Integrated analyses of functional genomics data have enormous potential for identifying phenotype-associated genes. Tissue-specificity is an important aspect of many genetic diseases, reflecting the potentially different roles of proteins and pathways in diverse cell lineages. Accounting for tissue specificity in global integration of functional genomics data is challenging, as “functionality” and “functional relationships” are often not resolved for specific tissue types. We address this challenge by generating tissue-specific functional networks, which can effectively represent the diversity of protein function for more accurate identification of phenotype-associated genes in the laboratory mouse. Specifically, we created 107 tissue-specific functional relationship networks through integration of genomic data utilizing knowledge of tissue-specific gene expression patterns. Cross-network comparison revealed significantly changed genes enriched for functions related to specific tissue development. We then utilized these tissue-specific networks to predict genes associated with different phenotypes. Our results demonstrate that prediction performance is significantly improved through using the tissue-specific networks as compared to the global functional network. We used a testis-specific functional relationship network to predict genes associated with male fertility and spermatogenesis phenotypes, and experimentally confirmed one top prediction, Mbyl1. We then focused on a less-common genetic disease, ataxia, and identified candidates uniquely predicted by the cerebellum network, which are supported by both literature and experimental evidence. Our systems-level, tissue-specific scheme advances over traditional global integration and analyses and establishes a prototype to address the tissue-specific effects of genetic perturbations, diseases and drugs.

SUMMARY Many functionally important interactions between genes and proteins involved in immunological diseases and processes are unknown. The exponential growth in public high-throughput data offers an opportunity to expand this knowledge. To unlock human-immunology-relevant insight contained in the global biomedical research effort, including all public high-throughput datasets, we performed immunological-pathway-focused Bayesian integration of a comprehensive, heterogeneous compendium comprising 38,088 genome-scale experiments. The distillation of this knowledge into immunological networks of functional relationships between molecular entities (ImmuNet), and tools to mine this resource, are accessible to the public at http://immunet.princeton.edu. The predictive capacity of ImmuNet, established by rigorous statistical validation, is easily accessed by experimentalists to generate data-driven hypotheses. We demonstrate the power of this approach through the identification of unique host-virus interaction responses, and we show how ImmuNet complements genetic studies by predicting disease-associated genes. ImmuNet should be widely beneficial for investigating the mechanisms of the human immune system and immunological diseases.

PURPOSE To create a risk prediction model that identifies patients at high risk for a potentially preventable acute care visit (PPACV). PATIENTS AND METHODS We developed a risk model that used electronic medical record data from initial visit to first antineoplastic administration for new patients at Memorial Sloan Kettering Cancer Center from January 2014 to September 2018. The final time-weighted least absolute shrinkage and selection operator model was chosen on the basis of clinical and statistical significance. The model was refined to predict risk on the basis of 270 clinically relevant data features spanning sociodemographics, malignancy and treatment characteristics, laboratory results, medical and social history, medications, and prior acute care encounters. The binary dependent variable was occurrence of a PPACV within the first 6 months of treatment. There were 8,067 observations for new-start antineoplastic therapy in our training set, 1,211 in the validation set, and 1,294 in the testing set. RESULTS A total of 3,727 patients experienced a PPACV within 6 months of treatment start. Specific features that determined risk were surfaced in a web application, riskExplorer, to enable clinician review of patient-specific risk. The positive predictive value of a PPACV among patients in the top quartile of model risk was 42%. This quartile accounted for 35% of patients with PPACVs and 51% of potentially preventable inpatient bed days. The model C-statistic was 0.65. CONCLUSION Our clinically relevant model identified the patients responsible for 35% of PPACVs and more than half of the inpatient beds used by the cohort. Additional research is needed to determine whether targeting these high-risk patients with symptom management interventions could improve care delivery by reducing PPACVs.

Understanding the changes in diverse molecular pathways underlying the development of breast tumors is critical for improving diagnosis, treatment, and drug development. Here, we used RNA-profiling of canine mammary tumors (CMTs) coupled with a robust analysis framework to model molecular changes in human breast cancer. Our study leveraged a key advantage of the canine model, the frequent presence of multiple naturally occurring tumors at diagnosis, thus providing samples spanning normal tissue, benign and malignant tumors from each patient. We demonstrated human breast cancer signals, at both expression and mutation level, are evident in CMTs. Profiling multiple tumors per patient enabled by the CMT model allowed us to resolve statistically robust transcription patterns and biological pathways specific to malignant tumors versus those arising in benign tumors or shared with normal tissues. We demonstrated that multiple-histological-samples per patient is necessary to effectively capture these progressionrelated signatures, and that carcinoma-specific signatures are predictive of survival for human breast cancer patients. To catalyze and support similar analyses and use of the CMT model by other biomedical researchers, we provide FREYA, a robust data processing pipeline and statistical analyses framework. molecular progression signal (Crawford and Oleksiak 2007;Storey et al. 2007;Hughes et al. 2015). In vitro approaches using human cell lines have been used to control for this sample heterogeneity; however, they are not fully reflective of in vivo tumor progression, including the effects of the microenvironment and the immune system (Gillet et al. 2011;Stein et al. 2004).The (in vivo) murine model of breast cancer has proven incredibly useful in deciphering cancer mechanisms, however, it requires experimental modification of the host via genetic modification (transgenic mice) or the transplantation of foreign tissue (xenografts) (Rangarajan and Weinberg 2003; Boone et al. 2015), which alters the tumor dynamics (Ben-David et al. 2017).Canine mammary tumor (CMT) is a promising emerging model for studying naturally occurring breast tumors (Klopfleisch et al. 2011;Liu et al. 2014;Pinho et al. 2012). CMTs and human breast cancer (BRCA) have similar histopathological profiles, including incidence rates, relationship with age and body mass index, hormonal influence, and clinical presentation as demonstrated in many clinical and smaller scale studies (Cekanova and Rathore 2014;Paoloni and Khanna 2008;Rowell et al. 2011;Kol et al. 2015;Kristiansen et al. 2016). Canine simple carcinomas share especially strong similarities with human breast cancer in terms of both histological and genetic features (Liu et al. 2014). Additionally, BRCA and CMT share chromosomal abnormalities such as copy number variations in several key breast cancer marker genes like MYC and PTEN (Borge et al. 2015). A significant advantage of the canine model is the high incidence of multiple naturally occurring tumors in the same patient (Sorenmo et al. ...

Uncontrolled symptoms are associated with unplanned acute care. Recognition of the complexity of symptom co-occurrence can drive improved management strategies.