Related Experiment Video
Updated: May 6, 2026
Profiling Sensitivity to Targeted Therapies in EGFR-Mutant NSCLC Patient-Derived Organoids
Published on: November 22, 2021
Serum proteomic study on EGFR-TKIs target treatment for patients with NSCLC
Xuan Wu1, Wenhua Liang, Xue Hou
1State Key Laboratory of Oncology in South China, Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, People's Republic of China.
Background:
Although epidermal growth factor receptor (EGFR)-tyrosine kinase inhibitors (TKIs) are widely used for EGFR mutated non-small-cell lung cancer (NSCLC) patients, tumor sample availability and heterogeneity of the tumor remain challenging for physicians' selection of these patients. Here, we developed a serum proteomic classifier based on matrix assisted laser desorption ionization time of flight mass spectrometry (MALDI-TOF-MS) to predict the clinical outcome of patients treated with EGFR-TKIs.
Method:
A total of 68 patients were included in this study. All patients received EGFR-TKIs as second or third line treatment and blood samples were collected before treatment. Using magnetic bead assisted serum peptide capture coupled to MALDI-TOF-MS, pretreatment serum from 24 NSCLC patients was analyzed to develop a proteomic classifier (training set). In a blinded test set with 44 patients, each sample was classified into "good" or "poor" groups using this classifier. Survival analysis of each group was done based on this classification.
Result:
A 3-peptide proteomic classifier was developed from the training set. In the testing set, the classifier was able to distinguish patients of "good" or "poor" outcomes with 93% accuracy, sensitivity, and specificity. The overall survival and progression free survival of the predicted good group were found to be significantly longer than the poor group, not only in the whole population but also in certain subgroups, such as pathological adenocarcinoma and nonsmokers. With respect to the tumor samples available for EGFR mutation detection, all eight EGFR mutant tumors and three of the 12 wild type EGFR tumors were classified as good while nine of the 12 wild type EGFR tumors were classified as poor.
Conclusion:
The current study has shown that a proteomic classifier can predict the outcome of patients treated with EGFR-TKIs and may aid in patient selection in the absence of available tumor tissue. Further studies are necessary to confirm these findings.
Insights
A new serum proteomic classifier accurately predicts treatment outcomes for non-small-cell lung cancer (NSCLC) patients receiving epidermal growth factor receptor (EGFR)-tyrosine kinase inhibitors (TKIs). This blood-based test aids patient selection when tumor tissue is unavailable.
Area of Science:
- Oncology
- Proteomics
- Molecular Diagnostics
Background:
- Epidermal growth factor receptor (EGFR)-tyrosine kinase inhibitors (TKIs) are standard for EGFR-mutated non-small-cell lung cancer (NSCLC).
- Tumor sample availability and heterogeneity pose challenges for selecting patients for EGFR-TKI therapy.
- A non-invasive method to predict treatment response is needed.
Purpose of the Study:
- To develop and validate a serum proteomic classifier to predict clinical outcomes in NSCLC patients treated with EGFR-TKIs.
- To assess the classifier's accuracy and utility in patient selection, especially when tumor tissue is limited.
Main Methods:
- Serum samples from 68 NSCLC patients treated with EGFR-TKIs were analyzed.
- A 3-peptide classifier was developed using matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF-MS) on a training set of 24 patients.
- The classifier was validated on a blinded test set of 44 patients, categorizing them into "good" or "poor" outcome groups.
Main Results:
- The serum proteomic classifier achieved 93% accuracy, sensitivity, and specificity in predicting patient outcomes in the test set.
- Patients classified as "good" responders exhibited significantly longer overall survival and progression-free survival compared to the "poor" responders.
- The classifier showed promising results in predicting outcomes for specific subgroups, including pathological adenocarcinoma and nonsmokers.
Conclusions:
- A serum proteomic classifier can effectively predict clinical outcomes for NSCLC patients undergoing EGFR-TKI treatment.
- This blood-based classifier may assist in patient selection for EGFR-TKI therapy, particularly when tumor tissue is unavailable.
- Further validation studies are warranted to confirm these findings and clinical utility.
More Related Videos
07:42Assessment of Resistance to Tyrosine Kinase Inhibitors by an Interrogation of Signal Transduction Pathways by Antibody Arrays
Published on: September 19, 2018
08:49Exosomal miRNA Analysis in Non-small Cell Lung Cancer NSCLC Patients' Plasma Through qPCR: A Feasible Liquid Biopsy Tool
Published on: May 27, 2016
