Yoonha Choi

Usual Interstitial Pneumonia Can Be Detected in Transbronchial Biopsies Using Machine Learning

Rationale: Usual interstitial pneumonia (UIP) is the histopathologic hallmark of idiopathic pulmonary fibrosis. Although UIP can be detected by high‐resolution computed tomography of the chest, the results are frequently inconclusive, and pathology from transbronchial biopsy (TBB) has poor sensitivity. Surgical lung biopsy may be necessary for a definitive diagnosis. Objectives: To develop a genomic classifier in tissue obtained by TBB that distinguishes UIP from non‐UIP, trained against central pathology as the reference standard. Methods: Exome enriched RNA sequencing was performed on 283 TBBs from 84 subjects. Machine learning was used to train an algorithm with high rule‐in (specificity) performance using specimens from 53 subjects. Performance was evaluated by cross‐validation and on an independent test set of specimens from 31 subjects. We explored the feasibility of a single molecular test per subject by combining multiple TBBs from upper and lower lobes. To address whether classifier accuracy depends upon adequate alveolar sampling, we tested for correlation between classifier accuracy and expression of alveolar‐specific genes. Results: The top‐performing algorithm distinguishes UIP from non‐UIP conditions in single TBB samples with an area under the receiver operator characteristic curve (AUC) of 0.86, with specificity of 86% (confidence interval = 71‐95%) and sensitivity of 63% (confidence interval = 51‐74%) (31 test subjects). Performance improves to an AUC of 0.92 when three to five TBB samples per subject are combined at the RNA level for testing. Although we observed a wide range of type I and II alveolar‐specific gene expression in TBBs, expression of these transcripts did not correlate with classifier accuracy. Conclusions: We demonstrate proof of principle that genomic analysis and machine learning improves the utility of TBB for the diagnosis of UIP, with greater sensitivity and specificity than pathology in TBB alone. Combining multiple individual subject samples results in increased test accuracy over single sample testing. This approach requires validation in an independent cohort of subjects before application in the clinic.

Genome-wide survey in African Americans demonstrates potential epistasis of fitness in the human genome.

The role played by epistasis between alleles at unlinked loci in shaping population fitness has been debated for many years and the existing evidence has been mainly accumulated from model organisms. In model organisms, fitness epistasis can be systematically inferred by detecting nonindependence of genotypic values between loci in a population and confirmed through examining the number of offspring produced in two‐locus genotype groups. No systematic study has been conducted to detect epistasis of fitness in humans owing to experimental constraints. In this study, we developed a novel method to detect fitness epistasis by testing the correlation between local ancestries on different chromosomes in an admixed population. We inferred local ancestry across the genome in 16,252 unrelated African Americans and systematically examined the pairwise correlations between the genomic regions on different chromosomes. Our analysis revealed a pair of genomic regions on chromosomes 4 and 6 that show significant local ancestry correlation (P‐value = 4.01 × 10−8) that can be potentially attributed to fitness epistasis. However, we also observed substantial local ancestry correlation that cannot be explained by systemic ancestry inference bias. To our knowledge, this study is the first to systematically examine evidence of fitness epistasis across the human genome.

Analytical performance of Envisia: a genomic classifier for usual interstitial pneumonia

BackgroundClinical guidelines specify that diagnosis of interstitial pulmonary fibrosis (IPF) requires identification of usual interstitial pneumonia (UIP) pattern. While UIP can be identified by high resolution CT of the chest, the results are often inconclusive, making surgical lung biopsy necessary to reach a definitive diagnosis (Raghu et al., Am J Respir Crit Care Med 183(6):788–824, 2011). The Envisia genomic classifier differentiates UIP from non-UIP pathology in transbronchial biopsies (TBB), potentially allowing patients to avoid an invasive procedure (Brown et al., Am J Respir Crit Care Med 195:A6792, 2017). To ensure patient safety and efficacy, a laboratory developed test (LDT) must meet strict regulatory requirements for accuracy, reproducibility and robustness. The analytical characteristics of the Envisia test are assessed and reported here.MethodsThe Envisia test utilizes total RNA extracted from TBB samples to perform Next Generation RNA Sequencing. The gene count data from 190 genes are then input to the Envisia genomic classifier, a machine learning algorithm, to output either a UIP or non-UIP classification result. We characterized the stability of RNA in TBBs during collection and shipment, and evaluated input RNA mass and proportions on the limit of detection of UIP. We evaluated potentially interfering substances such as blood and genomic DNA. Intra-run, inter-run, and inter-laboratory reproducibility of test results were also characterized.ResultsRNA content within TBBs preserved in RNAprotect is stable for up to 14 days with no detectable change in RNA quality. The Envisia test is tolerant to variation in RNA input (5 to 30 ng), with no impact on classifier results. The Envisia test can tolerate dilution of non-UIP and UIP classification signals at the RNA level by up to 60% and 20%, respectively. Analytical specificity studies utilizing UIP and non-UIP samples mixed with genomic DNA (up to 30% relative input) demonstrated no impact to classifier results. The Envisia test tolerates up to 22% of blood contamination, well beyond the level observed in TBBs. The test is reproducible from RNA extraction through to Envisia test result (standard deviation of 0.20 for Envisia classification scores on > 7-unit scale).ConclusionsThe Envisia test demonstrates the robust analytical performance required of an LDT. Envisia can be used to inform the diagnoses of patients with suspected IPF.

AB029. Clinical utility of a molecular diagnostic in evaluation of interstitial lung disease

Background The accurate diagnosis of idiopathic pulmonary fibrosis (IPF) continues to be challenging due to its overlapping features with other interstitial lung diseases (ILDs). With the approval of pirfenidone and nintedanib for treatment of IPF, there is greater urgency to identify patients with IPF without requiring surgical lung biopsy (SLB). In this study, we evaluated the clinical utility of a genomic classifier under development to identify usual interstitial pneumonia (UIP), the pathology pattern associated with IPF, using bronchoscopically collected samples.