A Blood-Based Metabolite Panel for Distinguishing Ovarian Cancer from Benign Pelvic Masses.


Journal

Clinical cancer research : an official journal of the American Association for Cancer Research
ISSN: 1557-3265
Titre abrégé: Clin Cancer Res
Pays: United States
ID NLM: 9502500

Informations de publication

Date de publication:
01 11 2022
Historique:
received: 06 04 2022
revised: 06 06 2022
accepted: 24 08 2022
pubmed: 30 8 2022
medline: 3 11 2022
entrez: 29 8 2022
Statut: ppublish

Résumé

To assess the contributions of circulating metabolites for improving upon the performance of the risk of ovarian malignancy algorithm (ROMA) for risk prediction of ovarian cancer among women with ovarian cysts. Metabolomic profiling was performed on an initial set of sera from 101 serous and nonserous ovarian cancer cases and 134 individuals with benign pelvic masses (BPM). Using a deep learning model, a panel consisting of seven cancer-related metabolites [diacetylspermine, diacetylspermidine, N-(3-acetamidopropyl)pyrrolidin-2-one, N-acetylneuraminate, N-acetyl-mannosamine, N-acetyl-lactosamine, and hydroxyisobutyric acid] was developed for distinguishing early-stage ovarian cancer from BPM. The performance of the metabolite panel was evaluated in an independent set of sera from 118 ovarian cancer cases and 56 subjects with BPM. The contributions of the panel for improving upon the performance of ROMA were further assessed. A 7-marker metabolite panel (7MetP) developed in the training set yielded an AUC of 0.86 [95% confidence interval (CI): 0.76-0.95] for early-stage ovarian cancer in the independent test set. The 7MetP+ROMA model had an AUC of 0.93 (95% CI: 0.84-0.98) for early-stage ovarian cancer in the test set, which was improved compared with ROMA alone [0.91 (95% CI: 0.84-0.98); likelihood ratio test P: 0.03]. In the entire specimen set, the combined 7MetP+ROMA model yielded a higher positive predictive value (0.68 vs. 0.52; one-sided P < 0.001) with improved specificity (0.89 vs. 0.78; one-sided P < 0.001) for early-stage ovarian cancer compared with ROMA alone. A blood-based metabolite panel was developed that demonstrates independent predictive ability and complements ROMA for distinguishing early-stage ovarian cancer from benign disease to better inform clinical decision making.

Identifiants

pubmed: 36037307
pii: 708818
doi: 10.1158/1078-0432.CCR-22-1113
pmc: PMC9633421
mid: NIHMS1834140
doi:

Substances chimiques

CA-125 Antigen 0
WAP Four-Disulfide Core Domain Protein 2 0
Proteins 0
Biomarkers, Tumor 0

Types de publication

Journal Article Research Support, Non-U.S. Gov't Research Support, N.I.H., Extramural

Langues

eng

Sous-ensembles de citation

IM

Pagination

4669-4676

Subventions

Organisme : NCI NIH HHS
ID : P30 CA016672
Pays : United States
Organisme : NCI NIH HHS
ID : P50 CA140388
Pays : United States
Organisme : NCI NIH HHS
ID : P50 CA217685
Pays : United States
Organisme : NCATS NIH HHS
ID : UL1 TR003167
Pays : United States
Organisme : NCI NIH HHS
ID : U01 CA200462
Pays : United States

Informations de copyright

©2022 American Association for Cancer Research.

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Auteurs

Ehsan Irajizad (E)

Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas.

Chae Y Han (CY)

Department of Experimental Therapeutics, The University of Texas MD Anderson Cancer Center, Houston, Texas.

Joseph Celestino (J)

Department of Experimental Therapeutics, The University of Texas MD Anderson Cancer Center, Houston, Texas.

Ranran Wu (R)

Department of Clinical Cancer Prevention, The University of Texas MD Anderson Cancer Center, Houston, Texas.

Eunice Murage (E)

Department of Clinical Cancer Prevention, The University of Texas MD Anderson Cancer Center, Houston, Texas.

Rachelle Spencer (R)

Department of Clinical Cancer Prevention, The University of Texas MD Anderson Cancer Center, Houston, Texas.

Jennifer B Dennison (JB)

Department of Clinical Cancer Prevention, The University of Texas MD Anderson Cancer Center, Houston, Texas.

Jody Vykoukal (J)

Department of Clinical Cancer Prevention, The University of Texas MD Anderson Cancer Center, Houston, Texas.

James P Long (JP)

Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas.

Kim Anh Do (KA)

Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas.

Charles Drescher (C)

Translational Research Program, Fred Hutchinson Cancer Research Center, Seattle, Washington.
Division of Gynecologic Oncology, Swedish Cancer Institute, Seattle, Washington.

Karen Lu (K)

Department of Gynecological Oncology and Reproductive Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas.

Zhen Lu (Z)

Department of Gynecological Oncology and Reproductive Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas.

Robert C Bast (RC)

Department of Gynecological Oncology and Reproductive Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas.

Sam Hanash (S)

Department of Clinical Cancer Prevention, The University of Texas MD Anderson Cancer Center, Houston, Texas.

Johannes F Fahrmann (JF)

Department of Clinical Cancer Prevention, The University of Texas MD Anderson Cancer Center, Houston, Texas.

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Classifications MeSH