We have a track-record in developing mathematical methods of cancer evolution to illuminate cancer detectability, tumor progression, intratumoral heterogeneity, and treatment response (Reiter et al, Nature Reviews Cancer 2019, Bozic et al, eLife 2013). We implemented publicly available computational tools to reconstruct the evolutionary history of cancers from DNA sequencing data which have been used in >50 studies (Reiter et al, Nature Commun 2017). These algorithms allowed us to refine the step-wise progression of pancreatic cancers and demonstrated the distinct and common origin of lymphatic and distant metastases in colorectal cancer patients (Naxerova et al, Science 2017; Makohon-Moore et al, Nature 2018). We discovered that mutated cancer driver genes that are heterogeneous within primary tumors or among untreated metastases lack predicted function and therefore single biopsies typically capture the essential information for initial therapeutic decision making (Reiter et al, Science 2018; Reiter et al, Nature Reviews Cancer 2019). We developed a statistical framework to show that distant metastases cluster together much more often than expected by chance as well as more often than lymph node metastases indicating a stricter evolutionary bottleneck for distant metastases (Reiter et al, Nature Genetics 2020). Most recently, we developed the first stochastic model of circulating tumor DNA shedding to approximate the potential of liquid biopsies for lung cancer early detection (Avanzini et al, Science Adv 2020). We closely collaborate with many other research labs, physician-scientists, and clinicians at Stanford, Harvard, Johns Hopkins, Memorial Sloan Kettering Cancer Center and others to tackle the most pressing scientific and clinical challenges.