Number of meetings attended (by attendee)

Attendees by meeting

DateAgendaNotes



 

Discuss Cancer Cell example data


1. Team agrees to use the word 'subset' in the TEST name. Otherwise we'd have instances like monocytes/monocytes, which in fact is monocyte subset/monocyte. This would only be used when the 'sublineage marker' variable is filled in.

2. Craig reviews Cancer cell immunophenotyping. We worked through some examples. Might need to create an example that ties cancer cell immunophenotyping to a genetics dataset, e.g., translocation information, etc.


  

Discuss rules for representing marker strings in MRKSTR variable.

 


  1. Introduction of New Team Members: Devangi Mehta from Biogen and Piercesare Balestra from Merck KGaA
  2. Develop rules around creating test names (e.g., abbreviations, etc. for shortening cell names) and test codes. 
    1. The team agreed it would be good to do this as we now want to use the Domain Example for Immunophenotyping worksheet that I uploaded to our Reference Library page on the Wiki.  We think that by using this worksheet to create sets of examples, we can ‘real-world’ pilot our proposed model while concurrently building our CT.  I’m still working on adding to the worksheet (cells we already identified or are on our list to discuss) and will upload the most recent copy before the meeting on Wed.  We’re thinking to start with the T-Cells as there are a lot of tests associated with this lineage and should provide a place to start these ‘rules’ discussions.
  3. FlowCytometry_controlled_terminology_Package_30_DRAFT_2018-05-23.xlsx

 

Discussion around the use of Receptor Occupancy across Flow Cytometery, Lab, IHC, and other contexts

Receptor Occupancy Discussion:
There are multiple scenarios that make use of the concept of ‘receptor occupancy’:
1. RO as measured by Flow Cytometry: When one is looking at the amount of ligand associated with a cell-bound receptor. Result being expressed as a binding % - % of receptors that have bound ligand (total receptor capacity, actual bound receptors, actual/total %, control results [background binding, isotype control binding]). This can be used for biotherapeutics, for natural ligands that may be modulated by a therapeutic agent, for natural ligands used as a biomarker for a diagnostic (soluble vs membrane-bound, free vs bound, bound vs total - % or Ratio type results, free vs total, bound vs unbound, saturated vs non-saturated/unsaturated). These values can also be normalized to a baseline result. We can report %RO different ways; not necessarily aware in a clinical study what is going to be significant. Results can also be reported as concentrations. All of these things can be applied to a subset of cells on which these values are being measured so it will be necessary to ensure this can be modeled (a mix of receptor occupancy info and target cell information).
a. RO and immunophenotyping by Flow Cytometry: There are combination assays where you are reporting RO along with immunophenotyping data. These would be different test names (different rows) but all related to each other.

2. RO for Immunohistochemistry: For one patient, data collected from a tumor specimen. Looking for a protein (e.g., PDL-1) via IHC but using two separate antibodies. May run both antibodies in parallel and they may yield different results, as part of companion diagnostics.
a. Is this a good use case for using ‘Binding Agent/BDAGNT’ NSV; how is this NSV defined? Is the antibody information in this instance just a method qualifier?
i. Definition of BDAGNT: The textual description of the entity that's binding to the entity in the --TEST variable. The --BDAGNT variable is used to indicate that there is a binding relationship between the entities in the --TEST and --BDAGNT variables, regardless of direction. The binding agent may be, but not limited to, a test article, a portion of the test article, a related compound, an endogenous molecule, an allergen and an infectious agent.
ii. A GGG-approved use case is the ISTEST of ‘Anti-drug Antibody’ with the drug/metabolite/whatever.
b. Is test condition/TSCOND appropriate for this information instead? Janssen created a suppqual called --CLONE to identify the antibody clone used in the assay; created a suppqual called –ASTA (anti-study treatment antibody). The Method look up table being discussed by the METHOD subteam may also be of use here.

3. Target Occupancy or Target Engagement: MSD ELISA assay that identifies concentrations of free, bound, total, etc. of a target of interest (ligand, enzyme, etc.). https://www.mesoscale.com/~/media/files/brochures/techbrochure.pdf (these types of assays are used for quantitation of PK concentrations). The drug is administered and binds to an enzyme, which may have a physiological effect. This assay captures the concentration of enzyme that is bound to the drug. It is measuring a binding interaction of interest such as a drug to an enzyme, not the concentration of the enzyme in its natural state. The ‘Binding Agent’ is the agent of interest for the assessment.
a. Target Engagement is a broader category than target occupancy. Target engagement may be assessed by occupancy or other assays that measure downstream physiologic effects (functional assays). – ELISAs, Ligand binding assays, cell-derivative vs cell-based assays, etc.
b. The flow domain may be specific for cell-based assays instead of cell-derived assays…may be a good way to draw a boundary around our new domain?
c. Example: Quantify free soluble cMET (unbound to drug) via MSD Assay (this would most likely be modeled in lab)
i. a biotinylated capture anti-cMET antibody was coated onto the wells of a streptavidin plate.
ii. Subject’s serum was added (ANALYTE)
iii. addition of the detection SulfoTag (Ruthenium) labeled monoclonal anti-cMET antibody
iv. the ECL signal due to free soluble cMET was detected
d. We do not want methodologies to drive which domain things go into, however depending on methodology, you may need specific variables.



 


A. We agree that the Lab and Microbiology team (IS) will handle the modeling of receptor occupancy within their own contexts/domains. We (the flow cytometry/immunophenotying team) will not be responsible for that (items 2 and 3 above). Instead we will focus on creating examples for the item 1 ONLY above. (IS domain=Immunogenicity Domain ; Antibodies that form as a result of an intervention, this has been expanded though to include other things)

1. RO as measured by Flow Cytometry: When one is looking at the amount of ligand associated with a cell-bound receptor. Result being expressed as a binding % - % of receptors that have bound ligand (total receptor capacity, actual bound receptors, actual/total %, control results [background binding, isotype control binding]). This can be used for biotherapeutics, for natural ligands that may be modulated by a therapeutic agent, for natural ligands used as a biomarker for a diagnostic (soluble vs membrane-bound, free vs bound, bound vs total - % or Ratio type results, free vs total, bound vs unbound, saturated vs non-saturated/unsaturated). These values can also be normalized to a baseline result. We can report %RO different ways; not necessarily aware in a clinical study what is going to be significant. Results can also be reported as concentrations. All of these things can be applied to a subset of cells on which these values are being measured so it will be necessary to ensure this can be modeled (a mix of receptor occupancy info and target cell information).
a. RO and immunophenotyping by Flow Cytometry: There are combination assays where you are reporting RO along with immunophenotyping data. These would be different test names (different rows) but all related to each other.

B. Team to put together an example based on the use case above. See rows 2-7 in the Example_RO tab here: tbl_Domain_Example_for_Immunophenotyping_2018-11-07.xlsx

C. We need to next work on receptor occupancy data where the results are percentages  ; both standard percentage (calculated at each timepoint) and baseline percentage → LisaP will prep the content ahead of time.

Meeting next week has to be canceled: Craig and Erin both out of office. Resume on Nov 21, 2018.


 

Action plan for CP domain draft

Cell Phenotype Findings (CP)

Order of Actions:

  1. Data set metadata - DONE!
  2. CP Specification:
    1. Add lab variables into table DONE!
    2. Add new variables into table DONE!
    3. Review variables in table to determine which should stay and which should go.
    4. Refine language in CDISC notes for all variables
    5. Review core expectancy (last column
  3. CP Examples:
    1. Identify the list of examples we put into IG
      1. Craig/Manjula/Trish to put together a draft
      2. Considerations: NSVs to include (PanelID)
      3. Considerations: Any examples that link to other domains? Any other suppquals to consider using?
  4. CP Assumptions:
    1. Review lab assumptions and identify which we should co-opt for our own usage
    2. Review IS assumptions for BDAGNT variable
    3. Re-look at Domain specification to make sure there is alignment with assumptions.

  File Modified
Microsoft Word Document CDISC Domain RULES for Immunophenotyping_v2.docx May 22, 2018 by Erin Muhlbradt
Microsoft Word Document CDISC Domain RULES for Immunophenotyping_v3.docx May 22, 2018 by Erin Muhlbradt
Microsoft Word Document CDISC Domain RULES for Immunophenotyping_v4.docx May 23, 2018 by Erin Muhlbradt
Microsoft Word Document CDISC Domain RULES for Immunophenotyping.docx May 22, 2018 by Erin Muhlbradt
Microsoft Excel Spreadsheet FlowCytometry_controlled_terminology_Package_30_DRAFT_2017-03-15_em_em.xlsx May 22, 2018 by Erin Muhlbradt
Microsoft Excel Spreadsheet FlowCytometry_controlled_terminology_Package_30_DRAFT_2018-05-23.xlsx May 23, 2018 by Erin Muhlbradt
Microsoft Word Document Rules for Representing Marker Strings in MRKSTR variable_2017-03-23.docx Jan 31, 2018 by Erin Muhlbradt
Microsoft Word Document Rules for Representing Marker Strings in MRKSTR variable_2018-01-31.docx Jan 31, 2018 by Erin Muhlbradt
Microsoft Word Document Rules for Representing Marker Strings in MRKSTR variable_2018-02-21.docx May 22, 2018 by Erin Muhlbradt
Microsoft Excel Spreadsheet tbl_Domain_Example_for_Immunophenotyping_2018-11-07.xlsx Nov 07, 2018 by Erin Muhlbradt

  • No labels