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C. Tang
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MINI 10 - ALK and EGFR (ID 105)
- Event: WCLC 2015
- Type: Mini Oral
- Track: Biology, Pathology, and Molecular Testing
- Presentations: 1
- Moderators:T. Yap, T. Li
- Coordinates: 9/07/2015, 16:45 - 18:15, Mile High Ballroom 1a-1f
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MINI10.11 - Classification of EGFR Gene Mutation Status Using Serum Proteomic Profiling Predicts Tumor Response in Patients with Stage IIIB or IV NSCLC (ID 874)
17:45 - 17:50 | Author(s): C. Tang
- Abstract
Background:
Epidermal growth factor receptor (EGFR) gene mutations in tumors predict tumor response to EGFR tyrosine kinase inhibitors (EGFR-TKIs) in non-small-cell lung cancer (NSCLC). However, obtaining tumor tissue for mutation analysis is challenging. Recently, peptide mass fingerprinting based on matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) has been widely used to detect diagnostic, prognostic, and predictive proteomic biomarkers. Here, we aimed to detect serum peptides/proteins associated with EGFR gene mutation status, and test whether a classification algorithm based on serum proteomic profiling could be developed to analyze EGFR gene mutation status to aid therapeutic decision-making.
Methods:
Serum collected from 223 stage IIIB or IV NSCLC patients with known EGFR gene mutation status in their tumors prior to therapy was analyzed by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) and ClinProTools software. Differences in serum peptides/proteins between patients with EGFR gene TKI-sensitive mutations and wild-type EGFR genes were detected in a training group of 100 patients; based on this analysis, a serum proteomic classification algorithm was developed to classify EGFR gene mutation status and tested in an independent validation group of 123 patients. The correlation between EGFR gene mutation status, as identified with the serum proteomic classifier and response to EGFR-TKIs was analyzed.
Results:
Nine peptide/protein peaks were significantly different between NSCLC patients with EGFR gene TKI-sensitive mutations and wild-type EGFR genes in the training group. A genetic algorithm model consisting of five peptides/proteins (m/z 4092.4, 4585.05, 1365.1, 4643.49 and 4438.43) was developed from the training group to separate patients with EGFR gene TKI-sensitive mutations and wild-type EGFR genes. The classifier exhibited a sensitivity of 84.6% and a specificity of 77.5% in the validation group. In the 81 patients from the validation group treated with EGFR-TKIs, 28 (59.6%) of 47 patients whose matched samples were labeled as “mutant” by the classifier and 3 (8.8%) of 34 patients whose matched samples were labeled as “wild” achieved an objective response (p<0.0001). Patients whose matched samples were labeled as “mutant” by the classifier had a significantly longer progression-free survival (PFS) than patients whose matched samples were labeled as “wild” (p=0.001).
Conclusion:
Peptides/proteins related to EGFR gene mutation status were found in the serum. Classification of EGFR gene mutation status using the serum proteomic classifier established in the present study in patients with stage IIIB or IV NSCLC is feasible and may predict tumor response to EGFR-TKIs.
