Predicting the Regulatory Dynamics of AML Disease Progression from Longitudinal Multi-Modal Clinical Data

Acute Myeloid Leukemia (AML) is a complex and heterogeneous disease identified by severe clinical progression, fast cellular proliferation, and often high mortality rates. Incorporating diverse longitudinal information on patients' medical histories is essential for developing effective disease predictive models applicable to both research and clinical settings. Here, we present a robust methodology for discovering the regulation of disease progression dynamics from a novel longitudinal, multimodal clinical dataset of patients diagnosed with AML. The medical data were analyzed to reveal the main clinical, genetic, and treatment features modulating disease progression. To discover dynamic mathematical models at the systems level-including the necessary regulatory interactions, parameters, and disease drivers-predictive of AML progression, we developed a de novo inference algorithm based on high-performance evolutionary computation. The results demonstrate that the predictive methodology could accurately estimate the drivers and clinical dynamics of AML progression in terms of blast percentages for both training and novel patients. This approach effectively predicted AML drivers, their mechanistic interactions, and disease progression by leveraging the heterogeneous and longitudinal dynamics of patients' clinical data. Importantly, this methodology shows significant potential for modeling progression dynamics in other acute diseases, providing a flexible and adaptable framework for advancing clinical and translational research.