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  2. POPS Lung Log In Page
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  4. POPS Lung Registration
  5. Fill out the required fields on the form.
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  9. POPS Lung Registration Activation
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Description: Query all screening data, including dose response curves, associated AUC and ED50 values. All resulting analyses were run using both ED50 and AUC as sensitivity metrics.

    Activity Query Search Field
  1. Enter search input using the text box or upload a file. Separate unique items by comma, space, or new line.
    • The uploaded file must comply with the following:
      • It must be a text file (.txt)
      • It must be smaller than 2kB
  2. Select input type from the following:
    • SWID: A chemical's unique UT Southwestern ID.
    • Common Name: A chemical's common name. (Limited availability)
  3. Activity Result Table
  4. The Results Table will be displayed below the search panel. Each row will contain:
    • SWID: The chemical's unique ID. Click to see its structure.
    • Sens. Med.: Median value of the sensitive chemicals
    • Resist. Med.: Median value of the resitant chemicals
    • Median Ratio: Ratio of the median sensitivity and median resistance values
    • Metric: Sensitivity metric (ED50 or AUC)
      • ED50: Median Effective Dose (ED50).
      • AUC: Area Under the Curve.
    • Median Value: The heatmap representing calculated AUC/ED50 values.
      • Hover over a cell to see the specific value and cell line in the information box in the lower left.
      • Click on a cell to open its cell line's dose response curve.
  5. Activity Result Customization
  6. Download your results in CSV format by using the buttons in the Filter Your Results panel.
  7. Toggle the visibility of the metrics by using the appropriate checkboxes.
  8. Sort the heatmap according to your metric of choice by selecting an option from the drop-down menu.
  9. Highlight a specific cell line throughout the heatmap by selecting it from the drop-down menu.
  10. Check to display relevant links for each chemical.
    • PubChem: Open a chemical structure search in PubChem.
  11. Click to filter individual chemicals from the list.
    • Select items from the Displayed Compounds list and click the chevrons to hide them.
    • Select items from the Hidden Compounds list and click the other chevrons to show them.
  12. Click to return to the previous panel.

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Description: Query quantitative predictive features assigned from measures of (1) Illumina Bead Array Expression Data (2) RNAseq (3) whole exome sequencing mutation calls (4) Illumina SNP array-based copy number quantification (5) reverse-phase protein arrays (RPPA) (5) carbon-tracing metabolomics flux analyses. Chemical/genetic relationships for 171 chemicals passed significance thresholds and are available.

    Elastic Net Query Search Field
  1. Enter search input using the text box or upload a file. Separate unique items by comma, space, or new line.
    • The uploaded file must comply with the following:
      • It must be a text file (.txt)
      • It must be smaller than 2kB
  2. Select corresponding search type from the following:
    • Marker: A predicted feature (eg. gene name).
    • SWID: A chemical's unique UT Southwestern ID.
    • Common Name: A chemical's common name. (Limited availability)
  3. Select one or more feature sets (optional).
    • If no feature set is selected, the query will include all feature sets available
    • Activate the checkmark next to the title to select/unselect all feature sets.
    • The following feature sets are available in the Elastic Net analysis:
      • Copy Number: Copy Number Variation of a gene from arrayCGH arrays.
      • Expression: Illumina V3 beadchip microarray expression values.
      • Metabolomics: Cells were pre-incubated with media containing either heavy labeled (13C) glucose or glutamine for either 6 or 24 hours. Mass spectrometry analysis was used to look at incorporation of heavy label into the metabolites fumarate, citrate, malate, lactate. All metabolites traces are in the form XXXYZZmN:
        • XXX = Metabolite (cit: citrate; mal: malate; lac: lactate; fum: fumarate)
        • Y = Carbon source (Q: glutamine; G: glucose)
        • ZZ = Time of incubation (24: 24 hours; 6: 6 hours)
        • N = Number of labeled carbons (0: no labeled carbons, 1: 1 labeled carbon, etc)
      • Mutation: Mutations defined by whole exome sequencing. Cell lines that did not have a matched normal pair were further filtered to identify most likely somatic variants. Values are binarized (1=mutant ; 0=wild-type).
      • RNA Seq: Gene expression by RNA sequence analysis.
      • Rppa: Protein expression by Reverse Phase Protein Array
      • SNP CNV: Copy Number Variation defined by illumina SNP arrays.
      • SNP MUT: Binarized value defining a gene's mutation, amplification, or deletion status. (1: mutant and/or amplified/deleted; 0: wild-type).
  4. Select a metric of your interest (optional).
    • ED50: Median Effective Dose (ED50).
    • AUC: Area Under the Curve.
  5. Elastic Net Result Table
  6. The Results Table will be displayed below the search panel.
    • 200 results will be listed at a time.
    • Request the previous or next set of 200 results using the navigation arrows flanking the table's title.
  7. Each entry will contain:
    • SWID: The chemical's unique ID. Click to see its structure.
    • Marker: The predictive feature from corresponding dataset.
      • Click to see the heatmap.
    • Weight: Elastic net derived weight.
    • Frequency: Bootstrapping frequency of occurrence (out of 100 permutations).
    • Type: Feature Set (as described above).
    • ROC pval: A P-value assessing predictive capacity of assigned features from a particular feature set.
      • Click to reveal the ROC curve (left) and the AUC/ED50 for each cell line plotted as a function of the prediction value (right). Vertical red lines define manually annotated cutoffs for sensitivity and resistance to each chemical. A horizontal red line is indicated at y=0 to indicate the cutoff for elastic net predictions. y>0 indicates predicted resistance and y<0 indicates predicted sensitivity. Values in the top right quadrant are true resistant cell lines (orange) and values in the bottom left quadrant are true sensitive cell lines (blue).
    • Metric: Metric on which the analysis was run (ED50 or AUC).
    • Scale: Indicates whether the sensitivity vector was log-transformed (Log10) or not (linear) prior to running the Elastic Net.
  8. Elastic Net Result Customization
  9. Download your results in CSV format by using the buttons in the Filter Your Results panel.
  10. Filter different metrics from the results by using the drop-down menu.
  11. Filter feature sets by using the drop-down menu.
  12. Filter the result's scale by using the droop-down menu.
  13. Filter weights by adjusting the handles on the corresponding slider.
    • Results with weight values between the minimum and maximum handles will be filtered out.
  14. Filter frequency by adjusting the handle on the corresponding slider.
    • Results with frequency values below the handle will be filtered out.
  15. Click to filter individual chemicals from the list.
  16. Select items from the Displayed Compounds list and click the chevrons to hide them.
  17. Select items from the Hidden Compounds list and click the other chevrons to show them.
  18. Select filter criteria from the drop-down menu.
  19. Click to return to the previous panel.

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Description: A modification to a Kolmogorov-Smirnov (KS) test was made to query single gene mutations or pairwise combinations of co-occurring gene mutations to rank those that can predict the best selective sensitivity to each chemical.

    Scanning KS Query Search Field
  1. Enter search input using the text box or upload a file. Separate unique items by comma, space, or new line.
    • The uploaded file must comply with the following:
      • It must be a text file (.txt)
      • It must be smaller than 2kB
  2. Select input type from the following:
    • SWID: A chemical's unique UT Southwestern ID.
    • Common Name: A chemical's common name. (Limited availability)
  3. Enter a gene of interest. (optional).
  4. Enter a maximum P-value filter. (optional).
  5. Enter a maximum median ratio filter. (optional).
  6. Select a sensitivity metric of interest (optional).
    • ED50: Median Effective Dose (ED50).
    • AUC: Area Under the Curve.
  7. Select output type from the following:
    • Table: A text table listing all results.
    • Plots: A grid containing the plot images of all results.
  8. Scanning KS Result Table
  9. The Results Table will be displayed below the search panel.
    • 200 results will be listed at a time.
    • Request the previous or next set of 200 results using the navigation arrows flanking the result's title.
  10. Each row will contain:
    • SWID: The chemical's unique ID. Click to see its structure.
    • Marker: Single gene or pairwise combinations of co-occuring mutations. Click to see the corresponding ECDF plot (red=mutant; blue=wild-type) and activity heatmap.
    • Med Ratio: log2 of the median mutant ED50 (or AUC) value divided by the median wild-type ED50 (or AUC) value.
    • Pvalue: P-value determined by the Scanning KS test.
    • Metric: Sensitivity metric (ED50 or AUC).
    • Frequency of Occurrence in TCGA: Frequency in which mutations co-occurr in TCGA dataset.
    • Mutant Count: Number of mutant cell lines in mutant distribution.
    • Plots: Corresponding ECDF plot. Click to enlarge the plot and show activity heatmap.
  11. Scanning KS Result Customization
  12. Download your results in CSV format by using the buttons in the Filter Your Results panel.
  13. Filter different metrics from the results by using the drop-down menu.
  14. Filter median ratio by adjusting the handles on the corresponding slider.
    • Results with median ratios above the handle will be filtered out.
  15. Filter occurrence in TCGApercentage by adjusting the handles on the corresponding slider.
    • Results with percentages below the handle will be filtered out.
  16. Filter mutant count by adjusting the handle on the corresponding slider.
    • Results with mutant counts below the handle will be filtered out.
  17. Filter P-values by adjusting the handle on the corresponding slider.
    • Results with mutant counts above the handle will be filtered out.
    • If no results above 2x10-5 are available, the slider will be disabled.
  18. Click to filter individual chemicals from the list.
  19. Select items from the Displayed Compounds list and click the chevrons to hide them.
  20. Select items from the Hidden Compounds list and click the other chevrons to show them.
  21. Select filter criteria from the drop-down menu.
  22. Click to return to the previous panel.

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Description: A panel of 124 NSCLC cell lines was subjected to whole exome sequencing (average 60X coverage). 34/124 have corresponding tumor and matched B-cell non-tumorigenic DNA from which definitive somatically acquired lesions can be determined. For the remaining 90 cell lines, a series of filters leveraging the tumor/normal matched dataset and publically available data were used to filter out probable germline alterations and enrich for somatically acquired mutations. This mutation query page displays searchable filtered mutation calls for all 124 cell lines. Lollipop plots also compare positional mutation calls in the UTSW cell line panel to LUAD and LUSC tumors in the TCGA and in an MD Anderson (MDACC) tumor panel.

    Mutation Query Search Field
  1. Enter search input using the text box or upload a file. Separate unique items by comma, space, or new line.
    • The uploaded file must comply with the following:
      • It must be a text file (.txt)
      • It must be smaller than 2kB
  2. Select input type from the following:
    • Exact: Search for the exact gene symbol.
    • Similar: Search for a gene symbol that contains the queried text.
  3. Select one or more cell lines (optional).
    • If no cell line is selected, the query will include all cell lines available
    • Activate the checkmark next to the title to select/unselect all cell lines.
  4. Mutation Result Table
  5. The Results Table will be displayed below the search panel.
    • 200 results will be listed at a time.
    • Request the previous or next set of 200 results using the navigation arrows flanking the table's title.
  6. Each row will contain:
    • Gene: Gene's name.
      • Click to see lolliplot plots comparing mutation positional information in the UTSW cell line panel to tumors in the TCGA and an MD Anderson Cancer Center (MDACC) dataset.
      • The top panel indicates PFAM annotated domain for a given gene. The remaining second through fourth panels indicate mutation frequency (y-axis) as a function of amino acid position along the protein (x-axis) in the UTSW cell line panel, TCGA, and MDACC tumor panels, respectively. A ‘M’ or a ‘U’ in the UTSW panel indicates the corresponding cell line was derived from the tumor/matched (34 cell lines) or tumor cell line dataset (90 cell lines). Pegs are colored according to mutation type (red = non-sense; black=missense) and circles are colored according to tumor of origin.
    • Mutation Status: Gene mutation status for all cell lines requested.
      • Black cells are mutated cell lines for the given gene.
      • White cells are wild type cell lines for the given gene.
      • Hover over a cell to see the mutation summary (number of mutations in gene, location).
      • Click on a cell to open the mutation details.
  7. Mutation Result Customization
  8. Download your results' mutation status in CSV format by using the buttons in the Filter Your Results panel.
  9. Highlight the variant type of interest by using the drop-down menu.
  10. Highlight the data set of interest by using the drop-down menu.
  11. Highlight the cell line of interest by using the droop-down menu.
  12. Adjust allele frequencies by adjusting the handles on the corresponding slider.
    • Results with allele frequency values outside the minimum and maximum handles will be grayed out.
  13. Click to filter individual genes from the list.
  14. Select items from the Displayed Genes list and click the chevrons to hide them.
  15. Select items from the Hidden Genes list and click the other chevrons to show them.
  16. Click to return to the previous panel.
  1. Log In to your POPS account.
  2. In the navigation menu, click on Your Name to access the User Settings page.
  3. POPS User Settings Page
  4. The fields will be automatically populated with your information.
  5. Replace the appropriate personal information field with your updated data.
  6. To change your password, enter your new password and confirm it.
  7. Enter your current password to confirm your changes.
  8. Click in the form to save changes.
  9. Check your registered email account for an email confirmation from POPS-Lung Admin.
    • The message may be found in your junk folder.
UT Southwestern Medical Center