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DOI	10.1073/pnas.2026663118
	Computational modeling of ovarian cancer dynamics suggests optimal strategies for therapy and screening
	Gu S.; Lheureux S.; Sayad A.; Cybulska P.; Hogen L.; Vyarvelska I.; Tu D.; Parulekar W.R.; Nankivell M.; Kehoe S.; Chi D.S.; Levine D.A.; Bernardini M.Q.; Rosen B.; Oza A.; Brown M.; Neel B.G.
发表日期	2021
ISSN	0027-8424
卷号	118 期号:25
英文摘要	High-grade serous tubo-ovarian carcinoma (HGSC) is a major cause of cancer-related death. Treatment is not uniform, with some patients undergoing primary debulking surgery followed by chemotherapy (PDS) and others being treated directly with chemotherapy and only having surgery after three to four cycles (NACT). Which strategy is optimal remains controversial. We developed a mathematical framework that simulates hierarchical or stochastic models of tumor initiation and reproduces the clinical course of HGSC. After estimating parameter values, we infer that most patients harbor chemoresistant HGSC cells at diagnosis and that, if the tumor burden is not too large and complete debulking can be achieved, PDS is superior to NACT due to better depletion of resistant cells. We further predict that earlier diagnosis of primary HGSC, followed by complete debulking, could improve survival, but its benefit in relapsed patients is likely to be limited. These predictions are supported by primary clinical data from multiple cohorts. Our results have clear implications for these key issues in HGSC management. © 2021 National Academy of Sciences. All rights reserved.
英文关键词	computational model; neoadjuvant chemotherapy; Ovarian cancer; primary debunking surgery
语种	英语
scopus关键词	Article; cancer screening; cancer survival; cancer therapy; cohort analysis; female; high grade serous tubo ovarian carcinoma; human; mathematical model; ovary carcinoma; tumor volume
来源期刊	Proceedings of the National Academy of Sciences of the United States of America
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/238890
作者单位	Princess Margaret Cancer Center, University Health Network, Toronto, ON M5G 2M9, Canada; Department of Medical Biophysics, University of Toronto, Toronto, ON M5G 1L7, Canada; Division of Medical Oncology and Hematology, University of Toronto, Toronto, ON M5G 2M9, Canada; Division of Gynecologic Oncology, University of Toronto, Toronto, ON M5G 2M9, Canada; Canadian Cancer Trials Group, Queens University, Kingston, ON K7L 3N6, Canada; Medical Research Council Clinical Trials Unit, University College London, London, WC1V6LJ, United Kingdom; Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, B152TT, United Kingdom; Gynecology Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY 10065, United States; Department of Obstetrics and Gynecology, Weill Cornell Medical College, New York, NY 10021, United States; Laura and Isaac Perlmutter Cancer Center, New York University–Langone Medical Center, New York, NY 10016, United States; Center for Functio...
推荐引用方式 GB/T 7714	Gu S.,Lheureux S.,Sayad A.,et al. Computational modeling of ovarian cancer dynamics suggests optimal strategies for therapy and screening[J],2021,118(25).
APA	Gu S..,Lheureux S..,Sayad A..,Cybulska P..,Hogen L..,...&Neel B.G..(2021).Computational modeling of ovarian cancer dynamics suggests optimal strategies for therapy and screening.Proceedings of the National Academy of Sciences of the United States of America,118(25).
MLA	Gu S.,et al."Computational modeling of ovarian cancer dynamics suggests optimal strategies for therapy and screening".Proceedings of the National Academy of Sciences of the United States of America 118.25(2021).