Cancer will be responsible for an estimated 585,720 American deaths in 2014 alone, highlighting the urgent need for improved disease detection and monitoring methods. Circulating tumor DNA (ctDNA) has the potential to revolutionize the identification and monitoring of cancer, but its detection in the blood plasma of most patients has remained costly and challenging. I recently helped develop an economical method (called CAPP- Seq) that combines ultra-deep sequencing and novel bioinformatics methods to achieve highly sensitive and specific noninvasive assessment of ctDNA with broad patient coverage. With this foundation, I hypothesize that ctDNA is a widely applicable biomarker for (1) sensitive and specific detection of residual disease, (2) monitoring of response to therapy, (3) and biopsy-free cancer screening and genotyping. To address this hypothesis, during the K99 and R00 phases, I propose to further develop and refine the computational and molecular biology framework of CAPP-Seq to lower its ctDNA detection limit by an order of magnitude. This will require introducing and validating novel statistical models and algorithms for genome analysis, and will involve wet laboratory research to both optimize the recovery of ctDNA molecules from plasma and eliminate sources of DNA error. During the training phase, I will further evaluate ctDNA detection levels at diagnosis in early stage patients with non-small cell lung cancer (NSCLC), the number one cause of cancer-related mortality, and will extend CAPP-Seq to diffuse large B-cell lymphoma (DLBCL), the commonest hematological malignancy. These two cancers are contrasted by the relative disparity of patient outcomes, an important consideration for disease surveillance, and will be studied as representatives of carcinomas and hematologic malignancies, respectively. Finally, during the independent phase, I will work with my group to devise statistical and genomic approaches for biopsy-free detection, genotyping, and classification of tumors that will be evaluated on plasma samples from diverse cancer patients, initially those with advanced disease as proof-of-principle, but ultimately on early-stage patients This project, if successful, will accelerate the early detection and monitoring of cancer.