al. (2009) Rearrangement of CRLF2 in B-progenitor- and Down syndrome-associated acute lymphoblastic leukemia. Nature Genetics 41, 1243–126.CrossRefGoogle ScholarPubMed
Nikolaev, SI et al. (2014) Frequent cases of RAS-mutated Down syndrome acute lymphoblastic leukaemia lack JAK2 mutations. Nature Communications 5, 4654.CrossRefGoogle ScholarPubMed
Buitenkamp, TD et al. (2014) Acute lymphoblastic leukemia in children with Down syndrome: a retrospective analysis from the Ponte di Legno study group. Blood 123, 7077.CrossRefGoogle ScholarPubMed
Stratton, MR, Campbell, PJ and Futreal, PA (2009) The cancer genome. Nature 458, 719724.CrossRefGoogle ScholarPubMed
Hasaart, KAL et al. (2020) Mutation accumulation and developmental lineages in normal and Down syndrome human fetal haematopoiesis. Scientific Reports 10, 12991.CrossRefGoogle ScholarPubMed
Lee-Six, H et al. (2018) Population dynamics of normal human blood inferred from somatic mutations. Nature 561, 473478.CrossRefGoogle ScholarPubMed
Osorio, FG et al. (2018) Somatic mutations reveal lineage relationships and age-related mutagenesis in human hematopoiesis. Cell Reports 25, 23082316, e4.CrossRefGoogle ScholarPubMed
Greaves, M (2018) A causal mechanism for childhood acute lymphoblastic leukaemia. Nature Reviews Cancer 18, 471484.CrossRefGoogle ScholarPubMed
Chatterjee, N and Walker, GC (2017) Mechanisms of DNA damage, repair, and mutagenesis. Environmental and Molecular Mutagenesis 58, 235263.CrossRefGoogle ScholarPubMed
Blokzijl, F et al. (2016) Tissue-specific mutation accumulation in human adult stem cells during life. Nature 538, 260264.CrossRefGoogle ScholarPubMed
Vogelstein, B et al. (2013) Cancer genome landscapes. Science 339, 15461558.CrossRefGoogle ScholarPubMed
Laurent, AP, Kotecha, RS and Malinge, S (2020) Gain of chromosome 21 in hematological malignancies: lessons from studying leukemia in children with Down syndrome. Leukemia 34, 19841999.CrossRefGoogle ScholarPubMed
Hama, A et al. (2012) Molecular lesions in childhood and adult acute megakaryoblastic leukaemia. British Journal of Haematology 156, 316325.CrossRefGoogle ScholarPubMed
Letourneau, A et al. (2014) Domains of genome-wide gene expression dysregulation in Down's syndrome. Nature 508, 345350.CrossRefGoogle ScholarPubMed
Yoshida, K et al. (2013) The landscape of somatic mutations in Down syndrome-related myeloid disorders. Nature Genetics 45, 12931299.CrossRefGoogle ScholarPubMed
Nikolaev, SI et al. (2013) Exome sequencing identifies putative drivers of progression of transient myeloproliferative disorder to AMKL in infants with Down syndrome. Blood 122, 554561.CrossRefGoogle ScholarPubMed
Taub, JW et al. (2004) Prenatal origin of GATA1 mutations may be an initiating step in the development of megakaryocytic leukemia in Down syndrome. Blood 104 15881589.Google Scholar
Hitzler, JK et al. (2003) GATA1 Mutations in transient leukemia and acute megakaryoblastic leukemia of Down syndrome. Blood 101, 43014434.CrossRefGoogle ScholarPubMed
Garnett, C, Cruz Hernandez, D and Vyas, P (2020) GATA1 and cooperating mutations in myeloid leukaemia of Down syndrome. IUBMB Life 72, 119130.CrossRefGoogle ScholarPubMed
Klusmann, JH et al. (2010) Developmental stage-specific interplay of GATA1 and IGF signaling in fetal megakaryopoiesis and leukemogenesis. Genes & Development 24, 16591672.CrossRefGoogle ScholarPubMed
Maroz, A et al. (2014) GATA1s induces hyperproliferation of eosinophil precursors in Down syndrome transient leukemia. Leukemia 28, 12591270.CrossRefGoogle ScholarPubMed
Rylski, M et al. (2003) GATA-1-mediated proliferation arrest during erythroid maturation. Molecular and Cellular Biology 23, 50315042.CrossRefGoogle ScholarPubMed
Byrska-Bishop, M et al. (2015) Pluripotent stem cells reveal erythroid-specific activities of the GATA1 N-terminus. Journal of Clinical Investigation 125, 9931005.CrossRefGoogle ScholarPubMed
Vyas, P and Crispino, JD (2007) Molecular insights into Down syndrome-associated leukemia. Current Opinion in Pediatrics 19, 914.CrossRefGoogle ScholarPubMed
Li, Z et al. (2005) Developmental stage-selective effect of somatically mutated leukemogenic transcription factor GATA1. Nature Genetics 37, 613619.CrossRefGoogle ScholarPubMed
Labuhn, M et al. (2019) Mechanisms of progression of myeloid preleukemia to transformed myeloid leukemia in children with Down syndrome. Cancer Cell 36, 340.CrossRefGoogle ScholarPubMed
Viny, AD and Levine, RL (2018) Cohesin mutations in myeloid malignancies made simple. Current Opinion in Hematology 25, 6166.CrossRefGoogle ScholarPubMed
Cuartero, S, Innes, AJ and Merkenschlager, M (2019) Towards a better understanding of cohesin mutations in AML. Frontiers in Oncology 9, 867876.CrossRefGoogle ScholarPubMed
Fisher, JB et al. (2017) Cohesin mutations in myeloid malignancies. Trends in Cancer 3, 282293.CrossRefGoogle ScholarPubMed
Ernst, T et al. (2010) Inactivating mutations of the histone methyltransferase gene EZH2 in myeloid disorders. Nature Genetics 42, 722726.CrossRefGoogle ScholarPubMed
Vainchenker, W and Constantinescu, SN (2013) JAK/STAT signaling in hematological malignancies. Oncogene 32, 26012613.CrossRefGoogle ScholarPubMed
Mercher, T and Schwaller, J (2019) Pediatric acute myeloid leukemia (AML): from genes to models toward targeted therapeutic intervention. Frontiers in Pediatrics 7, 401.CrossRefGoogle ScholarPubMed
Bache, I et al. (2020) A shared somatic translocation involving CUX1 in monozygotic twins as an early driver of AMKL in Down syndrome. Blood Cancer Journal 10, 2730.Google Scholar
de Rooij, JD et al. (2017) Pediatric non-Down syndrome acute megakaryoblastic leukemia is characterized by distinct genomic subsets with varying outcomes. Nature Genetics 49, 451456.CrossRefGoogle ScholarPubMed
Lopez, CK et al. (2017) Pediatric acute megakaryoblastic leukemia: multitasking fusion proteins and oncogenic cooperations. Trends in Cancer 3, 631642.CrossRefGoogle ScholarPubMed
Hertzberg, L et al. (2010) Down syndrome acute lymphoblastic leukemia, a highly heterogeneous disease in which aberrant expression of CRLF2 is associated with mutated JAK2: a report from the international BFM study group. Blood 115, 10061017.CrossRefGoogle ScholarPubMed
Potter, N et al. (2019) Single-cell analysis identifies CRLF2 rearrangements as both early and late events in down syndrome and non-Down syndrome acute lymphoblastic leukaemia. Leukemia 33, 893904.CrossRefGoogle ScholarPubMed
Bercovich, D et al. (2008) Mutations of JAK2 in acute lymphoblastic leukaemias associated with Down's syndrome. Lancet 372, 14841492.CrossRefGoogle ScholarPubMed
Levin, SD et al. (1999) Thymic stromal lymphopoietin: a cytokine that promotes the development of IgM + B cells in vitro and signals via a novel mechanism. Journal of Immunology 162, 677683.Google ScholarPubMed
Yoda, A et al. (2010) Functional screening identifies CRLF2 in precursor B-cell acute lymphoblastic leukemia. Proceedings of the National Academy of Sciences of the United States of America 107, 252257.CrossRefGoogle ScholarPubMed
Shochat, C et al. (2011) Gain-of-function mutations in interleukin-7 receptor-α (IL7R) in childhood acute lymphoblastic leukemias. Journal of Experimental Medicine 208, 901908.CrossRefGoogle Scholar
Paulsson, K et al. (2015) The genomic landscape of high hyperdiploid childhood acute lymphoblastic leukemia. Nature Genetics 47, 672676.CrossRefGoogle ScholarPubMed
Paulsson, K (2016) High hyperdiploid childhood acute lymphoblastic leukemia: chromosomal gains as the main driver event. Molecular & Cellular Oncology 3, e1064555.CrossRefGoogle ScholarPubMed
Malinowska-Ozdowy, K et al. (2015) KRAS And CREBBP mutations: a relapse-linked malicious liaison in childhood high hyperdiploid acute lymphoblastic leukemia. Leukemia 29, 16561667.CrossRefGoogle ScholarPubMed
Bhatnagar, N et al. (2016) Transient abnormal myelopoiesis and AML in Down syndrome: an update. Current Hematologic Malignancy Reports 11, 333341.CrossRefGoogle ScholarPubMed
Ahmed, M et al. (2004) Natural history of GATA1 mutations in Down syndrome. Blood 103, 24802489.CrossRefGoogle ScholarPubMed
Matzke, MA et al. (2003) Does the intrinsic instability of aneuploid genomes have a causal role in cancer? Trends in Genetics 19, 253256.CrossRefGoogle ScholarPubMed
Sheltzer, JM et al. (2011) Aneuploidy drives genomic instability in yeast. Science 333, 10261030.CrossRefGoogle ScholarPubMed
Greaves, M (2005) In utero origins of childhood leukaemia. Early Human Development 81, 123129.CrossRefGoogle ScholarPubMed
Sabharwal, SS and Schumacker, PT (2014) Mitochondrial ROS in cancer: initiators, amplifiers or an achilles’ heel? Nature Reviews Cancer 14, 709721.CrossRefGoogle ScholarPubMed
Schieber, M and Chandel, NS (2014) ROS Function in redox signaling and oxidative stress. Current Biology 24, R453R462.CrossRefGoogle ScholarPubMed
Zorov, DB, Juhaszova, M and Sollott, SJ (2014) Mitochondrial reactive oxygen species (ROS) and ROS-induced ROS release. Physiological Reviews 94, 909950.CrossRefGoogle ScholarPubMed
Piccoli, C et al. (2013) Chronic pro-oxidative state and mitochondrial dysfunctions are more pronounced in fibroblasts from Down syndrome foeti with congenital heart defects. Human Molecular Genetics 22, 12181232.CrossRefGoogle ScholarPubMed
Helguera, P et al. (2013) Adaptive downregulation of mitochondrial function in Down syndrome. Cell Metabolism 17, 132140.CrossRefGoogle ScholarPubMed
Izzo, A et al. (2017) Metformin restores the mitochondrial network and reverses mitochondrial dysfunction in Down syndrome cells. Human Molecular Genetics 26, 10561069.Google ScholarPubMed
Valenti, D et al. (2011) Deficit of complex I activity in human skin fibroblasts with chromosome 21 trisomy and overproduction of reactive oxygen species by mitochondria: involvement of the cAMP/PKA signalling pathway. Biochemical Journal 435, 679688.CrossRefGoogle ScholarPubMed
Perluigi, M et al. (2011) Oxidative stress occurs early in Down syndrome pregnancy: a redox proteomics analysis of amniotic fluid. Proteomics Clinical Applications 5, 167178.CrossRefGoogle ScholarPubMed
Busciglio, J et al. (2002) Altered metabolism of the amyloid beta precursor protein is associated with mitochondrial dysfunction in Down's syndrome. Neuron 33, 677688.CrossRefGoogle ScholarPubMed
Vilardell, M et al. (2011) Meta-analysis of heterogeneous Down syndrome data reveals consistent genome-wide dosage effects related to neurological processes. BMC Genomics 12, 229.CrossRefGoogle ScholarPubMed
Conti, A et al. (2007) Altered expression of mitochondrial and extracellular matrix genes in the heart of human fetuses with chromosome 21 trisomy. BMC Genomics 8, 268.CrossRefGoogle ScholarPubMed
Izzo, A et al. (2018) Mitochondrial dysfunction in Down syndrome: molecular mechanisms and therapeutic targets. Molecular Medicine 24, 2.CrossRefGoogle ScholarPubMed
Burrows, CJ and Muller, JG (1998) Oxidative nucleobase modifications leading to strand scission. Chemical Reviews 98, 11091152.CrossRefGoogle ScholarPubMed
Cabelof, DC et al. (2009) Mutational spectrum at GATA1 provides insights into mutagenesis and leukemogenesis in Down syndrome. Blood 114, 27532763.CrossRefGoogle ScholarPubMed
Alexandrov, LB et al. (2013) Signatures of mutational processes in human cancer. Nature 500, 415421.CrossRefGoogle ScholarPubMed
Alexandrov, LB et al. (2020) The repertoire of mutational signatures in human cancer. Nature 578, 94101.CrossRefGoogle ScholarPubMed
Viel, A et al. (2017) A specific mutational signature associated with DNA 8-oxoguanine persistence in MUTYH-defective colorectal cancer. EBioMedicine 20, 3949.CrossRefGoogle ScholarPubMed
Morawiec, Z et al. (2008) DNA Damage and repair in children with Down's syndrome. Mutation Research 637, 118123.CrossRefGoogle ScholarPubMed
Eckmann-Scholz, C et al. (2012) DNA-methylation profiling of fetal tissues reveals marked epigenetic differences between chorionic and amniotic samples. PLoS ONE 7, e39014.CrossRefGoogle ScholarPubMed
Kerkel, K et al. (2010) Altered DNA methylation in leukocytes with trisomy 21. PLoS Genetics 6, e1001212.CrossRefGoogle ScholarPubMed
Malinge, S et al. (2013) Development of acute megakaryoblastic leukemia in Down syndrome is associated with sequential epigenetic changes. Blood 122, e33e43.CrossRefGoogle ScholarPubMed
Mowery, CT et al. (2018) Trisomy of a Down syndrome critical region globally amplifies transcription via HMGN1 overexpression. Cell Reports 25, 18981911, e5.CrossRefGoogle ScholarPubMed
Sailani, MR et al. (2015) DNA-Methylation patterns in trisomy 21 using cells from monozygotic twins. PLoS ONE 10, e0135555.CrossRefGoogle ScholarPubMed
Laufer, BI et al. (2020) Low-Pass whole genome bisulfite sequencing of neonatal dried blood spots identifies a role for RUNX1 in Down syndrome DNA methylation profiles. Human Molecular Genetics 29, 34653476.CrossRefGoogle Scholar
Henneman, P et al. (2018) Widespread domain-like perturbations of DNA methylation in whole blood of Down syndrome neonates. PLoS One 13, e0194938.CrossRefGoogle ScholarPubMed
Bourquin, JP et al. (2006) Identification of distinct molecular phenotypes in acute megakaryoblastic leukemia by gene expression profiling. Proceedings of the National Academy of Sciences of the USA 103, 33393344.CrossRefGoogle ScholarPubMed
Kubota, Y et al. (2019) Integrated genetic and epigenetic analysis revealed heterogeneity of acute lymphoblastic leukemia in Down syndrome. Cancer Science 110, 33583367.CrossRefGoogle ScholarPubMed
Bernt, KM and Hunger, SP (2014) Current concepts in pediatric Philadelphia chromosome-positive acute lymphoblastic leukemia. Frontiers in Oncology 4, 54.CrossRefGoogle ScholarPubMed
Lane, AA et al. (2014) Triplication of a 21q22 region contributes to B cell transformation through HMGN1 overexpression and loss of histone H3 Lys27 trimethylation. Nature Genetics 46, 618623.CrossRefGoogle Scholar
Roy, A et al. (2012) Perturbation of fetal liver hematopoietic stem and progenitor cell development by trisomy 21. Proceedings of the National Academy of Sciences of the United States of America 109, 1757917584.CrossRefGoogle ScholarPubMed
Rozhok, AI and DeGregori, J (2016) The evolution of lifespan and age-dependent cancer risk. Trends in Cancer 2, 552560.CrossRefGoogle ScholarPubMed
Ivanovs, A et al. (2017) Human haematopoietic stem cell development: from the embryo to the dish. Development 144, 23232337.CrossRefGoogle ScholarPubMed
Khan, JA et al. (2016) Fetal liver hematopoietic stem cell niches associate with portal vessels. Science 351, 176180.CrossRefGoogle ScholarPubMed
Tamplin, OJ et al. (2015) Hematopoietic stem cell arrival triggers dynamic remodeling of the perivascular niche. Cell 160, 241252.CrossRefGoogle ScholarPubMed
Chou, S and Lodish, HF (2010) Fetal liver hepatic progenitors are supportive stromal cells for hematopoietic stem cells. Proceedings of the National Academy of Sciences of the USA 107, 77997804.CrossRefGoogle ScholarPubMed
Brouard, N et al. (2017) A unique microenvironment in the developing liver supports the expansion of megakaryocyte progenitors. Blood Advances 1, 18541866.CrossRefGoogle ScholarPubMed
Chiang, JC et al. (2018) Trisomy silencing by XIST normalizes Down syndrome cell pathogenesis demonstrated for hematopoietic defects in vitro. Nature Communications 9, 5180.CrossRefGoogle ScholarPubMed
Chen, HZ, Tsai, SY and Leone, G (2009) Emerging roles of E2Fs in cancer: an exit from cell cycle control. Nature Reviews Cancer 9, 785797.CrossRefGoogle ScholarPubMed
Yamada, H et al. (2013) Effects of insulin-like growth factor-1 on B-cell precursor acute lymphoblastic leukemia. International Journal of Hematology 97, 7382.CrossRefGoogle ScholarPubMed
Vorwerk, P et al. (2002) Expression of components of the IGF signalling system in childhood acute lymphoblastic leukaemia. Molecular Pathology 55, 4045.CrossRefGoogle ScholarPubMed
Miyauchi, J and Kawaguchi, H (2014) Fetal liver stromal cells support blast growth in transient abnormal myelopoiesis in Down syndrome through GM-CSF. Journal of Cellular Biochemistry 115, 11761186.CrossRefGoogle ScholarPubMed
Palucka, AK and Coussens, LM (2016) The basis of oncoimmunology. Cell 164, 12331247.CrossRefGoogle ScholarPubMed
Dunn, GP, Old, LJ and Schreiber, RD (2004) The three Es of cancer immunoediting. Annual Review of Immunology 22, 329360.CrossRefGoogle ScholarPubMed
Martincorena, I et al. (2017) Universal patterns of selection in cancer and somatic tissues. Cell 171, 10291041, e21.CrossRefGoogle ScholarPubMed
Vermeulen, L et al. (2013) Defining stem cell dynamics in models of intestinal tumor initiation. Science 342, 995998.CrossRefGoogle ScholarPubMed
Toomer, KH and Chen, Z (2014) Autoimmunity as a double agent in tumor killing and cancer promotion. Frontiers in Immunology 5, 116.CrossRefGoogle ScholarPubMed
de Visser, KE, Eichten, A and Coussens, LM (2006) Paradoxical roles of the immune system during cancer development. Nature Reviews Cancer 6, 2437.CrossRefGoogle ScholarPubMed
Coussens, LM and Werb, Z (2002) Inflammation and cancer. Nature 420, 860867.CrossRefGoogle ScholarPubMed
Kawanishi, S et al. (2017) Crosstalk between DNA damage and inflammation in the multiple steps of carcinogenesis. International Journal of Molecular Sciences 18, 18081821.CrossRefGoogle ScholarPubMed
Ram, G and Chinen, J (2011) Infections and immunodeficiency in Down syndrome. Clinical and Experimental Immunology 164, 916.CrossRefGoogle ScholarPubMed
Sullivan, KD et al. (2017) Trisomy 21 causes changes in the circulating proteome indicative of chronic autoinflammation. Scientific Reports 7, 14818.CrossRefGoogle ScholarPubMed
Huggard, D et al. (2020) Increased systemic inflammation in children with Down syndrome. Cytokine 127, 154938.CrossRefGoogle ScholarPubMed
Waugh, KA et al. (2019) Mass cytometry reveals global immune remodeling with multi-lineage hypersensitivity to type I interferon in Down syndrome. Cell Reports 29, 18931908, e4.CrossRefGoogle ScholarPubMed
Sullivan, KD et al. (2016) Trisomy 21 consistently activates the interferon response. Elife 5, 1622016248.CrossRefGoogle ScholarPubMed
Bloemers, BL et al. (2010) Distinct abnormalities in the innate immune system of children with Down syndrome. The Journal of Pediatrics 156, 804809, 09.e1–09.e5.CrossRefGoogle ScholarPubMed
Tunstall-Pedoe, O et al. (2008) Abnormalities in the myeloid progenitor compartment in Down syndrome fetal liver precede acquisition of GATA1 mutations. Blood 112, 45074511.CrossRefGoogle ScholarPubMed
Chou, ST et al. (2008) Trisomy 21 enhances human fetal erythro-megakaryocytic development. Blood 112, 45034506.CrossRefGoogle ScholarPubMed
de Hingh, YC et al. (2005) Intrinsic abnormalities of lymphocyte counts in children with Down syndrome. The Journal of Pediatrics 147, 744747.CrossRefGoogle ScholarPubMed
Mirantes, C, Passegué, E and Pietras, EM (2014) Pro-inflammatory cytokines: emerging players regulating HSC function in normal and diseased hematopoiesis. Experimental Cell Research 329, 248254.CrossRefGoogle ScholarPubMed
Essers, MA et al. (2009) IFNAlpha activates dormant haematopoietic stem cells in vivo. Nature 458, 904908.CrossRefGoogle ScholarPubMed
Matatall, KA et al. (2014) Type II interferon promotes differentiation of myeloid-biased hematopoietic stem cells. Stem Cells 32, 30233030.CrossRefGoogle ScholarPubMed
Matatall, KA et al. (2016) Chronic infection depletes hematopoietic stem cells through stress-induced terminal differentiation. Cell Reports 17, 25842595.CrossRefGoogle ScholarPubMed
Kim, PG et al. (2016) Interferon-α signaling promotes embryonic HSC maturation. Blood 128, 204216.CrossRefGoogle ScholarPubMed
Li, Y et al. (2020) Single-cell analysis of neonatal HSC ontogeny reveals gradual and uncoordinated transcriptional reprogramming that begins before birth. Cell Stem Cell 27, 732747.CrossRefGoogle ScholarPubMed
Woo, AJ et al. (2013) Developmental differences in IFN signaling affect GATA1s-induced megakaryocyte hyperproliferation. Journal of Clinical Investigation 123, 32923304.CrossRefGoogle Scholar
Wang, Q et al. (2000) Interferon-alpha directly represses megakaryopoiesis by inhibiting thrombopoietin-induced signaling through induction of SOCS-1. Blood 96, 20932099.CrossRefGoogle ScholarPubMed
Roberts, KG (2018) Genetics and prognosis of ALL in children vs adults. Hematology. American Society of Hematology. Education Program 2018, 137145.CrossRefGoogle ScholarPubMed
Tomasetti, C, Li, L and Vogelstein, B (2017) Stem cell divisions, somatic mutations, cancer etiology, and cancer prevention. Science 355, 13301334.CrossRefGoogle ScholarPubMed
Rozhok, AI, Salstrom, JL and DeGregori, J (2016) Stochastic modeling reveals an evolutionary mechanism underlying elevated rates of childhood leukemia. Proceedings of the National Academy of Sciences of the USA 113, 10501055.CrossRefGoogle ScholarPubMed
Bolouri, H et al. (2018) The molecular landscape of pediatric acute myeloid leukemia reveals recurrent structural alterations and age-specific mutational interactions. Nature Medicine 24, 103112.CrossRefGoogle ScholarPubMed
Hara, Y et al. (2017) Prognostic impact of specific molecular profiles in pediatric acute megakaryoblastic leukemia in non-Down syndrome. Genes Chromosomes & Cancer 56, 394404.CrossRefGoogle ScholarPubMed
Reinhardt, K et al. (2012) High frequency of GATA1 mutations in childhood non-Down syndrome acute megakaryoblastic leukemia. Blood 120, 888888.CrossRefGoogle Scholar
Ling, T and Crispino, JD (2020) GATA1 mutations in red cell disorders. IUBMB Life 72, 106118.CrossRefGoogle ScholarPubMed
Xu, G et al. (2006) Development of acute megakaryoblastic leukemia from a minor clone in a Down syndrome patient with clinically overt transient myeloproliferative disorder. Journal of Pediatric Hematology/Oncology 28, 696698.CrossRefGoogle Scholar
Rainis, L et al. (2003) Mutations in exon 2 of GATA1 are early events in megakaryocytic malignancies associated with trisomy 21. Blood 102, 981986.CrossRefGoogle ScholarPubMed
Uhara, H et al. (2009) Transient myeloproliferative disorder with vesiculopustular eruption: early smear is useful for quick diagnosis. Journal of the American Academy of Dermatology 60, 869871.CrossRefGoogle ScholarPubMed
Hoeller, S et al. (2009) New GATA1 mutation in codon 2 leads to the earliest known premature stop codon in transient myeloproliferative disorder. Blood 114, 37173718.CrossRefGoogle ScholarPubMed
Groet, J et al. (2005) Independent clones at separable stages of differentiation, bearing different GATA1 mutations, in the same TMD patient with Down syndrome. Blood 106, 18871888.CrossRefGoogle ScholarPubMed
Kanezaki, R et al. (2010) Down syndrome and GATA1 mutations in transient abnormal myeloproliferative disorder: mutation classes correlate with progression to myeloid leukemia. Blood 116, 46314638.CrossRefGoogle ScholarPubMed
Tsai, MH et al. (2011) Transient myeloproliferative disorder and GATA1 mutation in neonates with and without Down syndrome. Indian Journal of Pediatrics 78, 826832.CrossRefGoogle ScholarPubMed
Koga, H et al. (2012) Improving circulatory disturbance in transient abnormal myelopoiesis. Journal of Pediatric Hematology/Oncology 34, e149e151.CrossRefGoogle ScholarPubMed
Loh, TJ et al. (2014) Congenital GATA1-mutated myeloproliferative disorder in trisomy 21 complicated by placental fetal thrombotic vasculopathy. Human Pathology 45, 23642367.CrossRefGoogle ScholarPubMed
Shen, W et al. (2016) Concurrent detection of targeted copy number variants and mutations using a myeloid malignancy next generation sequencing panel allows comprehensive genetic analysis using a single testing strategy. British Journal of Haematology 173, 4958.CrossRefGoogle ScholarPubMed
Groet, J et al. (2003) Acquired mutations in GATA1 in neonates with down's syndrome with transient myeloid disorder. Lancet 361, 16171620.CrossRefGoogle ScholarPubMed
McElwaine, S et al. (2004) Microarray transcript profiling distinguishes the transient from the acute type of megakaryoblastic leukaemia (M7) in Down's syndrome, revealing PRAME as a specific discriminating marker. British Journal of Haematology 125, 729742.CrossRefGoogle ScholarPubMed
Figure 0

Fig. 1. GATA1 aberrations in transient abnormal myelopoiesis (TAM). Analysis of all GATA1 mutations identified in TAM, which are annotated in the Catalogue of Somatic Mutations in Cancer (COSMIC) Cancer Gene Census database and GATA1 mutations from the recently published study of Labuhn et al. (Refs 30, 31, 41, 61, 78, 138–149). (a) Pie chart showing the different types of GATA1 mutations observed in TAM (pre)leukaemic blasts. (b) 96-trinucleotide spectrum of all single nucleotide variants (SNVs) observed in GATA1. (c) The spectrum of insertions and deletions (indels) within GATA1.

Figure 1

Fig. 2. Development of transient abnormal myelopoiesis (TAM) and myeloid leukaemia of Down syndrome (ML-DS). Model for the development of TAM and ML-DS in Down syndrome individuals. Schematic representation, indicating the initiation of TAM before birth when trisomy 21 (T21) fetal haematopoietic stem and progenitor cells (HSPCs) acquire a GATA1 mutation. The proliferation of (pre)leukaemic TAM blasts in the fetal liver is supported by secretion of granulocyte-macrophage colony-stimulating factor (GM-CSF) in combination with increased insulin growth factor-2 (IGF) signalling and lower levels of IFN signalling. TAM resolves when haematopoiesis migrates to the bone marrow after birth and interferon (IFN) signalling increases. Additional oncogenic mutations in an existing GATA1 mutated clone provide these cells with an additional growth advantage and are required for progression towards ML-DS.

Figure 2

Fig. 3. Development of DS acute lymphoblastic leukemia (DS-ALL). Model for the development of acute lymphoblastic leukemia (ALL) in Down syndrome individuals. Trisomy 21 (T21) haematopoietic stem and progenitor cells (HSPCs) show a B-cell maturation defect. An extra copy of genes encoding chromatin modifiers on chromosome 21 causes a decrease in H3k27me3 marks and an increase in H3K27ac and H3K4me3 marks on genes involved in B-cell proliferation and B-ALL development, which will result in overexpression of these genes. The acquisition of cancer driver mutations and additional oncogenic mutations is sufficient for the development of DS-ALL.