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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

Plots illustrating the relationship between acute phase response pathway intensity score and clinical variable score.(A) Plot of the acute phase response intensity against the combined clinical variable score measuring the likelihood of a subject being a case calculated from a model predicting case-control status based only on the clinical variables WHO PS, smoking history, extent of normal lung coverage on CT scan, and severity of pre-existing ILD, with boxplots comparing the distribution of these measures in cases and controls. In each boxplot, the upper/right and lower/left sides of the box represent the higher and lower quartile values (Q3 and Q1), respectively. The black bar in each box represents the median value. (B) Receiver operating characteristics curve of cross-validated predictions from clinical data, the acute phase response intensity and a combination of the clinical data and acute phase response intensity.
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pone-0022062-g008: Plots illustrating the relationship between acute phase response pathway intensity score and clinical variable score.(A) Plot of the acute phase response intensity against the combined clinical variable score measuring the likelihood of a subject being a case calculated from a model predicting case-control status based only on the clinical variables WHO PS, smoking history, extent of normal lung coverage on CT scan, and severity of pre-existing ILD, with boxplots comparing the distribution of these measures in cases and controls. In each boxplot, the upper/right and lower/left sides of the box represent the higher and lower quartile values (Q3 and Q1), respectively. The black bar in each box represents the median value. (B) Receiver operating characteristics curve of cross-validated predictions from clinical data, the acute phase response intensity and a combination of the clinical data and acute phase response intensity.

Mentions: Figure 8A shows the acute phase response pathway intensity plotted against a combined clinical variable score measuring the likelihood of a subject being a case calculated from a logistic regression of case-control status against the clinical variables WHO PS, smoking history, extent of normal lung coverage on CT scan, and severity of pre-existing ILD. This shows both sources of information contributing to predicting ILD outcome, although these two measures are fairly strongly correlated, so that much of the information is duplicated. Figure 8B considers the implications for predicting ILD by showing the receiver operating characteristics (ROC) curves for the clinical variables, the acute phase pathway intensity, and the combination of the two sources of information. This shows comparable levels of predictive power from the clinical variables and acute phase pathway intensities, and some potential benefit from combining them together which, however, is limited, reflecting their correlation.


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)

Plots illustrating the relationship between acute phase response pathway intensity score and clinical variable score.(A) Plot of the acute phase response intensity against the combined clinical variable score measuring the likelihood of a subject being a case calculated from a model predicting case-control status based only on the clinical variables WHO PS, smoking history, extent of normal lung coverage on CT scan, and severity of pre-existing ILD, with boxplots comparing the distribution of these measures in cases and controls. In each boxplot, the upper/right and lower/left sides of the box represent the higher and lower quartile values (Q3 and Q1), respectively. The black bar in each box represents the median value. (B) Receiver operating characteristics curve of cross-validated predictions from clinical data, the acute phase response intensity and a combination of the clinical data and acute phase response intensity.
© Copyright Policy
Related In: Results  -  Collection

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

pone-0022062-g008: Plots illustrating the relationship between acute phase response pathway intensity score and clinical variable score.(A) Plot of the acute phase response intensity against the combined clinical variable score measuring the likelihood of a subject being a case calculated from a model predicting case-control status based only on the clinical variables WHO PS, smoking history, extent of normal lung coverage on CT scan, and severity of pre-existing ILD, with boxplots comparing the distribution of these measures in cases and controls. In each boxplot, the upper/right and lower/left sides of the box represent the higher and lower quartile values (Q3 and Q1), respectively. The black bar in each box represents the median value. (B) Receiver operating characteristics curve of cross-validated predictions from clinical data, the acute phase response intensity and a combination of the clinical data and acute phase response intensity.
Mentions: Figure 8A shows the acute phase response pathway intensity plotted against a combined clinical variable score measuring the likelihood of a subject being a case calculated from a logistic regression of case-control status against the clinical variables WHO PS, smoking history, extent of normal lung coverage on CT scan, and severity of pre-existing ILD. This shows both sources of information contributing to predicting ILD outcome, although these two measures are fairly strongly correlated, so that much of the information is duplicated. Figure 8B considers the implications for predicting ILD by showing the receiver operating characteristics (ROC) curves for the clinical variables, the acute phase pathway intensity, and the combination of the two sources of information. This shows comparable levels of predictive power from the clinical variables and acute phase pathway intensities, and some potential benefit from combining them together which, however, is limited, reflecting their correlation.

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