Distinct genes related to drug response identified in ER positive and ER negative breast cancer cell lines

Breast cancer patients have different responses to chemotherapeutic treatments. Genes associated with drug response can provide insight to understand the mechanisms of drug resistance, identify promising therapeutic opportunities, and facilitate personalized treatment. Estrogen receptor (ER) positive and ER negative breast cancer have distinct clinical behavior and molecular properties. However, to date, few studies have rigorously assessed drug response genes in them. In this study, our goal was to systematically identify genes associated with multidrug response in ER positive and ER negative breast cancer cell lines. We tested 27 human breast cell lines for response to seven chemotherapeutic agents (cyclophosphamide, docetaxel, doxorubicin, epirubicin, fluorouracil, gemcitabine, and paclitaxel). We integrated publicly available gene expression profiles of these cell lines with their in vitro drug response patterns, then applied meta-analysis to identify genes related to multidrug response in ER positive and ER negative cells separately. One hundred eighty-eight genes were identified as related to multidrug response in ER positive and 32 genes in ER negative breast cell lines. Of these, only three genes (DBI, TOP2A, and PMVK) were common to both cell types. TOP2A was positively associated with drug response, and DBI was negatively associated with drug response. Interestingly, PMVK was positively associated with drug response in ER positive cells and negatively in ER negative cells. Functional analysis showed that while cell cycle affects drug response in both ER positive and negative cells, most biological processes that are involved in drug response are distinct. A number of signaling pathways that are uniquely enriched in ER positive cells have complex cross talk with ER signaling, while in ER negative cells, enriched pathways are related to metabolic functions. Taken together, our analysis indicates that distinct mechanisms are involved in multidrug response in ER positive and ER negative breast cells. © 2012 Shen et al

Predicting Academic Performance: A Systematic Literature Review

The ability to predict student performance in a course or program creates opportunities to improve educational outcomes. With effective performance prediction approaches, instructors can allocate resources and instruction more accurately. Research in this area seeks to identify features that can be used to make predictions, to identify algorithms that can improve predictions, and to quantify aspects of student performance. Moreover, research in predicting student performance seeks to determine interrelated features and to identify the underlying reasons why certain features work better than others. This working group report presents a systematic literature review of work in the area of predicting student performance. Our analysis shows a clearly increasing amount of research in this area, as well as an increasing variety of techniques used. At the same time, the review uncovered a number of issues with research quality that drives a need for the community to provide more detailed reporting of methods and results and to increase efforts to validate and replicate work.Peer reviewe

Modification of Peck Formula to Predict Surface Settlement of Tunnel Construction in Water-Rich Sandy Cobble Strata and Its Program Implementation

There are few studies on the land subsidence induced by shield tunneling in the water-rich sandy gravel stratum, which is of high research value. Linear regression and measured data were employed in this study to investigate the land subsidence induced by shield tunneling when crossing the water-rich sandy gravel stratum from Mudan Dadao Station to Longmen Dadao station of Luoyang Metro Line 2. The maximum land subsidence correction coefficient, α, and the settlement trough width correction coefficient, β, were introduced to modify the peck formula to predict land subsidence induced by shield tunneling in Luoyang’s water-rich sandy gravel stratum. It was discovered that the original Peck formula needs to be modified because its prediction result was significantly larger than the actual value. When the value ranges of α and β in the modified Peck formula were 0.379~0.690 and 0.455~0.508, respectively, the modified Peck formula presented a minor error, in terms of the prediction curve, compared with the original formula, and the prediction result was more reliable. The best prediction result could be obtained when α = 0.535 and β = 0.482. In addition, Python could effectively improve the calculation efficiency of the Peck formula modification

Association between gene expression of three genes [TOP2A (A), DBI (B) and PMVK(C)] and drug response in ER positive and ER negative breast cell lines.

<p>The x-axis represents cell line drug response, represented as AUC value; higher AUC values are correlated with drug resistance, while low AUC values are correlated with drug sensitivity. The y-axis represents the expression of genes in cell lines.</p

Heatmap of gene-drug correlation.

<p>Each block represents a gene-drug correlation in ER positive or ER negative cell lines. Red boxes represent high negative gene-drug correlations, i.e., cell lines with higher gene expression tend to be more resistant, and green boxes represent high positive gene-drug correlations, i.e. cell lines with higher gene expression tend to be more sensitive. The bar across the top indicates the multidrug response genes identified in ER positive and ER negative cell lines. Yellow corresponds to ER negative and blue corresponds to ER positive.</p

Summary of chemosensitivity of 27 breast cell lines to 7 different drugs, measured by ChemoFx, their ER status and subtype.

<p>Cell lines are ranked in descending order of the average of chemosensitivity score (AUC), with lower AUC scores indicating greater sensitivity. ER status and subtype information was from <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0040900#pone.0040900-Neve1" target="_blank">[14]</a>.</p>a<p>is a non-malignant cell line since it was derived from a reduction mammoplasty.</p

2D scatter plot of chemotherapeutic agents with respect to the first and second principal components.

<p>2D scatter plot of chemotherapeutic agents with respect to the first and second principal components.</p

Enriched pathways identified in ER positive and negative breast cancer cells by IPA.

<p>Enriched pathways identified in ER positive and negative breast cancer cells by IPA.</p