A mathematical model of ctDNA shedding predicts tumor detection size

Abstract

AbstractEarly cancer detection aims to find tumors before they progress to an incurable stage. We developed a stochastic mathematical model of tumor evolution and circulating tumor DNA (ctDNA) shedding to determine the potential and the limitations of cancer early detection tests. We inferred normalized ctDNA shedding rates from 176 early stage lung cancer subjects and calculated that a 15 mL blood sample contains on average 1.7 genome equivalents of ctDNA for lung tumors with a volume of 1 cm3. For annual screening, the model predicts median detection sizes between 3.8 and 6.6 cm3corresponding to lead times between 310 and 450 days compared to current lung tumor sizes at diagnosis. For monthly cancer relapse testing based on 20 a priori known mutations, the model predicts a median detection size of 0.26 cm3corresponding to a lead time of 150 days. This mechanistic framework can help to optimize early cancer detection approaches.