Circulating nucleic acids (CNA) isolated from serum or plasma are increasingly recognized as biomarkers for cancers. Recently developed next generation sequencing provides high numbers of DNA sequences to detect the trace amounts of unique serum biomarkers associated with breast carcinoma. Serum CNA of 38 women with ductal carcinoma was extracted and sequenced on a 454/Roche high-throughput GS-FLX platform and compared with healthy controls and patients with other medical conditions. Repetitive elements present in CNA were detected and classified, and each repetitive element was normalized based on total sequence count or repeat count. Multivariate regression models were calculated using an information-theoretical approach and multimodel inference. A total of 423,150 and 953,545 sequences for the cancer patients and controls, respectively, were obtained. Data from 26 patients with stages II to IV tumors and from 67 apparently healthy female controls were used as the training data set. Using a bootstrap method to avoid sampling bias, a five-parameter model was developed. When this model was applied to a validation data set consisting of patients with tumor stage I (n = 10) compared with healthy and nonmalignant disease controls (n = 87; 1,261,561 sequences) a sensitivity of 70% at a specificity of 100% was obtained. At a diagnostic specificity level of 95%, a sensitivity of 90% was calculated. Identification of specific breast cancer-related CNA sequences provides the basis for the development of a serum-based routine laboratory test for breast cancer screening and monitoring.