A Blood-Based Metabolite Panel for Distinguishing Ovarian Cancer from Benign Pelvic Masses
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 nonsero...
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| Vydané v: | Clinical cancer research Ročník 28; číslo 21; s. 4669 |
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| Hlavní autori: | , , , , , , , , , , , , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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United States
01.11.2022
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| ISSN: | 1557-3265, 1557-3265 |
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| Abstract | 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. |
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| AbstractList | 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.PURPOSETo 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.EXPERIMENTAL DESIGNMetabolomic 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.RESULTSA 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.CONCLUSIONSA 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. 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. |
| Author | Fahrmann, Johannes F Lu, Zhen Do, Kim Anh Vykoukal, Jody Irajizad, Ehsan Hanash, Sam Long, James P Han, Chae Y Spencer, Rachelle Murage, Eunice Celestino, Joseph Drescher, Charles Dennison, Jennifer B Bast, Robert C Wu, Ranran Lu, Karen |
| Author_xml | – sequence: 1 givenname: Ehsan orcidid: 0000-0001-7510-4849 surname: Irajizad fullname: Irajizad, Ehsan organization: Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas – sequence: 2 givenname: Chae Y orcidid: 0000-0002-5196-1579 surname: Han fullname: Han, Chae Y organization: Department of Experimental Therapeutics, The University of Texas MD Anderson Cancer Center, Houston, Texas – sequence: 3 givenname: Joseph orcidid: 0000-0002-7758-1558 surname: Celestino fullname: Celestino, Joseph organization: Department of Experimental Therapeutics, The University of Texas MD Anderson Cancer Center, Houston, Texas – sequence: 4 givenname: Ranran orcidid: 0000-0002-6276-1425 surname: Wu fullname: Wu, Ranran organization: Department of Clinical Cancer Prevention, The University of Texas MD Anderson Cancer Center, Houston, Texas – sequence: 5 givenname: Eunice orcidid: 0000-0001-9630-3046 surname: Murage fullname: Murage, Eunice organization: Department of Clinical Cancer Prevention, The University of Texas MD Anderson Cancer Center, Houston, Texas – sequence: 6 givenname: Rachelle orcidid: 0000-0002-1573-6120 surname: Spencer fullname: Spencer, Rachelle organization: Department of Clinical Cancer Prevention, The University of Texas MD Anderson Cancer Center, Houston, Texas – sequence: 7 givenname: Jennifer B orcidid: 0000-0003-3067-0972 surname: Dennison fullname: Dennison, Jennifer B organization: Department of Clinical Cancer Prevention, The University of Texas MD Anderson Cancer Center, Houston, Texas – sequence: 8 givenname: Jody orcidid: 0000-0001-7797-627X surname: Vykoukal fullname: Vykoukal, Jody organization: Department of Clinical Cancer Prevention, The University of Texas MD Anderson Cancer Center, Houston, Texas – sequence: 9 givenname: James P orcidid: 0000-0001-5853-5938 surname: Long fullname: Long, James P organization: Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas – sequence: 10 givenname: Kim Anh orcidid: 0000-0001-8710-7131 surname: Do fullname: Do, Kim Anh organization: Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas – sequence: 11 givenname: Charles orcidid: 0000-0002-6579-0416 surname: Drescher fullname: Drescher, Charles organization: Division of Gynecologic Oncology, Swedish Cancer Institute, Seattle, Washington – sequence: 12 givenname: Karen orcidid: 0000-0002-5317-9927 surname: Lu fullname: Lu, Karen organization: Department of Gynecological Oncology and Reproductive Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas – sequence: 13 givenname: Zhen orcidid: 0000-0002-9596-0148 surname: Lu fullname: Lu, Zhen organization: Department of Gynecological Oncology and Reproductive Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas – sequence: 14 givenname: Robert C orcidid: 0000-0003-4621-8462 surname: Bast fullname: Bast, Robert C organization: Department of Gynecological Oncology and Reproductive Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas – sequence: 15 givenname: Sam orcidid: 0000-0002-4210-1593 surname: Hanash fullname: Hanash, Sam organization: Department of Clinical Cancer Prevention, The University of Texas MD Anderson Cancer Center, Houston, Texas – sequence: 16 givenname: Johannes F orcidid: 0000-0001-5088-0198 surname: Fahrmann fullname: Fahrmann, Johannes F organization: Department of Clinical Cancer Prevention, The University of Texas MD Anderson Cancer Center, Houston, Texas |
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| Title | A Blood-Based Metabolite Panel for Distinguishing Ovarian Cancer from Benign Pelvic Masses |
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