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Updated: May 14, 2026

Implementation of In Vitro Drug Resistance Assays: Maximizing the Potential for Uncovering Clinically Relevant Resistance Mechanisms
Published on: December 9, 2015
Personalized cancer medicine: molecular diagnostics, predictive biomarkers, and drug resistance
D Gonzalez de Castro1, P A Clarke, B Al-Lazikani
1Molecular Diagnostics Department, The Institute of Cancer Research and the Royal Marsden NHS Foundation Trust, London, UK.
Abstract:
The progressive elucidation of the molecular pathogenesis of cancer has fueled the rational development of targeted drugs for patient populations stratified by genetic characteristics. Here we discuss general challenges relating to molecular diagnostics and describe predictive biomarkers for personalized cancer medicine. We also highlight resistance mechanisms for epidermal growth factor receptor (EGFR) kinase inhibitors in lung cancer. We envisage a future requiring the use of longitudinal genome sequencing and other omics technologies alongside combinatorial treatment to overcome cellular and molecular heterogeneity and prevent resistance caused by clonal evolution.
Insights
Targeted cancer drugs are improving treatment for genetically defined patients. Future strategies involve longitudinal genome sequencing and combinatorial therapies to overcome drug resistance and cancer evolution.
Area of Science:
- Oncology
- Molecular Biology
- Genetics
Background:
- Advances in understanding cancer molecular pathogenesis enable targeted drug development.
- Patient stratification based on genetic profiles is crucial for personalized medicine.
Purpose of the Study:
- To discuss challenges in molecular diagnostics for cancer.
- To describe predictive biomarkers for personalized cancer medicine.
- To highlight resistance mechanisms to epidermal growth factor receptor (EGFR) kinase inhibitors in lung cancer.
Main Methods:
- Review of current literature on molecular diagnostics and biomarkers.
- Analysis of resistance mechanisms in EGFR-targeted lung cancer therapy.
- Envisioning future applications of omics technologies and combinatorial treatments.
Main Results:
- Molecular diagnostics face general challenges in implementation.
- Predictive biomarkers are essential for effective personalized cancer medicine.
- Epidermal growth factor receptor (EGFR) kinase inhibitors in lung cancer are subject to various resistance mechanisms.
Conclusions:
- Overcoming cellular and molecular heterogeneity requires longitudinal genome sequencing and other omics technologies.
- Combinatorial treatment strategies are necessary to prevent resistance driven by clonal evolution.
- Personalized cancer medicine necessitates continuous adaptation to evolving tumor biology.
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