Purpose: To determine the efficacy of the therapeutic DNA vaccine GX-188E for inducing regression of cervical intraepithelial neoplasia (CIN) 3. Experimental Design: We conducted a prospective, randomized, multicenter, open-label, phase 2 clinical trial of GX-188E in CIN3 patients positive for HPV type 16/18. The primary endpoint was to determine the histopathological regression to less than or equal to CIN1 at visit 7 (V7; 20 weeks after the first GX-188E injection), and an extension study was pursued until visit 8 (V8; 36 weeks after the first GX-188E injection). HPV sequencing analysis and an ex vivo IFN-γ ELISPOT assay were performed using the collected cervical biopsy and blood samples from patients. Results: In total, 72 patients were enrolled and underwent randomization. Of them, 64 patients were included in per-protocol analysis (V7) and 52 in extension analysis (V8). Our data showed 52% (33/64) at V7 and 67% (35/52) of patients at V8 presented histopathological regression after receiving the GX-188E injection. We found that 73% (V7) and 77% (V8) of the patients with histological regression showed HPV clearance. HPV clearance and histopathological regression were significantly associated at V7 and at V8. Compared to the measurements at V1 (baseline), the patients at V8 with HPV clearance showed significantly higher fold-changes in their IFN-γ ELISPOT responses compared to those without HPV clearance. The HPV sequence analysis revealed that the HPV type 16 E6/E7 variants D25E, V83L, and N29S were inversely associated with histopathological regression at V8. Conclusions: GX-188E is an effective therapeutic vaccine against a cohort containing only CIN3 patients.

Background: Human papillomavirus (HPV) is known to be more prevalent in spontaneous abortions than in elective terminations of pregnancy. More recently, placental infection with HPV was shown to be associated with spontaneous preterm delivery. However, no study has evaluated the prevalence of HPV infection in pregnant Korean females and its association with adverse pregnancy outcomes.Methods: We conducted a cross-sectional study of 311 females who gave birth at Korea University Medical Center. Our sample included 45 preterm deliveries, 50 cases of premature rupture of the membranes (PROM), 21 preeclampsia cases, and 8 gestational diabetes mellitus (GDM) patients. We used the Hybrid Capture II system to detect high-risk (HR)-HPV infection at six weeks postpartum.Results: The prevalence of HR-HPV infection was 14.1%. Women with HR-HPV infection had a higher incidence of PROM than those without HR-HPV. HR-HPV infection was associated with an increased risk of PROM (OR, 2.380; 95% CI, 1.103-5.134). The prevalence of preterm delivery, preeclampsia, or GDM was not different between the two groups.Conclusions: We observed a high prevalence of HR-HPV infection in pregnant women. Moreover, HR-HPV infection was associated with a risk of PROM at term. Further studies are needed to evaluate mechanisms by which HR-HPV infection induces PROM.

Cho HW, et al. J Gynecol Oncol. 2021 Oct;33(1):e4. https://doi.org/10.3802/jgo.2022.33.e4

Min KJ, et al. J Gynecol Oncol. 2015 Jul;26(3):232-239. https://doi.org/10.3802/jgo.2015.26.3.232

Chung YW, et al. J Gynecol Oncol. 2018 Aug;30:e75. https://doi.org/10.3802/jgo.2019.30.e75

Cervical cancer, the fourth most common cancer among women worldwide, often proves fatal and stems from precursor lesions caused by high-risk human papillomavirus (HR-HPV) infection. Accurate and early diagnosis is crucial for effective treatment. Current screening methods, such as the Pap test, liquid-based cytology (LBC), visual inspection with acetic acid (VIA), and HPV DNA testing, have limitations, requiring confirmation through colposcopy. This study introduces CerviCARE AI, an artificial intelligence (AI) analysis software, to address colposcopy challenges. It automatically analyzes Tele-cervicography images, distinguishing between low-grade and high-grade lesions. In a multicenter retrospective study, CerviCARE AI achieved a remarkable sensitivity of 98% for high-risk groups (P2, P3, HSIL or higher, CIN2 or higher) and a specificity of 95.5%. These findings underscore CerviCARE AI's potential as a valuable diagnostic tool for highly accurate identification of cervical precancerous lesions. While further prospective research is needed to validate its clinical utility, this AI system holds promise for improving cervical cancer screening and lessening the burden of this deadly disease.