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

Elkin, Rena, Oh, Jung Hun, Liu, Ying L, Selenica, Pier, Weigelt, Britta, Reis-Filho, Jorge S, Zamarin, Dmitriy, Deasy, Joseph O, Norton, Larry, Levine, Arnold J, Tannenbaum, Allen R (November 2021) Geometric network analysis provides prognostic information in patients with high grade serous carcinoma of the ovary treated with immune checkpoint inhibitors. npj Genomic Medicine, 6 (1). p. 99. ISSN 2056-7944

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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.

Item Type:	Paper
Subjects:	bioinformatics diseases & disorders > cancer diseases & disorders bioinformatics > computational biology diseases & disorders > cancer > cancer types > ovarian cancer diseases & disorders > cancer > drugs and therapies > patient outcomes diseases & disorders > cancer > cancer types
CSHL Authors:	Norton, Larry
Communities:	CSHL labs > Wigler lab
SWORD Depositor:	CSHL Elements
Depositing User:	CSHL Elements
Date:	24 November 2021
Date Deposited:	09 Dec 2021 20:28
Last Modified:	23 Jan 2024 19:58
PMCID:	PMC8613272
URI:	https://repository.cshl.edu/id/eprint/40454

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