Statistical analysis of molecular markers associated with response The PG 11047 GI50 levels for all cell lines were correlated with the molecular features measured for each cell lines selleck Seliciclib as reported by Neve et al. We used a statistical approach based on adaptive linear splines in order to identify the molecular correlates of response Inhibitors,Modulators,Libraries to PG 11047. These are variants of the linear splines method described previously. Briefly, we used a non parametric regression method to model non linear relationships between molecular cor relates and response. The goodness of fit was assessed by evaluating a P value corresponding to the F statistic for the fit. P values were corrected for multiple hypothe ses testing using the false discovery rate method. This process identified 250 genes whose transcripts were asso ciated with response.

From these, a 13 gene set was devel oped in order to predict a quantitative response among the cell lines using Monte Carlo cross validation. The complete panel of cell lines was used for this purpose. In MCCV, the samples are randomly partitioned into training sets and test sets. The Inhibitors,Modulators,Libraries marker genes found to be significant in the training set are Inhibitors,Modulators,Libraries then evaluated for their predictive accuracy in the test set. This random partitioning process is iterated multiple times. The 13 genes were consistently found to be significant across the iterations. More details are described in. The final model was determined via leave one out cross validation. Ingenuity Pathway Analysis knowledgebase molecular interactome was applied to the 250 predictor genes to identify the networks of genes, generated algorithmically based on their connectiv ity.

Network genes were further analysed for significant pathways associations. Results Effect Inhibitors,Modulators,Libraries of polyamine analogue PG 11047 on breast cancer cell lines Forty two breast cancer cell lines representing luminal, basal and claudin low breast cancer subtypes and six non malignant breast cell lines were treated with PG 11047 in doses ranging from 13 nM to 5 mM for 72 h. The GI50 dose was calculated for each of the cell lines and ranged from 0. 4 M to 5 mM with a median GI50 at 31. 5 M. The distribution of GI50 values for the cell lines arranged from most sensitive to most resistant is shown in Figure 1 along with subtype classification. These data show that the basal and claudin low subtypes Inhibitors,Modulators,Libraries were inhib ited at the lowest levels of PG 11047.

The TGI showed similar subtype specificity. Predictive markers for PG 11047 response We correlated the GI50 values for the cell lines in the panel with their pretreatment genomic, transcriptional and pro teomic profiles in order to identify molecular factors asso ciated with cellular response to PG 11047. www.selleckchem.com/products/Imatinib-Mesylate.html Additional Files 2, 3 and 4 list mRNA, DNA and protein features that were significantly associated with response. The molecu lar features associated with response or resistance when present at elevated levels are listed as response predictors.