Computational modeling of ovarian cancer dynamics suggests optimal strategies for therapy and screening Journal Article


Authors: 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.
Article Title: Computational modeling of ovarian cancer dynamics suggests optimal strategies for therapy and screening
Abstract: 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.
Keywords: cancer survival; ovarian cancer; tumor volume; cohort analysis; cancer screening; cancer therapy; mathematical model; ovary carcinoma; neoadjuvant chemotherapy; computational model; human; female; article; primary debunking surgery; high grade serous tubo ovarian carcinoma
Journal Title: Proceedings of the National Academy of Sciences of the United States of America
Volume: 118
Issue: 25
ISSN: 0027-8424
Publisher: National Academy of Sciences  
Date Published: 2021-06-22
Start Page: e2026663118
Language: English
DOI: 10.1073/pnas.2026663118
PROVIDER: scopus
PMCID: PMC8237655
PUBMED: 34161278
DOI/URL:
Notes: Article -- Export Date: 1 July 2021 -- Source: Scopus
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  1. Dennis S Chi
    707 Chi