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November 20th, 2020 


1:00pm - 1:45pm 


MITRE → Caroline PotteigerSteve Bratt Salim Semy Zach Lister Lauren Levine → Elly Cohen

UTSW → Melanie Hullings Shaalan Beg Brandi Cantarel 

ACS CAN →  Brittany A. McKelvey Devon Adams Mark Fleury 

Action items

Discussion notes in blueDecisions in greenAction items in red.

Planned Agenda Topics

  • <Hold for hot topics from project team>
    • Welcome Brittany! 
    • Operating Committee meeting next week
  • Past Action Items 11/13
  • Thanksgiving next week. Reschedule? Cancel? 
    • Cancel next week. 
  • Engagement Update
    • TrialJectory - initial kickoff on 11/12. Scheduling another for next week or the week after. 
    • Ciitizen - having internal discussions. 
    • TriNetX - having internal discussions
      • No updates 
    • Inspirata - continuing discussions. 
      • Could work with UCSF dataset. Inspirata has an NLP engine that could work with unstructured data.  
    • Informa - having internal discussions. 
    • Phase 2 Site Engagement - met up this week to discuss a plan. Several touch points scheduled for the future. 
    • RXL CRO - no updates 
    • IBM Watson Health - CodeX had a few initial discussions, but it dropped off. Mark to look into IBM contacts and make an introduction. 
  • Phase 1 Update
    • Working on IRB approvals for UTSW (Phase 1B) and MITRE/Cancer Insights (Phase 1A)
      • Phase 1A -
        • MITRE IRB has been re-submitted and DUA has been approved. 
      • Phase 1B - 
        • MITRE is working on the DUA. 
        • 3 main options - DUA with UTSW and CodeX, DUA with MITRE (and MITRE is able to re-distribute data), DUA with MITRE and each matching company.
          • Will develop a model that is helpful for other CodeX use cases. 
          • MITRE legal and UTSW legal to discuss. 
    • Massive Bio - in progress. 
      • Getting initial results, but they need some work. Troubleshooting now. 
      • TrialScope issue - labeling problem. Is getting fixed. 
      • - still working on finding the problem. 
    • UCSF - consult held on 11/16. 
      • Didn't have many answers for legal/DUA/privacy questions, but Elly can access the data and share with us what's included. 
      • If the data is what we need, the next step is a security consultation, which will answer some of those legal/DUA/privacy questions. 
      • Elly's affiliate status allows her to access the tool. 
      • UCSF has 2 datasets (structured and unstructured). This tool allows us to look at the structured dataset. We would need a UCSF team to look at the unstructured data. 
  • Phase 1 Analysis 
    • Plan for analysis?
      • Potential elements to add - measurable disease (bone metastasis is not eligible), line of therapy 
      • Potential elements to remove - elements that may not be readily available (histology, biomarkers) 
      • If something is not present, how do we know it's because it's absent or it just wasn't recorded?
        • Caroline meeting with spec team next week. 
    • Our analysis on how many "true matches" there are is going to depend on how many total matches come back 
      • 1 match in 5 = good filtering, 20 matches in 100 = bad filtering 
    • Patient data elements we add must be populated by both clinical trials and what's available from the patient record.
    • False negatives are ones we really want to eliminate and make sure they don't happen. 
    • Mark put together a document for Phase 1 Goals - Trial Matching - Documents 
      • How discriminatory are our 7 variables in matching patients when compared to a match on the full record?

        • Can we drop some of 7 and have just as good of a match?
          • May have to drop because of feasibility, but should understand hierarchy.
            • Test all seven variables,
            • Test each permutation of 6,
            • Test permutations of 5 based on either variable-extraction feasibility or based on results from 6-variable permutations showing least important variables
          • Should we add/replace variables to get a sufficient match?
          • How does the match quality with these variables vary by cancer type?
          • How does geographic restrictions play into overall number of trials returned (We may be able to filter on fewer clinical variables if geographic restrictions reduce trial count significantly)
    • Right now all trials are breast cancer, but we need to look at other cancers. 
      • Blood cancers as another cancer? Breast cancer falls under the solid cancer category. 
      • Complexity of treatment information may be different between cancer type.
      • Other cancer type data could be taken from the UCSF data
    • We need to publish the data from this to be transparent. 
    • Wouldn't expect the same results from each matching service.
    • In the future, we may want to consider some sort of threshold for matching services to become "mCODE-enabled".
    • Semi-commercial matching services may compete against each other, but other matching services that are disease specific aren't as competitive. 
    • Put together a smaller group to work on this analysis plan. 
      • Brittany, Caroline, Salim
      • Come up with a reasonable set of goals and matching services
      • Goal is not to find all the knowledge, but make sure the next Phase will have good enough results