Early dynamics of chronic myeloid leukemia on nilotinib predicts deep molecular response

Yuji Okamoto; Mitsuhito Hirano; Kai Morino; Masashi K. Kajita; Shinji Nakaoka; Mayuko Tsuda; Kei ji Sugimoto; Shigehisa Tamaki; Junichi Hisatake; Hisayuki Yokoyama; Tadahiko Igarashi; Atsushi Shinagawa; Takeaki Sugawara; Satoru Hara; Kazuhisa Fujikawa; Seiichi Shimizu; Toshiaki Yujiri; Hisashi Wakita; Kaichi Nishiwaki; Arinobu Tojo; Kazuyuki Aihara

doi:10.1038/s41540-022-00248-3

Early dynamics of chronic myeloid leukemia on nilotinib predicts deep molecular response

Yuji Okamoto, Mitsuhito Hirano, Kai Morino, Masashi K. Kajita, Shinji Nakaoka, Mayuko Tsuda, Kei ji Sugimoto, Shigehisa Tamaki, Junichi Hisatake, Hisayuki Yokoyama, Tadahiko Igarashi, Atsushi Shinagawa, Takeaki Sugawara, Satoru Hara, Kazuhisa Fujikawa, Seiichi Shimizu, Toshiaki Yujiri, Hisashi Wakita, Kaichi Nishiwaki, Arinobu TojoKazuyuki Aihara

Research output: Contribution to journal › Article › peer-review

Abstract

Chronic myeloid leukemia (CML) is a myeloproliferative disorder caused by the BCR-ABL1 tyrosine kinase. Although ABL1-specific tyrosine kinase inhibitors (TKIs) including nilotinib have dramatically improved the prognosis of patients with CML, the TKI efficacy depends on the individual patient. In this work, we found that the patients with different nilotinib responses can be classified by using the estimated parameters of our simple dynamical model with two common laboratory findings. Furthermore, our proposed method identified patients who failed to achieve a treatment goal with high fidelity according to the data collected only at three initial time points during nilotinib therapy. Since our model relies on the general properties of TKI response, our framework would be applicable to CML patients who receive frontline nilotinib or other TKIs.

Original language	English
Article number	39
Journal	npj Systems Biology and Applications
Volume	8
Issue number	1
DOIs	https://doi.org/10.1038/s41540-022-00248-3
Publication status	Published - 2022 Dec
Externally published	Yes

ASJC Scopus subject areas

Modelling and Simulation
Biochemistry, Genetics and Molecular Biology(all)
Drug Discovery
Computer Science Applications
Applied Mathematics

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10.1038/s41540-022-00248-3

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Okamoto, Y., Hirano, M., Morino, K., Kajita, M. K., Nakaoka, S., Tsuda, M., Sugimoto, K. J., Tamaki, S., Hisatake, J., Yokoyama, H., Igarashi, T., Shinagawa, A., Sugawara, T., Hara, S., Fujikawa, K., Shimizu, S., Yujiri, T., Wakita, H., Nishiwaki, K., ... Aihara, K. (2022). Early dynamics of chronic myeloid leukemia on nilotinib predicts deep molecular response. npj Systems Biology and Applications, 8(1), [39]. https://doi.org/10.1038/s41540-022-00248-3

Okamoto, Y, Hirano, M, Morino, K, Kajita, MK, Nakaoka, S, Tsuda, M, Sugimoto, KJ, Tamaki, S, Hisatake, J, Yokoyama, H, Igarashi, T, Shinagawa, A, Sugawara, T, Hara, S, Fujikawa, K, Shimizu, S, Yujiri, T, Wakita, H, Nishiwaki, K, Tojo, A & Aihara, K 2022, 'Early dynamics of chronic myeloid leukemia on nilotinib predicts deep molecular response', npj Systems Biology and Applications, vol. 8, no. 1, 39. https://doi.org/10.1038/s41540-022-00248-3

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title = "Early dynamics of chronic myeloid leukemia on nilotinib predicts deep molecular response",

abstract = "Chronic myeloid leukemia (CML) is a myeloproliferative disorder caused by the BCR-ABL1 tyrosine kinase. Although ABL1-specific tyrosine kinase inhibitors (TKIs) including nilotinib have dramatically improved the prognosis of patients with CML, the TKI efficacy depends on the individual patient. In this work, we found that the patients with different nilotinib responses can be classified by using the estimated parameters of our simple dynamical model with two common laboratory findings. Furthermore, our proposed method identified patients who failed to achieve a treatment goal with high fidelity according to the data collected only at three initial time points during nilotinib therapy. Since our model relies on the general properties of TKI response, our framework would be applicable to CML patients who receive frontline nilotinib or other TKIs.",

author = "Yuji Okamoto and Mitsuhito Hirano and Kai Morino and Kajita, {Masashi K.} and Shinji Nakaoka and Mayuko Tsuda and Sugimoto, {Kei ji} and Shigehisa Tamaki and Junichi Hisatake and Hisayuki Yokoyama and Tadahiko Igarashi and Atsushi Shinagawa and Takeaki Sugawara and Satoru Hara and Kazuhisa Fujikawa and Seiichi Shimizu and Toshiaki Yujiri and Hisashi Wakita and Kaichi Nishiwaki and Arinobu Tojo and Kazuyuki Aihara",

note = "Funding Information: This work was partially supported by JSPS KAKENHI Grant Number JP15H05707 (to K.M., M.K.K., S.N., and K.A.), JST PRESTO Grant Number JPMJPR16E9 (to S.N.), JST Moonshot R&D Grant Number JPMJMS2021 (to K.A.), AMED under Grant Number JP22dm0307009 (to K.A.) and Institute of AI and Beyond of The University of Tokyo (to K.A.). The N-road study had been funded by Novartis Pharma K.K. Publisher Copyright: {\textcopyright} 2022, The Author(s).",

year = "2022",

month = dec,

doi = "10.1038/s41540-022-00248-3",

language = "English",

volume = "8",

journal = "npj Systems Biology and Applications",

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T1 - Early dynamics of chronic myeloid leukemia on nilotinib predicts deep molecular response

AU - Okamoto, Yuji

AU - Hirano, Mitsuhito

AU - Morino, Kai

AU - Kajita, Masashi K.

AU - Nakaoka, Shinji

AU - Tsuda, Mayuko

AU - Sugimoto, Kei ji

AU - Tamaki, Shigehisa

AU - Hisatake, Junichi

AU - Yokoyama, Hisayuki

AU - Igarashi, Tadahiko

AU - Shinagawa, Atsushi

AU - Sugawara, Takeaki

AU - Hara, Satoru

AU - Fujikawa, Kazuhisa

AU - Shimizu, Seiichi

AU - Yujiri, Toshiaki

AU - Wakita, Hisashi

AU - Nishiwaki, Kaichi

AU - Tojo, Arinobu

AU - Aihara, Kazuyuki

N1 - Funding Information: This work was partially supported by JSPS KAKENHI Grant Number JP15H05707 (to K.M., M.K.K., S.N., and K.A.), JST PRESTO Grant Number JPMJPR16E9 (to S.N.), JST Moonshot R&D Grant Number JPMJMS2021 (to K.A.), AMED under Grant Number JP22dm0307009 (to K.A.) and Institute of AI and Beyond of The University of Tokyo (to K.A.). The N-road study had been funded by Novartis Pharma K.K. Publisher Copyright: © 2022, The Author(s).

PY - 2022/12

Y1 - 2022/12

N2 - Chronic myeloid leukemia (CML) is a myeloproliferative disorder caused by the BCR-ABL1 tyrosine kinase. Although ABL1-specific tyrosine kinase inhibitors (TKIs) including nilotinib have dramatically improved the prognosis of patients with CML, the TKI efficacy depends on the individual patient. In this work, we found that the patients with different nilotinib responses can be classified by using the estimated parameters of our simple dynamical model with two common laboratory findings. Furthermore, our proposed method identified patients who failed to achieve a treatment goal with high fidelity according to the data collected only at three initial time points during nilotinib therapy. Since our model relies on the general properties of TKI response, our framework would be applicable to CML patients who receive frontline nilotinib or other TKIs.

AB - Chronic myeloid leukemia (CML) is a myeloproliferative disorder caused by the BCR-ABL1 tyrosine kinase. Although ABL1-specific tyrosine kinase inhibitors (TKIs) including nilotinib have dramatically improved the prognosis of patients with CML, the TKI efficacy depends on the individual patient. In this work, we found that the patients with different nilotinib responses can be classified by using the estimated parameters of our simple dynamical model with two common laboratory findings. Furthermore, our proposed method identified patients who failed to achieve a treatment goal with high fidelity according to the data collected only at three initial time points during nilotinib therapy. Since our model relies on the general properties of TKI response, our framework would be applicable to CML patients who receive frontline nilotinib or other TKIs.

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JO - npj Systems Biology and Applications

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ER -

Early dynamics of chronic myeloid leukemia on nilotinib predicts deep molecular response

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