Background
Uterine cervical cancer is one of the most leading causes of cancer
death among women worldwide, especially among women in developing
countries1,2. According to the updated data from
International Agency for Research on Cancer (IARC)3,
it is estimated that of all the 604,127 new cases and 341,831
deaths4,5 of cervical cancer worldwide in 2020, and
more than 1/6 of those cases occurred in China6. It
was well acknowledged that persistent infection with high-risk human
papillomavirus (hrHPV) is the cause of almost all cervical
cancer7. However, only a small proportion of hrHPV
infections persist8 and develop into cervical squamous
intraepithelial lesion (SIL) which may, if left untreated, progress to
cancer.
In 2019, the World Health Organization issued a call for action to
eliminate cervical cancer9, and then advocated a
series of approaches includes increasing HPV vaccine
coverage10, increasing screening of women aged more
than 30 years with hrHPV testing and treatment for hrHPV-positive
(hrHPV+) women that are suspicious of cervical cancer precursor lesions.
However, great discrepancies exist in the popularity and quality of
screening methods. A combination of hrHPV+ DNA testing and cytology
detection has been implemented as a routine classifier for cervical
cancer screening in many countries, there are still several limitations
including that cytology is subjective and requirement for pathologists,
and hrHPV test with low specificity and might result in high colposcopy
referral 8. Although colposcopy is helpful to detect
the cervical lesion, most HPV infections will not give rise to
(pre)malignant disease, increased unnecessary colposcopy referrals would
lead to unnecessary treatment and further would negatively affect
childbearing11,12. Thus, an adequate screening
classifier is urgently needed for women with abnormal HPV and cytology
results13.
An objective triage strategy which could be automated and incorporated
molecular test combining with HPV detection might be able to solve these
issues. As a primary form of epigenetic inheritance14,
DNA methylation15,16 has been extensively studied and
widely used for tumor classification17,18, early
detection19, therapy target, and predictive
biomarker20,21 of metastasis and
recurrence22. What’s more, methylation assays can be
automated, have accurate quantitation, are robust to operator variations
and can be performed in the same specimen as the HPV testing. Of the
more than 100 human methylation biomarker23,24 genes
detected so far in cervical tissue13, several
biomarkers have been repeated shown to have elevated methylation in
cervical cancers and high-grade squamous intraepithelial lesion, which
including ZNF67117, TERT20,
SOX120, CADM125,
MAL25, FAM19A426,
miR-124-227, PAX128,
JAM329, and EPB41L3 30.
The S5 methylation classifier is a test based on DNA methylation of the
late regions L1 and L2 of HPV16, HPV18, HPV31 and HPV33 combined with
the promoter region of human tumor suppressor gene
EPB41L330 that identifies women with HSIL or more
worse lesions. Here, S5 was developed in a colposcopy study, and well
tested in the UK, Canada, and Mexico.
Although earlier studies in developed countries have shown that the
performance of the S5 classifier to detect precancer lesion, there are
few studies validating S5 in developing countries and such low and
middle incomes settings. In this study, we figured out the efficiency
and potential of S5 methylation in a screening population in China,
including the 2246 women with abnormal results between HPV and cytology
(2246 women who had any abnormal results for HPV and cytology). We
compared the ability of the S5 methylation, repeated conventional
cytology and HPV16/18 genotyping in detecting HSIL+ cases at the
timepoint of 6 months, 12 months, 18 months and 24 months within the
2-year endpoint, among women with abnormal results between HPV and
cytology selected from a multi-center study (the study recruited women
from routine opportunistic screening services of China, a middle-income
country) and attempted to find the evidence to support S5 as an optimal
triage classifier.