A combined score of clinical factors and serum proteins can predict time to recurrence in high grade serous ovarian cancer

David Mysona, Adam Pyrzak, Sharad B Purohit, Wenbo Zhi, Ashok Kumar Sharma, Lynn Tran, Paul Tran, Shan Bai, Bunja Jane Rungruang, Sharad A Ghamande, Jin-Xiong She

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

Objective: To investigate the utility of a combined panel of protein biomarkers and clinical factors to predict recurrence in serous ovarian cancer patients. Methods: Women at Augusta University diagnosed with ovarian cancer were enrolled between 2005 and 2015 (n = 71). Blood was drawn at enrollment and follow-up visits. Patient serum collected at remission was analyzed using the SOMAscan array (n = 35) to measure levels of 1129 proteins. The best 26 proteins were confirmed using Luminex assays in the same 35 patients and in an additional 36 patients (n total = 71) as orthogonal validation. The data from these 26 proteins was combined with clinical factors using an elastic net multivariate model to find an optimized combination predictive of progression-free survival (PFS). Results: Of the 26 proteins, Brain Derived Neurotrophic Factor and Platelet Derived Growth Factor molecules were significant for predicting PFS on both univariate and multivariate analyses. All 26 proteins were combined with clinical factors using the elastic net algorithm. Ten components were determined to predict PFS (HR of 6.55, p-value 1.12 × 10 −6 , CI 2.57–16.71). This model was named the serous high grade ovarian cancer (SHOC) score. Conclusion: The SHOC score can predict patient prognosis in remission. This tool will hopefully lead to early intervention and consolidation therapy strategies in remission patients destined to recur.

Original languageEnglish (US)
Pages (from-to)574-580
Number of pages7
JournalGynecologic Oncology
Volume152
Issue number3
DOIs
StatePublished - Mar 1 2019

Fingerprint

Ovarian Neoplasms
Blood Proteins
Recurrence
Disease-Free Survival
Proteins
Platelet-Derived Growth Factor
Brain-Derived Neurotrophic Factor
Multivariate Analysis
Biomarkers
Serum

Keywords

  • Biomarkers
  • Ovarian neoplasm
  • Prognosis
  • Serum proteomics

ASJC Scopus subject areas

  • Oncology
  • Obstetrics and Gynecology

Cite this

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title = "A combined score of clinical factors and serum proteins can predict time to recurrence in high grade serous ovarian cancer",
abstract = "Objective: To investigate the utility of a combined panel of protein biomarkers and clinical factors to predict recurrence in serous ovarian cancer patients. Methods: Women at Augusta University diagnosed with ovarian cancer were enrolled between 2005 and 2015 (n = 71). Blood was drawn at enrollment and follow-up visits. Patient serum collected at remission was analyzed using the SOMAscan array (n = 35) to measure levels of 1129 proteins. The best 26 proteins were confirmed using Luminex assays in the same 35 patients and in an additional 36 patients (n total = 71) as orthogonal validation. The data from these 26 proteins was combined with clinical factors using an elastic net multivariate model to find an optimized combination predictive of progression-free survival (PFS). Results: Of the 26 proteins, Brain Derived Neurotrophic Factor and Platelet Derived Growth Factor molecules were significant for predicting PFS on both univariate and multivariate analyses. All 26 proteins were combined with clinical factors using the elastic net algorithm. Ten components were determined to predict PFS (HR of 6.55, p-value 1.12 × 10 −6 , CI 2.57–16.71). This model was named the serous high grade ovarian cancer (SHOC) score. Conclusion: The SHOC score can predict patient prognosis in remission. This tool will hopefully lead to early intervention and consolidation therapy strategies in remission patients destined to recur.",
keywords = "Biomarkers, Ovarian neoplasm, Prognosis, Serum proteomics",
author = "David Mysona and Adam Pyrzak and Purohit, {Sharad B} and Wenbo Zhi and Sharma, {Ashok Kumar} and Lynn Tran and Paul Tran and Shan Bai and Rungruang, {Bunja Jane} and Ghamande, {Sharad A} and Jin-Xiong She",
year = "2019",
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T1 - A combined score of clinical factors and serum proteins can predict time to recurrence in high grade serous ovarian cancer

AU - Mysona, David

AU - Pyrzak, Adam

AU - Purohit, Sharad B

AU - Zhi, Wenbo

AU - Sharma, Ashok Kumar

AU - Tran, Lynn

AU - Tran, Paul

AU - Bai, Shan

AU - Rungruang, Bunja Jane

AU - Ghamande, Sharad A

AU - She, Jin-Xiong

PY - 2019/3/1

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N2 - Objective: To investigate the utility of a combined panel of protein biomarkers and clinical factors to predict recurrence in serous ovarian cancer patients. Methods: Women at Augusta University diagnosed with ovarian cancer were enrolled between 2005 and 2015 (n = 71). Blood was drawn at enrollment and follow-up visits. Patient serum collected at remission was analyzed using the SOMAscan array (n = 35) to measure levels of 1129 proteins. The best 26 proteins were confirmed using Luminex assays in the same 35 patients and in an additional 36 patients (n total = 71) as orthogonal validation. The data from these 26 proteins was combined with clinical factors using an elastic net multivariate model to find an optimized combination predictive of progression-free survival (PFS). Results: Of the 26 proteins, Brain Derived Neurotrophic Factor and Platelet Derived Growth Factor molecules were significant for predicting PFS on both univariate and multivariate analyses. All 26 proteins were combined with clinical factors using the elastic net algorithm. Ten components were determined to predict PFS (HR of 6.55, p-value 1.12 × 10 −6 , CI 2.57–16.71). This model was named the serous high grade ovarian cancer (SHOC) score. Conclusion: The SHOC score can predict patient prognosis in remission. This tool will hopefully lead to early intervention and consolidation therapy strategies in remission patients destined to recur.

AB - Objective: To investigate the utility of a combined panel of protein biomarkers and clinical factors to predict recurrence in serous ovarian cancer patients. Methods: Women at Augusta University diagnosed with ovarian cancer were enrolled between 2005 and 2015 (n = 71). Blood was drawn at enrollment and follow-up visits. Patient serum collected at remission was analyzed using the SOMAscan array (n = 35) to measure levels of 1129 proteins. The best 26 proteins were confirmed using Luminex assays in the same 35 patients and in an additional 36 patients (n total = 71) as orthogonal validation. The data from these 26 proteins was combined with clinical factors using an elastic net multivariate model to find an optimized combination predictive of progression-free survival (PFS). Results: Of the 26 proteins, Brain Derived Neurotrophic Factor and Platelet Derived Growth Factor molecules were significant for predicting PFS on both univariate and multivariate analyses. All 26 proteins were combined with clinical factors using the elastic net algorithm. Ten components were determined to predict PFS (HR of 6.55, p-value 1.12 × 10 −6 , CI 2.57–16.71). This model was named the serous high grade ovarian cancer (SHOC) score. Conclusion: The SHOC score can predict patient prognosis in remission. This tool will hopefully lead to early intervention and consolidation therapy strategies in remission patients destined to recur.

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