The shift towards primary human papillomavirus (HPV)‐based screening has necessitated the search for a secondary triage test that provides sufficient sensitivity to detect high‐grade cervical intraepithelial neoplasia (CIN) and cancer, but also brings an improved specificity to avoid unnecessary clinical work and colposcopy referrals. We evaluated the performance of the previously described DNA‐methylation test (S5) in detecting CIN3 and cancers from diverse geographic settings in high‐, medium‐ and low‐income countries, using the cut‐off of 0.80 and exploratory cut‐offs of 2.62 and 3.70. Assays were performed using exfoliated cervical specimens (n = 808) and formalin‐fixed biopsies (n = 166) from women diagnosed with cytology‐negative results (n = 220), CIN3 (n = 204) and cancer stages I (n = 245), II (n = 249), III (n = 28) and IV (n = 22). Methylation increased proportionally with disease severity (Cuzick test for trend, P < .0001). S5 accurately separated women with negative‐histology from CIN3 or cancer (P < .0001). At the 0.80 cut‐off, 543/544 cancers were correctly identified as S5 positive (99.81%). At cut‐off 3.70, S5 showed a sensitivity of 95.77% with improved specificity. The S5 odds ratios of women negative for cervical disease vs CIN3+ were significantly higher than for HPV16/18 genotyping at all cut‐offs (all P < .0001). At S5 cut‐off 0.80, 96.15% of consistently high‐risk human papillomavirus (hrHPV)‐negative cancers (tested with multiple hrHPV‐genotyping assay) were positive by S5. These cancers may have been missed in current primary hrHPV‐screening programmes. The S5 test can accurately detect CIN3 and malignancy irrespective of geographic context and setting. The test can be used as a screening and triage tool. Adjustment of the S5 cut‐off can be performed considering the relative importance given to sensitivity vs specificity.

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tumours, 1 primary juvenile granulosa cell tumour and 1 primary Sertoli-Leydig cell tumour. Three samples were obtained from treatment-naïve GCT (2 immature teratomas and one dysgerminoma). For each phenotype of tumour cells, immune cells, endothelial cells and cancer-associated fibroblasts, we identified specific transcriptomic markers. Results Based on differential expression analysis and expression of transcriptomic markers, we identified 27 clusters consisting of 9 tumour cell and 18 stromal cell clusters. The first results of subcluster analysis revealed nearly absence of B cells in all granulosa cell tumours. In addition, the immune cell subcluster mainly consists of T cells derived from the dysgerminoma (58%) and Sertoli-Leydig cell (20%) samples. Further characterisation and differentiation of distinct subclusters is currently ongoing and will be presented.Conclusion With this analysis we aim to generate a publicly accessible comprehensive blueprint of the tumour micro-environment, aiding other researchers to gain high-resolution insights in the heterogeneity and complexity of these rare ovarian cancers.