Respiratory viral infection promotes the awakening and outgrowth of dormant metastatic breast cancer cells in lungs.

Breast cancer is the second most common cancer globally. Most deaths from breast cancer are due to metastatic disease which often follows long periods of clinical dormancy1. Understanding the mechanisms that disrupt the quiescence of dormant disseminated cancer cells (DCC) is crucial for addressing metastatic progression. Infection with respiratory viruses (e.g. influenza or SARS-CoV-2) is common and triggers an inflammatory response locally and systemically2,3. Here we show that influenza virus infection leads to loss of the pro-dormancy mesenchymal phenotype in breast DCC in the lung, causing DCC proliferation within days of infection, and a greater than 100-fold expansion of carcinoma cells into metastatic lesions within two weeks. Such DCC phenotypic change and expansion is interleukin-6 (IL-6)-dependent. We further show that CD4 T cells are required for the maintenance of pulmonary metastatic burden post-influenza virus infection, in part through attenuation of CD8 cell responses in the lungs. Single-cell RNA-seq analyses reveal DCC-dependent impairment of T-cell activation in the lungs of infected mice. SARS-CoV-2 infected mice also showed increased breast DCC expansion in lungs post-infection. Expanding our findings to human observational data, we observed that cancer survivors contracting a SARS-CoV-2 infection have substantially increased risks of lung metastatic progression and cancer-related death compared to cancer survivors who did not. These discoveries underscore the significant impact of respiratory viral infections on the resurgence of metastatic cancer, offering novel insights into the interconnection between infectious diseases and cancer metastasis.

Altered DNA methylation profiles in blood from patients with sporadic Creutzfeldt-Jakob disease.

Prion diseases are fatal and transmissible neurodegenerative disorders caused by the misfolding and aggregation of prion protein. Although recent studies have implicated epigenetic variation in common neurodegenerative disorders, no study has yet explored their role in human prion diseases. Here we profiled genome-wide blood DNA methylation in the most common human prion disease, sporadic Creutzfeldt-Jakob disease (sCJD). Our case-control study (n = 219), when accounting for differences in cell type composition between individuals, identified 38 probes at genome-wide significance (p < 1.24 × 10-7). Nine of these sites were taken forward in a replication study, performed in an independent case-control (n = 186) cohort using pyrosequencing. Sites in or close to FKBP5, AIM2 (2 probes), UHRF1, KCNAB2 successfully replicated. The blood-based DNA methylation signal was tissue- and disease-specific, in that the replicated probe signals were unchanged in case-control studies using sCJD frontal-cortex (n = 84), blood samples from patients with Alzheimer's disease, and from inherited and acquired prion diseases. Machine learning algorithms using blood DNA methylation array profiles accurately distinguished sCJD patients and controls. Finally, we identified sites whose methylation levels associated with prolonged survival in sCJD patients. Altogether, this study has identified a peripheral DNA methylation signature of sCJD with a variety of potential biomarker applications.

A blood miRNA signature associates with sporadic Creutzfeldt-Jakob disease diagnosis.

Sporadic Creutzfeldt-Jakob disease (sCJD) presents as a rapidly progressive dementia which is usually fatal within six months. No clinical blood tests are available for diagnosis or disease monitoring. Here, we profile blood microRNA (miRNA) expression in sCJD. Sequencing of 57 sCJD patients, and healthy controls reveals differential expression of hsa-let-7i-5p, hsa-miR-16-5p, hsa-miR-93-5p and hsa-miR-106b-3p. Downregulation of hsa-let-7i-5p, hsa-miR-16-5p and hsa-miR-93-5p replicates in an independent cohort using quantitative PCR, with concomitant upregulation of four mRNA targets. Absence of correlation in cross-sectional analysis with clinical phenotypes parallels the lack of association between rate of decline in miRNA expression, and rate of disease progression in a longitudinal cohort of samples from 21 patients. Finally, the miRNA signature shows a high level of accuracy in discriminating sCJD from Alzheimer's disease. These findings highlight molecular alterations in the periphery in sCJD which provide information about differential diagnosis and improve mechanistic understanding of human prion diseases.

Reproducible parallel inference and simulation of stochastic state space models using odin, dust, and mcstate.

State space models, including compartmental models, are used to model physical, biological and social phenomena in a broad range of scientific fields. A common way of representing the underlying processes in these models is as a system of stochastic processes which can be simulated forwards in time. Inference of model parameters based on observed time-series data can then be performed using sequential Monte Carlo techniques. However, using these methods for routine inference problems can be made difficult due to various engineering considerations: allowing model design to change in response to new data and ideas, writing model code which is highly performant, and incorporating all of this with up-to-date statistical techniques. Here, we describe a suite of packages in the R programming language designed to streamline the design and deployment of state space models, targeted at infectious disease modellers but suitable for other domains. Users describe their model in a familiar domain-specific language, which is converted into parallelised C++ code. A fast, parallel, reproducible random number generator is then used to run large numbers of model simulations in an efficient manner. We also provide standard inference and prediction routines, though the model simulator can be used directly if these do not meet the user's needs. These packages provide guarantees on reproducibility and performance, allowing the user to focus on the model itself, rather than the underlying computation. The ability to automatically generate high-performance code that would be tedious and time-consuming to write and verify manually, particularly when adding further structure to compartments, is crucial for infectious disease modellers. Our packages have been critical to the development cycle of our ongoing real-time modelling efforts in the COVID-19 pandemic, and have the potential to do the same for models used in a number of different domains.