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Serum proteomic study on EGFR-TKIs target treatment for patients with NSCLC.

Wu X, Liang W, Hou X, Lin Z, Zhao H, Huang Y, Fang W, Zhao Y, Wu J, Yang Y, Xue C, Hu Z, Zhang J, Zhang J, Ma Y, Zhou T, Qin T, Zhang L - Onco Targets Ther (2013)

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: State Key Laboratory of Oncology in South China, Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, People's Republic of China.

ABSTRACT

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.

No MeSH data available.


Related in: MedlinePlus

Kaplan–Meier survival curves for overall survival in adenocarcinoma and nonsmoker subgroups.Notes: (A) Overall survival of the adenocarcinoma subgroup according to baseline proteomic classification (hazard ratio, 0.174; 95% confidence interval, 0.077–0.396; P<0.001). (B) Overall survival of nonsmokers according to baseline proteomic classification (hazard ratio, 0.394; 95% confidence interval, 0.183–0.846; P=0.014).
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f4-ott-6-1481: Kaplan–Meier survival curves for overall survival in adenocarcinoma and nonsmoker subgroups.Notes: (A) Overall survival of the adenocarcinoma subgroup according to baseline proteomic classification (hazard ratio, 0.174; 95% confidence interval, 0.077–0.396; P<0.001). (B) Overall survival of nonsmokers according to baseline proteomic classification (hazard ratio, 0.394; 95% confidence interval, 0.183–0.846; P=0.014).

Mentions: Patients classified as good outcome are expected to have a better OS or PFS than the predicted poor outcome patients. According to the 3-peptide proteomic classifier, we divided the patients of the testing sets into predicted good and poor outcome groups. Of the 44 NSCLC patients, 28 were classified as the predicted good outcome group and 16 were classified as the poor outcome group. The Kaplan–Meier survival curves for the two groups are shown in Figures 3 and 4. Patients in the predicted good group had significantly longer OS (hazard ratio [HR], 0.357; 95% confidence interval [CI], 0.186–0.688; P=0.002) and PFS (HR, 0.06; 95% CI, 0.022–0.158; P<0.001) than those in poor group (Table 3).


Serum proteomic study on EGFR-TKIs target treatment for patients with NSCLC.

Wu X, Liang W, Hou X, Lin Z, Zhao H, Huang Y, Fang W, Zhao Y, Wu J, Yang Y, Xue C, Hu Z, Zhang J, Zhang J, Ma Y, Zhou T, Qin T, Zhang L - Onco Targets Ther (2013)

Kaplan–Meier survival curves for overall survival in adenocarcinoma and nonsmoker subgroups.Notes: (A) Overall survival of the adenocarcinoma subgroup according to baseline proteomic classification (hazard ratio, 0.174; 95% confidence interval, 0.077–0.396; P<0.001). (B) Overall survival of nonsmokers according to baseline proteomic classification (hazard ratio, 0.394; 95% confidence interval, 0.183–0.846; P=0.014).
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3818102&req=5

f4-ott-6-1481: Kaplan–Meier survival curves for overall survival in adenocarcinoma and nonsmoker subgroups.Notes: (A) Overall survival of the adenocarcinoma subgroup according to baseline proteomic classification (hazard ratio, 0.174; 95% confidence interval, 0.077–0.396; P<0.001). (B) Overall survival of nonsmokers according to baseline proteomic classification (hazard ratio, 0.394; 95% confidence interval, 0.183–0.846; P=0.014).
Mentions: Patients classified as good outcome are expected to have a better OS or PFS than the predicted poor outcome patients. According to the 3-peptide proteomic classifier, we divided the patients of the testing sets into predicted good and poor outcome groups. Of the 44 NSCLC patients, 28 were classified as the predicted good outcome group and 16 were classified as the poor outcome group. The Kaplan–Meier survival curves for the two groups are shown in Figures 3 and 4. Patients in the predicted good group had significantly longer OS (hazard ratio [HR], 0.357; 95% confidence interval [CI], 0.186–0.688; P=0.002) and PFS (HR, 0.06; 95% CI, 0.022–0.158; P<0.001) than those in poor group (Table 3).

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: State Key Laboratory of Oncology in South China, Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, People's Republic of China.

ABSTRACT

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.

No MeSH data available.


Related in: MedlinePlus