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Cancer Res. 2019 May 22. pii: canres.1804.2018. doi: 10.1158/0008-5472.CAN-18-1804. [Epub ahead of print]

Towards personalized computer simulation of breast cancer treatment: a multi-scale pharmacokinetic and pharmacodynamic model informed by multi-type patient data.

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Oslo Centre for Biostatistics and Epidemiology, Faculty of Medicine, University of Oslo.
Department of Diagnostic Physics, Clinic of Radiology and Nuclear Medicine, Oslo University Hospital.
Institute for Cancer Research, Department of Cancer Genetics, Oslo University Hospital.
Department of Pathology, Oslo University Hospital.
Numerical Analysis And Scientific Computing, Simula Research Laboratory.
Department of Genetics, OUS Radium Hospital.
Department for Numerical Analysis and Scientific Computing, Simula Research Laboratory.
Division for Radiology and Nuclear Medicine, Oslo University Hospital.
Department of Cancer Genetics, Institute of Cancer Research.
Department of Clinical Molecular Oncology, Division of Medicine, Akershus University Hospital.
Department of Tumor Biology, Institute for Cancer Research, Oslo University Hospital Radiumhospitalet.
Department of Biostatistics, Faculty of Medicine, University of Oslo.
Dept. of Biostatistics, University of Oslo


The usefulness of mechanistic models to disentangle complex multi-scale cancer processes such as treatment response has been widely acknowledged. However, a major barrier for multi-scale models to predict treatment outcomes in individual patients lies in their initialization and parametrization which need to reflect individual cancer characteristics accurately. In this study, we used multi-type measurements acquired routinely on a single breast tumor, including histopathology, magnetic resonance imaging, and molecular profiling, to personalize parts of a complex multi-scale model of breast cancer treated with chemotherapeutic and anti-angiogenic agents. The model accounts for drug pharmacokinetics and pharmacodynamics. We developed an open-source computer program that simulates cross-sections of tumors under 12-week therapy regimes and used it to individually reproduce and elucidate treatment outcomes of four patients. Two of the tumors did not respond to therapy and model simulations were used to suggest alternative regimes with improved outcomes dependent on the tumor's individual characteristics. It was determined that more frequent and lower doses of chemotherapy reduce tumor burden in a low proliferative tumor while lower doses of anti-angiogenic agents improve drug penetration in a poorly perfused tumor. Furthermore, using this model we were able to predict correctly the outcome in another patient after 12 weeks of treatment. In summary, our model bridges multi-type clinical data to shed light on individual treatment outcomes.

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