Submitted:
22 September 2023
Posted:
25 September 2023
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Abstract
The molecular basis of Down syndrome (DS) predisposition to leukemia is not fully understood but involves various factors such as chromosomal abnormalities, oncogenic mutations, epigenetic alterations, and changes in selection dynamics.
Myeloid leukemia associated with DS (ML-DS) is preceded by a preleukemic phase called transient abnormal myelopoiesis driven by GATA1 gene mutations and progresses to ML-DS through additional mutations in cohesin genes, CTCF, RAS, or JAK/STAT pathway genes.
DS-related ALL (ALL-DS) differs from non-DS ALL in terms of cytogenetic subgroups and genetic driver events and aberrant expression of CRLF2, JAK2 mutations, and RAS pathway activating mutations are frequent in ALL-DS.
Recent advancements in single-cell multi-omics technologies have provided unprecedented insights into the cellular and molecular heterogeneity of DS-associated hematologic neoplasms. Single-cell RNA sequencing and digital spatial profiling enable the identification of rare cell subpopulations, characterization of clonal evolution dynamics, and exploration of the tumor microenvironment's role. These approaches may help identify new druggable targets and tailor therapeutic interventions based on distinct molecular profiles, ultimately improving patient outcomes with the potential to guide personalized medicine approaches and the development of targeted therapies.
Keywords:
acute myeloid leukemia
; acute lymphoblastic leukemia
; single-cell RNA sequencing
; Down syndrome
; trisomy 21
; personalized medicine
; multi-omics approach
1. Trisomy 21 and leukemia
Down syndrome (DS) is the most common chromosomal disorder in humans1. It results from a full trisomy of chromosome 21 (T21) in 90% of cases, with remaining patients harboring other chromosome 21 abnormalities or mosaicisms. Notably, while the risk of solid tumors is reduced throughout life, DS is associated with an increased risk of developing leukemia, especially during the first years of life. In particular, DS children have a 150-fold and 7-20-fold increased risk of developing acute myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL), respectively.
Although recent scientific progress, the molecular basis of DS predisposition to leukemia remains elusive. Patently, the perturbation of hematopoiesis in DS individuals is driven by chromosome 21. Besides T21-related mechanisms, several additional drivers have been described to be involved in DS-associated leukemogenesis, including concomitant oncogenic mutations, epigenetic and transcriptional alterations, and changes in selection dynamics within the fetal liver niche. Recently, single-cell multi-omics technologies has led to unbiased investigation of cellular profiles at unprecedented resolution in all hematological areas, including DS-associated hematologic neoplasms2,3.
Overall, DS represents the human phenotype model of genomic gain dosage imbalances, and the implementation of emerging single-cell analyses constitute an unprecedented opportunity to decipher the molecular consequences of genome dosage imbalance with potential groundbreaking consequences for non-DS leukemogenesis.
4. Single-cell analysis: extending the frontiers of ML/ALL-DS
In recent years, next generation sequencing (NGS) provided a paramount contribution in the understanding of cancer biology. Neverthless, bulk genomic profiling methods fail to accurately resolve the clonal architecture of tumor populations and are limited in the identification of relapse-driving clones.
The development of single-cell analysis techniques, including single-cell RNA sequencing (scRNA-seq) and spatial single-cell imaging, offers a promising opportunity to gain insights into the biology of cancer development and progression. The use of scRNA-seq may allow the identification of rare cell subpopulations and the characterization of clonal evolution dynamics in order to decipher the mechanisms supporting treatment resistance and disease relapse. Moreover, scRNA-seq and spatial single-cell imaging have the potential to investigate the role of tumor microenvironment (TME) in supporting leukemia cell growth and survival. Ultimately, such depth of exploration may allow for the development of novel targeted therapies aimed at improving the patient outcomes.
Finally, the integration of single-cell techniques within multi-omics (Figure 1), by allowing the joint analysis of genome, transcriptome, epigenome, and proteome at the single-cell level, may enable pivotal new insights into the complex interplay between intracellular and intercellular molecular mechanisms driving disease pathogenesis, evolution, and recurrence.
In conclusion, DS leukemogenesis represents a unique disease setting to study human preleukemia and the evolutionary steps that lead to fully transformed leukemia. The increasingly widespread access to platforms based on cell-by-cell technologies has allowed to overcome the limitations of conventional NGS, while shedding light on the complexity of tumor composition and clonal evolution In the era of personalized medicine, single-cell research has the potential to provide a more detailed understanding of the biology of DS leukemogenesis in order to identify new druggable targets and tailor the therapeutic intervention according to distinct molecular profiles at risk.
Author Contributions
EP and GC wrote the manuscript, MLR, AR, GM and LDA revised and provided important intellectual content.
Funding
This research received “Ricerca Corrente” funding from the Italian Ministry of Health to cover publication costs.
Consent for publication
All authors contributed to the research and approved the final manuscript.
Availability of data and material
Not applicable.
Competing interests
The authors declare no conflict of interest.
Ethics approval and consent to participate
Not applicable.
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Figure 1.
Several multiomics approaches combining genomic, transcrisptomic, proteomic and spatial imaging data from bone marrow biopsy and/or peripheral blood sample.
Figure 1.
Several multiomics approaches combining genomic, transcrisptomic, proteomic and spatial imaging data from bone marrow biopsy and/or peripheral blood sample.

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