Discovery of Airway Fluid Proteins Associated with Progressive Lung Disease and Damage in Early Childhood Cystic Fibrosis Public
Chou, Jeffrey (Spring 2019)
Published
Abstract
Background: Lung disease is typically the landmark clinical presentation in cystic fibrosis (CF) patients. However, much is still unknown regarding which immunological signaling proteins truly contribute to airway pathology, especially in young children. To assess such relationships, bronchoalveolar lavage (BAL) samples and lung computed tomography (CT) scans were obtained from a longitudinal cohort of CF children at one, three, and five years of age. Concentrations of select BAL proteins were determined using the Olink® Immuno-Oncology assay and overall lung disease from CT scans was quantified by the PRAGMA-CF (Perth-Rotterdam Annotated Grid Morphometric Analysis) method. Components of the PRAGMA score include PRAGMA-%Dis for total airway disease and PRAGMA-%Bx scores for bronchiectasis.
Methods: Spearman correlation coefficients were used to evaluate cross-sectional relationships of individual protein correlations with PRAGMA scores. A random-intercept model with AR(1) covariance structure was then built, with guidance from contrast tests for evaluating the effects of each individual protein on PRAGMA score at given time points. Calculated p-values for Spearman correlations and contrast tests were adjusted with the Benjamini-Hochberg method to control for false discovery rate (FDR). Finally, a lasso penalized regression algorithm for mixed models was utilized to select proteins that lead to a parsimonious model for predicting PRAGMA scores.
Results: After FDR correction, three BAL proteins (ARG1, CCL4, and CSF1) exhibited significant positive correlations with PRAGMA-%Dis in the cross-sectional analysis of the five-year-old subset. These findings are corroborated by the contrast test results based on the linear mixed model with age set to five years. In addition, the lasso method suggests that higher HGF, lower LAMP3, and older age are predictive of increased PRAGMA-%Dis, whereas the selected predictors for increased PRAGMA-%Bx include higher ICOSLG, higher TNFRSF9, lower LAMP3, and older age. These proteins had significant effects in the linear mixed models’ contrast tests except for the effect of LAMP3 on %Dis.
Conclusions: Our analyses demonstrate that select proteins have promising utility in predicting airway disease and damage in young CF children, both in a cross-sectional and longitudinal context. However, more research is needed to establish causal relationships for therapeutic drug development and improvement of precision medicine models.
Table of Contents
1. INTRODUCTION (1-5)
2. METHODS (6-12)
2.1 Data Acquisition and Cleaning (6)
2.1.1 I-BALL Clinical Demographics (6)
2.1.2 BALF & Olink Proteomics (6-8)
2.1.3 PRAGMA Scores (8)
2.2 Summary Statistics (8)
2.3 Cross-Sectional Spearman Correlations (8)
2.4 Linear Mixed Models of PRAGMA Scores to Evaluate Individual Protein Main Effects (9)
2.4.1 Model I: Protein concentration and age as a continuous covariate (9-10)
2.4.2 Model II: Protein concentration, age as a continuous covariate, and interaction of protein*age (10-11)
2.5 Lasso Penalized Regression for Building Multivariate Linear Mixed Models of PRAGMA Scores (11-12)
2.6 Transparency Statement (12)
3. RESULTS (13-24)
3.1 Summary Statistics (13)
3.2 Cross-Sectional Spearman Correlations (14-16)
3.3 Linear Mixed Models of PRAGMA Scores to Evaluate Individual Protein Main Effects (16-19)
3.3.1 Model I: Protein concentration and age as a continuous covariate (16-17)
3.3.2 Model II: Protein concentration, age as a continuous covariate, and interaction of protein*age (17-19)
3.4 Lasso Penalized Regression for Building Multivariate Protein Linear Mixed Models of PRAGMA Scores (19-24)
4. DISCUSSION (25-28)
4.1 Interpretation of Results (25-28)
4.2 Limitations (29)
4.3 Suggestions for Further Research (29-30)
5. REFERENCES (31-34)
6. APPENDIX A: Olink® Immuno-Oncology Panel Protein Names and Abbreviations (35-37)
7. APPENDIX B: Additional Tables (38-53)
8. APPENDIX C: Scatterplots of Significant Proteins (54-56)
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