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 multiscale 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 use 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 regimens and use 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 regimens 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. Research.

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Meibomian gland dysfunction (MGD) is the most common cause of dry eye disease (DED). In this study, we aimed to compare the effects of eyelid warming treatment using either TheraPearl Eye Mask (Bausch & Lomb Inc., New York, USA) or Blephasteam (Spectrum Thea Pharmaceuticals LTD, Macclesfield, UK) in a Norwegian population with mild to moderate MGD-related DED. An open label, randomized comparative trial with seventy patients (49 females, 21 males; mean age 53.6 years). Patients were randomly assigned to treatment with Blephasteam (n = 37) or TheraPearl (n = 33). All received a hyaluronic acid based artificial tear substitute (Hylo-Comod, Ursapharm, Saarbrücken, Germany). Patients were examined at baseline, and at three and six months initiation of treatment. Treatment efficacy was primarily evaluated by fluorescein breakup time (FBUT) and Ocular Surface Disease Index (OSDI) scores. Other outcome measures included ocular surface staining (OSS), Schirmer’s test, and meibomian quality and expressibility. Baseline parameter values did not differ between the groups. After six months of treatment, Blephasteam improved FBUT by 3.9 s (p < 0.01) and OSDI by 13.7 (p < 0.01), TheraPearl improved FBUT by 2.6 s (p < 0.01) and OSDI by 12.6 (p < 0.01). No difference between treatments was detected at 6 months (p = 0.11 for FBUT and p = 0.71 for OSDI), nor were there differences in the other tested parameters between the treatment groups. Blephasteam and TheraPearl are equally effective in treating mild to moderate MGD in a Norwegian population after 6-months of treatment.Clinicaltrials.gov ID: NCT03318874; Protocol ID: 2014/1983; First registration: 24/10/2017.

Approximate Bayesian computation (ABC) has advanced in two decades from a seminal idea to a practically applicable inference tool for simulator-based statistical models, which are becoming increasingly popular in many research domains. The computational feasibility of ABC for practical applications has been recently boosted by adopting techniques from machine learning to build surrogate models for the approximate likelihood or posterior and by the introduction of a general-purpose software platform with several advanced features, including automated parallelisation. Here we demonstrate the strengths of the advances in ABC by going beyond the typical benchmark examples and considering real applications in astronomy, infectious disease epidemiology, personalised cancer therapy and financial prediction. We anticipate that the emerging success of ABC in producing actual added value and quantitative insights in the real world will continue to inspire a plethora of further applications across different fields of science, social science and technology.

Mathematical modeling and simulation have emerged as a potentially powerful, time and cost effective approach to personalized cancer treatment. 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 the outcomes of therapeutic regimens in a particular patient lies in their initialization and parameterization which need to reflect individual cancer characteristics accurately. In this study we use multi-type routinely acquired measurements 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. We model the dynamics of drugs in tissue (pharmacokinetics) and the corresponding effects on their targets (pharmacodynamics). We developed a open-source computer program that simulates cross-sections of tumors under 12-week 1 therapy regimes and use it to individually reproduce and elucidate treatment outcomes of four patients. For two of the tumors that did not respond to therapy, we used model simulations to suggest alternative regimes, depending on their individual characteristics, with improved outcomes. We found that more frequent doses of chemothereapy reduce tumor burden in a low proliferative tumor while lower doses of anti-angiogenic agents improve drug penetration in a poorly perfused tumor. In addition to bridge multi-type clinical data to shed light on individual treatment outcomes, our approach identified a few tumor-related aspects that need to be clinically portraited better to allow for future model-driven personalized cancer therapy.