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  • Title: High-density sampling reveals volume growth in human tumours.
    Author: Angaji A, Owusu M, Velling C, Dick N, Weghorn D, Berg J.
    Journal: Elife; 2024 Nov 26; 13():. PubMed ID: 39587846.
    Abstract:
    In growing cell populations such as tumours, mutations can serve as markers that allow tracking the past evolution from current samples. The genomic analyses of bulk samples and samples from multiple regions have shed light on the evolutionary forces acting on tumours. However, little is known empirically on the spatio-temporal dynamics of tumour evolution. Here, we leverage published data from resected hepatocellular carcinomas, each with several hundred samples taken in two and three dimensions. Using spatial metrics of evolution, we find that tumour cells grow predominantly uniformly within the tumour volume instead of at the surface. We determine how mutations and cells are dispersed throughout the tumour and how cell death contributes to the overall tumour growth. Our methods shed light on the early evolution of tumours in vivo and can be applied to high-resolution data in the emerging field of spatial biology. Our bodies are made up of organs and tissues, which, in turn, are made up of individual cells. Normally, different types of cells in a tissue perform distinct roles, working together to keep the tissue healthy and functioning properly. However, a genetic mutation that makes them divide and grow uncontrollably can cause tumors to from. As the tumor grows, its cells can undergo further mutations. Each new mutation can serve as a ‘marker’, allowing scientists to track how a tumor has grown, by looking at which cells have specific mutations. Angaji et al. wanted to know if tumors grow through cells on the surface dividing more quickly and invading surrounding tissue; or if all cells in and across the tumor divide at the same rate. To answer this question, the researchers used high-resolution data looking at where in a tumor mutations accumulate. The experiments examined the early evolution of a tumor because only early mutations resulted in enough detectable cells through sequencing. The researchers then compared these 'tumor maps' to simulations of tumors growing in different ways, to see which growth mode fit the maps better. Angaji et al. found that the tumors they looked at grew uniformly across the tumor volume. They also established that the overall growth of the tumor was slow compared to the rate of growth predicted by the speed of the cells dividing. This means that the development of a tumor is finely balanced between net growth and shrinkage, and a small change in the external conditions could potentially kill a tumor. Angaji et al. have developed methods that will allow us to better track tumor growth, and provide further insights into cancer biology. These high-resolution tumor maps may provide clues about how to treat different types of tumors depending on how they grow.
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