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

Unsupervised machine learning shows significant promise for evaluating multicolor flow cytometry, particularly in advancing minimal residual disease (MRD) analysis. We implemented t-distributed stochastic neighbor embedding (t-SNE)-assisted MRD analysis in a challenging case of Philadelphia chromosome-positive acute lymphoblastic leukemia. This approach enabled us to identify MRD on an unbiased basis with a sensitivity comparable to that of genetic analysis while simultaneously reducing the workload. t-SNE is a valuable tool for the detection and verification of MRD, with its effectiveness significantly enhanced through the integration of optimized antibody panels, data preprocessing, and rigorous back-gating verification.

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Publication Details

Journal
Journal of Pediatric Hematology/Oncology
Published
2025-11-05
DOI
https://doi.org/10.1097/mph.0000000000003142
Primary Topic
Single-cell and spatial transcriptomics
Type
article
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article

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

Kuniaki Tanaka, Toshiro Maihara, Mariko Ikuo, Yuka Ohashi et al.
Journal of Pediatric Hematology/Oncology
Single-cell and spatial transcriptomics
article

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

Kuniaki Tanaka, Toshiro Maihara, Mariko Ikuo, Yuka Ohashi, Miyuki Okuda, Takeshi Ueshimo, Atsushi Iwai, Asami Watanabe, Hiroshi Takashima, Yusei Ohshima, Hiroyuki Kuroda, Ikuya Usami, Nobuyuki Watanabe, Kenichiro Kobayashi, Shumpei Mizuta, Toshio Heike, Saya Yoshida
article en

Abstract

Unsupervised machine learning shows significant promise for evaluating multicolor flow cytometry, particularly in advancing minimal residual disease (MRD) analysis. We implemented t-distributed stochastic neighbor embedding (t-SNE)-assisted MRD analysis in a challenging case of Philadelphia chromosome-positive acute lymphoblastic leukemia. This approach enabled us to identify MRD on an unbiased basis with a sensitivity comparable to that of genetic analysis while simultaneously reducing the workload. t-SNE is a valuable tool for the detection and verification of MRD, with its effectiveness significantly enhanced through the integration of optimized antibody panels, data preprocessing, and rigorous back-gating verification.

Journal of Pediatric Hematology/OncologyVol. 48(1)
Becton Dickinson (United States) (US), Palmetto Hematology Oncology (US), Hyogo Prefectural Amagasaki General Medical Center (JP)
Openalex Percentile: Top 71%
Single-cell and spatial transcriptomics
DOISourceOpenAlex
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