Background The cervicovaginal microbiome (CVM) plays a significant role in women’s cervical health and disease. Microbial alterations at the species level and characteristic community state types (CST) have been associated with acquisition and persistence of high-risk human papillomavirus (hrHPV) infections that may result in progression of cervical lesions to malignancy. Current sequencing methods, especially most commonly used multiplex 16S rRNA gene sequencing, struggle to fully clarify these changes because they generally fail to provide sufficient taxonomic resolution to adequately perform species-level associative studies. To improve CVM species designation, we designed a novel sequencing tool targeting microbes at the species taxonomic rank and examined its potential for profiling the CVM. Results We introduce an accessible and practical circular probe-based RNA sequencing (CiRNAseq) technology with the potential to profile and quantify the CVM. In vitro and in silico validations demonstrate that CiRNAseq can distinctively detect species in a mock mixed microbial environment, with the output data reflecting its ability to estimate microbes’ abundance. Moreover, compared to 16S rRNA gene sequencing, CiRNAseq provides equivalent results but with improved sequencing sensitivity. Analyses of a cohort of cervical smears from hrHPV-negative women versus hrHPV-positive women with high-grade cervical intraepithelial neoplasia confirmed known differences in CST occurring in the CVM of women with hrHPV-induced lesions. The technique also revealed variations in microbial diversity and abundance in the CVM of hrHPV-positive women when compared to hrHPV-negative women. Conclusions CiRNAseq is a promising tool for studying the interplay between the CVM and hrHPV in cervical carcinogenesis. This technology could provide a better understanding of cervicovaginal CST and microbial species during health and disease, prompting the discovery of biomarkers, additional to hrHPV, that can help detect high-grade cervical lesions.

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.