Digital twins of cancer patients: a step toward personalized treatments
The creation of digital twins (DTs) of cancer patients can assist us in predicting the evolution of an individual’s cancer through modeling each tumor’s characteristics and response to treatment. We therefore take advantage of new advances in computational approaches and combine mechanistic, machine learning, and stochastic modeling approaches to create a DT platform, which utilizes biological, biomedical, and EHR data sets. For each patient, the DT receives their information as input and predicts the evolution of their cancer.
We propose to develop a mechanistic model based on the quantitative systems pharmacology (QSP) modeling, which is one of the main computational approaches used to discover, test, and predict dose-exposure response. One of the main challenges of the QSP modeling is parameter estimation. Traditionally, these models assume all patients have similar diseases, and the values of parameters of the QSP model are identical for all patients. Therefore, parameters are commonly calibrated using the data often assembled from disparate sources.
To develop a personalized DT, we use patient-specific data for parameter estimations, sensitivity analysis, and uncertainty quantification. For each patient, we estimate the values of parameters of their QSP model using their data. We perform a multi-dimensional sensitivity analysis and uncertainty quantification on the mechanistic model to find a set of critical interactions and predict the intervals of confidence. Since this QSP model includes the data-driven mechanistic model of cells and molecules’ interaction networks, one of the ultimate results of this DT is the prediction of evolution of cancer in response to a given targeted therapy.