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

Lindsay, James; Fitz, Catherine Del Vecchio; Zwiesler, Zachary; Kumari, Priti; B, Van Der Veen; Monrose, Tamba; Mazor, Tali; Barry, Susan; Albayrak, Adem; Tung, Madelynn; Do, Kim‐Anh; Hector-Barry, Suzanne; Beardslee, Brian; Shapiro, Geoffrey I.; Methot, John; Sholl, Lynette M.; MacConaill, LE; Lindeman, Neal I.; Johnson, Bruce E.; Rollins, Barrett J.; Sander, Chris; Hassett, Michael J.; Cerami, Ethan

doi:10.1101/199489

Cited by 18 publications

(18 citation statements)

References 26 publications

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“…This challenge was evident in comparing MOAlmanac to the I-PREDICT trial, as differences in match selection were driven by differences in therapeutic availability at different time points, variable knowledge capture of the vast precision oncology hypothesis landscape, and levels of evidence to justify treatment selection. These results are suggestive of the urgency to standardize genomic-based clinical trial data and aggregate knowledge bases to parse the vast literature in precision oncology and enable principled, evidence-based clinical care 5,36 . Manual curation of literature is inherently laborious, and prior efforts have encouraged crowdsourcing and meta studies to address this challenge 4,5,37 .…”

Section: Discussionmentioning

confidence: 89%

Clinical interpretation of integrative molecular profiles to guide precision cancer medicine

Reardon

Moore

et al. 2020

Preprint

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Individual tumor molecular profiling is routinely used to detect single gene-variant ("first-order") genomic alterations that may inform therapeutic actions -- for instance, a tumor with a BRAF p.V600E variant might be considered for RAF/MEK inhibitor therapy. Interactions between such first-order events (e.g., somatic-germline) and global molecular features (e.g. mutational signatures) are increasingly associated with clinical outcomes, but these "second order" alterations are not yet generally accounted for in clinical interpretation algorithms and knowledge bases. Here, we introduce the Molecular Oncology Almanac (MOAlmanac), a clinical interpretation algorithm paired with a novel underlying knowledge base to enable integrative interpretation of genomic and transcriptional cancer data for point-of-care treatment decision-making and translational hypothesis generation. We compared MOAlmanac to first-order interpretation methodology in multiple retrospective patient cohorts and observed that the inclusion of preclinical and inferential evidence as well as second-order molecular features increased the number of nominated clinical hypotheses. MOAlmanac also performed matchmaking between patient molecular profiles and cancer cell lines to further expand individualized clinical actionability. When applied to a prospective precision oncology trial cohort, MOAlmanac nominated a median of two therapies per patient and identified therapeutic strategies administered in 46% of patient profiles. Overall, we present a novel computational method to perform integrative clinical interpretation of individualized molecular profiles. MOAlmanc increases clinical actionability over conventional approaches by considering second-order molecular features and additional evidence sources, and is available as an open-source framework.

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Section: Discussionmentioning

confidence: 89%

Clinical interpretation of integrative molecular profiles to guide precision cancer medicine

Reardon

Moore

et al. 2020

Preprint

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“…Besides that, some of our requirements identified are also met by tools not directly associated with cBioPortal, like MatchMiner [33] published by the Dana-Farber Cancer Institute. This tool, which has recently been prototypically integrated in cBioPortal [34], offers a service to match clinical trials to a patient case.…”

Section: Results and Future Workmentioning

confidence: 99%

Requirements Analysis and Specification for a Molecular Tumor Board Platform Based on cBioPortal

et al. 2020

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Clinicians in molecular tumor boards (MTB) are confronted with a growing amount of genetic high-throughput sequencing data. Today, at German university hospitals, these data Diagnostics 2020, 10, 93 2 of 15 are usually handled in complex spreadsheets from which clinicians have to obtain the necessary information. The aim of this work was to gather a comprehensive list of requirements to be met by cBioPortal to support processes in MTBs according to clinical needs. Therefore, oncology experts at nine German university hospitals were surveyed in two rounds of interviews. To generate an interview guideline a scoping review was conducted. For visual support in the second round, screenshot mockups illustrating the requirements from the first round were created. Requirements that cBioPortal already meets were skipped during the second round. In the end, 24 requirements with sometimes several conceivable options were identified and 54 screenshot mockups were created. Some of the identified requirements have already been suggested to the community by other users or are currently being implemented in cBioPortal. This shows, that the results are in line with the needs expressed by various disciplines. According to our findings, cBioPortal has the potential to significantly improve the processes and analyses of an MTB after the implementation of the identified requirements.

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“…By better recognizing the therapies a patient has received, a system built on our ontology could help with identifying appropriate clinical trials for hematology/oncology patients at scale and could complement clinical trial matching efforts such as MatchMiner. 31…”

Section: Discussionmentioning

confidence: 99%

Computerized Approach to Creating a Systematic Ontology of Hematology/Oncology Regimens

Malty

Jain

Yang

et al. 2018

JCO Clinical Cancer Informatics

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This approach represents, to our knowledge, the largest effort to date to systematically categorize and relate hematology/oncology drugs and regimens. The ontology can be used to reason individual components from regimens mentioned in electronic health records (eg, R-CHOP maps to rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone) and also to probabilistically reconstruct regimens from individual drug components. These capabilities may be particularly valuable in the implementation of rapid-learning health systems on the basis of real-world evidence. The derived Web Ontology Language ontology is freely available for noncommercial use through the Creative Commons 4.0 Attribution-NonCommercial-ShareAlike license.

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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

Cited by 18 publications

References 26 publications

Clinical interpretation of integrative molecular profiles to guide precision cancer medicine

Clinical interpretation of integrative molecular profiles to guide precision cancer medicine

Requirements Analysis and Specification for a Molecular Tumor Board Platform Based on cBioPortal

Computerized Approach to Creating a Systematic Ontology of Hematology/Oncology Regimens

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