26.02.2021 | Anja P. Einholm

Host: Danish Cancer Society Research Center.
Attendance: On Zoom. To attend, please fill the registration form.

Speaker

Prof. Arnoldo Frigessi, Department of Biostatistics, University of Oslo – Norway. More information.

Abstract

Current personalized cancer treatment is based on biomarkers which allow assigning each patient to a subtype of the disease, for which treatment has been established. Such stratified patient treatments represent a first important step away from one-size-fits-all treatment. However, the accuracy of disease classification comes short in the granularity of the personalization: it assigns patients to one of a few classes, within which heterogeneity in response to therapy usually is still very large. In addition, the combinatorial explosive quantity of combinations of cancer drugs, doses and regimens, makes clinical testing impossible. We propose a new strategy for personalised cancer therapy, based on producing a copy of the patient's tumour in a computer, and to expose this synthetic copy to multiple potential therapies. We show how mechanistic mathematical modelling, patient specific inference and simulation can be used to predict the effect of combination therapies in a breast cancer. The model accounts for complex interactions at the cellular and molecular level, and is able of bridging multiple spatial and temporal scales. The model is a combination of ordinary and partial differential equations, cellular automata and stochastic elements. The model is personalised by estimating multiple parameters from individual patient data, routinely acquired, including histopathology, imaging and molecular profiling. The results show that mathematical models can be personalized to predict the effect of therapies in each specific patient. The approach is tested with data from five breast tumours collected in a recent neoadjuvant clinical phase II trial. The model predicted correctly the outcome after 12 weeks treatment and showed by simulation how alternative treatment protocols would have produced different, and some times better, outcomes. This study is possibly the first one towards personalized computer simulation of breast cancer treatment incorporating relevant biologically-specific mechanisms and multi-type individual patient data in a mechanistic and multiscale manner: a first step towards virtual treatment comparison.