Molecular markers for cervical cancer screening

Figures & data

Figure 1. Flow chart of Dutch population-based screening for cervical cancer (numbers are from 2019). ASCUS (Atypical Squamous Cells of Undetermined Significance) [4]

Figure 2. Progression from normal cervical tissue to CIN3 and further to invasive cervical cancer after an hrHPV infection. Women with a diagnosis of CIN1 are usually left untreated and invited for the next screening interval. The treatment of most women with a diagnosis of CIN2 is successful in preventing progression to the more severe stages, which has greatly diminished the incidence of cervical cancer and the associated mortality, mostly in high-income countries. Treatment success diminishes considerably upon further progression to cervical carcinoma. LBC: a method for preparing cervical samples in cytopathology; PAP: a screening test for the presence of HPV

Table

(PCR) based methods rely on the ability to rapidly amplify pre-determined regions of double-stranded DNA and generally involve the use of a heat-stable DNA polymerase. PCR-based methods are essential for cervical cancer research and screening and are used for hrHPV and miRNA detection as well as for the analysis of DNA methylation (see the section on epigenetic markers for more details). Immunoassays are based on the ability of an antibody (or another affinity ligand) to recognize and bind to a macromolecule (most often a protein) in solution and provide a detectable signal. Immunoassays can be scaled up to enable high-throughput screening. They are also compatible with PoC assay formats, such as lateral flow immunoassays. IHC is an immunoassay performed on tissue slices or cytological samples. It allows to detect protein markers in their cellular context and notably to detect cancer cells or cells that have the potential to progress to cancer cells. Mass-spectrometry assays allow to identify and quantify biomarkers in complex biological matrices based on their characteristic mass-to-charge ratio (m/z). To enhance specificity, molecules may be fragmented in the mass spectrometer to give product ions with characteristic m/z ratios. Hybrid LBA-MS assays combine the enrichment of target analytes (often proteins) by binding to an affinity ligand (usually an antibody), with MS or LC-MS analyses to achieve the required limits of detection and specificity. They combine the specificity of affinity enrichment with that of mass-spectrometry detection.

Table

The term epigenetics refers primarily to the regulation of gene expression through the methylation of cytosine at position 5 (^5mC). Methylation of cytosine regulates accessibility to certain regions of the chromosome and thus controls gene expression. However, epigenetic regulation comprises other mechanisms, such as the acetylation or methylation of lysine residues in histones, which also regulate access to specific chromosomal locations. Epigenetic methylation patterns define the fate of progenitor cells derived from stem cells in cellular differentiation and are thus an essential natural regulatory mechanism. Epigenetic modifications are inherited but not in a Mendelian manner. While often rather stable, they can be affected by environmental factors such as viral infections, lifestyle (e.g. diet) and may be related to carcinogenic transformations. Epigenetic regulatory mechanisms may be tackled by pharmaceuticals, for example, inhibitors of histone deacetylases or histone acetyltransferases. Particular methylation patterns have been associated with the transforming potential of CIN lesions (notably CIN2+) and the development of cervical cancer. Epigenetic changes occur much more rapidly than genetic changes and may thus allow prognosis of disease development earlier and in a more personalized manner than looking for DNA mutations. Epigenetic changes may facilitate DNA mutations due to the induction of DNA and chromosomal instability. Changes in methylation patterns are likely involved in all types of cancer and many clinically relevant relationships have been established. Molecular biology techniques allow measuring ^5mC site-specifically using robust PCR-based assays. This opens the way for developing diagnostic or prognostic assays on small amounts of tissue, cells or body fluids. There is an increase in ^5mC in CpG islands of both HPV as well as host DNA in persistent hrHPV infections that progress to CIN2+ and further to squamous cell cervical carcinoma. CIN2+ lesions with persistent hrHPV infection (> 5 years) and high copy number alterations show consistently higher levels of promotor region methylation than lesions with a shorter history of hrHPV infection (< 5 years) and low copy number alterations.

Figure 3. Schematic representation of epigenetic processes following infection with hrHPV. While 80% of infections are cleared by the immune system, 20% persist leading to CIN1/2 lesions. Most of these persistent infections will be resolved by the immune system with clearance of the infection. Deregulation of E6 and E7 expression is a critical event that changes a productive CIN1/2 lesion (one that produces viral particles without progressing) into a transforming CIN2+ lesion. Transforming CIN2+ lesions may carry an elevated risk of progressing to cancer during the ensuing screening interval (high short-term risk) or not (low short-term risk). The risk of progression is correlated with changes in the methylation status of both host and viral DNA. Hallmarks of a high-risk lesion are a persistent infection of more than five years, elevated copy number alterations, low expression of E4, and DNA hypermethylation, while low-risk lesions show the opposite characteristics (adapted from Kremer et al., Bjog 2021, 128(3):504–514)

Table 1. Selected DNA methylation studies with high-grade CIN and cervical cancer as endpoints.¹ All studies used QMSP as detection method

Genes	Sample Type	Sensitivity	Specificity	Number of Subjects	Endpoint	Comments	Reference
Panel of 12 genes^2	Cervical scrapes	74% (hrHPV: 79%)	76% (hrHPV: 42%)	appr. 200	Triage after PAP test for CIN2+	Discovery and initial validation	[93]
FAM19A4, mir124-2	Self-sampling (lavage & brushes)^3	Lavage: 70.5% Brush: 69.4%	Lavage: 67.8% Brush: 76.4%	Lavage: 182 (training set) Lavage: 384 (testing set) Brush: 224 (training set) Brush: 254 (testing set)	Triage after hrHPV test for CIN3 +^4	Validation of self-sampling	[94]
FAM19A4	Self-sampling (lavage) versus physician-sampling (scrapes)	78.5% (≥30 years; self) 88.2% (≥30 years; physician) 37.5% (≤30 years; self) 45.8% ((≤30 years; physician)	Generally higher in self-samples than in scrapes	532 (lavage & scrape – matched) – 450 admitted to colposcopy to establish CIN status	histologically confirmed CIN3+ (primary) CIN2+ (secondary)	No significant difference between self-sampling and physician-sampling	[95]
Panel of 6 genes^5	Cervical scrapes	60.0% 33.3%	84.1% 87.6%	appr. 200	CIN3+ CIN2+	Panel of 6 genes shows higher specificity for triage after hrHPV screen	[37]
FAM19A4, miR124-2^6		74.3% 27.8%	68.2% 67.4%		CIN3+ CIN2+
EPB41L3, viral late gene regions of HPV16, HPV18, HPV31 and HPV33	Exfoliated cervical specimens	74%	65% (91% at the sensitivity of the genotyping test)	7 (341 hrHPV positive)	Triage after hrHPV for CIN2+	Comparison with HPV16/18 genotyping	[96]
ASCL1, LHX8, ST6GALNAC5, GHSR, SST and ZIC1 (ASCL1, LHX8, ZIC1) (ASCL1/LHX8)	Cervical scrapes	83.4% (CIN3) for ASCL1/LHX8	82.4%	577 (352 ≤ CIN1; 175 CIN3; 50 CA)	Triage after hrHPV for CIN3	AUC: 0.882	[97]
miR-149, miR-20a, miR-93	Cervical scrapes	Not given	Not given	209	Triage after hrHPV for CIN3	AUC: 0.834^Footnote7 AUC: 0.939	[98]
ASTN1, DLX1, ITGA4, RXFP3, SOX17,ZNF671^9	Cervical Scrapes	61.2% 44.4% 1.5%	94.6%	632 (553 normal)	CIN3 CIN2 Normal	Comparison to hrHPV test and p16/Ki-67 dual stain IHC	[99]

Table

Protein Panel: Usually a relatively small set of proteins that optimally separates groups of patients that were stratified according to specific clinical criteria. Panels are assembled after multivariate statistical analysis to show that proteins contribute independently to the discrimination. Microbiome: Bacteria that co-exist at or in our body (e.g. skin, gut, nose, cervix). These bacteria live in symbiosis with the host organism in a healthy situation and protect it by performing indispensable metabolic reactions that the host organism is incapable of. These bacteria can be very specific for the place where they live and respond to changes in their environment, which may make them a source of biomarkers for a given disease. Integration of genetic and proteomic information: The integration of information from different layers of biology (genetics, epigenetics, transcriptomics, proteomics and metabolomics) may provide more complete and specific insights into biological processes including the development of a given disease. The fact that changes at the DNA and RNA level may translate into changes at the protein level, opens the possibility to create personalized databases for proteomics analysis, which may ultimately lead to individualized protein panels as biomarkers. Proteins associated with DNA replication: Cervical cancer is almost entirely due to infection with hrHPV. While infection with hrHPV is a necessary condition it does not allow the prediction of cancer development. Proteins that are involved in controlling cell proliferation and notably those that are involved in DNA replication, recombination, and repair are thus of particular interest as potential biomarkers.

Table 2. Selected studies based on proteins related to cell division and DNA replication. All studies used IHC as detection method. The p16/Ki-67 studies were performed with the CINTec® PLUS assay (Roche)

Proteins	Sample Type	Sensitivity	Specificity	Number of Subjects	Endpoint	Comments	Reference
p16/Ki-67 (IHC)	Pap cytology or LBC	86.7% (all women)^10 84.7% (women >30 ys)	95.2% (all women) 96.2% (women >30 years)	27349	CIN2+	International clinical study	[101]
p16/Ki-67 (IHC)	Pap cytology (subgroup used LBC)	91%	64%	2628	ASCUS^11	Review of 7 studies	[11]
p16/Ki-67 (IHC)	LBC	93.2% (< 30 ys) 97.2% (> 30 ys)	46.1% (< 30 ys) 60.0% (> 30 ys)	625	CIN3+	CINtec plus cytology assay	[]
p16/Ki-67 (IHC)	LBC	90.0%	63.0%	93	CIN2+	Small-scale study	[10]
p16/Ki-67 (IHC)	LBC (clinician collected) and self-collected vaginal samples	100% (LBC) 68.2% (vaginal samples)	79.6% (LBC) 84.9% (vaginal samples)	1005	HSIL	Study from Papua New-Guinea	[]
p16/Ki-67 (IHC)	LBC	92.0% 93.0% (PAP)	61.0% 49.0% (PAP)	495	CIN3+	hrHPV positive on self-sampled material	[110]
MCM, TOP2A (IHC)^12	LBC	85.3%	71.7%	317	HSIL	Comparison to cytology	[111]

Table

ICERs are used in conjunction with a cost-effectiveness threshold, indicating the maximum willingness to pay for an additional (quality-adjusted) life year. The highest ICER up to this threshold is optimal since it represents the most cost-effective intervention [16]. The most universally used threshold is applied by the WHO [16]. An intervention is considered highly cost-effective if the ICER is less than the country’s per capita GDP and cost-effective if the ICER is less than three times the per capita GDP depending on the guidelines of a particular country. Cost-effectiveness analyses generally use Quality Adjusted Life Years (QALYs) or Life Years Gained (LYG) as a measure. In QALY, a quality (of life) -adjustment weight or utility is applied to each relevant health state (0 equals death and 1 equals perfect health) [124]. A QALY is calculated by multiplying perfect health during 1-year with the utility (e.g. 1 QALY = 1 year of life in perfect health x 1 utility, 6 months of perfect life x 1 utility = 0.5 QALY). Simulation models like the MISCAN-Cervix microsimulation model generate a large hypothetical population with individual life histories to simulate a cohort of people. Microsimulation models can be used to calculate the cost-effectiveness of screening strategies in a population.

Table 3. Selected cost-effectiveness analyses that calculate the cost per QALY gained of primary HPV screening strategies in different countries

Country	Threshold	Optimal Strategy	Start/stop age	ICER
The Netherlands*	€ 20,000/QALY	Every 6 years	25-,27-30-32/31-70	€ 10,300/QALY
Taiwan*	$ 1,620/QALY	Every 3 years	30/70	$ 1,357/QALY
USA*	$ 50,000/QALY	Every 3 years	18/85	$ 9,900/QALY
Uganda**	$ 1,690/LYG^a	Self-collection	30/40	$ 80/LYG
Italy*	€ 50,000/QALY	Every 5 years	25/65	€ 5,800/QALY
Norway*	$ 83,000/LYG	Every 6 years	25/70	$ 80,000/LYG
Canada*	$ 50,000/QALY	Every 3 years	18–25/70	$ 47,300/QALY
UK*	£ 20,000–30,000/QALY	Every 3–5 years	25/64	£18,600/QALY
Germany*	€ 33,000/QALY	Every 3 years	20–25/lifetime	€ 28,400/QALY
Sweden*	€ 80,000/LYG	Every 5 years	32/60	€ 43,000/LYG

^a life-years gained

Sources: * [131.], ** [132]

* hrHPV only

** hrHPV triage

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