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Comparison of global versus epidermal growth factor receptor pathway profiling for prediction of lapatinib
Dmytro M Havaleshko1, Steven Christopher Smith, HyungJun Cho
1Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville, VA 22908, USA.
Abstract:
Chemotherapy for metastatic bladder cancer is rarely curative. The recently developed small molecule, lapatinib, a dual epidermal growth factor receptor (EGFR)/human epidermal growth factor receptor-2 receptor tyrosine kinase inhibitor, might improve this situation. Recent findings suggest that identifying which patients are likely to benefit from targeted therapies is beneficial, although controversy remains regarding what types of evaluation might yield optimal candidate biomarkers of sensitivity. Here, we address this issue by developing and comparing lapatinib sensitivity prediction models for human bladder cancer cells. After empirically determining in vitro sensitivities (drug concentration necessary to cause a 50% growth inhibition) of a panel of 39 such lines to lapatinib treatment, we developed prediction models based on profiling the baseline transcriptome, the phosphorylation status of EGFR pathway signaling targets, or a combination of both data sets. We observed that models derived from microarray gene expression data showed better prediction performance (93%-98% accuracy) compared with models derived from EGFR pathway profiling of 23 selected phosphoproteins known to be involved in EGFR-driven signaling (54%-61% accuracy) or from a subset of the microarray data for transcripts in the EGFR pathway (86% accuracy). Combining microarray data and phosphoprotein profiling provided a combination model with 98% accuracy. Our findings suggest that transcriptome-wide profiling for biomarkers of lapatinib sensitivity in cancer cells provides models with excellent predictive performance and may be effectively combined with EGFR pathway phosphoprotein profiling data. These results have significant implications for the use of such tools in personalizing the approach to cancers treated with EGFR-directed targeted therapies.
Insights
Predicting lapatinib effectiveness in bladder cancer is crucial. Transcriptome profiling offers highly accurate biomarkers for personalized treatment with epidermal growth factor receptor (EGFR) inhibitors.
Area of Science:
- Oncology
- Molecular Biology
- Pharmacogenomics
Background:
- Metastatic bladder cancer treatment often lacks curative options.
- Lapatinib, a dual EGFR/HER2 inhibitor, shows potential for targeted therapy.
- Identifying predictive biomarkers for lapatinib sensitivity is critical for personalized medicine.
Purpose of the Study:
- To develop and compare models predicting lapatinib sensitivity in human bladder cancer cells.
- To evaluate the predictive performance of transcriptome-wide profiling versus EGFR pathway signaling analysis.
- To explore the utility of combining different data types for enhanced prediction.
Main Methods:
- Determined in vitro lapatinib sensitivity for 39 bladder cancer cell lines.
- Developed prediction models using baseline transcriptome data (microarray).
- Developed prediction models using EGFR pathway phosphoprotein profiling and combined data.
Main Results:
- Microarray-based models achieved 93%-98% accuracy in predicting lapatinib sensitivity.
- EGFR pathway phosphoprotein models showed lower accuracy (54%-61%).
- A combined model using microarray and phosphoprotein data reached 98% accuracy.
Conclusions:
- Transcriptome-wide profiling provides highly predictive biomarkers for lapatinib sensitivity.
- Combining transcriptome and phosphoprotein data further enhances predictive model accuracy.
- These findings support personalized treatment strategies for bladder cancer using EGFR-targeted therapies.
