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Analytical performance of Envisia: a genomic classifier for usual interstitial pneumonia
BACKGROUND: Clinical 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 neces...
Autores principales: | , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5693488/ https://www.ncbi.nlm.nih.gov/pubmed/29149880 http://dx.doi.org/10.1186/s12890-017-0485-4 |
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author | Choi, Yoonha Lu, Jiayi Hu, Zhanzhi Pankratz, Daniel G. Jiang, Huimin Cao, Manqiu Marchisano, Cristina Huiras, Jennifer Fedorowicz, Grazyna Wong, Mei G. Anderson, Jessica R. Tom, Edward Y. Babiarz, Joshua Imtiaz, Urooj Barth, Neil M. Walsh, P. Sean Kennedy, Giulia C. Huang, Jing |
author_facet | Choi, Yoonha Lu, Jiayi Hu, Zhanzhi Pankratz, Daniel G. Jiang, Huimin Cao, Manqiu Marchisano, Cristina Huiras, Jennifer Fedorowicz, Grazyna Wong, Mei G. Anderson, Jessica R. Tom, Edward Y. Babiarz, Joshua Imtiaz, Urooj Barth, Neil M. Walsh, P. Sean Kennedy, Giulia C. Huang, Jing |
author_sort | Choi, Yoonha |
collection | PubMed |
description | BACKGROUND: Clinical 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. METHODS: The 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. RESULTS: RNA 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). CONCLUSIONS: The Envisia test demonstrates the robust analytical performance required of an LDT. Envisia can be used to inform the diagnoses of patients with suspected IPF. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12890-017-0485-4) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-5693488 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-56934882017-11-24 Analytical performance of Envisia: a genomic classifier for usual interstitial pneumonia Choi, Yoonha Lu, Jiayi Hu, Zhanzhi Pankratz, Daniel G. Jiang, Huimin Cao, Manqiu Marchisano, Cristina Huiras, Jennifer Fedorowicz, Grazyna Wong, Mei G. Anderson, Jessica R. Tom, Edward Y. Babiarz, Joshua Imtiaz, Urooj Barth, Neil M. Walsh, P. Sean Kennedy, Giulia C. Huang, Jing BMC Pulm Med Research Article BACKGROUND: Clinical 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. METHODS: The 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. RESULTS: RNA 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). CONCLUSIONS: The Envisia test demonstrates the robust analytical performance required of an LDT. Envisia can be used to inform the diagnoses of patients with suspected IPF. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12890-017-0485-4) contains supplementary material, which is available to authorized users. BioMed Central 2017-11-17 /pmc/articles/PMC5693488/ /pubmed/29149880 http://dx.doi.org/10.1186/s12890-017-0485-4 Text en © The Author(s) 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License(http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Article Choi, Yoonha Lu, Jiayi Hu, Zhanzhi Pankratz, Daniel G. Jiang, Huimin Cao, Manqiu Marchisano, Cristina Huiras, Jennifer Fedorowicz, Grazyna Wong, Mei G. Anderson, Jessica R. Tom, Edward Y. Babiarz, Joshua Imtiaz, Urooj Barth, Neil M. Walsh, P. Sean Kennedy, Giulia C. Huang, Jing Analytical performance of Envisia: a genomic classifier for usual interstitial pneumonia |
title | Analytical performance of Envisia: a genomic classifier for usual interstitial pneumonia |
title_full | Analytical performance of Envisia: a genomic classifier for usual interstitial pneumonia |
title_fullStr | Analytical performance of Envisia: a genomic classifier for usual interstitial pneumonia |
title_full_unstemmed | Analytical performance of Envisia: a genomic classifier for usual interstitial pneumonia |
title_short | Analytical performance of Envisia: a genomic classifier for usual interstitial pneumonia |
title_sort | analytical performance of envisia: a genomic classifier for usual interstitial pneumonia |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5693488/ https://www.ncbi.nlm.nih.gov/pubmed/29149880 http://dx.doi.org/10.1186/s12890-017-0485-4 |
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