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Abstract
While paediatric blood cancers are deadly, modern medical advances have enabled clinicians to measure levels of residual cancer cells to manage therapeutic strategies for patients. However, blood cancers, including leukaemias and lymphomas, are highly heterogeneous and is comprised of complex clonal populations that can hinder efforts in detecting the cancer cells as well as managing treatments. Furthermore, the tumour microenvironment is comprised of heterogenous immune dynamics that may be different between patients. High-throughput sequencing has constributed to new discoveries in genetic and transcriptomic alterations underpinning cancer, including blood cancers, and has changed how patients are monitored and managed. Here we discuss the recent efforts using single-cell approach, particularly on efforts to track clonal heterogenity of paediatric blood cancer and the underlying immune response, highlighting avenues for novel biomarker discovery that may have significant impact on clinical oncology practice.
Keywords
blood cancer
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clonality
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paediatric
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Single-cell
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T-cell receptor
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Amos Choo, Zewen Kelvin Tuong.
Measuring single-cell immune clonality to track haematological cancers.
Clinical and Translational Medicine, 2024, 14(8): e1780 DOI:10.1002/ctm2.1780
| [1] |
Irac SE, Soon MSF, Borcherding N, Tuong ZK. Single-cell immune repertoire analysis. Nat Methods. 2024;21(5):777-792.
|
| [2] |
Haebe S, Shree T, Sathe A, et al. Single-cell analysis can define distinct evolution of tumor sites in follicular lymphoma. Blood. 2021;137(21):2869-2880.
|
| [3] |
Abbas HA, Hao D, Tomczak K, et al. Single cell T cell landscape and T cell receptor repertoire profiling of AML in context of PD-1 blockade therapy. Nat Commun. 2021;12(1):6071.
|
| [4] |
Wu W, Liang X, Li H, et al. Landscape of T cells in NK-AML(M4/M5) revealed by single-cell sequencing. J Leukoc Biol. 2022;112(4):745-758.
|
| [5] |
Gao S, Wu Z, Arnold B, et al. Single-cell RNA sequencing coupled to TCR profiling of large granular lymphocyte leukemia T cells. Nat Commun. 2022;13(1):1982.
|
| [6] |
Zhu B, Wang Y, Ku LT, et al. scNAT: a deep learning method for integrating paired single-cell RNA and T cell receptor sequencing profiles. Genome Biol. 2023;24(1):292.
|
| [7] |
Mi X, Griffin G, Lee W, et al. Genomic and clinical characterization of B/T mixed phenotype acute leukemia reveals recurrent features and T-ALL like mutations. Am J Hematol. 2018;93(11):1358-1367.
|
| [8] |
Lao ZT, Ding LW, An O, et al. Mutational and transcriptomic profiling of acute leukemia of ambiguous lineage reveals obscure but clinically important lineage bias. Haematologica. 2019;104(5):e200-e203.
|
| [9] |
Xie J, Jeon H, Xin G, Ma Q, Chung D. LRT: integrative analysis of scRNA-seq and scTCR-seq data to investigate clonal differentiation heterogeneity. PLOS Comp Biol. 2023;19(7):e1011300.
|
| [10] |
Suo C, Polanski K, Dann E, et al. Dandelion uses the single-cell adaptive immune receptor repertoire to explore lymphocyte developmental origins. Nat Biotechnol. 2024;42(1):40-51.
|
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2024 The Author(s). Clinical and Translational Medicine published by John Wiley & Sons Australia, Ltd on behalf of Shanghai Institute of Clinical Bioinformatics.