Managing a Large-Scale Multiomics Project: A Team Science Case Study in Proteogenomics.

Highly collaborative scientists are often called on to extend their expertise to different types of projects and to expand the scope and scale of projects well beyond their previous experience. For a large-scale project involving "big data" to be successful, several different aspects of the research plan need to be developed and tested, which include but are not limited to the experimental design, sample collection, sample preparation, metadata recording, technical capability, data acquisition, approaches for data analysis, methods for integration of different data types, recruitment of additional expertise as needed to guide the project, and strategies for clear communication throughout the project. To capture this process, we describe an example project in proteogenomics that built on our collective expertise and experience. Key steps included definition of hypotheses, identification of an appropriate clinical cohort, pilot projects to assess feasibility, refinement of experimental designs, and extensive discussions involving the research team throughout the process. The goal of this chapter is to provide the reader with a set of guidelines to support development of other large-scale multiomics projects.

Proteogenomic landscape of squamous cell lung cancer.

How genomic and transcriptomic alterations affect the functional proteome in lung cancer is not fully understood. Here, we integrate DNA copy number, somatic mutations, RNA-sequencing, and expression proteomics in a cohort of 108 squamous cell lung cancer (SCC) patients. We identify three proteomic subtypes, two of which (Inflamed, Redox) comprise 87% of tumors. The Inflamed subtype is enriched with neutrophils, B-cells, and monocytes and expresses more PD-1. Redox tumours are enriched for oxidation-reduction and glutathione pathways and harbor more NFE2L2/KEAP1 alterations and copy gain in the 3q2 locus. Proteomic subtypes are not associated with patient survival. However, B-cell-rich tertiary lymph node structures, more common in Inflamed, are associated with better survival. We identify metabolic vulnerabilities (TP63, PSAT1, and TFRC) in Redox. Our work provides a powerful resource for lung SCC biology and suggests therapeutic opportunities based on redox metabolism and immune cell infiltrates.

Descriptive Analysis of Mortality Predictors in H1n1 Influenza in South Indian Patients.

BACKGROUND: H1N1 (hemagglutinin-H-neuroaminidase-N) influenza infection is associated with high morbidity and mortality because of associated complications and related factors. Predictors of mortality in H1N1 patients are studied with very few without seasonal/pandemic declaration. This study was carried out to describe the clinical features, complications and different risk factors that affect the outcome in the patients with confirmed H1N1influenza infection. METHODS: A retrospective study was done in Kasturba Medical College Hospital, Manipal, India by analyzing the medical records of 141 patients admitted from January, 2011 to June, 2015. RESULTS: Of the 141 patients in the study, 51.1% of the patients were female with a mean age of 32±16.2 years. Fever with headache was observed in 92.9% patients while cough in 78.7% patients and breathlessness in 54.6% patients. On the basis of disease severity, 53.2% of the patients were put on mechanical ventilation. For all the patients, treatment for influenza management began with oseltemivir. Diuretics, antianxiety and corticosteroids were given as supportive and symptomatic care which contributed to high mortality in hospitalized patients. Mean hospitalization period was 8.5 days. During the hospitalization, patients developed different complications i.e. 31.20% patients developed respiratory tract infections, while 17.7% patients developed ARDS and 14.4% patients developed sepsis. The mortality rate of this study population was found to be 29.1 %. CONCLUSION: It was observed that low oxygen saturation during admission, high blood urea level, use of diuretics, corticosteroids, anti-anxiety drugs and complications like ARDS, sepsis influence the mortality rate of patients with H1N1 infection.