Details

Autor(en) / Beteiligte

• Beatriz Pelegrina
• Sonia Paytubi
• Fátima Marin
• José Manuel Martínez
• Álvaro Carmona
• Jon Frias-Gomez
• Paula Peremiquel-Trillas
• Eduard Dorca
• Alba Zanca
• Marta López-Querol
• Irene Onieva
• Yolanda Benavente
• Marc Barahona
• Sergi Fernandez-Gonzalez
• Javier De Francisco
• Víctor Caño
• August Vidal
• Lara Pijuan
• Júlia Canet-Hermida
• Núria Dueñas
• Joan Brunet
• Marta Pineda
• Xavier Matias-Guiu
• Jordi Ponce
• Francesc Xavier Bosch
• Silvia De Sanjosé
• Laia Alemany
• Laura Costas

Titel

Evaluation of somatic mutations in cervicovaginal samples as a non-invasive method for the detection and molecular classification of endometrial cancerResearch in context

Ist Teil von

• EBioMedicine, 2023-08, Vol.94, p.104716

Ort / Verlag

Elsevier

Erscheinungsjahr

2023

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• 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. Methods: 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. Findings: Overall, 73% (N = 94 out of 129 clinician-collected samples, and N = 66 out of 90 self-collected samples) 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 of endometrial cancer 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 the data on tumour samples. Interpretation: 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. Funding: 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.