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A Method for Screening and Validation of Resistant Mutations Against Kinase Inhibitors
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Resistance to Tyrosine Kinase Inhibitors in Chronic Myeloid Leukemia-From Molecular Mechanisms to Clinical Relevance

Raquel Alves1,2,3,4, Ana Cristina Gonçalves1,2,3,4, Sergio Rutella5

  • 1Laboratory of Oncobiology and Hematology, University Clinic of Hematology, Faculty of Medicine (FMUC), University of Coimbra, 3000-548 Coimbra, Portugal.

Cancers
|October 13, 2021
PubMed

Insights

Resistance to tyrosine kinase inhibitors (TKIs) in chronic myeloid leukemia (CML) can occur through various mechanisms. New sensitive techniques and AI can help understand and overcome this drug resistance for better CML treatment.

Area of Science:

  • Hematology
  • Oncology
  • Molecular Biology

Background:

  • Chronic myeloid leukemia (CML) is characterized by the BCR-ABL1 fusion gene, leading to uncontrolled tyrosine kinase activity.
  • While tyrosine kinase inhibitors (TKIs) like Imatinib are effective, resistance remains a significant clinical challenge.
  • Mechanisms of TKI resistance in CML are diverse, including BCR-ABL1-dependent and independent pathways, alongside patient adherence issues.

Purpose of the Study:

  • To review the molecular mechanisms underlying TKI resistance in CML.
  • To explore novel methodologies for detecting and understanding these resistance mechanisms.
  • To discuss therapeutic strategies for overcoming TKI resistance in CML patients.

Main Methods:

  • Review of existing literature on CML pathogenesis and TKI resistance.
  • Discussion of advanced techniques such as next-generation sequencing (NGS) and digital PCR (dPCR).
  • Exploration of artificial intelligence (AI) and computational modeling for predicting resistance.

Main Results:

  • Identified BCR-ABL1-dependent and independent mechanisms contributing to TKI resistance.
  • Highlighted the critical role of patient adherence in treatment efficacy.
  • Emphasized the potential of sensitive molecular techniques and AI in elucidating resistance pathways.

Conclusions:

  • Understanding the multifaceted mechanisms of TKI resistance is crucial for effective CML management.
  • Advanced technologies like NGS, dPCR, and AI offer promising avenues for identifying resistance drivers.
  • Developing novel therapeutic strategies targeting resistance mechanisms is essential to improve long-term outcomes for CML patients.
Keywords:
CMLTKI resistancebioinformatics and artificial intelligenceepigeneticsimmune systemnew targeted therapiespatient adherence

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