Summary We analyzed primary breast cancers by genomic DNA copy number arrays, DNA methylation, exome sequencing, mRNA arrays, microRNA sequencing and reverse phase protein arrays. Our ability to integrate information across platforms provided key insights into previously-defined gene expression subtypes and demonstrated the existence of four main breast cancer classes when combining data from five platforms, each of which shows significant molecular heterogeneity. Somatic mutations in only three genes (TP53, PIK3CA and GATA3) occurred at > 10% incidence across all breast cancers; however, there were numerous subtype-associated and novel gene mutations including the enrichment of specific mutations in GATA3, PIK3CA and MAP3K1 with the Luminal A subtype. We identified two novel protein expression-defined subgroups, possibly contributed by stromal/microenvironmental elements, and integrated analyses identified specific signaling pathways dominant in each molecular subtype including a HER2/p-HER2/HER1/p-HER1 signature within the HER2-Enriched expression subtype. Comparison of Basal-like breast tumors with high-grade Serous Ovarian tumors showed many molecular commonalities, suggesting a related etiology and similar therapeutic opportunities. The biologic finding of the four main breast cancer subtypes caused by different subsets of genetic and epigenetic abnormalities raises the hypothesis that much of the clinically observable plasticity and heterogeneity occurs within, and not across, these major biologic subtypes of breast cancer.

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Project management and project scheduling are crucial to help development teams keep track of timing as well as resource allocation. In order to manage software projects, project managers need to anticipate, analyze the risk factors that may occur as well as their impacts on the progress of the project, and assess and adapt the project resource allocation. This paper concentrates on a quantitative approach for risk analysis in software project scheduling by taking advantage of Bayesian networks capacity (including related mathematical calculations) in modeling and assessing uncertainty and incorporates them in software project scheduling with program evaluation and review technique (PERT). Common risk factors in project scheduling are also examined, and a Bayesian networks model of 19 common risk factors and their causal relationships is proposed and confirmed. The research also borrows and implements categories and levels of risk from construction projects into software projects. A tool was built to experiment and validate the proposed model.