Genetic diversity plays a central role in tumor progression, metastasis, and resistance to treatment. Experiments are shedding light on this diversity at ever finer scales, but interpretation is challenging. Using recent progress in numerical models, we simulate macroscopic tumors to investigate the interplay between global growth dynamics, microscopic composition, and circulating tumor cell cluster diversity. We find that modest differences in growth parameters can profoundly change microscopic diversity. Simple outwards expansion leads to spatially segregated clones, as expected, but a modest cell turnover can result in mixing at the microscopic scale, consistent with experimental observations. Whereas simple range expansion models predict maximum diversity at the tumor edge, turnover models predict maximum diversity near the core of the tumor and a higher potency of CTCs for metastasis. Using multi-region sequencing data from a Hepatocellular Carcinoma patient to validate our models, we propose that deep multi-region sequencing is well-powered to distinguish between some of the leading models of cancer evolution. The genetic composition of circulating tumor cell clusters, which can be obtained from noninvasive blood draws, is therefore informative about tumor evolution, the position of origin of the cluster within the tumor, and its metastatic potential. It is therefore a promising tool for both fundamental and medical research.