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Artificial Intelligence Enables the Label-Free Identification of Chronic Myeloid Leukemia Cells with Mitochondrial Morphological Alterations

Suzuki, K.; Watanabe, N.; Torii, S.; Arakawa, S.; Ochi, K.; Tsuchiya, S.; Yamada, K.; Kawamura, Y.; Ota, S.; Komatsu, N.; Shimizu, S.; Ando, M.; Takaku, T.

2023-07-28 cancer biology
10.1101/2023.07.26.550632 bioRxiv
Show abstract

Long-term tyrosine kinase inhibitor (TKI) treatment for patients with chronic myeloid leukemia (CML) causes various adverse events. Achieving a deep molecular response (DMR) is necessary for discontinuing TKIs and attaining treatment-free remission. Thus, early diagnosis is crucial as a lower DMR achievement rate has been reported in high-risk patients. Therefore, we attempted to identify CML cells using a novel technology that combines artificial intelligence (AI) with flow cytometry and investigated the basis for AI- mediated identification. Our findings indicate that BCR-ABL1-transduced cells and leukocytes from patients with CML showed significantly fragmented mitochondria and decreased mitochondrial membrane potential. Additionally, BCR-ABL1 enhanced the phosphorylation of Drp1 via the mitogen-activated protein kinase pathway, inducing mitochondrial fragmentation. Finally, the AI identified cell line models and patient leukocytes that showed mitochondrial morphological changes. Our study suggested that this AI- based technology enables the highly sensitive detection of BCR-ABL1-positive cells and early diagnosis of CML.

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