Abigail D Winder1, Kruti P Maniar2, Jian-Jun Wei2, Dachao Liu3, Denise M Scholtens3, John R Lurain1, Julian C Schink4, Barbara M Buttin1, Virginia L Filiaci5,6, Heather A Lankes5,6, Nilsa C Ramirez7, Kay Park8, Meenakshi Singh9, Richard W Lieberman10,11, Robert S Mannel12, Matthew A Powell13, Floor J Backes14, Cara A Mathews15, Michael L Pearl16, Angeles Alvarez Secord17, David J Peace18, David G Mutch13, William T Creasman19, J Julie Kim20. 1. Division of Gynecologic Oncology, Department of Obstetrics and Gynecology, Northwestern University, Chicago, Illinois. 2. Division of Surgical Pathology, Department of Pathology, Northwestern University, Chicago, Illinois. 3. Division of Biostatistics, Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois. 4. Division of Gynecologic Oncology, Department of Obstetrics and Gynecology, Spectrum Health, Grand Rapids, Michigan. 5. Statistics and Data Management Center, NRG Oncology, Buffalo, New York. 6. Roswell Park Cancer Institute, Buffalo, New York. 7. Biopathology Center and Gynecologic Oncology Group Tissue Bank, The Research Institute at Nationwide Children's Hospital, Columbus, Ohio. 8. Department of Pathology, Memorial Sloan-Kettering Cancer Center, New York, New York. 9. Department of Pathology, University of Kansas School of Medicine and Medical Center, Kansas City, Kansas. 10. Department of Obstetrics and Gynecology, University of Michigan Health System, Ann Arbor, Michigan. 11. Department of Pathology, University of Michigan Health System, Ann Arbor, Michigan. 12. Department of Gynecologic Oncology, The Peggy and Charles Stephenson Cancer Center, The University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma. 13. Division of Gynecologic Oncology, Washington University School of Medicine, St Louis, Missouri. 14. Division of Gynecologic Oncology, Ohio State University and James Comprehensive Cancer Center, Columbus, Ohio. 15. Division of Obstetrics and Gynecology, Women and Infants Hospital, Providence, Rhode Island. 16. Department of Obstetrics, Gynecology, and Reproductive Medicine, Stony Brook University Medical Center, Stony Brook, New York. 17. Department of Obstetrics/Gynecology, Division of Gynecology Oncology, Duke University Medical Center, Durham, North Carolina. 18. Division of Hematology/Oncology, University of Illinois Hospital and Health Sciences System, Chicago, Illinois. 19. Department of Obstetrics and Gynecology, Medical University of South Carolina, Charleston, South Carolina. 20. Division of Reproductive Science in Medicine, Department of Obstetrics and Gynecology, Northwestern University, Chicago, Illinois.
Abstract
BACKGROUND: Synuclein-γ (SNCG) is highly expressed in advanced solid tumors, including uterine serous carcinoma (USC). The objective of the current study was to determine whether SNCG protein was associated with survival and clinical covariates using the largest existing collection of USCs from the Gynecologic Oncology Group (GOG-8023). METHODS: High-density tissue microarrays (TMAs) of tumor tissues from 313 patients with USC were stained by immunohistochemistry for SNCG, p53, p16, FOLR1, pERK, pAKT, ER, PR, and HER2/neu. Associations of SNCG and other tumor markers with overall and progression-free survival were assessed using log-rank tests and Cox proportional-hazards models, which also were adjusted for age, race, and stage. RESULTS: The overall survival at 5 years was 46% for women with high SNCG expression and 62% for those with low SNCG expression (log-rank P = .021; hazard ratio [HR], 1.31; 95% confidence interval [CI], 0.91-1.9 in adjusted Cox model). The progression-free survival rate at 5 years was worse for women who had high SNCG expression, at 40%, compared with 56% for those who had low SNCG expression (log-rank P = .0081; HR, 1.36; 95% CI, 0.96-1.92 in adjusted Cox model). High levels of both p53 and p16 were significantly associated with worse overall survival (p53: HR, 4.20 [95% CI, 1.54-11.45]; p16: HR, 1.95 [95% CI, 1.01-3.75]) and progression-free survival (p53: HR, 2.16 [95% CI, 1.09-4.27]; p16: HR, 1.53 [95% CI, 0.87-2.69]) compared with low levels. CONCLUSIONS: This largest collection of USCs to date demonstrates that SNCG was associated with poor survival in univariate analyses. SNCG does not predict survival outcome independent of p53 and p16 in models that jointly consider multiple markers. Cancer 2017;123:1144-1155.
BACKGROUND: Synuclein-γ (SNCG) is highly expressed in advanced solid tumors, including uterine serous carcinoma (USC). The objective of the current study was to determine whetherSNCGprotein was associated with survival and clinical covariates using the largest existing collection of USCs from the Gynecologic Oncology Group (GOG-8023). METHODS: High-density tissue microarrays (TMAs) of tumor tissues from 313 patients with USC were stained by immunohistochemistry for SNCG, p53, p16, FOLR1, pERK, pAKT, ER, PR, and HER2/neu. Associations of SNCG and othertumor markers with overall and progression-free survival were assessed using log-rank tests and Cox proportional-hazards models, which also were adjusted for age, race, and stage. RESULTS: The overall survival at 5 years was 46% for women with high SNCG expression and 62% for those with low SNCG expression (log-rank P = .021; hazard ratio [HR], 1.31; 95% confidence interval [CI], 0.91-1.9 in adjusted Cox model). The progression-free survival rate at 5 years was worse for women who had high SNCG expression, at 40%, compared with 56% for those who had low SNCG expression (log-rank P = .0081; HR, 1.36; 95% CI, 0.96-1.92 in adjusted Cox model). High levels of both p53 and p16 were significantly associated with worse overall survival (p53: HR, 4.20 [95% CI, 1.54-11.45]; p16: HR, 1.95 [95% CI, 1.01-3.75]) and progression-free survival (p53: HR, 2.16 [95% CI, 1.09-4.27]; p16: HR, 1.53 [95% CI, 0.87-2.69]) compared with low levels. CONCLUSIONS: This largest collection of USCs to date demonstrates that SNCG was associated with poor survival in univariate analyses. SNCG does not predict survival outcome independent of p53 and p16 in models that jointly consider multiple markers. Cancer 2017;123:1144-1155.
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