David Mysona1, Adam Pyrzak1, Sharad Purohit2, Wenbo Zhi1, Ashok Sharma3, Lynn Tran3, Paul Tran3, Shan Bai3, Bunja Rungruang4, Sharad Ghamande4, Jin-Xiong She5. 1. Center for Biotechnology and Genomic Medicine, Medical College of Georgia, Augusta University, United States of America; Department of Obstetrics and Gynecology, Medical College of Georgia, Augusta University, United States of America. 2. Center for Biotechnology and Genomic Medicine, Medical College of Georgia, Augusta University, United States of America; Department of Medical Laboratory, Imaging, & Radiologic Sciences, College of Allied Health Sciences, Augusta University, United States of America. 3. Center for Biotechnology and Genomic Medicine, Medical College of Georgia, Augusta University, United States of America. 4. Department of Obstetrics and Gynecology, Medical College of Georgia, Augusta University, United States of America. 5. Center for Biotechnology and Genomic Medicine, Medical College of Georgia, Augusta University, United States of America; Department of Obstetrics and Gynecology, Medical College of Georgia, Augusta University, United States of America. Electronic address: jshe@augusta.edu.
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 (ntotal = 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.
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 (ntotal = 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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