| Autor: |
Gorla A; Bioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, CA, USA., Witonsky J; Division of Allergy, Immunology, and Bone Marrow Transplant, Department of Pediatrics, University of California San Francisco, San Francisco, CA, USA., Elhawary JR; Department of Medicine, University of California, San Francisco, San Francisco, CA, USA., Chen ZJ; Department of Computer Science, University of California Los Angeles, Los Angeles, CA, USA., Mefford J; Department of Neurology, University of California Los Angeles, Los Angeles, CA, USA., Perez-Garcia J; Genomics and Health Group, Department of Biochemistry, Microbiology, Cell Biology, and Genetics, University of La Laguna, La Laguna, Spain., Huntsman S; Department of Medicine, University of California, San Francisco, San Francisco, CA, USA., Hu D; Department of Medicine, University of California, San Francisco, San Francisco, CA, USA., Eng C; Department of Medicine, University of California, San Francisco, San Francisco, CA, USA., Woodruff PG; Department of Medicine, University of California, San Francisco, San Francisco, CA, USA., Sankararaman S; Department of Computer Science, University of California Los Angeles, Los Angeles, CA, USA.; Department of Computational Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.; Department of Human Genetics, University of California Los Angeles, Los Angeles, CA, USA., Ziv E; Department of Medicine, University of California, San Francisco, San Francisco, CA, USA., Flint J; Department of Psychiatry and Behavioral Sciences, Brain Research Institute, University of California Los Angeles, Los Angeles, CA, USA., Zaitlen N; Department of Computational Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.; Department of Human Genetics, University of California Los Angeles, Los Angeles, CA, USA.; Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA., Burchard E; Department of Medicine, University of California, San Francisco, San Francisco, CA, USA.; Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA., Rahmani E; Department of Computational Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA. |
| Abstrakt: |
Identifying and refining clinically significant patient stratification is a critical step toward realizing the promise of precision medicine in asthma. Several peripheral blood hallmarks, including total peripheral blood eosinophil count (BEC) and immunoglobulin E (IgE) levels, are routinely used in asthma clinical practice for endotype classification and predicting response to state-of-the-art targeted biologic drugs. However, these biomarkers appear ineffective in predicting treatment outcomes in some patients, and they differ in distribution between racially and ethnically diverse populations, potentially compromising medical care and hindering health equity due to biases in drug eligibility. Here, we propose constructing an unbiased patient stratification score based on DNA methylation (DNAm) and utilizing it to refine the efficacy of hallmark biomarkers for predicting drug response. We developed Phenotype Aware Component Analysis (PACA), a novel contrastive machine-learning method for learning combinations of DNAm sites reflecting biomedically meaningful patient stratifications. Leveraging whole-blood DNAm from Latino (discovery; n=1,016) and African American (replication; n=756) pediatric asthma case-control cohorts, we applied PACA to refine the prediction of bronchodilator response (BDR) to the short-acting β2-agonist albuterol, the most used drug to treat acute bronchospasm worldwide. While BEC and IgE correlate with BDR in the general patient population, our PACA-derived DNAm score renders these biomarkers predictive of drug response only in patients with high DNAm scores. BEC correlates with BDR in patients with upper-quartile DNAm scores (OR 1.12; 95% CI [1.04, 1.22]; P=7.9 e-4) but not in patients with lower-quartile scores (OR 1.05; 95% CI [0.95, 1.17]; P=0.21); and IgE correlates with BDR in above-median (OR for response 1.42; 95% CI [1.24, 1.63]; P=3.9e-7) but not in below-median patients (OR 1.05; 95% CI [0.92, 1.2]; P=0.57). These results hold within the commonly recognized type 2 (T2)-high asthma endotype but not in T2-low patients, suggesting that our DNAm score primarily represents an unknown variation of T2 asthma. Among T2-high patients with high DNAm scores, elevated BEC or IgE also corresponds to baseline clinical presentation that is known to benefit more from biologic treatment, including higher exacerbation scores, higher allergen sensitization, lower BMI, more recent oral corticosteroids prescription, and lower lung function. Our findings suggest that BEC and IgE, the traditional asthma biomarkers of T2-high asthma, are poor biomarkers for millions worldwide. Revisiting existing drug eligibility criteria relying on these biomarkers in asthma medical care may enhance precision and equity in treatment. |
