4-protein signature predicting tamoxifen treatment outcome in recurrent breast cancer

Estrogen receptor (ER) positive tumors represent the majority of breast malignancies, and are effectively treated with hormonal therapies, such as tamoxifen. However, in the recurrent disease resistance to tamoxifen therapy is common and a major cause of death. In recent years, in-depth proteome analyses have enabled identification of clinically useful biomarkers, particularly, when heterogeneity in complex tumor tissue was reduced using laser capture microdissection (LCM). In the current study, we performed high resolution proteomic analysis on two cohorts of ER positive breast tumors derived from patients who either manifested good or poor outcome to tamoxifen treatment upon recurrence. A total of 112 fresh frozen tumors were collected from multiple medical centers and divided into two sets: an in-house training and a multi-center test set. Epithelial tumor cells were enriched with LCM and analyzed by nano-LC Orbitrap mass spectrometry (MS), which yielded >3000 and >4000 quantified proteins in the training and test sets, respectively. Raw data are available via ProteomeXchange with identifiers PXD000484 and PXD000485. Statistical analysis showed differential abundance of 99 proteins, of which a subset of 4 proteins was selected through a multivariate step-down to develop a predictor for tamoxifen treatment outcome. The 4-protein signature significantly predicted poor outcome patients in the test set, independent of predictive histopathological characteristics (hazard ratio [HR] = 2.17; 95% confidence interval [CI] = 1.15 to 4.17; multivariate Cox regression p value = 0.017). Immunohistochemical (IHC) staining of PDCD4, one of the signature proteins, on an independent set of formalin-fixed paraffin-embedded tumor tissues provided and independent technical validation (HR = 0.72; 95% CI = 0.57 to 0.92; multivariate Cox regression p value = 0.009). We hereby report the first validated protein predictor for tamoxifen treatment outcome in recurrent ER-positive breast cancer. IHC further showed that PDCD4 is an independent marker

Dashboard by-example: A hypergraph-based approach to on-demand data warehousing systems

The usefulness and ease of use of dashboards are essential elements in supporting interactive queries in data warehousing systems, as they provide the analysts the view of critical business metrics that reflect the business performance. However, on-demand dashboard development presents a challenging task for linking semantic equivalent data facts, discovering structural dependencies between and within data sources. In this paper, we introduce an improved approach for dashboard development that is supported by by-example framework and semantic-based data linkage management. In this Dashboard-by-example (DBE) framework, hypergraph-based techniques are adopted to acquire knowledge from heterogeneous and disparate data into homogeneous ontological clusters and partitions. Moreover, the use of hypergraph-guided data linkage provides multiple perspectives on the knowledge space, supporting interaction model to explore, contextualize and aggregate the data depending on the application needs. The applicability of our approach is discussed in a case study scenario, highlight the flexibility and efficiency in handling on-demand requirements in data warehousing systems

Facilitating business process interoperability in the on-demand context: State of the art and open issues

In today's evolving world, On-demand Business intelligence (BI) has been sparked as a potential solution providing companies of all sizes a new approach to meet dynamic business needs. Especially, in on-demand BI, all the components needed are integrated and hosted behind the scenes in what is termed a "software-as-a-service" offering. To be successful, business process interoperability has become high priority to provide an efficient support for the whole process of on-demand BI deployment. In this context, the main contribution of this paper is to provide a survey on the state of the art towards business process interoperability in the on-demand environments. Specifically, this paper also aims at a discussion of significant practices provided in the literature along the dimensions of interoperability, flexibility and adaptability. On this basis, some of the challenges that current approaches may face and potential solution that can meet the adaptability and interoperability requirements of ondemand business processes can be highlighted

Towards the development of large-scale data warehouse application frameworks

Facing with growing data volumes and deeper analysis requirements, current development of Business Intelligence (BI) and Data warehousing systems (DWHs) is a challenging and complicated task, which largely involves in ad-hoc integration and data re-engineerng. This arises an increasing requirement for a scalable application framework which can be used for the implementation and administration of diverse BI appliations in a straight forward and cost-efficient way. In this context, this paper presents a large-scale application framework for standardized BI applications, suporting the ability to define and construct data warehouse processes, new data analytics capabilities as well as to support the deployment requirements of multiscalable front-end applications. The core of the framework consists of defined metadata repositories with pre-built and function specific information templates as well as application definition. Moreover, the application framework is also based on workflow mechanisms for developing and running automatic data processing tasks. Hence, the framework is capable of offering an unified reference architecture to end users, which spans various aspects of development lifecycle and can be adapted or extended to better meet application- specific BI engineering process

On efficient and effective association rule mining from XML data

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)3180497-50

Mining frequent binary expressions

In data mining, searching for frequent patterns is a common basic operation. It forms the basis of many interesting decision support processes. In this paper we present a new type of patterns, binary expressions. Based on the properties of a specified binary test, such as reflexivity, transitivity and symmetry, we construct a generic algorithm that mines all frequent binary expressions. We present three applications of this new type of expressions: mining for rules, for horizontal decompositions, and in intensional database relations. Since the number of binary expressions can become exponentially large, we use data mining techniques to avoid exponential execution times. We present results of the algorithm that show an exponential gain in time due to a well chosen pruning technique