Abstract
Risk stratification and treatment decisions for leukemia patients are regularly based on clinical markers determined at diagnosis, while measurements on system dynamics are often neglected. However, there is increasing evidence that linking quantitative time-course information to disease outcomes can improve the predictions for patient-specific treatment responses. We designed a synthetic experiment simulating response kinetics of 5,000 patients to compare different computational methods with respect to their ability to accurately predict relapse for chronic and acute myeloid leukemia treatment. Technically, we used clinical reference data to first fit a model and then generate de novo model simulations of individual patients’ time courses for which we can systematically tune data quality (i.e. measurement error) and quantity (i.e. number of measurements). Based hereon, we compared the prediction accuracy of three different computational methods, namely mechanistic models, generalized linear models, and deep neural networks that have been fitted to the reference data. Reaching prediction accuracies between 60 and close to 100%, our results indicate that data quality has a higher impact on prediction accuracy than the specific choice of the particular method. We further show that adapted treatment and measurement schemes can considerably improve the prediction accuracy by 10 to 20%. Our proof-of-principle study highlights how computational methods and optimized data acquisition strategies can improve risk assessment and treatment of leukemia patients.
Highlights
Myeloid leukemias are characterized by aberrations affecting the proliferation and maturation of myeloid progenitor cells, leading to the progressive displacement of functional blood cells by immature and dysfunctional leukemic cells
Computational relapse prediction and quality: (i) a dense (D) data set consisting of weekly (AML) or monthly (CML) measurements of the leukemic burden free of any measurement error. (ii) For the dense-noisy (DN) data we added a normally distributed “technical” noise to all data points of D to match the measuring error (AML) or the residuals observed between real data and their corresponding model fits (CML). (iii) In a third step, we reduced the total number of measurements per patient, creating a sparse-noisy (SN) data set that matches the measurement frequency in the real data. (iv) to make the data as realistic as possible, we added a detection limit for very low measurements, called artificial patient (AP) data
We showed that qualitatively different computational approaches, ranging from machine learning approaches to mechanistic models, are in principle suited to support relapse prediction based on time-series data of leukemia remission levels
Summary
Myeloid leukemias are characterized by aberrations affecting the proliferation and maturation of myeloid progenitor cells, leading to the progressive displacement of functional blood cells by immature and dysfunctional leukemic cells. Depending on the time scale of the displacement process, myeloid leukemias are further divided in chronic and acute leukemias. Q or at our repository https://gitlab.com/imb-dev/ aml-cml-relapse-prediction-method-comparison
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