Application of cell line based genomic predictors to predict response to targeted therapies in breast cancer

Cancer cell lines can be treated with a drug and the molecular comparison of responders and non-responders may yield potential predictors that could be tested in the clinic. It is a bioinformatics challenge to apply the cell line-derived multivariable response predictors to patients who respond to therapy. Using the gene expression data from 23 breast cancer cell lines, I developed three predictors of dasatinib sensitivity by selecting differentially expressed genes and applying different classification algorithms. The performance of these predictors on independent cell lines with known dasatinib response was tested. The predictor based on weighted voting method has the best overall performance. It correctly predicted dasatinib sensitivity in 11 out of 12 (92%) breast and 17 out of 23 (74%) lung cancer cell lines. These predictors were then applied to the gene expression data from 133 breast cancer patients in an attempt to predict how the patients might respond to dasatinib therapy. Two predictors identified 13 patients in common to be dasatinib sensitive. Sixty two percent of these cases are triple negative (ER-negative, HER2-negative and PR-negative) and 76% are double negative. The result is consistent with the findings from other studies, which identified a target population for dasatinib treatment to be triple negative or basal breast cancer subtype. In conclusion, we think that the cell line-derived dasatinib classifiers can be applied to the human patients. ^

Campaign contributions to congressional candidates by the healthcare sector from 1990 to 2008

Stakeholder groups with special interests as donors to finance congressional campaigns have been a controversial issue in the United Sates. While previous studies concentrated on whether a connection existed between the campaign contributions provided by stakeholder groups and the voting behavior of congressional members, there is little evidence to show the trend of allocation of their campaign contributions to their favorite candidates during the elections. This issue has become increasingly important in the health sector since the health care reform bill was passed in early 2010.^ This study examined the long-term trend of campaign contributions offered by various top healthcare stakeholder groups to particular political parties (i.e. Democrat and Republican). The main focus of this paper was to observe and describe the financial donations provided by these healthcare stakeholder groups in the congressional election cycles from 1990 to 2008 in order to obtain an overview of their patterns of campaign contributions. Their contributing behaviors were characterized based on the campaign finance data collected by the Center for Responsive Politics (CRP). Specifically, I answered the questions: (1) to which political party did specific healthcare stakeholder groups give money and (2) what was the pattern of their campaign contributions from 1990 to 2008?^ The findings of my study revealed that the healthcare stakeholder groups had different political party preferences and partisanship orientations regarding the Democratic or Republican Party. These differences were obvious throughout the election cycles from 1990 to 2008 and their distinct patterns of financial contribution were evident across industries in the health sector as well. Among all the healthcare stakeholder groups in this study, physicians were the top contributors in the congressional election. The pharmaceutical industry was the only group where the majority of contribution funds were allocated to Republicans in every election period studied. This study found that no interest group has succeeded in electing the preferred congressional candidate by giving the majority of its financial support to the winning party in every election. Chiropractors, hospitals/nursing homes, and health services/HMOs performed better than other healthcare stakeholder groups by supporting the electoral winner 8 out of 9 election cycles.^