Abstract
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 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 and low diversity, as expected. However, a modest cell turnover can result in an increased number of divisions and mixing among clones resulting in increased microscopic diversity in the tumor core. Using simulations to estimate power to detect such spatial trends, we find that multiregion sequencing data from contemporary studies is marginally powered to detect the predicted effects. Slightly larger samples, improved detection of rare variants, or sequencing of smaller biopsies or circulating tumor cell clusters would allow one to distinguish between leading models of tumor evolution. The genetic composition of circulating tumor cell clusters, which can be obtained from non-invasive blood draws, is therefore informative about tumor evolution and its metastatic potential.
Highlights
We show that fine-scale tumor heterogeneity, and CTC cluster composition, depend more sensitively on the turnover dynamics of the tumor
For simulations with low to moderate death rate, d ∈ {0.05, 0.1, 0.2} and s = 1%, we find that the frequency spectra are very similar across the three turnover models (Fig 1, S1, S2): A low death rate has little impact on the global composition of a tumor
Waclaw et al argued that turnover reduces diversity based on the observation that more high-frequency variants were observed in the tumor with turnover: A small number of clones make up a larger proportion of the tumor
Summary
Genetic diversity plays a central role in tumor progression, metastasis, and resistance to treatment. Biopsies containing tens of thousands of cells with a 10% frequency cutoff show an increase in diversity at the edge of the tumor across all turnover models, with the number of spatially distributed samples needed to detect the trend reliably close to 40, roughly twice the size of the HCC dataset. 30 deep sequenced small cluster (23-30 cells) samples are sufficient to reliably reveal qualitative difference between turnover models that neither single cells nor large biopsies capture, even at low (1%) frequency cutoffs (Fig S11). Vantage compared to the no turnover and surface turnover model (Fig S12)
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