As secondary antibody anti mouse, anti rat or anti rabbit horseradish peroxidase conjugated anti www.selleckchem.com/products/BI6727-Volasertib.html bodies were used. Protein bands were visualized with Western Lightning ECL and detected with a luminescent image analyzer. For all western blots at least three repetitions were performed. ELISA Microarray Phosphorylated proteins were quantified using an Array Tube based sand wich ELISA microarray, as previously described. 10 ul of protein sample was applied on the microarray. Phos phorylated proteins were detected using commercially available isotype specific capture antibodies and biotiny lated phospho specific detection antibodies. For the detection the microarray was incubated with streptavidin HRP conjugate followed by dye precipitation reaction using TrueBlue.

Transmission was measured with the Arraymate reader and protein concentration was quantified using standard calibration surfaces as described in Holenya et al. Background Predicting tumor response to radiotherapy is one of the major issues in cancer treatment. Predicting radiosensi tivity is important for improving clinical outcome and for personalized medicine decisions of the treatment needed, doses, and fractionation schedules. Under standing the mechanism of radiosensitiviy is also a major issue in identifying effective biomarkers and potential drug targets of radiosensitivity. Assays evaluating radiosensitivity have been developed and tested over the last 25 years. Recently, compre hensive gene expression analysis with high throughput technology has been used to identify radiosensitivity classifiers as well as to elucidate the radiosensitivity mechanism in many cancer types including colorectal, cervical, breast, head and neck cancer.

As treat ment response is related to the complex genetic biology of the cancer and host, biological interaction and factors that determine tumor response through the simultan eous genetic analysis of thousands of genes should be considered in predicting treatment outcome. The cancer cell line panel of the National Cancer Institute has been widely used for drug screening based on relevant gene expression. Although promising, these studies are confined to a single platform microarray and further validation and a larger dataset are needed. Moreover, individually identifying every gene with a statistically sig nificant response is not sufficient as a biological explan ation.

For this reason, gene set analysis is necessary, along with defining the biological processes or pathways in expression analysis. In this study, to identify a common radiosensitivity gene signature and relevant Dacomitinib biological processes from a large amount of data from multiple platforms, we ana lyzed four types of transcript microarray data from radiosensitivity profiling of the NCI 60 cell line panel.