State-dependent evolutionary models reveal modes of solid tumor growth (preprint)

Spatial properties of tumor growth have profound implications for cancer progression, therapeutic resistance and metastasis. Yet, how spatial position governs tumor cell division remains difficult to evaluate in clinical tumors. Here, we demonstrate that elevated cellular growth rates on the tumor periphery leave characteristic patterns in the genomes of cells sampled from different parts of a tumor, which become evident when they are used to construct a tumor phylogenetic tree. Namely, rapidly-dividing peripheral lineages branch more extensively and acquire more mutations than slower-dividing lineages in the tumor center. We develop a Bayesian state-dependent evolutionary phylodynamic model (SDevo) that quantifies these patterns to infer the differential cell division rates between peripheral and central cells jointly from the branching and mutational patterns of single-time point, multi-region sequencing data. We validate this approach on simulated tumors by demonstrating its ability to accurately infer spatially-varying birth rates under a range of growth conditions and sampling strategies. We then show that SDevo outperforms state-of-the-art, non-cancer multi-state phylodynamic methods which ignore differential mutational acquisition. Finally, we apply SDevo to multi-region sequencing data from clinical hepatocellular carcinomas and find evidence that cells on the tumor edge divide 2-4x faster than those in the center. As multi-region and single-cell sequencing increase in resolution and availability, we anticipate that SDevo will be useful in interrogating spatial restrictions on tumor growth and could be extended to model non-spatial factors that influence tumor progression, including hypoxia and immune infiltration.

Characterising the epidemic spread of Influenza A/H3N2 within a city through phylogenetics (preprint)

Infecting large portions of the global population, seasonal influenza is a major burden on societies around the globe. While the global source sink dynamics of the different seasonal influenza viruses have been studied intensively, it’s local spread remains less clear. In order to improve our understanding of how influenza is transmitted on a city scale, we collected an extremely densely sampled set of influenza sequences alongside patient metadata. To do so, we sequenced influenza viruses isolated from patients of two different hospitals, as well as private practitioners in Basel, Switzerland during the 2016/2017 influenza season. The genetic sequences reveal that repeated introductions into the city drove the influenza season. We then reconstruct how the effective reproduction number changed over the course of the season. We find trends in transmission dynamics correlated positively with trends in temperature, but not relative humidity nor school holidays. Alongside the genetic sequence data that allows us to see how individual cases are connected, we gathered patient information, such as the age or household status. Zooming into the local transmission outbreaks suggests that the elderly were to a large extent infected within their own transmission network, while school children likely drove the spread within the remaining transmission network. These patterns will be valuable to plan interventions combating the spread of respiratory diseases within cities given that similar patterns are observed for other influenza seasons and cities. Author summary As shown with the current SARS-CoV-2 pandemic, respiratory diseases can quickly spread around the globe. While it can be hugely important to understand how diseases spread around the globe, local spread is most often the main driver of novel infections of respiratory diseases such as SARS-CoV-2 or influenza. We here use genetic sequence data alongside patient information to better understand what the drives the local spread of influenza by looking at the 2016/2017 influenza season in Basel, Switzerland as an example. The genetic sequence data allows us to reconstruct the how the transmission dynamics changed over the course of the season, which we correlate to changes, but not humidity or school holidays. Additionally, the genetic sequence data allows us to see how individual cases are connected. Using patient information, such as age and household status our analyses suggest that the elderly mainly transmit within their own transmission network. Additionally, they suggest that school aged children, but not pre-school aged children are important drivers of the local spread of influenza.