A systematic review and meta-analysis of the prevalence of therapeutic targets in cervical cancer

Abstract

Cervical Cancer (CC) is a significantly prevalent disease in developing countries. Currently, targeted therapies are not a primary standard of care in CC. This information could be crucial for developing directed therapies and patient screening for biomarkers that would allow personalised treatment of CC. This systematic review aimed to estimate the prevalence of potential therapeutic targets such as the epidermal growth factor receptor (EGFR) and the PI3K/Akt/mTOR and Ras/Raf/MAPK pathways in patients with CC, identified through genomic and non-genomic testing. Studies were identified through an ad-hoc search strategy from the available on MEDLINE (Ovid), CENTRAL, LILACS, SCOPUS, through the Clinical Trial registry on Clinicaltrials.gov, International Clinical Trials Registry Platform, RENIS (Argentine National Registry of Health Research) and grey literature sources. We included 74 studies which represented a total pool of 7,862 participants. Forty-five studies informed mutations of EGFR, with a combined positivity rate of 53% (95%CI: 45%-60%; I² = 95%). Twenty studies informed the presence of mutations in PIK3CA with a combined positivity rate of 30% (95%CI: 21%-39%; I² = 96%). Twenty-three studies reported a mutation in Ras, with a combined positivity rate of 14% (95%CI: 8%-21%; I² = 95%). Raf mutations were informed in six studies. Six studies informed the presence of Akt mutations, two studies informed mTOR mutations and only one study reported mutations of MAPK. The most frequently described therapeutic targets were EGFR, and the PIK3CA and Ras pathways, though inconsistency in positivity rates was significant. Our study did not allow the identification of any specific clinical characteristics that might explain the observed heterogeneity. Despite the overall good quality of the included studies, the applicability of these results to patients' general population with CC is still unclear.

Keywords: cervical cancer; cervical neoplasia; mutation; prevalence; therapeutic targets.

Figure 1.. Meta-analysis of the EGFR positivity rates.

Figure 2.. Subgroup analysis. Positivity for EGFR according to the type of detection test (genomic versus non-genomic).

Figure 3.. Analysis of the effect of small studies on the EGFR positivity rate (assessment of publication bias).

Figure 4.. Meta-analysis of the PI3K positivity ratios.

Figure 5.. Subgroup analysis. Positivity for PI3K according to the type of histology.

Figure 6.. Analysis of the effect of small studies on the PI3K positivity rate (assessment of publication bias).

Figure 7.. Meta-analysis of the Ras positivity proportions.

Figure 8.. Subgroup analysis. Positivity for Ras according to the histological type.

Figure 9.. Subgroup analysis. Positivity for Ras according to the type of detection test (genomic versus non-genomic).

Figure 10.. Analysis of the effect of small studies on the Ras positivity rate (assessment of publication bias).

Figure 11.. Meta-analysis of the positivity proportions of Akt.

Figure 12.. Meta-analysis of the Raf positivity ratios.

Figure 1.. PRISMA flow diagram.

Figure 2.. Critical appraisal. Adaptation of the criteria of the ‘Joanna Briggs Institute Critical Appraisal Checklist for Prevalence Studies’.: 1. Was the sample frame appropriate to address the target population?: 2. Were study participants recruited in an appropriate way?: 3. Was the sample size adequate?: 4. Were the study subjects and setting described in detail?: 5. Was data analysis conducted with sufficient coverage of the identified sample?: 6. Was the condition measured in a standard, reliable way for the variable of interest in all cases?: 7. Was there appropriate statistical analysis?