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MatchMiner: An open source computational platform for real-time matching of cancer patients to precision medicine clinical trials using genomic and clinical criteria

View ORCID ProfileJames Lindsay, View ORCID ProfileCatherine Del Vecchio Fitz, Zachary Zwiesler, Priti Kumari, Bernd Van Der Veen, Tamba Monrose, Tali Mazor, Susan Barry, Adem Albayrak, Michael Tung, Khanh Do, Suzanne Hector-Barry, Brian Beardslee, Geoffrey Shapiro, John Methot, Lynette Sholl, Laura E. MacConaill, Neil Lindeman, Bruce Johnson, Barrett Rollins, Chris Sander, Michael Hassett, Ethan Cerami
doi: https://doi.org/10.1101/199489
James Lindsay
1Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, USA.
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  • ORCID record for James Lindsay
Catherine Del Vecchio Fitz
1Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, USA.
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  • For correspondence: cdvfitz@jimmy.harvard.edu
Zachary Zwiesler
1Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, USA.
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Priti Kumari
1Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, USA.
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Bernd Van Der Veen
2The Hyve, Utrecht, The Netherlands.
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Tamba Monrose
1Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, USA.
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Tali Mazor
1Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, USA.
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Susan Barry
8Dana-Farber Cancer Institute, Boston, USA.
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Adem Albayrak
3Informatics and Translational Research, Dana-Farber Cancer Institute, Boston, USA.
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Michael Tung
3Informatics and Translational Research, Dana-Farber Cancer Institute, Boston, USA.
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Khanh Do
4Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, USA.
5Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, USA.
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Suzanne Hector-Barry
4Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, USA.
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Brian Beardslee
4Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, USA.
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Geoffrey Shapiro
4Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, USA.
5Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, USA.
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John Methot
3Informatics and Translational Research, Dana-Farber Cancer Institute, Boston, USA.
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Lynette Sholl
4Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, USA.
5Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, USA.
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Laura E. MacConaill
6Department of Pathology, Brigham and Women’s Hospital, Boston, USA.
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Neil Lindeman
6Department of Pathology, Brigham and Women’s Hospital, Boston, USA.
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Bruce Johnson
4Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, USA.
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Barrett Rollins
4Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, USA.
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Chris Sander
1Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, USA.
7Department of Cell Biology, Harvard Medical School, Boston, USA.
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Michael Hassett
4Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, USA.
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Ethan Cerami
1Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, USA.
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  • Abstract
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Abstract

Background Molecular profiling of cancers is now routine at many cancer centers, and the number of precision cancer medicine clinical trials, which are informed by profiling, is steadily rising. Additionally, these trials are becoming increasingly complex, often having multiple arms and many genomic eligibility criteria. Currently, it is a challenging for physicians to match patients to relevant clinical trials using the patient’s genomic profile, which can lead to missed opportunities. Automated matching against uniformly structured and encoded genomic eligibility criteria is essential to keep pace with the complex landscape of precision medicine clinical trials.

Results To meet these needs, we built and deployed an automated clinical trial matching platform called MatchMiner at the Dana-Farber Cancer Institute (DFCI). The platform has been integrated with Profile, DFCI’s enterprise genomic profiling project, which contains tumor profile data for >20,000 patients, and has been made available to physicians across the Institute. As no current standard exists for encoding clinical trial eligibility criteria, a new language called Clinical Trial Markup Language (CTML) was developed, and over 178 genomically-driven clinical trials were encoded using this language. The platform is open source and freely available for adoption by other institutions.

Conclusion MatchMiner is the first open platform developed to enable computational matching of patient-specific genomic profiles to precision cancer medicine clinical trials. Creating MatchMiner required developing clinical trial eligibility standards to support genome-driven matching and developing intuitive interfaces to support practical use-cases. Given the complexity of tumor profiling and the rapidly changing multi-site nature of genome-driven clinical trials, open source software is the most efficient, scalable, and economical option for matching cancer patients to clinical trials.

  • Abbreviations

    AACR
    American Association for Cancer Research
    API
    Application programming interface
    CNA
    DNA copy number alteration
    CLIA
    Clinical Laboratory Improvement Amendments
    CTML
    Clinical Trial Markup Language
    DFCI
    Dana-Farber Cancer Institute
    EMR
    Electronic Medical Record
    FFPE
    Formalin-fixed paraffin-embedded
    GA4GH
    Global Alliance for Genome and Health
    HIPAA
    Health Insurance Portability and Accountability Act
    HUGO
    Human Genome Organization
    Indel
    Insertion / deletion
    NCT
    National Clinical Trial
    MPS
    Massively Parallel Sequencing
    REST
    RepresEntational State Transfer
    SNV
    Single Nucleotide Variant
    SAML
    Security Assertion Markup Language
  • Copyright 
    The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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    Posted October 23, 2017.
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    MatchMiner: An open source computational platform for real-time matching of cancer patients to precision medicine clinical trials using genomic and clinical criteria
    James Lindsay, Catherine Del Vecchio Fitz, Zachary Zwiesler, Priti Kumari, Bernd Van Der Veen, Tamba Monrose, Tali Mazor, Susan Barry, Adem Albayrak, Michael Tung, Khanh Do, Suzanne Hector-Barry, Brian Beardslee, Geoffrey Shapiro, John Methot, Lynette Sholl, Laura E. MacConaill, Neil Lindeman, Bruce Johnson, Barrett Rollins, Chris Sander, Michael Hassett, Ethan Cerami
    bioRxiv 199489; doi: https://doi.org/10.1101/199489
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    MatchMiner: An open source computational platform for real-time matching of cancer patients to precision medicine clinical trials using genomic and clinical criteria
    James Lindsay, Catherine Del Vecchio Fitz, Zachary Zwiesler, Priti Kumari, Bernd Van Der Veen, Tamba Monrose, Tali Mazor, Susan Barry, Adem Albayrak, Michael Tung, Khanh Do, Suzanne Hector-Barry, Brian Beardslee, Geoffrey Shapiro, John Methot, Lynette Sholl, Laura E. MacConaill, Neil Lindeman, Bruce Johnson, Barrett Rollins, Chris Sander, Michael Hassett, Ethan Cerami
    bioRxiv 199489; doi: https://doi.org/10.1101/199489

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