Cargando…

Clinical Interest of Combining Transcriptomic and Genomic Signatures in High-Grade Serous Ovarian Cancer

High-grade serous ovarian cancer is one of the deadliest gynecological malignancies and remains a clinical challenge. There is a critical need to effectively define patient stratification in a clinical setting. In this study, we address this question and determine the optimal number of molecular sub...

Descripción completa

Detalles Bibliográficos
Autores principales: Kieffer, Yann, Bonneau, Claire, Popova, Tatiana, Rouzier, Roman, Stern, Marc-Henri, Mechta-Grigoriou, Fatima
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7089941/
https://www.ncbi.nlm.nih.gov/pubmed/32256521
http://dx.doi.org/10.3389/fgene.2020.00219
_version_ 1783509824603947008
author Kieffer, Yann
Bonneau, Claire
Popova, Tatiana
Rouzier, Roman
Stern, Marc-Henri
Mechta-Grigoriou, Fatima
author_facet Kieffer, Yann
Bonneau, Claire
Popova, Tatiana
Rouzier, Roman
Stern, Marc-Henri
Mechta-Grigoriou, Fatima
author_sort Kieffer, Yann
collection PubMed
description High-grade serous ovarian cancer is one of the deadliest gynecological malignancies and remains a clinical challenge. There is a critical need to effectively define patient stratification in a clinical setting. In this study, we address this question and determine the optimal number of molecular subgroups for ovarian cancer patients. By studying several independent patient cohorts, we observed that classifying high-grade serous ovarian tumors into four molecular subgroups using a transcriptomic-based approach did not reproducibly predict patient survival. In contrast, classifying these tumors into only two molecular subgroups, fibrosis and non-fibrosis, could reliably inform on patient survival. In addition, we found complementarity between transcriptomic data and the genomic signature for homologous recombination deficiency (HRD) that helped in defining prognosis of ovarian cancer patients. We also established that the transcriptomic and genomic signatures underlined independent biological processes and defined four different risk populations. Thus, combining genomic and transcriptomic information appears as the most appropriate stratification method to reliably subgroup high-grade serous ovarian cancer patients. This method can easily be transferred into the clinical setting.
format Online
Article
Text
id pubmed-7089941
institution National Center for Biotechnology Information
language English
publishDate 2020
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-70899412020-03-31 Clinical Interest of Combining Transcriptomic and Genomic Signatures in High-Grade Serous Ovarian Cancer Kieffer, Yann Bonneau, Claire Popova, Tatiana Rouzier, Roman Stern, Marc-Henri Mechta-Grigoriou, Fatima Front Genet Genetics High-grade serous ovarian cancer is one of the deadliest gynecological malignancies and remains a clinical challenge. There is a critical need to effectively define patient stratification in a clinical setting. In this study, we address this question and determine the optimal number of molecular subgroups for ovarian cancer patients. By studying several independent patient cohorts, we observed that classifying high-grade serous ovarian tumors into four molecular subgroups using a transcriptomic-based approach did not reproducibly predict patient survival. In contrast, classifying these tumors into only two molecular subgroups, fibrosis and non-fibrosis, could reliably inform on patient survival. In addition, we found complementarity between transcriptomic data and the genomic signature for homologous recombination deficiency (HRD) that helped in defining prognosis of ovarian cancer patients. We also established that the transcriptomic and genomic signatures underlined independent biological processes and defined four different risk populations. Thus, combining genomic and transcriptomic information appears as the most appropriate stratification method to reliably subgroup high-grade serous ovarian cancer patients. This method can easily be transferred into the clinical setting. Frontiers Media S.A. 2020-03-17 /pmc/articles/PMC7089941/ /pubmed/32256521 http://dx.doi.org/10.3389/fgene.2020.00219 Text en Copyright © 2020 Kieffer, Bonneau, Popova, Rouzier, Stern and Mechta-Grigoriou. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Genetics
Kieffer, Yann
Bonneau, Claire
Popova, Tatiana
Rouzier, Roman
Stern, Marc-Henri
Mechta-Grigoriou, Fatima
Clinical Interest of Combining Transcriptomic and Genomic Signatures in High-Grade Serous Ovarian Cancer
title Clinical Interest of Combining Transcriptomic and Genomic Signatures in High-Grade Serous Ovarian Cancer
title_full Clinical Interest of Combining Transcriptomic and Genomic Signatures in High-Grade Serous Ovarian Cancer
title_fullStr Clinical Interest of Combining Transcriptomic and Genomic Signatures in High-Grade Serous Ovarian Cancer
title_full_unstemmed Clinical Interest of Combining Transcriptomic and Genomic Signatures in High-Grade Serous Ovarian Cancer
title_short Clinical Interest of Combining Transcriptomic and Genomic Signatures in High-Grade Serous Ovarian Cancer
title_sort clinical interest of combining transcriptomic and genomic signatures in high-grade serous ovarian cancer
topic Genetics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7089941/
https://www.ncbi.nlm.nih.gov/pubmed/32256521
http://dx.doi.org/10.3389/fgene.2020.00219
work_keys_str_mv AT kiefferyann clinicalinterestofcombiningtranscriptomicandgenomicsignaturesinhighgradeserousovariancancer
AT bonneauclaire clinicalinterestofcombiningtranscriptomicandgenomicsignaturesinhighgradeserousovariancancer
AT popovatatiana clinicalinterestofcombiningtranscriptomicandgenomicsignaturesinhighgradeserousovariancancer
AT rouzierroman clinicalinterestofcombiningtranscriptomicandgenomicsignaturesinhighgradeserousovariancancer
AT sternmarchenri clinicalinterestofcombiningtranscriptomicandgenomicsignaturesinhighgradeserousovariancancer
AT mechtagrigorioufatima clinicalinterestofcombiningtranscriptomicandgenomicsignaturesinhighgradeserousovariancancer