Introduction/Background The incidence of endometrial cancer is increasing worldwide. While delays in diagnosis reduce survival, case molecular misclassification might be associated with under- and over-treatment. The objective of this study was to evaluate genetic alterations to detect and molecularly classify cases of endometrial cancer using non-invasive samples.

Methodology Consecutive patients with incident endometrial cancer (N = 139) and controls (N = 107) from a recent Spanish case-control study were included in this analysis. Overall, 339 cervicovaginal samples (out of which 228 were clinician-collected and 111 were self-collected) were analysed using a test based on next-generation sequencing (NGS), which targets 47 genes. Immunohistochemical markers were evaluated in 133 tumour samples. A total of 159 samples were used to train the detection algorithm and 180 samples were used for validation.

Results Overall, 73% of endometrial cancer cases had detectable mutations in clinician-collected and self-collected samples, while the specificity was 80% (79/99) for clinician-collected samples and 90% (19/21) for self-collected samples. The molecular classifications obtained using tumour samples and non-invasive gynaecologic samples in our study showed moderate-to-good agreement. The molecular classification of cases into four groups using NGS of both clinician-collected and self-collected cervicovaginal samples yielded significant differences in disease-free survival. The cases with mutations in POLE had an excellent prognosis, whereas the cases with TP53 mutations had the poorest clinical outcome, which is consistent with data on tumour samples.

Conclusion This study classified endometrial cancer cases into four molecular groups based on the analysis of cervicovaginal samples that showed significant differences in disease-free survival. The molecular classification of endometrial cancer in non-invasive samples may improve patient care and survival by indicating the early need for aggressive surgery, as well as reducing referrals to highly specialized hospitals in cancers with good prognosis. Validation in independent sets will confirm the potential for molecular classification in non-invasive samples.

Disclosures This study was funded by a competitive grant from Instituto de Salud Carlos III through the projects PI19/01835, PI23/00790, and FI20/00031, CIBERESP CB06/02/0073 and CIBERONC CB16/12/00231, CB16/12/00234 (Co-funded by European Regional Development Fund. ERDF: A way to build Europe). Samples and data were provided by Biobank HUB-ICO-IDIBELL, integrated into the Spanish Biobank Network, and funded by the Instituto de Salud Carlos III (PT20/00171) and by Xarxa de Bancs de Tumors de Catalunya (XBTC) sponsored by Pla Director d’Oncologia de Catalunya. This work was supported in part by the AECC, Grupos estables (GCTRA18014MATI). It also counts with the support of the Secretariat for Universities and Research of the Department of Business and Knowledge of the Generalitat de Catalunya, and grants to support the activities of research groups 2021SGR01354 and 2021SGR1112.