Cargando…
Improving lung cancer risk stratification leveraging whole transcriptome RNA sequencing and machine learning across multiple cohorts
BACKGROUND: Bronchoscopy for suspected lung cancer has low diagnostic sensitivity, rendering many inconclusive results. The Bronchial Genomic Classifier (BGC) was developed to help with patient management by identifying those with low risk of lung cancer when bronchoscopy is inconclusive. The BGC wa...
Autores principales: | , , , , , , , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7579926/ https://www.ncbi.nlm.nih.gov/pubmed/33087128 http://dx.doi.org/10.1186/s12920-020-00782-1 |
_version_ | 1783598692942479360 |
---|---|
author | Choi, Yoonha Qu, Jianghan Wu, Shuyang Hao, Yangyang Zhang, Jiarui Ning, Jianchang Yang, Xinwu Lofaro, Lori Pankratz, Daniel G. Babiarz, Joshua Walsh, P. Sean Billatos, Ehab Lenburg, Marc E. Kennedy, Giulia C. McAuliffe, Jon Huang, Jing |
author_facet | Choi, Yoonha Qu, Jianghan Wu, Shuyang Hao, Yangyang Zhang, Jiarui Ning, Jianchang Yang, Xinwu Lofaro, Lori Pankratz, Daniel G. Babiarz, Joshua Walsh, P. Sean Billatos, Ehab Lenburg, Marc E. Kennedy, Giulia C. McAuliffe, Jon Huang, Jing |
author_sort | Choi, Yoonha |
collection | PubMed |
description | BACKGROUND: Bronchoscopy for suspected lung cancer has low diagnostic sensitivity, rendering many inconclusive results. The Bronchial Genomic Classifier (BGC) was developed to help with patient management by identifying those with low risk of lung cancer when bronchoscopy is inconclusive. The BGC was trained and validated on patients in the Airway Epithelial Gene Expression in the Diagnosis of Lung Cancer (AEGIS) trials. A modern patient cohort, the BGC Registry, showed differences in key clinical factors from the AEGIS cohorts, with less smoking history, smaller nodules and older age. Additionally, we discovered interfering factors (inhaled medication and sample collection timing) that impacted gene expressions and potentially disguised genomic cancer signals. METHODS: In this study, we leveraged multiple cohorts and next generation sequencing technology to develop a robust Genomic Sequencing Classifier (GSC). To address demographic composition shift and interfering factors, we synergized three algorithmic strategies: 1) ensemble of clinical dominant and genomic dominant models; 2) development of hierarchical regression models where the main effects from clinical variables were regressed out prior to the genomic impact being fitted in the model; and 3) targeted placement of genomic and clinical interaction terms to stabilize the effect of interfering factors. The final GSC model uses 1232 genes and four clinical covariates – age, pack-years, inhaled medication use, and specimen collection timing. RESULTS: In the validation set (N = 412), the GSC down-classified low and intermediate pre-test risk subjects to very low and low post-test risk with a specificity of 45% (95% CI 37–53%) and a sensitivity of 91% (95%CI 81–97%), resulting in a negative predictive value of 95% (95% CI 89–98%). Twelve percent of intermediate pre-test risk subjects were up-classified to high post-test risk with a positive predictive value of 65% (95%CI 44–82%), and 27% of high pre-test risk subjects were up-classified to very high post-test risk with a positive predictive value of 91% (95% CI 78–97%). CONCLUSIONS: The GSC overcame the impact of interfering factors and achieved consistent performance across multiple cohorts. It demonstrated diagnostic accuracy in both down- and up-classification of cancer risk, providing physicians actionable information for many patients with inconclusive bronchoscopy. |
format | Online Article Text |
id | pubmed-7579926 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-75799262020-10-22 Improving lung cancer risk stratification leveraging whole transcriptome RNA sequencing and machine learning across multiple cohorts Choi, Yoonha Qu, Jianghan Wu, Shuyang Hao, Yangyang Zhang, Jiarui Ning, Jianchang Yang, Xinwu Lofaro, Lori Pankratz, Daniel G. Babiarz, Joshua Walsh, P. Sean Billatos, Ehab Lenburg, Marc E. Kennedy, Giulia C. McAuliffe, Jon Huang, Jing BMC Med Genomics Research BACKGROUND: Bronchoscopy for suspected lung cancer has low diagnostic sensitivity, rendering many inconclusive results. The Bronchial Genomic Classifier (BGC) was developed to help with patient management by identifying those with low risk of lung cancer when bronchoscopy is inconclusive. The BGC was trained and validated on patients in the Airway Epithelial Gene Expression in the Diagnosis of Lung Cancer (AEGIS) trials. A modern patient cohort, the BGC Registry, showed differences in key clinical factors from the AEGIS cohorts, with less smoking history, smaller nodules and older age. Additionally, we discovered interfering factors (inhaled medication and sample collection timing) that impacted gene expressions and potentially disguised genomic cancer signals. METHODS: In this study, we leveraged multiple cohorts and next generation sequencing technology to develop a robust Genomic Sequencing Classifier (GSC). To address demographic composition shift and interfering factors, we synergized three algorithmic strategies: 1) ensemble of clinical dominant and genomic dominant models; 2) development of hierarchical regression models where the main effects from clinical variables were regressed out prior to the genomic impact being fitted in the model; and 3) targeted placement of genomic and clinical interaction terms to stabilize the effect of interfering factors. The final GSC model uses 1232 genes and four clinical covariates – age, pack-years, inhaled medication use, and specimen collection timing. RESULTS: In the validation set (N = 412), the GSC down-classified low and intermediate pre-test risk subjects to very low and low post-test risk with a specificity of 45% (95% CI 37–53%) and a sensitivity of 91% (95%CI 81–97%), resulting in a negative predictive value of 95% (95% CI 89–98%). Twelve percent of intermediate pre-test risk subjects were up-classified to high post-test risk with a positive predictive value of 65% (95%CI 44–82%), and 27% of high pre-test risk subjects were up-classified to very high post-test risk with a positive predictive value of 91% (95% CI 78–97%). CONCLUSIONS: The GSC overcame the impact of interfering factors and achieved consistent performance across multiple cohorts. It demonstrated diagnostic accuracy in both down- and up-classification of cancer risk, providing physicians actionable information for many patients with inconclusive bronchoscopy. BioMed Central 2020-10-22 /pmc/articles/PMC7579926/ /pubmed/33087128 http://dx.doi.org/10.1186/s12920-020-00782-1 Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. 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 in a credit line to the data. |
spellingShingle | Research Choi, Yoonha Qu, Jianghan Wu, Shuyang Hao, Yangyang Zhang, Jiarui Ning, Jianchang Yang, Xinwu Lofaro, Lori Pankratz, Daniel G. Babiarz, Joshua Walsh, P. Sean Billatos, Ehab Lenburg, Marc E. Kennedy, Giulia C. McAuliffe, Jon Huang, Jing Improving lung cancer risk stratification leveraging whole transcriptome RNA sequencing and machine learning across multiple cohorts |
title | Improving lung cancer risk stratification leveraging whole transcriptome RNA sequencing and machine learning across multiple cohorts |
title_full | Improving lung cancer risk stratification leveraging whole transcriptome RNA sequencing and machine learning across multiple cohorts |
title_fullStr | Improving lung cancer risk stratification leveraging whole transcriptome RNA sequencing and machine learning across multiple cohorts |
title_full_unstemmed | Improving lung cancer risk stratification leveraging whole transcriptome RNA sequencing and machine learning across multiple cohorts |
title_short | Improving lung cancer risk stratification leveraging whole transcriptome RNA sequencing and machine learning across multiple cohorts |
title_sort | improving lung cancer risk stratification leveraging whole transcriptome rna sequencing and machine learning across multiple cohorts |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7579926/ https://www.ncbi.nlm.nih.gov/pubmed/33087128 http://dx.doi.org/10.1186/s12920-020-00782-1 |
work_keys_str_mv | AT choiyoonha improvinglungcancerriskstratificationleveragingwholetranscriptomernasequencingandmachinelearningacrossmultiplecohorts AT qujianghan improvinglungcancerriskstratificationleveragingwholetranscriptomernasequencingandmachinelearningacrossmultiplecohorts AT wushuyang improvinglungcancerriskstratificationleveragingwholetranscriptomernasequencingandmachinelearningacrossmultiplecohorts AT haoyangyang improvinglungcancerriskstratificationleveragingwholetranscriptomernasequencingandmachinelearningacrossmultiplecohorts AT zhangjiarui improvinglungcancerriskstratificationleveragingwholetranscriptomernasequencingandmachinelearningacrossmultiplecohorts AT ningjianchang improvinglungcancerriskstratificationleveragingwholetranscriptomernasequencingandmachinelearningacrossmultiplecohorts AT yangxinwu improvinglungcancerriskstratificationleveragingwholetranscriptomernasequencingandmachinelearningacrossmultiplecohorts AT lofarolori improvinglungcancerriskstratificationleveragingwholetranscriptomernasequencingandmachinelearningacrossmultiplecohorts AT pankratzdanielg improvinglungcancerriskstratificationleveragingwholetranscriptomernasequencingandmachinelearningacrossmultiplecohorts AT babiarzjoshua improvinglungcancerriskstratificationleveragingwholetranscriptomernasequencingandmachinelearningacrossmultiplecohorts AT walshpsean improvinglungcancerriskstratificationleveragingwholetranscriptomernasequencingandmachinelearningacrossmultiplecohorts AT billatosehab improvinglungcancerriskstratificationleveragingwholetranscriptomernasequencingandmachinelearningacrossmultiplecohorts AT lenburgmarce improvinglungcancerriskstratificationleveragingwholetranscriptomernasequencingandmachinelearningacrossmultiplecohorts AT kennedygiuliac improvinglungcancerriskstratificationleveragingwholetranscriptomernasequencingandmachinelearningacrossmultiplecohorts AT mcauliffejon improvinglungcancerriskstratificationleveragingwholetranscriptomernasequencingandmachinelearningacrossmultiplecohorts AT huangjing improvinglungcancerriskstratificationleveragingwholetranscriptomernasequencingandmachinelearningacrossmultiplecohorts |