Milene T Saavedra1,2, Bradley S Quon3,4, Anna Faino5, Silvia M Caceres1, Katie R Poch1, Linda A Sanders1,2, Kenneth C Malcolm1, David P Nichols6, Scott D Sagel7, Jennifer L Taylor-Cousar1,7,2,8,9, Sonia M Leach10, Matthew Strand5, Jerry A Nick1,2. 1. 1 Department of Medicine. 2. 2 Department of Medicine, University of Colorado Denver, Aurora, Colorado. 3. 3 Centre for Heart Lung Innovation, Department of Medicine, University of British Columbia and. 4. 4 St. Paul's Hospital, Vancouver, British Columbia, Canada. 5. 5 Division of Biostatistics and Bioinformatics. 6. 6 Department of Pediatrics, Seattle Children's Hospital, University of Washington, Seattle, Washington; and. 7. 7 Department of Pediatrics, and. 8. 8 Department of Pediatrics, Children's Hospital Colorado and. 9. 9 University of Colorado School of Medicine, Aurora, Colorado. 10. 10 Department of Biomedical Research, National Jewish Health, Denver, Colorado.
Abstract
RATIONALE: Cystic fibrosis pulmonary exacerbations accelerate pulmonary decline and increase mortality. Previously, we identified a 10-gene leukocyte panel measured directly from whole blood, which indicates response to exacerbation treatment. We hypothesized that molecular characteristics of exacerbations could also predict future disease severity. OBJECTIVES: We tested whether a 10-gene panel measured from whole blood could identify patient cohorts at increased risk for severe morbidity and mortality, beyond standard clinical measures. METHODS: Transcript abundance for the 10-gene panel was measured from whole blood at the beginning of exacerbation treatment (n = 57). A hierarchical cluster analysis of subjects based on their gene expression was performed, yielding four molecular clusters. An analysis of cluster membership and outcomes incorporating an independent cohort (n = 21) was completed to evaluate robustness of cluster partitioning of genes to predict severe morbidity and mortality. RESULTS: The four molecular clusters were analyzed for differences in forced expiratory volume in 1 second, C-reactive protein, return to baseline forced expiratory volume in 1 second after treatment, time to next exacerbation, and time to morbidity or mortality events (defined as lung transplant referral, lung transplant, intensive care unit admission for respiratory insufficiency, or death). Clustering based on gene expression discriminated between patient groups with significant differences in forced expiratory volume in 1 second, admission frequency, and overall morbidity and mortality. At 5 years, all subjects in cluster 1 (very low risk) were alive and well, whereas 90% of subjects in cluster 4 (high risk) had suffered a major event (P = 0.0001). In multivariable analysis, the ability of gene expression to predict clinical outcomes remained significant, despite adjustment for forced expiratory volume in 1 second, sex, and admission frequency. The robustness of gene clustering to categorize patients appropriately in terms of clinical characteristics, and short- and long-term clinical outcomes, remained consistent, even when adding in a secondary population with significantly different clinical outcomes. CONCLUSIONS: Whole blood gene expression profiling allows molecular classification of acute pulmonary exacerbations, beyond standard clinical measures, providing a predictive tool for identifying subjects at increased risk for mortality and disease progression.
RATIONALE: Cystic fibrosis pulmonary exacerbations accelerate pulmonary decline and increase mortality. Previously, we identified a 10-gene leukocyte panel measured directly from whole blood, which indicates response to exacerbation treatment. We hypothesized that molecular characteristics of exacerbations could also predict future disease severity. OBJECTIVES: We tested whether a 10-gene panel measured from whole blood could identify patient cohorts at increased risk for severe morbidity and mortality, beyond standard clinical measures. METHODS: Transcript abundance for the 10-gene panel was measured from whole blood at the beginning of exacerbation treatment (n = 57). A hierarchical cluster analysis of subjects based on their gene expression was performed, yielding four molecular clusters. An analysis of cluster membership and outcomes incorporating an independent cohort (n = 21) was completed to evaluate robustness of cluster partitioning of genes to predict severe morbidity and mortality. RESULTS: The four molecular clusters were analyzed for differences in forced expiratory volume in 1 second, C-reactive protein, return to baseline forced expiratory volume in 1 second after treatment, time to next exacerbation, and time to morbidity or mortality events (defined as lung transplant referral, lung transplant, intensive care unit admission for respiratory insufficiency, or death). Clustering based on gene expression discriminated between patient groups with significant differences in forced expiratory volume in 1 second, admission frequency, and overall morbidity and mortality. At 5 years, all subjects in cluster 1 (very low risk) were alive and well, whereas 90% of subjects in cluster 4 (high risk) had suffered a major event (P = 0.0001). In multivariable analysis, the ability of gene expression to predict clinical outcomes remained significant, despite adjustment for forced expiratory volume in 1 second, sex, and admission frequency. The robustness of gene clustering to categorize patients appropriately in terms of clinical characteristics, and short- and long-term clinical outcomes, remained consistent, even when adding in a secondary population with significantly different clinical outcomes. CONCLUSIONS: Whole blood gene expression profiling allows molecular classification of acute pulmonary exacerbations, beyond standard clinical measures, providing a predictive tool for identifying subjects at increased risk for mortality and disease progression.
Authors: Frederick W Woodley; Emrah Gecili; Rhonda D Szczesniak; Chandra L Shrestha; Christopher J Nemastil; Benjamin T Kopp; Don Hayes Journal: Respir Med Date: 2021-11-23 Impact factor: 3.415
Authors: Silvia M Caceres; Linda A Sanders; Noel M Rysavy; Katie R Poch; Caroline R Jones; Kyle Pickard; Tasha E Fingerlin; Roland A Marcus; Kenneth C Malcolm; Jennifer L Taylor-Cousar; David P Nichols; Jerry A Nick; Matthew Strand; Milene T Saavedra Journal: PLoS One Date: 2022-05-05 Impact factor: 3.240