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

Yuji OkamotoMitsuhito HiranoKai MorinoMasashi K KajitaShinji NakaokaMayuko TsudaKei-Ji SugimotoShigehisa TamakiJunichi HisatakeHisayuki YokoyamaTadahiko IgarashiAtsushi ShinagawaTakeaki SugawaraSatoru HaraKazuhisa FujikawaSeiichi ShimizuToshiaki YujiriHisashi WakitaKaichi NishiwakiArinobu TojoKazuyuki Aihara
Published in: NPJ systems biology and applications (2022)
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.
Keyphrases
  • chronic myeloid leukemia
  • tyrosine kinase
  • epidermal growth factor receptor
  • case report
  • electronic health record
  • stem cells
  • big data
  • machine learning
  • artificial intelligence