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Prediction of outcome of non-small cell lung cancer patients treated with chemotherapy and bortezomib by time-course MALDI-TOF-MS serum peptide profiling.

Voortman J, Pham TV, Knol JC, Giaccone G, Jimenez CR - Proteome Sci (2009)

Bottom Line: Prediction of long PFS was associated with longer overall survival.Inclusion of 7 peptide ions showing differential changes in abundance during treatment led to a 13-peptide ion signature with 86% accuracy at 100% sensitivity and 73% specificity.A 5-peptide ion signature could separate patients with a partial response vs. non-responders with 89% accuracy at 100% sensitivity and 83% specificity.

View Article: PubMed Central - HTML - PubMed

Affiliation: Dept Medical Oncology, VUmc-Cancer Center Amsterdam, the Netherlands. j.voortman@gmail.com

ABSTRACT

Background: Only a minority of patients with advanced non-small cell lung cancer (NSCLC) benefit from chemotherapy. Serum peptide profiling of NSCLC patients was performed to investigate patterns associated with treatment outcome.Using magnetic bead-assisted serum peptide capture coupled to matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS), serum peptide mass profiles of 27 NSCLC patients treated with cisplatin-gemcitabine chemotherapy and bortezomib were obtained. Support vector machine-based algorithms to predict clinical outcome were established based on differential pre-treatment peptide profiles and dynamic changes in peptide abundance during treatment.

Results: A 6-peptide ion signature distinguished with 82% accuracy, sensitivity and specificity patients with a relatively short vs. long progression-free survival (PFS) upon treatment. Prediction of long PFS was associated with longer overall survival. Inclusion of 7 peptide ions showing differential changes in abundance during treatment led to a 13-peptide ion signature with 86% accuracy at 100% sensitivity and 73% specificity. A 5-peptide ion signature could separate patients with a partial response vs. non-responders with 89% accuracy at 100% sensitivity and 83% specificity. Differential peptide profiles were also found when comparing the NSCLC serum profiles to those from cancer-free control subjects.

Conclusion: This study shows that serum peptidome profiling using MALDI-TOF-MS coupled to pattern diagnostics may aid in prediction of treatment outcome of advanced NSCLC patients treated with chemotherapy.

No MeSH data available.


Related in: MedlinePlus

Comparison serum profiles NSCLC vs. cancer-free controls. A, Principle Component Analysis (PCA) NSCLC vs. cancer-free control comparison. B, heat map of the 47 differential peaks. The peaks are ordered by median fold change between the two groups. C, spectra overlay of the 8 most differential peaks in the healthy (red) versus NSCLC (blue) comparison. Spectra overlay of the 8 peaks randomly selected out of the remaining 635 peptides.
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Figure 5: Comparison serum profiles NSCLC vs. cancer-free controls. A, Principle Component Analysis (PCA) NSCLC vs. cancer-free control comparison. B, heat map of the 47 differential peaks. The peaks are ordered by median fold change between the two groups. C, spectra overlay of the 8 most differential peaks in the healthy (red) versus NSCLC (blue) comparison. Spectra overlay of the 8 peaks randomly selected out of the remaining 635 peptides.

Mentions: Finally, in an exploratory additional analysis, we compared the serum peptide spectra of 13 cancer-free control subjects (median age: 38 years old; range: 27-58 years old) and the pre-treatment serum spectra of the 27 NSCLC patients included in this study. We performed a principal component analysis (PCA) analysis of the 40 profiles using all 682 peptides, see Figure 5A. While there is overlap between the two groups in the three dimensional plot, the healthy profiles (in red) are clustered at the bottom right region. Furthermore, we observed no indication of outliers in the dataset. Next we performed a supervised analysis to identify peptides that were significantly differential in intensity between the two groups. For this purpose, the Mann-Whitney U test was carried out on each of the 682 peptides using all profiles. The peptides were selected based on the criteria outlined in Methods, resulting in 47 peptides. A heat map of the intensities of the 47 peptides is shown in Figure 5B (see also Table 7). Figure 5C shows the spectra overlay of the top 8 most discriminating peaks, all of which have a p-value < 0.0001. Note that for example the peak at m/z 1777.966 has a higher intensity in NSCLC patients (blue) compared cancer-free controls (red) and the peak at m/z 1039.6249 has a lower intensity in NSCLC patients. We carried out classification analysis using support vector machine. A grid search for parameters was employed to find the best model according to LOOCV. Using all 682 peptides, an LOOCV accuracy of 93% was achieved. When the 47 peptides selected by the Mann-Whitney U test were used, the LOOCV accuracy was 98% with 100% sensitivity and 96% specificity. To substantiate the result, we compared it to a random selection of peptides. Using the same model selection mechanism for support vector machine with 47 different peptides randomly selected the average accuracy over 10 runs was 90%.


Prediction of outcome of non-small cell lung cancer patients treated with chemotherapy and bortezomib by time-course MALDI-TOF-MS serum peptide profiling.

Voortman J, Pham TV, Knol JC, Giaccone G, Jimenez CR - Proteome Sci (2009)

Comparison serum profiles NSCLC vs. cancer-free controls. A, Principle Component Analysis (PCA) NSCLC vs. cancer-free control comparison. B, heat map of the 47 differential peaks. The peaks are ordered by median fold change between the two groups. C, spectra overlay of the 8 most differential peaks in the healthy (red) versus NSCLC (blue) comparison. Spectra overlay of the 8 peaks randomly selected out of the remaining 635 peptides.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2746186&req=5

Figure 5: Comparison serum profiles NSCLC vs. cancer-free controls. A, Principle Component Analysis (PCA) NSCLC vs. cancer-free control comparison. B, heat map of the 47 differential peaks. The peaks are ordered by median fold change between the two groups. C, spectra overlay of the 8 most differential peaks in the healthy (red) versus NSCLC (blue) comparison. Spectra overlay of the 8 peaks randomly selected out of the remaining 635 peptides.
Mentions: Finally, in an exploratory additional analysis, we compared the serum peptide spectra of 13 cancer-free control subjects (median age: 38 years old; range: 27-58 years old) and the pre-treatment serum spectra of the 27 NSCLC patients included in this study. We performed a principal component analysis (PCA) analysis of the 40 profiles using all 682 peptides, see Figure 5A. While there is overlap between the two groups in the three dimensional plot, the healthy profiles (in red) are clustered at the bottom right region. Furthermore, we observed no indication of outliers in the dataset. Next we performed a supervised analysis to identify peptides that were significantly differential in intensity between the two groups. For this purpose, the Mann-Whitney U test was carried out on each of the 682 peptides using all profiles. The peptides were selected based on the criteria outlined in Methods, resulting in 47 peptides. A heat map of the intensities of the 47 peptides is shown in Figure 5B (see also Table 7). Figure 5C shows the spectra overlay of the top 8 most discriminating peaks, all of which have a p-value < 0.0001. Note that for example the peak at m/z 1777.966 has a higher intensity in NSCLC patients (blue) compared cancer-free controls (red) and the peak at m/z 1039.6249 has a lower intensity in NSCLC patients. We carried out classification analysis using support vector machine. A grid search for parameters was employed to find the best model according to LOOCV. Using all 682 peptides, an LOOCV accuracy of 93% was achieved. When the 47 peptides selected by the Mann-Whitney U test were used, the LOOCV accuracy was 98% with 100% sensitivity and 96% specificity. To substantiate the result, we compared it to a random selection of peptides. Using the same model selection mechanism for support vector machine with 47 different peptides randomly selected the average accuracy over 10 runs was 90%.

Bottom Line: Prediction of long PFS was associated with longer overall survival.Inclusion of 7 peptide ions showing differential changes in abundance during treatment led to a 13-peptide ion signature with 86% accuracy at 100% sensitivity and 73% specificity.A 5-peptide ion signature could separate patients with a partial response vs. non-responders with 89% accuracy at 100% sensitivity and 83% specificity.

View Article: PubMed Central - HTML - PubMed

Affiliation: Dept Medical Oncology, VUmc-Cancer Center Amsterdam, the Netherlands. j.voortman@gmail.com

ABSTRACT

Background: Only a minority of patients with advanced non-small cell lung cancer (NSCLC) benefit from chemotherapy. Serum peptide profiling of NSCLC patients was performed to investigate patterns associated with treatment outcome.Using magnetic bead-assisted serum peptide capture coupled to matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS), serum peptide mass profiles of 27 NSCLC patients treated with cisplatin-gemcitabine chemotherapy and bortezomib were obtained. Support vector machine-based algorithms to predict clinical outcome were established based on differential pre-treatment peptide profiles and dynamic changes in peptide abundance during treatment.

Results: A 6-peptide ion signature distinguished with 82% accuracy, sensitivity and specificity patients with a relatively short vs. long progression-free survival (PFS) upon treatment. Prediction of long PFS was associated with longer overall survival. Inclusion of 7 peptide ions showing differential changes in abundance during treatment led to a 13-peptide ion signature with 86% accuracy at 100% sensitivity and 73% specificity. A 5-peptide ion signature could separate patients with a partial response vs. non-responders with 89% accuracy at 100% sensitivity and 83% specificity. Differential peptide profiles were also found when comparing the NSCLC serum profiles to those from cancer-free control subjects.

Conclusion: This study shows that serum peptidome profiling using MALDI-TOF-MS coupled to pattern diagnostics may aid in prediction of treatment outcome of advanced NSCLC patients treated with chemotherapy.

No MeSH data available.


Related in: MedlinePlus