Mathematical models and treatment response in Glioblastoma

Friday, March 2, 2018 - 12:15pm


Kyle Singleton
Postdoctoral Research Fellow
Precision Neurotherapeutics Innovations Laboratory (PNT Innovations Lab)
Mayo Clinic


Accurate clinical assessment of a patient's response to treatment is a critical task in the era of precision medicine. However, disease and treatment evaluations often use population averages that fail to account for patient-specific variation. Glioblastoma (GBM), a primary brain tumor with dismal median survival times of 12-14 months, has a highly heterogeneous, invasive profile causing tumor dynamics and responsive to therapy to vary widely from patient to patient. These variations have made it difficult for physicians to determine effective therapies or appropriate treatment schedules. Using computational mathematical models, the unique kinetics of individual patients' tumors can be simulated by our group using net rates of cell proliferation (ρ) and invasion (D) derived from serial magnetic resonance imaging (MRI). These models serve as untreated virtual controls of tumor growth, enabling comparisons against post-treatment imaging to generate a patient-specific “Days Gained” (DG) response metric. Significant DG thresholds have been found across a variety of radio-, chemo-, and immuno-therapies that distinguish long and short-term survivors. In addition, DG thresholds show promise as an early identifier of response in patients undergoing complex imaging transformations, such as pseudo-progression. Thus, computational models and the DG metric add useful patient-specific understanding of GBM that can inform clinical decisions and future research in efforts to extend patient survival times.