Geometric network analysis provides prognostic information in patients with high grade serous carcinoma of the ovary treated with immune checkpoint inhibitors

Abstract Network analysis methods can potentially quantify cancer aberrations in gene networks without introducing fitted parameters or variable selection. A new network curvature-based method is introduced to provide an integrated measure of variability within cancer gene networks. The method is ap...

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Autores principales:	Rena Elkin, Jung Hun Oh, Ying L. Liu, Pier Selenica, Britta Weigelt, Jorge S. Reis-Filho, Dmitriy Zamarin, Joseph O. Deasy, Larry Norton, Arnold J. Levine, Allen R. Tannenbaum
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Genetics QH426-470
Acceso en línea:	https://doaj.org/article/14677a3e0f704e37a93284a2df2fb8ea
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spelling	oai:doaj.org-article:14677a3e0f704e37a93284a2df2fb8ea2021-11-28T12:42:13ZGeometric network analysis provides prognostic information in patients with high grade serous carcinoma of the ovary treated with immune checkpoint inhibitors10.1038/s41525-021-00259-92056-7944https://doaj.org/article/14677a3e0f704e37a93284a2df2fb8ea2021-11-01T00:00:00Zhttps://doi.org/10.1038/s41525-021-00259-9https://doaj.org/toc/2056-7944Abstract Network analysis methods can potentially quantify cancer aberrations in gene networks without introducing fitted parameters or variable selection. A new network curvature-based method is introduced to provide an integrated measure of variability within cancer gene networks. The method is applied to high-grade serous ovarian cancers (HGSOCs) to predict response to immune checkpoint inhibitors (ICIs) and to rank key genes associated with prognosis. Copy number alterations (CNAs) from targeted and whole-exome sequencing data were extracted for HGSOC patients (n = 45) treated with ICIs. CNAs at a gene level were represented on a protein–protein interaction network to define patient-specific networks with a fixed topology. A version of Ollivier–Ricci curvature was used to identify genes that play a potentially key role in response to immunotherapy and further to stratify patients at high risk of mortality. Overall survival (OS) was defined as the time from the start of ICI treatment to either death or last follow-up. Kaplan–Meier analysis with log-rank test was performed to assess OS between the high and low curvature classified groups. The network curvature analysis stratified patients at high risk of mortality with p = 0.00047 in Kaplan–Meier analysis in HGSOC patients receiving ICI. Genes with high curvature were in accordance with CNAs relevant to ovarian cancer. Network curvature using CNAs has the potential to be a novel predictor for OS in HGSOC patients treated with immunotherapy.Rena ElkinJung Hun OhYing L. LiuPier SelenicaBritta WeigeltJorge S. Reis-FilhoDmitriy ZamarinJoseph O. DeasyLarry NortonArnold J. LevineAllen R. TannenbaumNature PortfolioarticleMedicineRGeneticsQH426-470ENnpj Genomic Medicine, Vol 6, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Genetics QH426-470
spellingShingle	Medicine R Genetics QH426-470 Rena Elkin Jung Hun Oh Ying L. Liu Pier Selenica Britta Weigelt Jorge S. Reis-Filho Dmitriy Zamarin Joseph O. Deasy Larry Norton Arnold J. Levine Allen R. Tannenbaum Geometric network analysis provides prognostic information in patients with high grade serous carcinoma of the ovary treated with immune checkpoint inhibitors
description	Abstract Network analysis methods can potentially quantify cancer aberrations in gene networks without introducing fitted parameters or variable selection. A new network curvature-based method is introduced to provide an integrated measure of variability within cancer gene networks. The method is applied to high-grade serous ovarian cancers (HGSOCs) to predict response to immune checkpoint inhibitors (ICIs) and to rank key genes associated with prognosis. Copy number alterations (CNAs) from targeted and whole-exome sequencing data were extracted for HGSOC patients (n = 45) treated with ICIs. CNAs at a gene level were represented on a protein–protein interaction network to define patient-specific networks with a fixed topology. A version of Ollivier–Ricci curvature was used to identify genes that play a potentially key role in response to immunotherapy and further to stratify patients at high risk of mortality. Overall survival (OS) was defined as the time from the start of ICI treatment to either death or last follow-up. Kaplan–Meier analysis with log-rank test was performed to assess OS between the high and low curvature classified groups. The network curvature analysis stratified patients at high risk of mortality with p = 0.00047 in Kaplan–Meier analysis in HGSOC patients receiving ICI. Genes with high curvature were in accordance with CNAs relevant to ovarian cancer. Network curvature using CNAs has the potential to be a novel predictor for OS in HGSOC patients treated with immunotherapy.
format	article
author	Rena Elkin Jung Hun Oh Ying L. Liu Pier Selenica Britta Weigelt Jorge S. Reis-Filho Dmitriy Zamarin Joseph O. Deasy Larry Norton Arnold J. Levine Allen R. Tannenbaum
author_facet	Rena Elkin Jung Hun Oh Ying L. Liu Pier Selenica Britta Weigelt Jorge S. Reis-Filho Dmitriy Zamarin Joseph O. Deasy Larry Norton Arnold J. Levine Allen R. Tannenbaum
author_sort	Rena Elkin
title	Geometric network analysis provides prognostic information in patients with high grade serous carcinoma of the ovary treated with immune checkpoint inhibitors
title_short	Geometric network analysis provides prognostic information in patients with high grade serous carcinoma of the ovary treated with immune checkpoint inhibitors
title_full	Geometric network analysis provides prognostic information in patients with high grade serous carcinoma of the ovary treated with immune checkpoint inhibitors
title_fullStr	Geometric network analysis provides prognostic information in patients with high grade serous carcinoma of the ovary treated with immune checkpoint inhibitors
title_full_unstemmed	Geometric network analysis provides prognostic information in patients with high grade serous carcinoma of the ovary treated with immune checkpoint inhibitors
title_sort	geometric network analysis provides prognostic information in patients with high grade serous carcinoma of the ovary treated with immune checkpoint inhibitors
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/14677a3e0f704e37a93284a2df2fb8ea
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Geometric network analysis provides prognostic information in patients with high grade serous carcinoma of the ovary treated with immune checkpoint inhibitors

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