Identification of transcriptional subtypes in lung adenocarcinoma and squamous cell carcinoma through integrative analysis of microarray and RNA sequencing data

Abstract Classification of tumors into subtypes can inform personalized approaches to treatment including the choice of targeted therapies. The two most common lung cancer histological subtypes, lung adenocarcinoma and lung squamous cell carcinoma, have been previously divided into transcriptional s...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	François Fauteux, Anuradha Surendra, Scott McComb, Youlian Pan, Jennifer J. Hill
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/fc18a6f066cc476284b3a6f6ed028a10
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:fc18a6f066cc476284b3a6f6ed028a10
record_format	dspace
spelling	oai:doaj.org-article:fc18a6f066cc476284b3a6f6ed028a102021-12-02T17:32:57ZIdentification of transcriptional subtypes in lung adenocarcinoma and squamous cell carcinoma through integrative analysis of microarray and RNA sequencing data10.1038/s41598-021-88209-42045-2322https://doaj.org/article/fc18a6f066cc476284b3a6f6ed028a102021-04-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-88209-4https://doaj.org/toc/2045-2322Abstract Classification of tumors into subtypes can inform personalized approaches to treatment including the choice of targeted therapies. The two most common lung cancer histological subtypes, lung adenocarcinoma and lung squamous cell carcinoma, have been previously divided into transcriptional subtypes using microarray data, and corresponding signatures were subsequently used to classify RNA-seq data. Cross-platform unsupervised classification facilitates the identification of robust transcriptional subtypes by combining vast amounts of publicly available microarray and RNA-seq data. However, cross-platform classification is challenging because of intrinsic differences in data generated using the two gene expression profiling technologies. In this report, we show that robust gene expression subtypes can be identified in integrated data representing over 3500 normal and tumor lung samples profiled using two widely used platforms, Affymetrix HG-U133 Plus 2.0 Array and Illumina HiSeq RNA sequencing. We tested and analyzed consensus clustering for 384 combinations of data processing methods. The agreement between subtypes identified in single-platform and cross-platform normalized data was then evaluated using a variety of statistics. Results show that unsupervised learning can be achieved with combined microarray and RNA-seq data using selected preprocessing, cross-platform normalization, and unsupervised feature selection methods. Our analysis confirmed three lung adenocarcinoma transcriptional subtypes, but only two consistent subtypes in squamous cell carcinoma, as opposed to four subtypes previously identified. Further analysis showed that tumor subtypes were associated with distinct patterns of genomic alterations in genes coding for therapeutic targets. Importantly, by integrating quantitative proteomics data, we were able to identify tumor subtype biomarkers that effectively classify samples on the basis of both gene and protein expression. This study provides the basis for further integrative data analysis across gene and protein expression profiling platforms.François FauteuxAnuradha SurendraScott McCombYoulian PanJennifer J. HillNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q François Fauteux Anuradha Surendra Scott McComb Youlian Pan Jennifer J. Hill Identification of transcriptional subtypes in lung adenocarcinoma and squamous cell carcinoma through integrative analysis of microarray and RNA sequencing data
description	Abstract Classification of tumors into subtypes can inform personalized approaches to treatment including the choice of targeted therapies. The two most common lung cancer histological subtypes, lung adenocarcinoma and lung squamous cell carcinoma, have been previously divided into transcriptional subtypes using microarray data, and corresponding signatures were subsequently used to classify RNA-seq data. Cross-platform unsupervised classification facilitates the identification of robust transcriptional subtypes by combining vast amounts of publicly available microarray and RNA-seq data. However, cross-platform classification is challenging because of intrinsic differences in data generated using the two gene expression profiling technologies. In this report, we show that robust gene expression subtypes can be identified in integrated data representing over 3500 normal and tumor lung samples profiled using two widely used platforms, Affymetrix HG-U133 Plus 2.0 Array and Illumina HiSeq RNA sequencing. We tested and analyzed consensus clustering for 384 combinations of data processing methods. The agreement between subtypes identified in single-platform and cross-platform normalized data was then evaluated using a variety of statistics. Results show that unsupervised learning can be achieved with combined microarray and RNA-seq data using selected preprocessing, cross-platform normalization, and unsupervised feature selection methods. Our analysis confirmed three lung adenocarcinoma transcriptional subtypes, but only two consistent subtypes in squamous cell carcinoma, as opposed to four subtypes previously identified. Further analysis showed that tumor subtypes were associated with distinct patterns of genomic alterations in genes coding for therapeutic targets. Importantly, by integrating quantitative proteomics data, we were able to identify tumor subtype biomarkers that effectively classify samples on the basis of both gene and protein expression. This study provides the basis for further integrative data analysis across gene and protein expression profiling platforms.
format	article
author	François Fauteux Anuradha Surendra Scott McComb Youlian Pan Jennifer J. Hill
author_facet	François Fauteux Anuradha Surendra Scott McComb Youlian Pan Jennifer J. Hill
author_sort	François Fauteux
title	Identification of transcriptional subtypes in lung adenocarcinoma and squamous cell carcinoma through integrative analysis of microarray and RNA sequencing data
title_short	Identification of transcriptional subtypes in lung adenocarcinoma and squamous cell carcinoma through integrative analysis of microarray and RNA sequencing data
title_full	Identification of transcriptional subtypes in lung adenocarcinoma and squamous cell carcinoma through integrative analysis of microarray and RNA sequencing data
title_fullStr	Identification of transcriptional subtypes in lung adenocarcinoma and squamous cell carcinoma through integrative analysis of microarray and RNA sequencing data
title_full_unstemmed	Identification of transcriptional subtypes in lung adenocarcinoma and squamous cell carcinoma through integrative analysis of microarray and RNA sequencing data
title_sort	identification of transcriptional subtypes in lung adenocarcinoma and squamous cell carcinoma through integrative analysis of microarray and rna sequencing data
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/fc18a6f066cc476284b3a6f6ed028a10
work_keys_str_mv	AT francoisfauteux identificationoftranscriptionalsubtypesinlungadenocarcinomaandsquamouscellcarcinomathroughintegrativeanalysisofmicroarrayandrnasequencingdata AT anuradhasurendra identificationoftranscriptionalsubtypesinlungadenocarcinomaandsquamouscellcarcinomathroughintegrativeanalysisofmicroarrayandrnasequencingdata AT scottmccomb identificationoftranscriptionalsubtypesinlungadenocarcinomaandsquamouscellcarcinomathroughintegrativeanalysisofmicroarrayandrnasequencingdata AT youlianpan identificationoftranscriptionalsubtypesinlungadenocarcinomaandsquamouscellcarcinomathroughintegrativeanalysisofmicroarrayandrnasequencingdata AT jenniferjhill identificationoftranscriptionalsubtypesinlungadenocarcinomaandsquamouscellcarcinomathroughintegrativeanalysisofmicroarrayandrnasequencingdata
_version_	1718380161905197056

Identification of transcriptional subtypes in lung adenocarcinoma and squamous cell carcinoma through integrative analysis of microarray and RNA sequencing data

Ejemplares similares