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Explore. Analyze. Learn.
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Welcome to Oncoscape.

A data visualization platform that empowers researchers to discover novel patterns and relationships between clinical and molecular data. Through a suite of interoperable tools, Oncoscape offers a unique and intuitive approach to hypothesis refinement.

Iterative Analysis

Seamlessly transfer knowledge among analytical tools. Discover new patterns and relationships by connecting diverse questions and answers.

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Cohort Discovery

Easily define patient sets of interest. Build, refine, and scale cohorts based on clinical and/or molecular factors.

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Interactive Visualizations

Access data and methods through a suite of visual tools. Combine the power of analysis and discovery through the simple click of a mouse.

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Interdisciplinary Science


Researchers

Interested in tumor progression and molecular functions for an individual patient or patients within a scalable population.

Bioinformaticians

Interested in developing, comparing, or validating models of response for given diseases.

Clinicians

Interested in determining the best clinical treatment for a patient given their demographic and tumor molecular profile.

Available Tools


Datasets


Oncoscape hosts level 3 public TCGA datasets representing gene and patient data downloaded from UCSC Xena. To review this data in depth visit our data API. User planning to publish on the provided data must adhere to all publishing guideline set by the NIH. Datasets in Oncoscape are classified by disease type according to TCGA studies.

Developers


Interested in contributing new methods or visualizations? A version of Oncoscape exist for that! Please visit our development site to view upcoming features or discover areas to contribute. Oncoscape is an open source project hosted on GitHub that utlizes many other open source project such as Docker, MongoDB and Kong API.

Acknowledgements


Oncoscape is developed at the Fred Hutchinson Cancer Research Center under the auspices of the Seattle Translational Tumor Research initiative.

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Use Cases

Define patient cohorts with multiple genetic alterations

Save and view cohorts with multiple genetic alterations

  1. Tool Selection: From the Oncoscape header, click Datasets and select Gliomas, then click Tools and select Markers + Patients
  2. Gene Search: From the Control Bar, type in the Search Box (middle right side) “EGFR” and click “Go”. Above the Search box a count of genes will appear and the selected node will be highlighted orange with a black border
  3. Find Alterations: From the Control Bar, in the Commands section (middle right side, blue text) click Show Edges of Selected Edges [Mutation, Amplification, Gain, Loss, Deletion] connecting EGFR to the patient nodes represent alterations
  4. Patients with Alterations: From the Control Bar, in the Commands section, click Select Connected Nodes. The patient nodes will now be outlined in black
  5. Hide Unassociated Nodes: From the Control Bar, in the Commands section, click Hide Unselected Nodes. Any patient nodes that are not associated with EGFR will be hidden
  6. Deselect Nodes: In the M+P window, click on the open white space to deselect the patient nodes
  7. Gene Search: From the Control Bar, type in the Search Box (middle right side) “IDH1” and click “Go”. Above the Search box a count of genes will appear and the selected node will be highlighted orange with a black border
  8. Find Alterations: In the Commands section, click Show Edges of Selected Edges [Mutation, Amplification, Gain, Loss, Deletion] connecting IDH1 to the patient nodes represent alterations
  9. Hide Patients w/o Alterations: In the Commands section, click Select Connected Nodes, followed by Hide Unselected Nodes. Any patient nodes that are not associated with IDH1 will be removed
  10. Save Cohort: From the Cohort Panel, type a name in the Selected Cohort box (e.g. EGFR + IDH1 Altered) and click the “+” button The EGFR + IDH1 cohort will be renamed and saved in the Cohorts Section
  11. Compare to Population: From the Command Section, click Show All Nodes

Create cohorts in Survival analyze treatments in Timelines

Survival cohorts can be pushed to other tools for analysis

  1. Tool Selection: From the Oncoscape header, click Datasets and select Gliomas, then click Tools and select Survival The default single survival plot (in blue). All Patients + Samples is shown
  2. Create a Cohort: Hover over the Survival window until the crosshairs appear, then left-click and drag the cursor to select the “All Patient + Sample curve from 11 years to tail end. The patients falling within this selected region will define a new cohort and be displayed in a new curve
  3. Save Cohort: From the Cohort Panel, type a name in the Selected Cohort box (e.g. Glioma Long-Term Survivors) and click the “+” button. The Glioma Long-Term Survivors cohort will be renamed and saved in the Cohorts Section
  4. Transfer Cohort: From the Oncoscape header click Tools and select Timelines. The Glioma Long-Term Survivors cohorts will have a gray line over their clinical history. Patients will be ordered so those with a known death date are shown at the bottom of the graph and the rest are shown according to their last known alive date
  5. Timelines Zoom: Using the darker grey side-bar (left or top) left-click and drag a small section of the bar. The blue region acts as a scroll bar that can be resized and moved by dragging it lengthwise to show patients and events within that region
  6. Analyzing Saved Cohorts: View event details (e.g. average ages and treatment cycles) of the long term survivors by hovering over the colored blocks. NOTE: The saved survival cohort tends to be a younger group and most of them do not receive chemotherapy or radiation

Build patient cohorts with color options within TCGA Breast

Define and save different cohorts based on PR Status

  1. Tool Selection: From the Oncoscape header, click Datasets and select Breast, then click Tools and select PCA
  2. Color Options: From the Control Bar (middle right side), click the Color Options button. A pop up of color options will appear
  3. Color Code: Select PR IHC Status PCA will reload the color-coded data by PR IHC Status and a legend with a color-key selector (right middle side), will appear in the Control Bar
  4. Select: From the color-key selector in the Control Bar, click the select button (circle with dot) next to the purple Negative value. All tumor samples associated with a Negative PR status will be selected and outlined in black
  5. Save Cohort: From the Cohort Panel, type a name in the Selected Cohort box (e.g. PR Negative) and click the “+” button. The PR IHC cohort will be renamed and saved in the Cohorts Section
  6. Deselect: From the PR IHC Status color-key selector, click the deselect (open circle) button next to the purple Negative value. All outlined Negative PR samples should deselect
  7. Additional Cohorts (optional): Repeat steps 5-7 to create cohorts for PR Status Positive and Indeterminate
  8. Integrate Analysis: From the Oncoscape header, click Tools and select Survival Survival will show the pairwise log-rank p-values on the Control Bar (upper right side) for each cohort. NOTE: Cohorts can be created and accessed in all tools
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Get Started

The power of Oncoscape lies in the ability to create and move cohorts through the various tools.

Creating a saved cohort is easy. On the left side of all tools is the cohort panel. All functions related to cohorts are preformed here. Summaries for the various sections are below. It is important to note that as cohorts are selected and toggled between the clinical histograms, survival curves and summaries will update.


Selected Cohort: Add, edit or delete cohorts. Click the "+ " to save, "x " to delete or click the down arrow to select a cohort for edits.

Clinical Histogram: Snapshot of clinical information, per cohort. The histogram will adjust with each cohort selection.Click the down arrow to view other filters.   e.g.   Age At Diagnosis, Gender, Race, Ethnicity, Vital, Tumor.

Survival Curve: Snapshot of Kaplan Meier survival curves, per cohort. For more in depth analysis push the saved cohorts to the survival tool.

Cohorts: All saved cohorts are listed in this area. Each cohort is clickable and will highlight the selections on the main window, as well as adjust any survival curves or clinical histograms that are associated. The last line will always show your current selection, which will also update in the Selected Cohort box at the top of the cohort panel.

Cohort Summary: Every cohort includes a summary of available information based on patients and samples.


Transfer Cohorts


Transfer cohorts at the click of a button.

Once cohorts have been created it is easy to move them to different tools for additional analysis. In the main header find the Analysis Tools button and select a new tool. The left cohort panel will appear in each tool. Additional help for individual tools can be found in the Available Tools section.

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Data

All genesets used in Oncoscape can be viewed at Geneset Details.

Oncoscape's data is based on a gene/patient relationship as defined: “non-silent somatic mutation (nonsense, missense, frame-shift indels, splice site mutations, stop codon readthroughs, change of start codon, inframe indels) was identified in the protein coding region of a gene, or any mutation identified in a non-coding gene”. Credit to Xena UCSC.

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Frequently Asked Questions

What tools have export features?

Currently, the export feature is only available on the Spreadsheet tool. Users can push saved cohorts to the spreadsheet tool by clicking the the Analysis Tool button at the top of the screen. Once there click the CSV Export button. An Excel file will get generated.


How is data saved for return visits?

All data for selections and cohorts will be automatically saved for a return visit assuming users are on the same computer to login. User logins that allow stored sessions from any device will be deployed in a future release.


How to contact the Oncoscape team directly?

On the header, click Feedback, fill out the form and someone from the Oncoscape team will contact you shortly.