Estimating clonal growth rates and the relation to malignancy in human blood

Brian Johnson

University of California, San Diego

"Estimating clonal growth rates and the relation to malignancy in human blood"

While evolutionary approaches to medicine hold great potential, measuring evolution is difficult due to experimental constraints and the dynamic nature of biology. It is impossible to continuously observe the evolution of cancer, and obtaining multiple longitudinal samples over time is rare. Advancements in single-cell DNA sequencing have allowed for new evolutionary approaches to studying somatic clonal expansion, which are likely to improve mechanistic understanding of cancer and our ability to effectively prognosticate patients. We present coalescent methods to estimate the growth rate of clones from reconstructed evolutionary trees, eliminating the need for complex simulations. We apply our methods to four recently published single-cell whole genome sequencing datasets, estimating the growth rate of clonal expansions in blood, and validating these estimates with longitudinal data. We show that our estimates lead to new insights on evolutionary parameters, which have implications for early detection of high-risk clones. For example, compared to clones with a single or unknown driver mutation, clones with multiple drivers have increased growth rates (median 0.94 vs. 0.25 per cell per year; p = 1.6 x10^-6). Additionally, patients diagnosed with Myeloproliferative Neoplasm (MPN), a group of malignant conditions characterized by overproduction of blood cells, were found to harbor more aggressively expanding clones (median 0.55 vs. 0.23 per cell per year; p = 0.029) compared to healthy individuals. Further, stratifying patients with MPN by the growth rate of their fittest clone uncovered that higher growth rates are associated with shorter time from clone initiation to MPN diagnosis (median 13.9 vs. 26.4 months; p = 0.0026). As genomic sequencing technology continues to advance, we demonstrate that clonal growth rates can be accurately estimated and have potential for clinical application. To make our methods widely available, we created cloneRate, a user-friendly R package for researchers to apply to their own datasets.
Additional authors: Yubo Shuai, Department of Mathematics at UC San Diego; Jason Schweinsberg, Department of Mathematics at UC San Diego; Kit Curtius, Division of Biomedical Informatics at UC San Diego

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