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Evaluation of somatic mutations in cervicovaginal samples as a non-invasive method for the detection and molecular classification of endometrial cancer
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...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10393602/ https://www.ncbi.nlm.nih.gov/pubmed/37480623 http://dx.doi.org/10.1016/j.ebiom.2023.104716 |
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author | Pelegrina, Beatriz Paytubi, Sonia Marin, Fátima Martínez, José Manuel Carmona, Álvaro Frias-Gomez, Jon Peremiquel-Trillas, Paula Dorca, Eduard Zanca, Alba López-Querol, Marta Onieva, Irene Benavente, Yolanda Barahona, Marc Fernandez-Gonzalez, Sergi De Francisco, Javier Caño, Víctor Vidal, August Pijuan, Lara Canet-Hermida, Júlia Dueñas, Núria Brunet, Joan Pineda, Marta Matias-Guiu, Xavier Ponce, Jordi Bosch, Francesc Xavier De Sanjosé, Silvia Alemany, Laia Costas, Laura |
author_facet | Pelegrina, Beatriz Paytubi, Sonia Marin, Fátima Martínez, José Manuel Carmona, Álvaro Frias-Gomez, Jon Peremiquel-Trillas, Paula Dorca, Eduard Zanca, Alba López-Querol, Marta Onieva, Irene Benavente, Yolanda Barahona, Marc Fernandez-Gonzalez, Sergi De Francisco, Javier Caño, Víctor Vidal, August Pijuan, Lara Canet-Hermida, Júlia Dueñas, Núria Brunet, Joan Pineda, Marta Matias-Guiu, Xavier Ponce, Jordi Bosch, Francesc Xavier De Sanjosé, Silvia Alemany, Laia Costas, Laura |
author_sort | Pelegrina, Beatriz |
collection | PubMed |
description | 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 10.13039/501100004587Instituto 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 10.13039/501100008530European 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 10.13039/501100004587Instituto de Salud Carlos III (PT20/00171) and by 10.13039/501100014540Xarxa 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 10.13039/501100002809Generalitat de Catalunya, and grants to support the activities of research groups 2021SGR01354 and 2021SGR1112. |
format | Online Article Text |
id | pubmed-10393602 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-103936022023-08-03 Evaluation of somatic mutations in cervicovaginal samples as a non-invasive method for the detection and molecular classification of endometrial cancer Pelegrina, Beatriz Paytubi, Sonia Marin, Fátima Martínez, José Manuel Carmona, Álvaro Frias-Gomez, Jon Peremiquel-Trillas, Paula Dorca, Eduard Zanca, Alba López-Querol, Marta Onieva, Irene Benavente, Yolanda Barahona, Marc Fernandez-Gonzalez, Sergi De Francisco, Javier Caño, Víctor Vidal, August Pijuan, Lara Canet-Hermida, Júlia Dueñas, Núria Brunet, Joan Pineda, Marta Matias-Guiu, Xavier Ponce, Jordi Bosch, Francesc Xavier De Sanjosé, Silvia Alemany, Laia Costas, Laura eBioMedicine Articles 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 10.13039/501100004587Instituto 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 10.13039/501100008530European 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 10.13039/501100004587Instituto de Salud Carlos III (PT20/00171) and by 10.13039/501100014540Xarxa 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 10.13039/501100002809Generalitat de Catalunya, and grants to support the activities of research groups 2021SGR01354 and 2021SGR1112. Elsevier 2023-07-20 /pmc/articles/PMC10393602/ /pubmed/37480623 http://dx.doi.org/10.1016/j.ebiom.2023.104716 Text en © 2023 The Author(s) https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Articles Pelegrina, Beatriz Paytubi, Sonia Marin, Fátima Martínez, José Manuel Carmona, Álvaro Frias-Gomez, Jon Peremiquel-Trillas, Paula Dorca, Eduard Zanca, Alba López-Querol, Marta Onieva, Irene Benavente, Yolanda Barahona, Marc Fernandez-Gonzalez, Sergi De Francisco, Javier Caño, Víctor Vidal, August Pijuan, Lara Canet-Hermida, Júlia Dueñas, Núria Brunet, Joan Pineda, Marta Matias-Guiu, Xavier Ponce, Jordi Bosch, Francesc Xavier De Sanjosé, Silvia Alemany, Laia Costas, Laura Evaluation of somatic mutations in cervicovaginal samples as a non-invasive method for the detection and molecular classification of endometrial cancer |
title | Evaluation of somatic mutations in cervicovaginal samples as a non-invasive method for the detection and molecular classification of endometrial cancer |
title_full | Evaluation of somatic mutations in cervicovaginal samples as a non-invasive method for the detection and molecular classification of endometrial cancer |
title_fullStr | Evaluation of somatic mutations in cervicovaginal samples as a non-invasive method for the detection and molecular classification of endometrial cancer |
title_full_unstemmed | Evaluation of somatic mutations in cervicovaginal samples as a non-invasive method for the detection and molecular classification of endometrial cancer |
title_short | Evaluation of somatic mutations in cervicovaginal samples as a non-invasive method for the detection and molecular classification of endometrial cancer |
title_sort | evaluation of somatic mutations in cervicovaginal samples as a non-invasive method for the detection and molecular classification of endometrial cancer |
topic | Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10393602/ https://www.ncbi.nlm.nih.gov/pubmed/37480623 http://dx.doi.org/10.1016/j.ebiom.2023.104716 |
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