ATS 2021 will feature presentations of original research from accepted abstracts. Mini Symposia and Thematic Poster Sessions are abstract based sessions.
Please use the form below to browse scientific abstracts and case reports accepted for ATS 2021. Abstracts presented at the ATS 2021 will be published in the Online Abstract Issue of the American Journal of Respiratory and Critical Care Medicine, Volume 203, May 3, 2021.
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Bridging the Envisia Genomic Classifier to the nCounter Platform: A Proof of Concept Study
Session Title
TP112 - TP112 PROTEOMICS/GENOMICS/METABOLOMICS IN LUNG DISEASE
Abstract
A4352 - Bridging the Envisia Genomic Classifier to the nCounter Platform: A Proof of Concept Study
Author Block: H. Jiang1, J. Qu2, M. Wong1, G. Fedorowicz1, D. Pankratz1, K. Lee3, A. Sopory4, J. Storhoff1, S. Bhorade5, J. Huang2, P. Walsh1, G. C. Kennedy1; 1R&D, Veracyte, South San Francisco, CA, United States, 2Research Discovery - DAT, Veracyte, South San Francisco, CA, United States, 3IT-dev, Veracyte, South San Francisco, CA, United States, 4CLIA, Veracyte, South San Francisco, CA, United States, 5Medical Affairs, Veracyte, south san francisco, CA, United States.
Rationale: The Envisia Genomic Classifier (EGC) is a molecular test, offered in a single US CLIA reference laboratory, which classifies usual interstitial pneumonia (UIP) pattern in transbronchial biopsies (TBBs) using the Unified Assay (UA), a proprietary next-generation RNA sequencing platform. The nCounter® Analysis System is a CE-marked and FDA-cleared decentralized molecular testing platform that can simultaneously measure the expression of up to 800 genes per sample and can accommodate up to 12 samples including controls per run. EGC testing on the nCounter platform would facilitate global test access with reduced costs and faster turnaround time. We report preliminary results on the bridging of the EGC to the nCounter platform.
Methods: RNA-seq UA data was used to define an nCounter CodeSet targeting 243 genes, including the 190 EGC genes. TBB RNA samples that were processed via UA were reprocessed on the nCounter platform at varying total RNA input amounts (50ng - 200ng) to assess gene count variability and correlation to the UA platform. To assess UIP limits of detection on nCounter, TBB RNAs classifying as UIP or non-UIP were mixed at varying proportions and processed to both platforms. Thirty-three TBB RNA samples with variable RNA quality were processed on both platforms and results compared.
Results: Gene count variability on nCounter improved with increasing RNA inputs and was equivalent to UA at 100ng RNA input. Correlation of measured gene expression levels between UA and nCounter data was moderate (R2 = 0.71), while UIP classifier scores using the original 190 EGC genes showed higher correlation across platforms across a range of RNA quality (R2 = 0.94 after calibration to accommodate the nCounter platform, Figure 1). EGC classification scores of TBB mixtures show similar UIP limits of detection on nCounter and UA data.
Conclusions: These results show that the gene expression differences and machine learning algorithm underlying the EGC are robust across detection platforms. Bridging the EGC to the nCounter test platform is in progress, with minimal expected algorithm modification. We thus expect the Envisia nCounter test clinical performance to maintain at approximately 91% specificity and 63% sensitivity, as shown by two independent clinical validation studies of EGC. The technical feasibility of performing EGC testing on the nCounter platform will increase accessibility and timeliness to diagnostic information for patients with undiagnosed interstitial lung disease by identifying a molecular UIP pattern in TBB samples within the local testing laboratory.
Author Block: H. Jiang1, J. Qu2, M. Wong1, G. Fedorowicz1, D. Pankratz1, K. Lee3, A. Sopory4, J. Storhoff1, S. Bhorade5, J. Huang2, P. Walsh1, G. C. Kennedy1; 1R&D, Veracyte, South San Francisco, CA, United States, 2Research Discovery - DAT, Veracyte, South San Francisco, CA, United States, 3IT-dev, Veracyte, South San Francisco, CA, United States, 4CLIA, Veracyte, South San Francisco, CA, United States, 5Medical Affairs, Veracyte, south san francisco, CA, United States.
Rationale: The Envisia Genomic Classifier (EGC) is a molecular test, offered in a single US CLIA reference laboratory, which classifies usual interstitial pneumonia (UIP) pattern in transbronchial biopsies (TBBs) using the Unified Assay (UA), a proprietary next-generation RNA sequencing platform. The nCounter® Analysis System is a CE-marked and FDA-cleared decentralized molecular testing platform that can simultaneously measure the expression of up to 800 genes per sample and can accommodate up to 12 samples including controls per run. EGC testing on the nCounter platform would facilitate global test access with reduced costs and faster turnaround time. We report preliminary results on the bridging of the EGC to the nCounter platform.
Methods: RNA-seq UA data was used to define an nCounter CodeSet targeting 243 genes, including the 190 EGC genes. TBB RNA samples that were processed via UA were reprocessed on the nCounter platform at varying total RNA input amounts (50ng - 200ng) to assess gene count variability and correlation to the UA platform. To assess UIP limits of detection on nCounter, TBB RNAs classifying as UIP or non-UIP were mixed at varying proportions and processed to both platforms. Thirty-three TBB RNA samples with variable RNA quality were processed on both platforms and results compared.
Results: Gene count variability on nCounter improved with increasing RNA inputs and was equivalent to UA at 100ng RNA input. Correlation of measured gene expression levels between UA and nCounter data was moderate (R2 = 0.71), while UIP classifier scores using the original 190 EGC genes showed higher correlation across platforms across a range of RNA quality (R2 = 0.94 after calibration to accommodate the nCounter platform, Figure 1). EGC classification scores of TBB mixtures show similar UIP limits of detection on nCounter and UA data.
Conclusions: These results show that the gene expression differences and machine learning algorithm underlying the EGC are robust across detection platforms. Bridging the EGC to the nCounter test platform is in progress, with minimal expected algorithm modification. We thus expect the Envisia nCounter test clinical performance to maintain at approximately 91% specificity and 63% sensitivity, as shown by two independent clinical validation studies of EGC. The technical feasibility of performing EGC testing on the nCounter platform will increase accessibility and timeliness to diagnostic information for patients with undiagnosed interstitial lung disease by identifying a molecular UIP pattern in TBB samples within the local testing laboratory.
