Abstract The most basic differential expression analysis involves the identification of genes that exhibit distinct expression patterns in two cohorts. Surprisingly, even though there are numerous computational methods for detecting and assessing the statistical significance of somatic DNA copy number (CN) gains and losses in a single cohort, there are no tools for detecting and assessing the statistical significance of CN differences between two cohorts. Instead, typically each cohort is analyzed separately and distinct findings are presented as evidence of CN differences. Cyclic shift testing was originally introduced as a method to identify and assess the statistical significance of recurrent somatic CN gains and losses. Subsequent work explored theoretical statistical underpinnings of cyclic shift testing. Here we extend the previous results by showing that cyclic shift testing can be applied to detect and assess the statistical significance of CN differences between two cohorts. Human papilloma virus (HPV) infection is a risk factor for head and neck squamous cell carcinoma (HNSC), and it is known that HPV+ and HPV- HNSC have distinct mutational and gene expression profiles. We apply cyclic shift testing to detect and assess the statistical significance of CN differences between HPV+ and HPV- HNSC using data from The Cancer Genome Atlas HNSC cohort. Our approach detects statistically significant CN differences in regions that are known to contain HNSC drivers (7p, 9p21, 11q13, 11q14-qter), as well as regions of chr14, chr16, and others that are less well characterized. Citation Format: Vonn Walter, Hyo Young Choi, Xiaobei Zhao, Jose Zevallos, D. Neil Hayes. Detecting somatic DNA copy number differences with cyclic shift testing [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 5460.