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Proteomic biomarkers for acute interstitial lung disease in gefitinib-treated Japanese lung cancer patients.

Nyberg F, Ogiwara A, Harbron CG, Kawakami T, Nagasaka K, Takami S, Wada K, Tu HK, Otsuji M, Kyono Y, Dobashi T, Komatsu Y, Kihara M, Akimoto S, Peers IS, South MC, Higenbottam T, Fukuoka M, Nakata K, Ohe Y, Kudoh S, Clausen IG, Nishimura T, Marko-Varga G, Kato H - PLoS ONE (2011)

Bottom Line: After alignment, scaling, and measurement batch adjustment, we identified 41 peptide peaks representing 29 proteins best predicting ILD.The acute phase response pathway was strongly represented (17 of 29 proteins, p = 1.0×10(-25)), suggesting a key role with potential utility as a marker for increased risk of acute ILD events.Validation by Western blotting showed correlation for identified proteins, confirming that robust results can be generated from an MS/MS platform implementing strict quality control.

View Article: PubMed Central - PubMed

Affiliation: Global Epidemiology, AstraZeneca R&D, Mölndal, Sweden. Fredrik.Nyberg@astrazeneca.com

ABSTRACT
Interstitial lung disease (ILD) events have been reported in Japanese non-small-cell lung cancer (NSCLC) patients receiving EGFR tyrosine kinase inhibitors. We investigated proteomic biomarkers for mechanistic insights and improved prediction of ILD. Blood plasma was collected from 43 gefitinib-treated NSCLC patients developing acute ILD (confirmed by blinded diagnostic review) and 123 randomly selected controls in a nested case-control study within a pharmacoepidemiological cohort study in Japan. We generated ∼7 million tandem mass spectrometry (MS/MS) measurements with extensive quality control and validation, producing one of the largest proteomic lung cancer datasets to date, incorporating rigorous study design, phenotype definition, and evaluation of sample processing. After alignment, scaling, and measurement batch adjustment, we identified 41 peptide peaks representing 29 proteins best predicting ILD. Multivariate peptide, protein, and pathway modeling achieved ILD prediction comparable to previously identified clinical variables; combining the two provided some improvement. The acute phase response pathway was strongly represented (17 of 29 proteins, p = 1.0×10(-25)), suggesting a key role with potential utility as a marker for increased risk of acute ILD events. Validation by Western blotting showed correlation for identified proteins, confirming that robust results can be generated from an MS/MS platform implementing strict quality control.

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Related in: MedlinePlus

Exploratory data analysis of MS signal intensities using PCA.(A) Plot of first two principal components from PCA analysis of the full proteomic data from all 11 analysis batches (numbered 1–11 in time sequence). Each sample is represented by a single point, with the range of points within each batch being shown by a polygon joining the extreme points in that batch. (B) Plots of the first two principal components for the repeated batches of samples (1 and 10, 3 and 11). Individual samples are represented by a line, connecting the two replicates in different batches. (C) Reproducibility of an example differentially expressed peptide between two duplicate batch runs of proteomic analysis. The intensities of the first and second runs for each replicated sample are plotted against each other. Samples colored by batch (batch 1 repeated as batch 10 – blue; batch 3, repeated as batch 11 – red). Allowing for between-sequence differences there is a good correlation between replicate runs.
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pone-0022062-g002: Exploratory data analysis of MS signal intensities using PCA.(A) Plot of first two principal components from PCA analysis of the full proteomic data from all 11 analysis batches (numbered 1–11 in time sequence). Each sample is represented by a single point, with the range of points within each batch being shown by a polygon joining the extreme points in that batch. (B) Plots of the first two principal components for the repeated batches of samples (1 and 10, 3 and 11). Individual samples are represented by a line, connecting the two replicates in different batches. (C) Reproducibility of an example differentially expressed peptide between two duplicate batch runs of proteomic analysis. The intensities of the first and second runs for each replicated sample are plotted against each other. Samples colored by batch (batch 1 repeated as batch 10 – blue; batch 3, repeated as batch 11 – red). Allowing for between-sequence differences there is a good correlation between replicate runs.

Mentions: We then used a principal component analysis (PCA) to explore the data in order to identify the largest sources of variation. Figure 2A shows a plot of this analysis, with each sample colored according to batch. Measurements from the same batch tend to cluster together, separate from other batches, implying that the largest differences between samples arise from the batch-wise processing.


Proteomic biomarkers for acute interstitial lung disease in gefitinib-treated Japanese lung cancer patients.

Nyberg F, Ogiwara A, Harbron CG, Kawakami T, Nagasaka K, Takami S, Wada K, Tu HK, Otsuji M, Kyono Y, Dobashi T, Komatsu Y, Kihara M, Akimoto S, Peers IS, South MC, Higenbottam T, Fukuoka M, Nakata K, Ohe Y, Kudoh S, Clausen IG, Nishimura T, Marko-Varga G, Kato H - PLoS ONE (2011)

Exploratory data analysis of MS signal intensities using PCA.(A) Plot of first two principal components from PCA analysis of the full proteomic data from all 11 analysis batches (numbered 1–11 in time sequence). Each sample is represented by a single point, with the range of points within each batch being shown by a polygon joining the extreme points in that batch. (B) Plots of the first two principal components for the repeated batches of samples (1 and 10, 3 and 11). Individual samples are represented by a line, connecting the two replicates in different batches. (C) Reproducibility of an example differentially expressed peptide between two duplicate batch runs of proteomic analysis. The intensities of the first and second runs for each replicated sample are plotted against each other. Samples colored by batch (batch 1 repeated as batch 10 – blue; batch 3, repeated as batch 11 – red). Allowing for between-sequence differences there is a good correlation between replicate runs.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3140475&req=5

pone-0022062-g002: Exploratory data analysis of MS signal intensities using PCA.(A) Plot of first two principal components from PCA analysis of the full proteomic data from all 11 analysis batches (numbered 1–11 in time sequence). Each sample is represented by a single point, with the range of points within each batch being shown by a polygon joining the extreme points in that batch. (B) Plots of the first two principal components for the repeated batches of samples (1 and 10, 3 and 11). Individual samples are represented by a line, connecting the two replicates in different batches. (C) Reproducibility of an example differentially expressed peptide between two duplicate batch runs of proteomic analysis. The intensities of the first and second runs for each replicated sample are plotted against each other. Samples colored by batch (batch 1 repeated as batch 10 – blue; batch 3, repeated as batch 11 – red). Allowing for between-sequence differences there is a good correlation between replicate runs.
Mentions: We then used a principal component analysis (PCA) to explore the data in order to identify the largest sources of variation. Figure 2A shows a plot of this analysis, with each sample colored according to batch. Measurements from the same batch tend to cluster together, separate from other batches, implying that the largest differences between samples arise from the batch-wise processing.

Bottom Line: After alignment, scaling, and measurement batch adjustment, we identified 41 peptide peaks representing 29 proteins best predicting ILD.The acute phase response pathway was strongly represented (17 of 29 proteins, p = 1.0×10(-25)), suggesting a key role with potential utility as a marker for increased risk of acute ILD events.Validation by Western blotting showed correlation for identified proteins, confirming that robust results can be generated from an MS/MS platform implementing strict quality control.

View Article: PubMed Central - PubMed

Affiliation: Global Epidemiology, AstraZeneca R&D, Mölndal, Sweden. Fredrik.Nyberg@astrazeneca.com

ABSTRACT
Interstitial lung disease (ILD) events have been reported in Japanese non-small-cell lung cancer (NSCLC) patients receiving EGFR tyrosine kinase inhibitors. We investigated proteomic biomarkers for mechanistic insights and improved prediction of ILD. Blood plasma was collected from 43 gefitinib-treated NSCLC patients developing acute ILD (confirmed by blinded diagnostic review) and 123 randomly selected controls in a nested case-control study within a pharmacoepidemiological cohort study in Japan. We generated ∼7 million tandem mass spectrometry (MS/MS) measurements with extensive quality control and validation, producing one of the largest proteomic lung cancer datasets to date, incorporating rigorous study design, phenotype definition, and evaluation of sample processing. After alignment, scaling, and measurement batch adjustment, we identified 41 peptide peaks representing 29 proteins best predicting ILD. Multivariate peptide, protein, and pathway modeling achieved ILD prediction comparable to previously identified clinical variables; combining the two provided some improvement. The acute phase response pathway was strongly represented (17 of 29 proteins, p = 1.0×10(-25)), suggesting a key role with potential utility as a marker for increased risk of acute ILD events. Validation by Western blotting showed correlation for identified proteins, confirming that robust results can be generated from an MS/MS platform implementing strict quality control.

Show MeSH
Related in: MedlinePlus