Background Given the severity and scope of the current COVID-19 pandemic, it is critical to determine predictive features of COVID-19 mortality and medical resource usage to effectively inform health, risk-based physical distancing, and work accommodation policies. Non-clinical sociodemographic features are important explanatory variables of COVID-19 outcomes, revealing existing disparities in large health care systems.
BackgroundA major problem in identifying the best therapeutic targets for cancer is the molecular heterogeneity of the disease. Cancer is often caused by an accumulation of mutations which produce irreversible damage to the cell’s control mechanisms of survival and proliferation. Different mutations may affect these cellular anachronisms through a combination of molecular interactions which may be dynamically changing during cancer progression. It has been previously shown that cancer accumulates mutations over time. In this paper we address the problem of cancer heterogeneity by modeling cancer progression using somatic mutation and gene expression cross-sectional data.ResultsWe propose a novel formulation of integrating somatic mutation and gene expression data to infer the temporal sequence of events from cross-sectional data. Using a mixed integer linear program we model the interaction between groups of different mutated genes and the resulting modifications at the gene expression level. Our approach identifies a partition of mutation events which gradually produce gene expression changes to a partition of genes over time. The proposed formulation is tested using both simulated data and real breast cancer data with matched somatic mutations and gene expression measurements from The Cancer Genome Atlas. First, we classify the genes as oncogenes or tumor suppressors based on the frequency of driver mutations. As expected, the most frequently mutated genes in breast cancer are PIK3CA and TP53 genes. Then, we select those genes with most frequent driver mutations and a set of genes known to play roles in cancer development. Furthermore, we apply the proposed mixed integer linear program to identify the temporal order in which genes mutate and, simultaneously, the changes they produce at the gene expression level during cancer progression. In addition, we are able to identify known causal relationships between mutations and gene expression changes in PI3K/AKT and TP53 pathways.ConclusionsThis paper proposes a new model to infer the temporal sequence in which mutations occur and lead to changes at the gene expression level during cancer progression. The approach is general and can be applied to any data sets with available somatic mutations and gene expression measurements.Electronic supplementary materialThe online version of this article (doi:10.1186/s12918-016-0255-6) contains supplementary material, which is available to authorized users.
ObjectiveTo better understand the immune microenvironment of pancreatic ductal adenocarcinomas (PDACs), here we explored the relevance of T and B cell compartmentalisation into tertiary lymphoid structures (TLSs) for the generation of local antitumour immunity.DesignWe characterised the functional states and spatial organisation of PDAC-infiltrating T and B cells using single-cell RNA sequencing (scRNA-seq), flow cytometry, multicolour immunofluorescence, gene expression profiling of microdissected TLSs, as well as in vitro assays. In addition, we performed a pan-cancer analysis of tumour-infiltrating T cells using scRNA-seq and sc T cell receptor sequencing datasets from eight cancer types. To evaluate the clinical relevance of our findings, we used PDAC bulk RNA-seq data from The Cancer Genome Atlas and the PRINCE chemoimmunotherapy trial.ResultsWe found that a subset of PDACs harbours fully developed TLSs where B cells proliferate and differentiate into plasma cells. These mature TLSs also support T cell activity and are enriched with tumour-reactive T cells. Importantly, we showed that chronically activated, tumour-reactive T cells exposed to fibroblast-derived TGF-β may act as TLS organisers by producing the B cell chemoattractant CXCL13. Identification of highly similar subsets of clonally expandedCXCL13+tumour-infiltrating T cells across multiple cancer types further indicated a conserved link between tumour-antigen recognition and the allocation of B cells within sheltered hubs in the tumour microenvironment. Finally, we showed that the expression of a gene signature reflecting mature TLSs was enriched in pretreatment biopsies from PDAC patients with longer survival after receiving different chemoimmunotherapy regimens.ConclusionWe provided a framework for understanding the biological role of PDAC-associated TLSs and revealed their potential to guide the selection of patients for future immunotherapy trials.
The standard treatment for advanced prostate cancer is hormone therapy in the form of continuous androgen suppression (CAS), which unfortunately frequently leads to resistance and relapse. An alternative scheme is intermittent androgen suppression (IAS), in which patients are submitted to cycles of treatment (in the form of androgen deprivation) and off-treatment periods in an alternating manner. In spite of extensive recent clinical experience with IAS, the design of ideal protocols for any given patient remains a challenge. The level of prostate specific antigen (PSA) is frequently monitored to determine when patients will be taken off therapy and when therapy will resume. In this work, we propose a threshold-based policy for optimal IAS therapy design that is parameterized by lower and upper PSA threshold values and is associated with a cost metric that combines clinically relevant measures of therapy success. We use a Stochastic Hybrid Automaton (SHA) model of prostate cancer evolution under IAS and perform Infinitesimal Perturbation Analysis (IPA) to adaptively adjust PSA threshold values so as to improve therapy outcomes. We also apply this methodology to clinical data from real patients, and obtain promising results and valuable insights for personalized IAS therapy design.
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