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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; ... Neel, BG; + view all (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 (PNAS) , 118 (25) , Article e2026663118. 10.1073/pnas.2026663118. Green open access

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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.

Type: Article
Title: Computational modeling of ovarian cancer dynamics suggests optimal strategies for therapy and screening
Open access status: An open access version is available from UCL Discovery
DOI: 10.1073/pnas.2026663118
Publisher version: http://doi.org/10.1073/pnas.2026663118
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Science & Technology, Multidisciplinary Sciences, Science & Technology - Other Topics, ovarian cancer, computational, neoadjuvant chemotherapy, primary debunking surgery, PRIMARY DEBULKING SURGERY, NEOADJUVANT CHEMOTHERAPY, CISPLATIN RESISTANCE, SURGICAL CYTOREDUCTION, PERITONEAL CARCINOMA, DRUG-RESISTANCE, TIME-INTERVAL, CELL-LINES, EXPRESSION, EVOLUTION
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Inst of Clinical Trials and Methodology
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Inst of Clinical Trials and Methodology > MRC Clinical Trials Unit at UCL
URI: https://discovery.ucl.ac.uk/id/eprint/10137016
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