Diagnosis of solid malignancies at a late stage often results in poor prognosis. Dr. Dan Landau and collaborators at the Institute of Computational Biomedicine, Weill Cornell Medicine are developing an ultra-sensitive analytical platform that will increase the success of detecting early-stage cancer by the analysis of circulating tumor DNA in blood. Cell-free DNA (cfDNA) from tumors is shed into circulation, and cancer mutations can be detected through next-generation sequencing of DNA in plasma from cancer patients. However, the fraction of cfDNA in blood from tumors in patients with low-burden early-stage disease is often significantly less than 1%, and current screening approaches are not sensitive enough to accurately identify tumor DNA at these low levels. To enhance sensitivity, some early detection approaches focus on very deep sequencing to detect mutations in a small number of select genes. However, if a patient’s cancer does not have a mutation in any of the genes tested, the cancer will be missed. Additionally, large and clinically burdensome volumes of blood from patients may be required to achieve a reasonable probability that tumor DNA bearing mutations in those specific genes are actually in the sample. Rather than striving for depth of sequencing, Landau and his team have designed an algorithm that searches across the genome for cumulative patterns of mutations plus region-specific changes in the number of copies of tumor cfDNA. This approach will enable detection of early stage cancer as well as quantitative tracking of disease after treatment, using standard volume patient blood samples. In addition to enabling detection of low-burden cancers, increased sensitivity could help physicians decide whether patients with no detectable residual disease might avoid follow-up chemotherapy and its associated toxicities.