BaseSpace Cohort Analyzer FAQs

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  • General


    • Conditions/Diseases: Cohort Analyzer includes information from two kinds of samples: cell lines and human subjects.
      Data from individual patients and healthy subjects is grouped by disease (Colon cancer, Glioblastoma, etc) or as 'Normal' individuals. The Cell Line Encyclopedia includes genomic profiles and drug sensitivity information for over 2000 cell lines. You can also import data from cell lines or patients for other diseases and conditions into your domain.
    • Molecular Data types: Currently, the Cohort Analyzer platform includes data for copy number variation, RNA expression, DNA methylation, miRNA perturbations, and somatic mutations linked to disease conditions.
    • Clinical information: Phenotypical and physical characteristics are reported for each patient. Where relevant, we report attributes for each condition based on common classification systems, such as Karnofsky scores and the American Joint Committee on Cancer TNM grading.
    • TCGA data: The TCGA data results are based upon data generated by the TCGA Research Network.
    • NCI TARGET data: The NCI TARGET data results are based upon data generated by the National Cancer Institute.

    Biomarker Repository was formerly known as KnowledgeBase. Biomarker Repository is a continually growing library of information on genomic markers that are relevant to clinical research and diagnosis. The information in Biomarker Repository is derived from various sources such as OMIM, PharmGKB, and Cohort Analyzer-curated content. Markers are sorted based on molecular data type, such as RNA expression or copy number variation.

    Biomarkers in the Cohort Analyzer database are aggregated, analyzed for scientific relevance and individually curated by Cohort Analyzer scientists. We categorize biomarkers based on a scale of clinical confidence as:

    • FDA-approved: FDA-approved biomarkers are currently used to inform clinical choices and may have specific drugs indicated. Information on drugs targeting FDA-approved biomarkers is available in the Predictive Guidance report.
    • CLIA test available: Clinically testable biomarkers that can inform a patient's disease prognosis or drug response are grouped under this category. Clicking on these biomarkers in a report will direct you to a Biomarker Repository entry with information on CPT codes and vendors who can perform these tests.
    • In clinical trials: Biomarkers in later stages of diagnostics or drug discovery can inform a therapeutic choice for your patients. You can also use this information to identify appropriate clinical trials to enroll patients with unresponsive disease.
    • Experimental: These biomarkers of disease are at early stages of laboratory testing for validation; either in cell lines or in model animals. Use them carefully if treating patients.

    To get started with Cohort Analyzer, we provide several Quick Start Guides which can be accessed by clicking the help button (?) in the left upper corner of each page. In addition, this FAQ provides background information. For other questions, you can contact us in several ways on our customer support page

    In addition, WalkMe tutorials are available within the application to help users learn and discover workflows within the platform. The WalkMe tutorials can be accessed by the “Interactive Help” link on the top of every page.

    Cohort Analyzer uses many advanced browser features in order to display complex visualizations rapidly. We support Chrome, Firefox and Internet Explorer and strongly recommend using their latest versions. Using a different browser or an outdated version, can reduce performance or functionality. As of August 15, 2014, we will have limited support for Internet Explorer 8. This means that any new features introduced after August 15 2014, might have reduced or no functionality in Internet Explorer 8 or other old browser versions.

  • Data Sharing and Security


  • Each user of Cohort Analyzer has their own secure, private data center-based domain where their private data is stored. Only users within your domain can access this information.

    Yes. Cohort Analyzer reports can be exported and saved locally in PDF format. You can also download molecular data such as RNA expression, copy number variation, DNA methylation and somatic mutation information as *.csv files.

    Users can import private studies into Cohort Analyzer through the Data Uploader. Supported data types are somatic mutation, copy number variation, and RNA-seq data.

    If you have loaded a study into Cohort Analyzer, customer administrators can then permission the study to share with collaborators.

  • Patient Registry Page


  • Patient registry is a full list of all your subjects (or cell lines) as well as curated content from public repositories of patient information like The Cancer Genome Atlas and Gene Expression Omnibus. The Registry allows you to review the clinical attributes of your selected population. Also, by clicking on the "EDIT PATIENT FILTERS" button, you can filter your population.

    Click on "EDIT PATIENT FILTERS" button to view patients with a different disease, normal subjects or go to the Cell Line Registry. Within the cell line registry, you can also click on the 'patient summary' icon to find patients with predicted drug responses that match a cell line or set of cell lines.

    Clinical and physical characteristics are reported for each individual and cell line. Some of these can help you quickly sort through to specific populations, such as all those within a particular age group, or cell lines with a similar tissue of origin.

    In addition, some clinical attributes serve as measures of patient health, disease prognosis or response to treatment. We report clinical attributes for each condition based on common classification systems, such as Karnofsky scores and the American Joint Committee on Cancer TNM grading. For example, the TNM grades as defined by the AJCC can tell you how many patients on a particular therapy had metastatic cancers.

    Race refers to a person's biological background (eg Caucasian, African-American), whereas ethnicity refers to a person's cultural background (eg Ashkenazi Jewish). Cohort Analyzer uses the US census classification of race. Note that Hispanic or Latino is used to indicate ethnicity. So it is possible for a person to be a Caucasian or African-American by race, but a Latino by ethnicity.

  • Basic Patient Finder


  • Basic Patient Finder allows you to define your population by selecting a condition (and optionally a project). You can then filter your population of patients or cell lines for clinical and molecular attributes. Cell lines can also be filtered for drug sensitivity. This is done by clicking on an attribute on a tab and then applying it. You can access the Basic Patient Finder by clicking the "EDIT PATIENT FILTERS" button from the Patient Registry page.

    Filters can be used to select a sub-set of patients or cell lines that match specific criteria. For example, you can use clinical filters to select all patients within a particular age group, on the same medication, or with similar TNM stages. The molecular filter can be used to select patients with one or more genetic variants. Genes can be filtered based on changes in RNA expression, DNA methylation, copy number variation or with somatic mutations. The Drug Sensitivity Filter finds cell lines that are sensitive or resistant to the selected drugs. The dynamically updated graphs show you how many patients or cell lines match your filter criteria.

    Filtering patients based on molecular criteria updates the Clinical Summary provided in Patient Registry to show the frequency at which the selected molecular marker occurs in different clinical classes. For example, you can quickly identify if a potential biomarker is more frequent in advanced stages of cancer or associated with a particular class of tumors more than other classes.

    Cohort Analyzer remembers the condition you selected on your last visit and displays it automatically when you next log in. The first time you log on, all Cancer Patients will be the default condition.

    You can choose to see only the private data from your organization with the "All Patients" dropdown box located in the top left.

    These Healthy subjects are part of the 1000 Genomes Project.

    Selecting a project under "Projects" tab will update the total number of patients in your registry whereas the study information under the "Clinical" tab will apply filters based on the selected studies to your cohort. Pleases note that not all studies are represented in the "Projects" tab or in the study information under "Clinical".

    The "Drug Sensitivity" tab is visible when you select "Cancer cell lines" under the "Condition" tab.

    When multiple attributes are selected (gender, race, etc.), they will be linked automatically by an AND operator. When multiple bins within an attribute (eg, male or female) are selected they will be linked automatically by an OR operator, since they are typically mutually exclusive.

    Drugs for cell lines are typically listed by generic name if available, otherwise by their code name from the National Cancer Institute's Developmental Therapeutics Program. If available, the drug name will link out to the PubChem record, which has formula, molecular weight, CAS number, etc.

    All of the filters you have selected will be listed on the right hand side of the screen. To remove one, click the x next to the attribute of interest. It is also possible to clear entire groups or subgroups of attributes.

  • Patient Data Page


  • When you select an individual patient (or cell line), the Patient Data page becomes accessible and provides you with all the clinical and molecular details specific to an individual patient. You can view full details of a patient's treatment regimen under the Clinical Data summary.

    View the genomic location, impact and statistical significance of specific somatic mutations and DNA methylation, RNA expression or copy number variation of a particular gene or genomic region.

  • Patient Reports Page


  • When you select an individual patient (or cell line), Patient Reports provide patient-specific reports on Biomarkers in your patient that might affect the molecular classification.

  • Reports Page


  • When you have more than one patient or cell line selected (ie, a population), the reports page allows you to do analyses on your population.

    Biomarker Analysis helps you compare data from two groups of patients to identify biomarkers for disease prognosis or drug response. Comparing groups of patients through the Biomarker Analysis report identifies statistically significant differences in gene expression, DNA methylation, copy number variation or somatic mutations between the two groups selected.

    Click on the Group Comparison Set-up button to choose parameters for each group. You can compare groups based on physical or clinical attributes by clicking 'Add' next to each attribute. Select or remove group-specific criteria in the lists that appear below each group on the left-hand side windows.

    Add molecular criteria to groups through the 'Molecular' attributes tab. Start typing the name of a gene or SNP of interest and select from the auto-complete list that will appear. Choose a data type and add your criteria to groups. For example, you may like to compare the gene expression profiles of patients over-expressing and under-expressing the EGFR gene.

    Results for Biomarker Analysis are ranked based on how different a genetic variant is between the two groups defined in the comparison. By default, results are ordered based on the statistical significance of the difference in a particular gene's data between the two groups, as indicated by the p-value. You can reorder results based on the Group change (see below), or filter based on p-value and group change cut-off values you choose.

    Group change is a measure of the difference of a molecular parameter between the two groups being compared. The algorithm used to compute the group change for a biomarker depends on the type of molecular data being studied. Group change for each of the data types currently available is as below.

    • RNA expression: Difference in expression of a gene between two groups is represented as a ratio of the fold-change expression in each group.
    • DNA methylation: Group change in gene methylation is represented as the difference of mean percentage differential methylation for the two groups created.
    • Copy number variation: Difference of mean copy number change in each of the two groups.
    • Somatic mutations: A mutation severity score is computed for each patient in the groups. The group change is calculated as a difference of the mean mutation severity score for each of the groups.

    Click the arrow on the left-hand side of a gene to reveal a drop down iconographic representation of the way a particular biomarker is altered across patients within each group. For gene expression, DNA methylation and copy number variation, you can view how each biomarker is increased, decreased or remains unchanged in the group. Somatic mutations are graded on a scale of severity based on the location of a mutation as intergenic, promoter and severe (affecting a coding region).

    The Patient Stratification report provides you a summary view of the number of patients who match your clinical and molecular criteria.

    Molecular classification biomarkers provide detailed information on the sub-category of a particular patient's tumor. This can help to identify diagnostic tests to administer, treatment options that may or may not work, and other disease risks. For example, the breast cancer patient TCGA-C8-A12T's report shows that her tumor over-expresses the ERBB2 gene, a molecular classification biomarker that is also linked to an improved response to the FDA-approved drugs pertuzumab and lapatinib.

    We categorize biomarkers based on a scale of clinical confidence as:

    • FDA-approved: FDA-approved biomarkers are currently used to inform clinical choices and may have specific drugs indicated. Information on drugs targeting FDA-approved biomarkers is available in the Predictive Guidance report.
    • CLIA test available: Clinically testable biomarkers that can inform a patient's disease prognosis or drug response are grouped under this category. Clicking on these biomarkers in a report will direct you to a Biomarker Repository entry with information on CPT codes and vendors who can perform these tests.
    • In clinical trials: Biomarkers in later stages of diagnostics or drug discovery can inform a therapeutic choice for your patients. You can also use this information to identify appropriate clinical trials to enroll patients with unresponsive disease.
    • Clinically observed: Biomarkers observed as being associated with a disease in patients, but not tested in clinical trials or otherwise validated.
    • Experimental: These biomarkers of disease are at early stages of laboratory testing for validation; either in cell lines or in model animals. use them carefully if treating patients.

    The Prognosis report is only available for patients (not cell lines or normal individuals). It includes information on biomarkers that can influence the course of a patient's disease. We categorize biomarkers based on a scale of clinical confidence as:

    • FDA-approved: FDA-approved biomarkers are currently used to inform clinical choices and may have specific drugs targeting them. Information on drugs targeting FDA-approved biomarkers is available in the Predictive Guidance report.
    • CLIA test available: Clinically testable biomarkers that can inform a patient's disease prognosis or drug response are grouped under this category. Clicking on these biomarkers in a report will direct you to a Biomarker Repository entry with information on CPT codes and vendors who offer laboratory services for these tests.
    • In clinical trials: Biomarkers in later stages of diagnostics or drug discovery can inform a therapeutic choice for your patients. You can also use this information to identify appropriate clinical trials to enroll patients with unresponsive disease.
    • Clinically observed: Biomarkers observed as being associated with a disease in patients, but not tested in clinical trials or otherwise validated.
    • Experimental: These biomarkers of disease are at early stages of laboratory testing for validation; either in cell lines or in model animals. Use them carefully if treating patients.

    The Predictive Guidance report for patients lists predictive biomarkers that impact a patient's response to drugs. Use this report to identify which drugs a patient will have an improved response to, drugs contra-indicated for a patient's biomarkers and off-label therapies that may be useful for a particular patient.

    The report contains two sections: Approved drugs and Off-label biomarker based therapies. FDA- approved drugs labeled for specific biomarkers are listed under Approved therapies, along with the clinical relevance of the biomarker.

    Off-label biomarker based therapies are computed by Cohort Analyzer based on a patient's genomic information. Genetic markers that are known targets for available drugs are identified in a patient's genome, and listed along with the associated drug, the disease indication for which the drug is currently approved, and the evidence status of the off-label indication. Off-label biomarkers are classified on a scale of clinical confidence as:

    • FDA-approved: FDA-approved biomarkers are currently used to label specific drugs available in the market.
    • CLIA test available: Clinically testable biomarkers that can inform a patient's disease prognosis or drug response are grouped under this category. Clicking on these biomarkers in a report will direct you to a Biomarker Repository entry with information on CPT codes and vendors who can perform these tests.
    • In clinical trials: Biomarkers that are currently being evaluated for their predictive value in clinical trials are labeled this way. You can also use this information to identify appropriate clinical trials to enroll patients with unresponsive disease.
    • Clinically observed: Biomarkers observed as being associated with a disease in patients, but not tested in clinical trials or otherwise validated.
    • Experimental: These biomarkers of disease are at early stages of laboratory testing for validation; either in cell lines or in model animals. Use this information conservatively when interpreting clinical data.

    The Genetic Predisposition report is only available for individual healthy subjects with germline variant data. The report includes information about biomarkers that can impact an individual's chances of developing disease later in life, the way they may respond to certain drugs or their carrier status for recessive genetic diseases. For example, the drug response tab may include information on a patient's status for the rs339097 variant, which makes affected individuals less responsive to warfarin than those without this biomarker.

    Depending on genotype, a patient's status may be:

    • Affected: The variant directly influences a person's disease risk or drug response.
    • Carrier: Though the individual is unaffected, they may pass on a risk factor to their offspring
    • Inconclusive: Based on Cohort Analyzer analysis, the variant is indicated as a risk factor but is not conclusively associated with disease.
    • Unknown: Biomarkers marked 'unknown' are still in the process of being analyzed by our scientists. We constantly update content in the platform based on current research, so check back for updates in future releases.