Targeted RNA next generation sequencing analysis of cervical smears can predict the presence of hrHPV-induced cervical lesions.
Cervical intraepithelial neoplasia
High risk human papilloma virus
Machine learning
Screening
Targeted RNA sequencing
Journal
BMC medicine
ISSN: 1741-7015
Titre abrégé: BMC Med
Pays: England
ID NLM: 101190723
Informations de publication
Date de publication:
09 06 2022
09 06 2022
Historique:
received:
11
02
2022
accepted:
26
04
2022
entrez:
8
6
2022
pubmed:
9
6
2022
medline:
11
6
2022
Statut:
epublish
Résumé
Because most cervical cancers are caused by high-risk human papillomaviruses (hrHPVs), cervical cancer prevention programs increasingly employ hrHPV testing as a primary test. The high sensitivity of HPV tests is accompanied by low specificity, resulting in high rates of overdiagnosis and overtreatment. Targeted circular probe-based RNA next generation sequencing (ciRNAseq) allows for the quantitative detection of RNAs of interest with high sequencing depth. Here, we examined the potential of ciRNAseq-testing on cervical scrapes to identify hrHPV-positive women at risk of having or developing high-grade cervical intraepithelial neoplasia (CIN). We performed ciRNAseq on 610 cervical scrapes from the Dutch cervical cancer screening program to detect gene expression from 15 hrHPV genotypes and from 429 human genes. Differentially expressed hrHPV- and host genes in scrapes from women with outcome "no CIN" or "CIN2+" were identified and a model was built to distinguish these groups. Apart from increasing percentages of hrHPV oncogene expression from "no CIN" to high-grade cytology/histology, we identified genes involved in cell cycle regulation, tyrosine kinase signaling pathways, immune suppression, and DNA repair being expressed at significantly higher levels in scrapes with high-grade cytology and histology. Machine learning using random forest on all the expression data resulted in a model that detected 'no CIN' versus CIN2+ in an independent data set with sensitivity and specificity of respectively 85 ± 8% and 72 ± 13%. CiRNAseq on exfoliated cells in cervical scrapes measures hrHPV-(onco)gene expression and host gene expression in one single assay and in the process identifies HPV genotype. By combining these data and applying machine learning protocols, the risk of CIN can be calculated. Because ciRNAseq can be performed in high-throughput, making it cost-effective, it can be a promising screening technology to stratify women at risk of CIN2+. Further increasing specificity by model improvement in larger cohorts is warranted.
Sections du résumé
BACKGROUND
Because most cervical cancers are caused by high-risk human papillomaviruses (hrHPVs), cervical cancer prevention programs increasingly employ hrHPV testing as a primary test. The high sensitivity of HPV tests is accompanied by low specificity, resulting in high rates of overdiagnosis and overtreatment. Targeted circular probe-based RNA next generation sequencing (ciRNAseq) allows for the quantitative detection of RNAs of interest with high sequencing depth. Here, we examined the potential of ciRNAseq-testing on cervical scrapes to identify hrHPV-positive women at risk of having or developing high-grade cervical intraepithelial neoplasia (CIN).
METHODS
We performed ciRNAseq on 610 cervical scrapes from the Dutch cervical cancer screening program to detect gene expression from 15 hrHPV genotypes and from 429 human genes. Differentially expressed hrHPV- and host genes in scrapes from women with outcome "no CIN" or "CIN2+" were identified and a model was built to distinguish these groups.
RESULTS
Apart from increasing percentages of hrHPV oncogene expression from "no CIN" to high-grade cytology/histology, we identified genes involved in cell cycle regulation, tyrosine kinase signaling pathways, immune suppression, and DNA repair being expressed at significantly higher levels in scrapes with high-grade cytology and histology. Machine learning using random forest on all the expression data resulted in a model that detected 'no CIN' versus CIN2+ in an independent data set with sensitivity and specificity of respectively 85 ± 8% and 72 ± 13%.
CONCLUSIONS
CiRNAseq on exfoliated cells in cervical scrapes measures hrHPV-(onco)gene expression and host gene expression in one single assay and in the process identifies HPV genotype. By combining these data and applying machine learning protocols, the risk of CIN can be calculated. Because ciRNAseq can be performed in high-throughput, making it cost-effective, it can be a promising screening technology to stratify women at risk of CIN2+. Further increasing specificity by model improvement in larger cohorts is warranted.
Identifiants
pubmed: 35676700
doi: 10.1186/s12916-022-02386-1
pii: 10.1186/s12916-022-02386-1
pmc: PMC9178797
doi:
Substances chimiques
RNA
63231-63-0
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
206Informations de copyright
© 2022. The Author(s).
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