PhD, University of Edinburgh, Applied and Computational Mathematics (2019)
MSc, Università di Bologna, Mathematics (2015)
BSc, Università degli Studi di Parma, Mathematics (2012)
Johannes Reiter, Postdoctoral Faculty Sponsor
A mathematical model of ctDNA shedding predicts tumor detection size.
2020; 6 (50)
Early cancer detection aims to find tumors before they progress to an incurable stage. To determine the potential of circulating tumor DNA (ctDNA) for cancer detection, we developed a mathematical model of tumor evolution and ctDNA shedding to predict the size at which tumors become detectable. From 176 patients with stage I to III lung cancer, we inferred that, on average, 0.014% of a tumor cell's DNA is shed into the bloodstream per cell death. For annual screening, the model predicts median detection sizes of 2.0 to 2.3 cm representing a ~40% decrease from the current median detection size of 3.5 cm. For informed monthly cancer relapse testing, the model predicts a median detection size of 0.83 cm and suggests that treatment failure can be detected 140 days earlier than with imaging-based approaches. This mechanistic framework can help accelerate clinical trials by precomputing the most promising cancer early detection strategies.
View details for DOI 10.1126/sciadv.abc4308
View details for PubMedID 33310847
ctDNA shedding dynamics dictate early lung cancer detection potential
AMER ASSOC CANCER RESEARCH. 2020: 25
View details for Web of Science ID 000537848000023
Cancer recurrence times from a branching process model.
PLoS computational biology
2019; 15 (11): e1007423
As cancer advances, cells often spread from the primary tumor to other parts of the body and form metastases. This is the main cause of cancer related mortality. Here we investigate a conceptually simple model of metastasis formation where metastatic lesions are initiated at a rate which depends on the size of the primary tumor. The evolution of each metastasis is described as an independent branching process. We assume that the primary tumor is resected at a given size and study the earliest time at which any metastasis reaches a minimal detectable size. The parameters of our model are estimated independently for breast, colorectal, headneck, lung and prostate cancers. We use these estimates to compare predictions from our model with values reported in clinical literature. For some cancer types, we find a remarkably wide range of resection sizes such that metastases are very likely to be present, but none of them are detectable. Our model predicts that only very early resections can prevent recurrence, and that small delays in the time of surgery can significantly increase the recurrence probability.
View details for DOI 10.1371/journal.pcbi.1007423
View details for PubMedID 31751332