Unraveling Clonal Architecture and Microenvironmental Complexity of Leukemia and Lymphoma Using Singlecell Multi-Omics in Nigeria
DOI:
https://doi.org/10.64229/8hzxek71Keywords:
Leukemia, Lymphoma, Single-Cell Sequencing, Heterogeneity, Tumor Microenvironment, Precision MedicineAbstract
Leukemia and lymphoma represent a significant burden in Nigeria, characterized by distinct molecular profiles and treatment responses compared to global patterns. This uniqueness can be attributed to several factors including genetic diversity within the Nigerian population, environmental exposures specific to the region, and healthcare access challenges that affect early diagnosis and treatment adherence. The comprehensive understanding of these hematological malignancies has been revolutionized by the advent of single-cell technologies, which have uncovered unprecedented resolution into tumor heterogeneity, evolution, and microenvironmental interactions. Unlike traditional bulk sequencing methods that average signals across thousands of cells, single-cell approaches allow researchers to examine individual cells, revealing rare but critical subpopulations that drive disease progression and therapy resistance.
This review synthesizes findings from Nigerian studies integrated with global advances in single-cell research, highlighting how clonal architecture, cellular ecosystems, and molecular signatures contribute to pathogenesis and treatment outcomes. We explore how single-cell multi-omics approaches—including transcriptomics, genomics, and proteomics—have identified novel therapeutic targets and resistance mechanisms in leukemia and lymphoma. Additionally, we discuss the potential for implementing these technologies in Nigeria to advance precision medicine, considering both the opportunities and challenges specific to the Nigerian healthcare context. The integration of single-cell analyses into clinical practice promises to improve risk stratification, therapy selection, and overall management of these malignancies in resource-limited settings through more targeted and effective treatment approaches.
References
[1]Chanock, S.J. Harnessing cancer genomes for precision oncology. Nat Genet 56, 1768–1769 (2024). https://doi.org/10.1038/s41588-024-01879-4
[2]Chinese Journal of Hematology. (2022). Chinese guidelines for diagnosis and treatment of Hodgkin lymphoma (2022). Chinese Journal of Hematology, 43(9), 705-715. https://doi.org/10.3760/cma.j.issn.0253-2727.2022.09.001
[3]Novel insights into Hodgkin lymphoma biology by single-cell analysisCrossref DOI link: https://doi.org/10.1182/blood.2022017147
[4]Mumme, H., Thomas, B.E., Bhasin, S.S. et al. Single-cell analysis reveals altered tumor microenvironments of relapse- and remission-associated pediatric acute myeloid leukemia. Nat Commun 14, 6209 (2023). https://doi.org/10.1038/s41467-023-41994-0
[5]Liu, J., Jiang, P., Lu, Z. et al. Decoding leukemia at the single-cell level: clonal architecture, classification, microenvironment, and drug resistance. Exp Hematol Oncol 13, 12 (2024). https://doi.org/10.1186/s40164-024-00479-6
[6]Frontiers in Oncology. (2024). Application and research progress of single cell sequencing technology in leukemia. Frontiers in Oncology, 14, 1389468. https://doi.org/10.3389/fonc.2024.1389468
[7]Abe, Y., Sakata-Yanagimoto, M., Fujisawa, M. et al. A single-cell atlas of non-haematopoietic cells in human lymph nodes and lymphoma reveals a landscape of stromal remodelling. Nat Cell Biol 24, 565–578 (2022). https://doi.org/10.1038/s41556-022-00866-3
[8]Cotorogea-Simion M, Pavel B, Isac S, Telecan T, Matache I-M, Bobirca A, Bobirca F-T, Rababoc R, Droc G. What Is Different in Acute Hematologic Malignancy-Associated ARDS? An Overview of the Literature. Medicina. 2022; 58(9):1215. https://doi.org/10.3390/medicina58091215
[9]Hagenbeek, T.J., Zbieg, J.R., Hafner, M. et al. An allosteric pan-TEAD inhibitor blocks oncogenic YAP/TAZ signaling and overcomes KRAS G12C inhibitor resistance. Nat Cancer 4, 812–828 (2023). https://doi.org/10.1038/s43018-023-00577-0
[10]Lali, R., Chong, M., Omidi, A. et al. Calibrated rare variant genetic risk scores for complex disease prediction using large exome sequence repositories. Nat Commun 12, 5852 (2021). https://doi.org/10.1038/s41467-021-26114-0
[11]Veronica Murianni, Giuseppe Luigi Banna,Response to letter to the editor: "Insights on nivolumab-based immunotherapy and prognostic stratification in older patients with metastatic renal cell carcinoma", Journal of Geriatric Oncology, 17, 1, (102771), (2026).https://doi.org/10.1016/j.jgo.2025.102771
[12]Puram, S. V., Tirosh, I., Parikh, A. S., et al. (2017). Single-cell transcriptomic analysis of primary and metastatic tumor ecosystems in head and neck cancer. Cell, 171(7), 1611–1624. https://doi.org/10.1016/j.cell.2017.10.044
[13]Lambrechts, D., Wauters, E., Boeckx, B. et al. Phenotype molding of stromal cells in the lung tumor microenvironment. Nat Med 24, 1277–1289 (2018). https://doi.org/10.1038/s41591-018-0096-5
[14]Marusyk, A., Janiszewska, M., & Polyak, K. (2020). Intratumor heterogeneity: The Rosetta Stone of therapy resistance. Cancer Cell, 37(4), 471–484. https://doi.org/10.1016/j.ccell.2020.03.007
[15]O’Donnell, J.S., Teng, M.W.L. & Smyth, M.J. Cancer immunoediting and resistance to T cell-based immunotherapy. Nat Rev Clin Oncol 16, 151–167 (2019). https://doi.org/10.1038/s41571-018-0142-8
[16]Braun, T.P., Estabrook, J., Schonrock, Z. et al. Asxl1 deletion disrupts MYC and RNA polymerase II function in granulocyte progenitors. Leukemia 37, 478–487 (2023). https://doi.org/10.1038/s41375-022-01792-x
[17]Lin, X., Wang, S., Sun, M. et al. Retraction Note: miR-195-5p/NOTCH2-mediated EMT modulates IL-4 secretion in colorectal cancer to affect M2-like TAM polarization. J Hematol Oncol 16, 41 (2023). https://doi.org/10.1186/s13045-023-01438-0
[18]Heidary, M., Auer, M., Ulz, P. et al. The dynamic range of circulating tumor DNA in metastatic breast cancer. Breast Cancer Res 16, 421 (2014). https://doi.org/10.1186/s13058-014-0421-y
[19]Carter, B., Zhao, K. The epigenetic basis of cellular heterogeneity. Nat Rev Genet 22, 235–250 (2021). https://doi.org/10.1038/s41576-020-00300-0
[20]Deaglio, S., & Ringshausen, I. (2023). Editorial: Tumour microenvironment heterogeneity in hematological malignancies. Frontiers in Oncology, 13, 1295996. https://doi.org/10.3389/fonc.2023.1295996
[21]Famous, K. R., Delucchi, K. L., Ware, L. B., et al. (2017). Acute respiratory distress syndrome subphenotypes respond differently to randomized fluid management strategy. American Journal of Respiratory and Critical Care Medicine, 195(3), 331–338. https://doi.org/10.1164/rccm.201603-0645OC
[22]Voisinne, G., Locard-Paulet, M., Froment, C. et al. Kinetic proofreading through the multi-step activation of the ZAP70 kinase underlies early T cell ligand discrimination. Nat Immunol 23, 1355–1364 (2022). https://doi.org/10.1038/s41590-022-01288-x
[23]Schore, R.J., Angiolillo, A.L., Kairalla, J.A. et al. Outstanding outcomes with two low intensity regimens in children with low-risk B-ALL: a report from COG AALL0932. Leukemia 37, 1375–1378 (2023). https://doi.org/10.1038/s41375-023-01870-8
[24]Xia, Y., Sun, T., Li, G. et al. Spatial single cell analysis of tumor microenvironment remodeling pattern in primary central nervous system lymphoma. Leukemia 37, 1499–1510 (2023). https://doi.org/10.1038/s41375-023-01908-x
[25]Yang, X., Wen, J., Yang, H. et al. Functional characterization of Alzheimer’s disease genetic variants in microglia. Nat Genet 55, 1735–1744 (2023). https://doi.org/10.1038/s41588-023-01506-8
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Leon Oluci Kochukgu (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
