Dimensionality reduction enables MRD visualization in single-sample flow cytometric analysis in acute leukemia

Berry DA, Zhou S, Higley H, Mukundan L, Fu S, Reaman GH, et al. Association of minimal residual disease with clinical outcome in pediatric and adult acute lymphoblastic leukemia: a meta-analysis. JAMA Oncol. 2017;3(7):e170580. https://doi.org/10.1001/jamaoncol.2017.0580.

Article  PubMed  PubMed Central  Google Scholar 

Short NJ, Zhou S, Fu C, Berry DA, Walter RB, Freeman SD, et al. Association of measurable residual disease with survival outcomes in patients with acute myeloid leukemia: a systematic review and meta-analysis. JAMA Oncol. 2020;6(12):1890–9. https://doi.org/10.1001/jamaoncol.2020.4600.

Article  PubMed  PubMed Central  Google Scholar 

Munshi NC, Avet-Loiseau H, Rawstron AC, Owen RG, Child JA, Thakurta A, et al. Association of minimal residual disease with superior survival outcomes in patients with multiple myeloma: a meta-analysis. JAMA Oncol. 2017;3(1):28–35. https://doi.org/10.1001/jamaoncol.2016.3160.

Article  PubMed  PubMed Central  Google Scholar 

Del Giudice I, Raponi S, Della Starza I, De Propris MS, Cavalli M, De Novi LA, et al. Minimal residual disease in chronic lymphocytic leukemia: a new goal? Front Oncol. 2019;9:689. https://doi.org/10.3389/fonc.2019.00689.

Article  PubMed  PubMed Central  Google Scholar 

Tettero JM, Freeman S, Buecklein V, Venditti A, Maurillo L, Kern W, et al. Technical aspects of flow cytometry-based measurable residual disease quantification in acute myeloid leukemia: experience of the European LeukemiaNet MRD Working Party. Hemasphere. 2021;6(1):e676. https://doi.org/10.1097/HS9.0000000000000676.

Article  PubMed  PubMed Central  Google Scholar 

Theunissen P, Mejstrikova E, Sedek L, Van der Sluijs-Gelling AJ, Gaipa G, Bartels M, et al. Standardized flow cytometry for highly sensitive MRD measurements in B-cell acute lymphoblastic leukemia. Blood. 2017;129(3):347–57. https://doi.org/10.1182/blood-2016-07-726307.

Article  CAS  PubMed  Google Scholar 

Flores-Montero J, Sanoja-Flores L, Paiva B, Puig N, García-Sánchez O, Böttcher S, et al. Next generation flow for highly sensitive and standardized detection of minimal residual disease in multiple myeloma. Leukemia. 2017;31(10):2094–103. https://doi.org/10.1038/leu.2017.29.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Rawstron AC, Fazi C, Agathangelidis A, Villamor N, Letestu R, Nomdedeu J, et al. A complementary role of multiparameter flow cytometry and high-throughput sequencing for minimal residual disease detection in chronic lymphocytic leukemia: an European Research Initiative on CLL study. Leukemia. 2016;30(4):929–36. https://doi.org/10.1038/leu.2015.313.

Article  CAS  PubMed  Google Scholar 

Béné MC, Lacombe F, Porwit A. Unsupervised flow cytometry analysis in hematological malignancies: a new paradigm. Int J Lab Hematol. 2021;43(Supplement 1):54–64. https://doi.org/10.1111/ijlh.13548.

Article  PubMed  Google Scholar 

van der Maaten L, Hinton G. Visualizing data using t-SNE. J Mach Learn Res. 2009;9:2579–605.

Google Scholar 

McInnes L, Healy J, Melville J. UMAP: uniform manifold approximation and projection for dimension reduction. arXiv preprint arXiv. 2018; 1802.03426. https://doi.org/10.21105/joss.00861

Wang Y, Huang H, Rudin C, Shaposhnik Y. Understanding how dimension reduction tools work: an empirical approach to deciphering t-SNE, UMAP, TriMAP and PaCMAP for data visualization. J Mach Learning Res. 2021;22:1–73.

Google Scholar 

Van Gassen S, Callebaut B, Van Helden MJ, Lambrecht BN, Demeester P, Dhaene T, et al. FlowSOM: using self-organizing maps for visualization and interpretation of cytometry data. Cytometry A. 2015;87(7):636–45. https://doi.org/10.1002/cyto.a.22625.

Article  PubMed  Google Scholar 

Levine JH, Simonds EF, Bendall SC, Davis KL, Amir e-AD, Tadmor MD, et al. Data-driven phenotypic dissection of AML reveals progenitor-like cells that correlate with prognosis. Cell. 2015;162(1):184–97. https://doi.org/10.1016/j.cell.2015.05.047.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Samusik N, Good Z, Spitzer MH, Davis KL, Nolan GP. Automated mapping of phenotype space with single-cell data. Nat Methods. 2016;13(6):493–6. https://doi.org/10.1038/nmeth.3863.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Vial JP, Lechevalier N, Lacombe F, Dumas PY, Bidet A, Leguay T, et al. Unsupervised flow cytometry analysis allows for an accurate identification of minimal residual disease assessment in acute myeloid leukemia. Cancers (Basel). 2021;13(4):629. https://doi.org/10.3390/cancers13040629.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Nguyen PC, Nguyen V, Baldwin K, Kankanige Y, Blombery P, Came N, et al. Computational flow cytometry provides accurate assessment of measurable residual disease in chronic lymphocytic leukaemia. Br J Haematol. 2023;202(4):760–70. https://doi.org/10.1111/bjh.18802.

Article  CAS  PubMed  Google Scholar 

Alaterre E, Raimbault S, Garcia JM, Rème T, Requirand G, Klein B, et al. Automated and simplified identification of normal and abnormal plasma cells in multiple myeloma by flow cytometry. Cytometry B Clin Cytom. 2018;94(3):484–92. https://doi.org/10.1002/cyto.b.21590.

Article  CAS  PubMed  Google Scholar 

Verbeek MWC, Rodríguez BS, Sedek L, Laqua A, Buracchi C, Buysse M, et al. Minimal residual disease assessment in B-cell precursor acute lymphoblastic leukemia by semi-automated identification of normal hematopoietic cells: a EuroFlow study. Cytometry B Clin Cytom. 2024;106(4):252–63. https://doi.org/10.1002/cyto.b.22143.

Article  CAS  PubMed  Google Scholar 

DiGiuseppe JA, Tadmor MD, Pe’er D. Detection of minimal residual disease in B lymphoblastic leukemia using viSNE. Cytometry B Clin Cytom. 2015;88(5):294–304. https://doi.org/10.1002/cyto.b.21252.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Kobayashi K, Mizuta S, Ohashi Y, Yoshida S, Watanabe A, Takashima H, et al. Optimized flow cytometry incorporating t-SNE enables minimal residual disease assessment in a Philadelphia chromosome-positive acute lymphoblastic leukemia at or near the lower detection limit. J Pediatr Hematol Oncol. 2026;48(1):e21–5. https://doi.org/10.1097/MPH.0000000000003142.

Article  CAS  PubMed  Google Scholar 

Heuser M, Freeman SD, Ossenkoppele GJ, Buccisano F, Hourigan CS, Ngai LL, et al. 2021 update on MRD in acute myeloid leukemia: a consensus document from the European LeukemiaNet MRD working party. Blood. 2021;138(26):2753–67. https://doi.org/10.1182/blood.2021013626.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Weijler L, Kowarsch F, Wödlinger M, Reiter M, Maurer-Granofszky M, Schumich A, et al. UMAP based anomaly detection for minimal residual disease quantification within acute myeloid leukemia. Cancers (Basel). 2022;14(4):898. https://doi.org/10.3390/cancers14040898.

Article  PubMed  PubMed Central  Google Scholar 

Ng DP, Simonson PD, Tarnok A, Lucas F, Kern W, Rolf N, et al. Recommendations for using artificial intelligence in clinical flow cytometry. Cytometry B Clin Cytom. 2024;106(4):228–38. https://doi.org/10.1002/cyto.b.22166.

Article  CAS  PubMed  Google Scholar 

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