Optimal feature selection for sparse linear discriminant analysis and its applications in gene expression data

This work studies the theoretical rules of feature selection in linear discriminant analysis (LDA), and a new feature selection method is proposed for sparse linear discriminant analysis. An $l_1$ minimization method is used to select the important features from which the LDA will be constructed. The asymptotic results of this proposed two-stage LDA (TLDA) are studied, demonstrating that TLDA is an optimal classification rule whose convergence rate is the best compared to existing methods. The experiments on simulated and real datasets are consistent with the theoretical results and show that TLDA performs favorably in comparison with current methods. Overall, TLDA uses a lower minimum number of features or genes than other approaches to achieve a better result with a reduced misclassification rate.Comment: 20 pages, 3 figures, 5 tables, accepted by Computational Statistics and Data Analysi

Phosphorylation-dependent differences in CXCR4-LASP1-AKT1 Interaction between breast cancer and chronic myeloid leukemia

The serine/threonine protein kinase AKT1 is a downstream target of the chemokine receptor4 (CXCR4), and both proteins play a central role in the modulation of diverse cellular processes,including proliferation and cell survival. While in chronic myeloid leukemia (CML) the CXCR4is downregulated, thereby promoting the mobilization of progenitor cells into blood, the receptoris highly expressed in breast cancer cells, favoring the migratory capacity of these cells. Recently,the LIM and SH3 domain protein 1 (LASP1) has been described as a novel CXCR4 binding partnerand as a promoter of the PI3K/AKT pathway. In this study, we uncovered a direct binding ofLASP1, phosphorylated at S146, to both CXCR4 and AKT1, as shown by immunoprecipitation assays,pull-down experiments, and immunohistochemistry data. In contrast, phosphorylation of LASP1at Y171 abrogated these interactions, suggesting that both LASP1 phospho-forms interact. Finally,findings demonstrating different phosphorylation patterns of LASP1 in breast cancer and chronicmyeloid leukemia may have implications for CXCR4 function and tyrosine kinase inhibitor treatment

Predicting Relapse Prior to Transplantation in Chronic Myeloid Leukemia by Integrating Expert Knowledge and Expression Data

Motivation: Selecting a small number of signature genes for accurate classification of samples is essential for the development of diagnostic tests. However, many genes are highly correlated in gene expression data, and hence, many possible sets of genes are potential classifiers. Because treatment outcomes are poor in advanced chronic myeloid leukemia (CML), we hypothesized that expression of classifiers of advanced phase CML when detected in early CML [chronic phase (CP) CML], correlates with subsequent poorer therapeutic outcome. Results: We developed a method that integrates gene expression data with expert knowledge and predicted functional relationships using iterative Bayesian model averaging. Applying our integrated method to CML, we identified small sets of signature genes that are highly predictive of disease phases and that are more robust and stable than using expression data alone. The accuracy of our algorithm was evaluated using cross-validation on the gene expression data. We then tested the hypothesis that gene sets associated with advanced phase CML would predict relapse after allogeneic transplantation in 176 independent CP CML cases. Our gene signatures of advanced phase CML are predictive of relapse even after adjustment for known risk factors associated with transplant outcomes