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1.
Figure 6

Figure 6. From: CellMiner: a web-based suite of genomic and pharmacologic tools to explore transcript and drug patterns in the NCI-60 cell line set.

Recognition of drugs with similar mechanism of action, using erlotinib as an input for pattern comparison. A. Use the “Pattern comparison” tool (Figure 4) selected for “Drug NSC” and input 718781 (the NSC for erlotinib). The bar graph shown is that from the “Z score determination” tool from Figure 2, selected for “Gene transcript level”. B. The top six rows of the “Significant drug correlations” output from the “Pattern comparison” tool, identifying two other FDA-approved drugs and one in advanced clinical trials. C. The bar graphs shown are from the “Z score determination” tool from Figure 2, selected for “Gene transcript level” for the two FDA-approved, and one in clinical trials drugs identified in B.

William C. Reinhold, et al. Cancer Res. ;72(14):3499-3511.
2.
Figure 5

Figure 5. From: CellMiner: a web-based suite of genomic and pharmacologic tools to explore transcript and drug patterns in the NCI-60 cell line set.

Pattern comparison example for a colon-specific input pattern. The input for this analysis consisted of ones for all non-colon cell lines, and fives for all colon. The three forms of output described in Figure 4, genes, microRNAs, and drugs, are shown. The top three genes by correlation are shown in tabular fashion. Bar graphs for two of the genes, generated as described in Figure 2, display the data visually. The top two microRNAs by correlation are shown next in graphical fashion, generated as described in Figure 3. The top three drugs with either the FDA-approved or clinical trials by correlation, and one clinically untested compound are shown in tabular fashion. Bar graphs for two of these, generated as described in Figure 2, are displayed. The red star in all cases indicates prior literature association with colon cancer.

William C. Reinhold, et al. Cancer Res. ;72(14):3499-3511.
3.
Figure 1

Figure 1. From: CellMiner: a web-based suite of genomic and pharmacologic tools to explore transcript and drug patterns in the NCI-60 cell line set.

Snapshot of the NCI-60 Analysis Website, a suite of web-based tools designed to facilitate rapid pharmacologic and genomic bioinformatics for the NCI-60 cell lines. A. These tools are accessible at the CellMiner website (http://discover.nci.nih.gov/cellminer/) by clicking on the NCI-60 Analysis Tools tab. B. The analysis of interest (Z score determination, Mean centered graphs for microRNAs, or Pattern comparisons) is selected in Step1 using the check boxes. The specific identifier or pattern of interest is selected in Step 2, either by typing in an identifier using the “Input list” function, or by uploading a file using the “Upload file” function. A maximum of 150 identifiers (genes, microRNAs or drugs) can be input at once. The results are e-mailed to the address entered in Step 3. Multiple check boxes may be selected for a single input. Radio buttons (circles) for an analysis type are mutually exclusive.

William C. Reinhold, et al. Cancer Res. ;72(14):3499-3511.
4.
Figure 3

Figure 3. From: CellMiner: a web-based suite of genomic and pharmacologic tools to explore transcript and drug patterns in the NCI-60 cell line set.

Relative microRNA transcript expression levels in the NCI-60. A. Input of data. Check the “microRNA mean centered graphs” check-box in Step 1. To access the list of microRNAs names to use, go to footnote 2, “List of microRNA identifiers” and click “download”. In Step 2, the user chooses whether to type in the input, or upload it as a file (.txt or .xls) by selecting “Input list” or “Upload file”, respectively. If typing in the input, input the microRNA name(s) in the “Input the identifiers” box. In Step 3, enter the e-mail address to send the results to. B. The output includes a plot of the mean-centered average log2 intensities along with their numerical values (data not shown). C. Mean log2 intensity values for range, minimum, maximum, average, and standard deviation for all cell lines are included to assist in data interpretation, as well as a histogram of the cell lines average intensities. For the histogram, the x-axis is the average intensity for the cell lines, and the y-axis the frequency at which they occur.

William C. Reinhold, et al. Cancer Res. ;72(14):3499-3511.
5.
Figure 4

Figure 4. From: CellMiner: a web-based suite of genomic and pharmacologic tools to explore transcript and drug patterns in the NCI-60 cell line set.

Pattern comparison to transcript expression, microRNA expression and drug activity levels in the NCI-60. A. Choose your input type by checking the “Pattern comparison” check-box, and then choose either the “Gene symbol”, “microRNA”, “Drug NSC#”, or “Pattern in 60 element array” radio buttons in Step 1. In Step 2, the user chooses whether to type in the input, or upload it as a file by selecting “Input list” or “Upload file”, respectively (use the same identifiers as in Figure 2 and 3). To input your own template, use the “Pattern comparison template file” download from footnote 3, with your numerical values and “na” for missing or to be ignored values. B. Significant gene correlation output, given for all genes that match your input pattern at a significance level of p<0.05. C. Significant microRNA correlations output, given for all microRNAs that match your input pattern at a significance level of p<0.05. D. Significant drug correlations output, given for all compounds that match your input pattern at a significance level of p<0.05. Only the top and bottom of the lists are shown in each case.

William C. Reinhold, et al. Cancer Res. ;72(14):3499-3511.
6.
Figure 2

Figure 2. From: CellMiner: a web-based suite of genomic and pharmacologic tools to explore transcript and drug patterns in the NCI-60 cell line set.

Relative transcript expression and drug activity levels in the NCI-60. A. Check the “Z score determination” check-box, and then choose either the “Gene transcript level” or the “Drug activity” radio button in step 1. In those instances in which multiple genes or drugs are entered, a cross correlation of all genes or drugs entered may be included by checking the “Include cross-correlations” check box. In Step 2, the user chooses whether to type in the input, or upload it as a file by selecting “Input list” or “Upload file” (as .txt or .xls), respectively. B. For relative transcript expression levels, input the gene name(s) using the “official” (HUGO) name. C. A graphical z score composite of all transcript probes that pass quality control is generated, along with the numerical values (data not shown). D. Mean log2 intensity values for range, minimum, maximum, average, and standard deviation for all Affymetrix probes are included to assist in data interpretation, as well as chromosomal location. E. For relative drug activity levels, input the drug “official” NSC number(s) (see “Download NSCs” file from Figure 1B, Step 1, for a listing of these). F. A graphical z score composite of all drug experiments that pass quality control is generated, along with the numerical values (data not shown). G. Mean log10 intensity values for range, minimum, maximum, average, and standard deviation for all experiments are included to assist in data interpretation, as well as a histogram of the cell lines average activities. For the histogram, the x-axis is the average experiment activity for the cell lines, and the y-axis the frequency at which they occur. The red arrow indicates the most resistant cell line to doxorubicin, NCI-ADR-RES.

William C. Reinhold, et al. Cancer Res. ;72(14):3499-3511.

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