Workshop in Biostatistics

DATE: March 3, 2016
TIME: 1:30 - 3:00 pm
LOCATION: Medical School Office Building, Rm x303
TITLE: Precision tumor monitoring and outcome prediction from mathematical model of circulating tumor DNA

Ash Alizadeh, MD, PhD
Assistant Professor of Medicine (Oncology), Stanford
David Kurtz, MD
Postdoctoral Fellow; Oncology, Stanford


Predicting an individual’s response to treatment remains a major challenge in clinical oncology. Current methods rely on clinical and biological risk factors identified prior to therapy, such as tumor stage, histological grade, or tumor genotype. These factors are associated with differences in response and survival in the population; however, their ability to predict outcome for an individual patient is limited. Emerging blood-based biomarkers, such as circulating tumor-derived DNA (ctDNA), allow opportunities to measure tumor dynamics over time, either prior to or during therapy. We created an ordinary differential equation (ODE) based mathematical model relating ctDNA to underlying tumor growth dynamics over time. By applying this model to ctDNA time series data, we can create a continuous view of tumor dynamics over time. We have tested this model in a cohort of patients with diffuse large B cell lymphoma (DLBCL), the most common blood cancer in adults, using ctDNA measurements over the course of their therapy. This model allowed patient-specific predictions of tumor volume and clinical outcomes, previously not possible from standard clinical data. Mathematical models of tumor dynamics grounded in mechanism provide wide opportunities in personalized medicine and tailored therapeutics.

Suggested readings:


Mathematical Modeling of Tumors: