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Assessment of Resistance to Tyrosine Kinase Inhibitors by an Interrogation of Signal Transduction Pathways by Antibody Arrays
Published on: September 19, 2018
A phosphoarray platform is capable of personalizing kinase inhibitor therapy in head and neck cancers
Konrad Klinghammer1, James Keller2, Jonathan George2
1Department of Hematology and Oncology, Charite University Medicine, Berlin, Germany.
Abstract:
Tyrosine kinase inhibitors are effective treatments for cancers. Knowing the specific kinase mutants that drive the underlying cancers predict therapeutic response to these inhibitors. Thus, the current protocol for personalized cancer therapy involves genotyping tumors in search of various driver mutations and subsequently individualizing the tyrosine kinase inhibitor to the patients whose tumors express the corresponding driver mutant. While this approach works when known driver mutations are found, its limitation is the dependence on driver mutations as predictors for response. To complement the genotype approach, we hypothesize that a phosphoarray platform is equally capable of personalizing kinase inhibitor therapy. We selected head and neck squamous cell carcinoma as the cancer model to test our hypothesis. Using the receptor tyrosine kinase phosphoarray, we identified the phosphorylation profiles of 49 different tyrosine kinase receptors in five different head and neck cancer cell lines. Based on these results, we tested the cell line response to the corresponding kinase inhibitor therapy. We found that this phosphoarray accurately informed the kinase inhibitor response profile of the cell lines. Next, we determined the phosphorylation profiles of 39 head and neck cancer patient derived xenografts. We found that absent phosphorylated EGFR signal predicted primary resistance to cetuximab treatment in the xenografts without phosphorylated ErbB2. Meanwhile, absent ErbB2 signaling in the xenografts with phosphorylated EGFR is associated with a higher likelihood of response to cetuximab. In summary, the phosphoarray technology has the potential to become a new diagnostic platform for personalized cancer therapy.
Insights
A novel phosphoarray accurately predicts cancer therapy response by analyzing tyrosine kinase phosphorylation profiles. This approach complements genetic testing for personalized cancer treatment, particularly in head and neck squamous cell carcinoma.
Area of Science:
- Oncology
- Molecular Biology
- Biotechnology
Background:
- Tyrosine kinase inhibitors (TKIs) are crucial for cancer therapy, with treatment response often predicted by specific kinase mutations.
- Current personalized cancer therapy relies on genotyping tumors for driver mutations to select appropriate TKIs.
- This genotype-dependent approach has limitations when known driver mutations are absent.
Purpose of the Study:
- To investigate the potential of a phosphoarray platform as a complementary method for personalizing TKI therapy.
- To evaluate the efficacy of a receptor tyrosine kinase phosphoarray in predicting TKI response in head and neck squamous cell carcinoma (HNSCC).
Main Methods:
- Utilized a receptor tyrosine kinase phosphoarray to determine phosphorylation profiles of 49 tyrosine kinase receptors in five HNSCC cell lines.
- Correlated phosphoarray results with cell line response to corresponding kinase inhibitor therapies.
- Analyzed phosphorylation profiles of 39 HNSCC patient-derived xenografts (PDXs).
Main Results:
- The phosphoarray accurately predicted kinase inhibitor response in HNSCC cell lines.
- In PDXs, absent phosphorylated epidermal growth factor receptor (EGFR) predicted resistance to cetuximab when phosphorylated ErbB2 was also absent.
- Absence of ErbB2 signaling in PDXs with phosphorylated EGFR correlated with a higher likelihood of cetuximab response.
Conclusions:
- Phosphoarray technology demonstrates potential as a diagnostic platform for personalized cancer therapy.
- This approach offers an alternative or complementary method to genetic profiling for predicting TKI efficacy.
- Phosphorylation profiling can guide individualized TKI selection for HNSCC and potentially other cancers.
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