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Adaptive therapy for ovarian cancer: An integrated approach to PARP inhibitor scheduling.
Strobl, Maximilian; Martin, Alexandra L; West, Jeffrey; Gallaher, Jill; Robertson-Tessi, Mark; Gatenby, Robert; Wenham, Robert; Maini, Philip; Damaghi, Mehdi; Anderson, Alexander.
Afiliação
  • Strobl M; Department of Integrated Mathematical Oncology, Moffitt Cancer Center, Tampa, FL, USA.
  • Martin AL; Department of Obstetrics and Gynecology, University of Tennessee Health Science Center, Memphis, TN, USA.
  • West J; Division of Gynecologic Oncology, West Cancer Center and Research Institute, Memphis, TN, USA.
  • Gallaher J; Department of Integrated Mathematical Oncology, Moffitt Cancer Center, Tampa, FL, USA.
  • Robertson-Tessi M; Department of Integrated Mathematical Oncology, Moffitt Cancer Center, Tampa, FL, USA.
  • Gatenby R; Department of Integrated Mathematical Oncology, Moffitt Cancer Center, Tampa, FL, USA.
  • Wenham R; Department of Integrated Mathematical Oncology, Moffitt Cancer Center, Tampa, FL, USA.
  • Maini P; Cancer Biology and Evolution Program, Moffitt Cancer Center, Tampa, FL, USA.
  • Damaghi M; Gynecologic Oncology Program, Moffitt Cancer Center, Tampa, FL, USA.
  • Anderson A; Wolfson Centre for Mathematical Biology, University of Oxford, Oxford, UK.
bioRxiv ; 2023 Mar 24.
Article em En | MEDLINE | ID: mdl-36993591
ABSTRACT
Toxicity and emerging drug resistance are important challenges in PARP inhibitor (PARPi) treatment of ovarian cancer. Recent research has shown that evolutionary-inspired treatment algorithms which adapt treatment to the tumor's treatment response (adaptive therapy) can help to mitigate both. Here, we present a first step in developing an adaptive therapy protocol for PARPi treatment by combining mathematical modelling and wet-lab experiments to characterize the cell population dynamics under different PARPi schedules. Using data from in vitro Incucyte Zoom time-lapse microscopy experiments and a step-wise model selection process we derive a calibrated and validated ordinary differential equation model, which we then use to test different plausible adaptive treatment schedules. Our model can accurately predict the in vitro treatment dynamics, even to new schedules, and suggests that treatment modifications need to be carefully timed, or one risks losing control over tumour growth, even in the absence of any resistance. This is because our model predicts that multiple rounds of cell division are required for cells to acquire sufficient DNA damage to induce apoptosis. As a result, adaptive therapy algorithms that modulate treatment but never completely withdraw it are predicted to perform better in this setting than strategies based on treatment interruptions. Pilot experiments in vivo confirm this conclusion. Overall, this study contributes to a better understanding of the impact of scheduling on treatment outcome for PARPis and showcases some of the challenges involved in developing adaptive therapies for new treatment settings.

Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Guideline / Prognostic_studies Idioma: En Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Guideline / Prognostic_studies Idioma: En Ano de publicação: 2023 Tipo de documento: Article