Leveraging sequences missing from the human genome to diagnose cancer

Cancer diagnosis using cell-free DNA (cfDNA) can significantly improve treatment and survival but has several technical limitations. Here, we show that tumor-associated mutations create neomers, DNA sequences 11-18bp in length that are absent in the human genome, that can accurately detect cancer subtypes and features. We show that we can detect twenty-one different tumor-types with higher accuracy than state-of-the-art methods using a neomer-based classifier. Refinement of this classifier via supervised learning identified additional cancer features with even greater precision. We also demonstrate that neomers can precisely diagnose cancer from cfDNA in liquid biopsy samples. Finally, we show that neomers can be used to detect cancer-associated non-coding mutations affecting gene regulatory activity. Combined, our results identify a novel, sensitive, specific and straightforward cancer diagnostic tool.