Circulating tumor DNA in cancer: Predictive molecular pathology meets mathematics

The cancer secretome is a valuable reservoir of cancer biomarkers. Besides containing circulating tumor cells, extracellular vesicles, and proteins, it is also rich in circulating tumor DNA (ctDNA)-a subpopulation of cell free DNA. The most efficient technology to capture ctDNA is next generation sequencing (NGS). Indeed, this analysis enables the identification of both quantitative (e.g., mutant allelic fraction - MAF) and qualitative (e.g., the variant type) information. Strikingly, by calculating these data in relation to time, cytopathologists can decodify and graphically report the ctDNA "message", which may help to diagnose cancer, define treatment, and monitor disease evolution. In this paper, we report the most compelling evidence steadily accumulating on the successful application of NGS-based ctDNA analysis in cancer diagnosis, treatment decision, and monitoring of cancer progression. We also propose a mathematical model that calculates MAF evolution in relation to time.