The RARA receptor has also been re cently identified as being co amplified with

As with the BT474 cell line there was no separation at 2 hours post treatment with 0. 1 uM and 1 uM lapati nib coloured magenta and grey respectively, suggesting that the affects of the drug are not yet apparent at this Amuvatinib PDGFR 阻害剤 time point in both cell lines. However, in this cell line the split occurred at both lapatinib dosages. Again, as with the BT474 data, these analyses were used to guide our comparisons for the supervised CIA and the expres sion analysis. The motifs associated with this split in the data are in the same orientation relative to the origin to our split of interest in Figure 2b. These include the VDRRXR hetero dimer. This heterodimer has been previously associated with numerous cancers, including breast cancer.

Validation of the 6 comparisons chosen for supervised CIA The results from unsupervised CIA suggests that there was no difference between control and treated cells at both the high and low dose lapatinib at the 2 hour time point in both cell lines, and that there was no difference between treated and untreated BT474 cells at the AT-406 6 hr and 12 hr time point when low dose lapatinib was used. If this is the case there should be few differentially regu lated genes at the early time point in both cell lines and at the low dose in the BT474 cell line. The results from these comparisons are shown in Additional file 1. On average there are 60 differentially regulated genes in these comparisons compared to over 2,500 differen tially regulated genes when using the comparisons out lined in Table 1. This marked difference is a strong validation of our approach.

Supervised CIA identifies 8 putative transcription factors associated with the response to lapatinib In order to systematically identify the TFBSs specifically associated with the response to lapatinib in these cell lines, we performed a supervised AG-490 EGFR 阻害剤 analysis of the data, combining CIA and BGA, as described. CIA was performed twice in the BT474 dataset and four times in the SKBR3 dataset. This resulted in six ranked lists of TFBS associated with a response to lapatinib treatment. The 6 transcrip tion factor motifs which were consistently ranked highly across the six comparisons are displayed in Table 2. The individual ranking for each of the 6 comparisons are available in Additional file 2. From these motifs we can infer the 8 transcription factors which are driving the re sponse to lapatinib in these cell lines.

Differential gene expression analysis of the BT474 and SKBR3 cell lines identifies a list of 421 genes associated with response to lapatinib The same six comparisons outlined in Table 1 were used to determine the genes which consistently respond to lapatinib treatment in both cell lines. In total, there were 421 distinct genes consistently dysregulated across the six comparisons. The full list of dysregulated genes, with associated fold changes and p values, is available in Additional file 3. A panel of 19 genes, in addition to the identified TFs, were selected for further analyses using qPCR based on varying combinations of the following criteria. the magnitude of response to lapatinib, whether the selected genes were predicted targets of the 8 TFs, the involvement of the gene in important oncogenic processes.