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Searching of Clinical Trials Made Easier in cBioPortal Using Patients' Genetic and Clinical ProfilesFunding This research has been conducted within the MIRACUM project. MIRACUM is funded by the German Federal Ministry of Education and Research (BMBF), grant IDs 01ZZ1801A (P.U., L.M., and J.C.) and 01ZZ1801B (M.B.). This work was supported by the BMBF funded HiGHmed project, grant ID 01ZZ1802Z (H.B., N.R.) and by the German Research Foundation (DFG), grant IDs CRC 850 and CRC 1160 (M.B.).
Background Molecular tumor boards (MTBs) cope with the complexity of an increased usage of genome sequencing data in cancer treatment. As for most of these patients, guideline-based therapy options are exhausted, finding matching clinical trials is crucial. This search process is often performed manually and therefore time consuming and complex due to the heterogeneous and challenging dataset.
Objectives In this study, a prototype for a search tool was developed to demonstrate how cBioPortal as a clinical and genomic patient data source can be integrated with ClinicalTrials.gov, a database of clinical studies to simplify the search for trials based on genetic and clinical data of a patient. The design of this tool should rest on the specific needs of MTB participants and the architecture of the integration should be as lightweight as possible and should not require manual curation of trial data in advance with the goal of quickly and easily finding a matching study.
Methods Based on a requirements analysis, interviewing MTB experts, a prototype was developed. It was further refined using a user-centered development process with multiple feedback loops. Finally, the usability of the application was evaluated with user interviews including the thinking-aloud protocol and the system usability scale (SUS) questionnaire.
Results The integration of ClinicalTrials.gov in cBioPortal is achieved by a new tab in the patient view where the genomic profile for the search is prefilled and additional parameters can be adjusted. These parameters are then used to query the application programming interface (API) of ClinicalTrials.gov. The returned search results subsequently are ranked and presented to the user. The evaluation of the application resulted in an SUS score of 83.5.
Conclusion This work demonstrates the integration of cBioPortal with ClinicalTrials.gov to use clinical and genomic patient data to search for appropriate trials within an MTB.
Keywordsclinical decision support - genomics - clinical trial - molecular tumor board - trial matching - genetic alteration
Protection of Human and Animal Subjects
The project was performed in compliance with the World Medical Association Declaration of Helsinki on Ethical Principles for Medical Research Involving Human Subjects. Ethical approval was not required.
* These authors contributed equally to this work.
Received: 06 September 2021
Accepted: 15 November 2021
30 March 2022 (online)
© 2022. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/)
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- 1 Service RF. Gene sequencing. The race for the $1000 genome. Science 2006; 311 (5767): 1544-1546
- 2 Shendure J, Ji H. Next-generation DNA sequencing. Nat Biotechnol 2008; 26 (10) 1135-1145
- 3 Garraway LA, Verweij J, Ballman KV. Precision oncology: an overview. J Clin Oncol 2013; 31 (15) 1803-1805
- 4 Alves RCP, Alves D, Guz B. et al. Advanced hepatocellular carcinoma. Review of targeted molecular drugs. Ann Hepatol 2011; 10 (01) 21-27
- 5 Brehmer D, Greff Z, Godl K. et al. Cellular targets of gefitinib. Cancer Res 2005; 65 (02) 379-382
- 6 Arora A, Scholar EM. Role of tyrosine kinase inhibitors in cancer therapy. J Pharmacol Exp Ther 2005; 315 (03) 971-979
- 7 Cunanan KM, Gonen M, Shen R. et al. Basket trials in oncology: a trade-off between complexity and efficiency. J Clin Oncol 2017; 35 (03) 271-273
- 8 Redig AJ, Jänne PA. Basket trials and the evolution of clinical trial design in an era of genomic medicine. J Clin Oncol 2015; 33 (09) 975-977
- 9 Arnold D, Bokemeyer C. Studien und personalisierte Medizin in der Onkologie?. Oncol Res Treat 2010; 33 (Suppl. 7): 25-29
- 10 Holch JW, Westphalen CB, Hiddemann W, Heinemann V, Jung A, Metzeler KH. Präzisionsonkologie und molekulare Tumorboards – Konzepte, Chancen und Herausforderungen. Dtsch Med Wochenschr 2017; 142 (22) 1676-1684
- 11 Lindeman NI, Cagle PT, Aisner DL. et al. Updated molecular testing guideline for the selection of lung cancer patients for treatment with targeted tyrosine kinase inhibitors: guideline from the College of American Pathologists, the International Association for the Study of Lung Cancer, and the Association for Molecular Pathology. Arch Pathol Lab Med 2018; 142 (03) 321-346
- 12 Penberthy LT, Dahman BA, Petkov VI, DeShazo JP. Effort required in eligibility screening for clinical trials. J Oncol Pract 2012; 8 (06) 365-370
- 13 Cerami E, Gao J, Dogrusoz U. et al. The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer Discov 2012; 2 (05) 401-404
- 14 Gao J, Aksoy BA, Dogrusoz U. et al. Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Sci Signal 2013; 6 (269) pl1-pl1
- 15 Buechner P, Hinderer M, Unberath P. et al. Requirements analysis and specification for a molecular tumor board platform based on cBioPortal. Diagnostics (Basel) 2020; 10 (02) 93
- 16 Lindsay J, Fitz CDV, Zwiesler Z. et al. MatchMiner: An open source computational platform for real-time matching of cancer patients to precision medicine clinical trials using genomic and clinical criteria. bioRxiv 2017; 199489
- 17 Micheel CM, Lovly CM, Levy MA. My cancer genome. Cancer Genet 2014; 207 (06) 289
- 18 Sahoo SS, Tao S, Parchman A. et al. Trial prospector: matching patients with cancer research studies using an automated and scalable approach. Cancer Inform 2014; 13: 157-166
- 19 MSKCC. . Patient P-0000465. Accessed May 7, 2021 at: https://www.cbioportal.org/patient/summary?studyId=msk_impact_2017&caseId=P-0000465
- 20 Likert R. A technique for the measurement of attitudes. Arch Psychol 1932
- 21 Brooke J. SUS-A quick and dirty usability scale. Usability Evaluation in Industry. 1996; 189 (194) 4-7
- 22 Gao M, Kortum P, Oswald FL. Multi-language toolkit for the system usability scale. Int J Hum Comput Interact 2020; 36 (20) 1883-1901
- 23 U.S. National Library of Medicine. . ClinicalTrials.gov API. Accessed February 11, 2022 at: https://clinicaltrials.gov/api/gui
- 24 Unberath P, Knell C, Prokosch H-U, Christoph J. Developing new analysis functions for a translational research platform: extending the cBioPortal for cancer genomics. Stud Health Technol Inform 2019; 258: 46-50
- 25 Bangor A, Kortum P, Miller J. Determining what individual SUS scores mean: adding an adjective rating scale. J Usability Stud 2009; 4 (03) 114-123
- 26 Comis RL, Miller JD, Aldigé CR, Krebs L, Stoval E. Public attitudes toward participation in cancer clinical trials. J Clin Oncol 2003; 21 (05) 830-835
- 27 Unger JM, Cook E, Tai E, Bleyer A. The role of clinical trial participation in cancer research: barriers, evidence, and strategies. Am Soc Clin Oncol Educ Book 2016; 35: 185-198
- 28 Lara Jr. PN, Higdon R, Lim N. et al. Prospective evaluation of cancer clinical trial accrual patterns: identifying potential barriers to enrollment. J Clin Oncol 2001; 19 (06) 1728-1733
- 29 Kaplan CP, Nápoles AM, Dohan D. et al. Clinical trial discussion, referral, and recruitment: physician, patient, and system factors. Cancer Causes Control 2013; 24 (05) 979-988
- 30 Parker BA, Schwaederlé M, Scur MD. et al. Breast cancer experience of the molecular tumor board at the University of California, San Diego Moores Cancer Center. J Oncol Pract 2015; 11 (06) 442-449
- 31 Bryce AH, Egan JB, Borad MJ. et al. Experience with precision genomics and tumor board, indicates frequent target identification, but barriers to delivery. Oncotarget 2017; 8 (16) 27145-27154
- 32 Halfmann M, Stenzhorn H, Gerjets P, Kohlbacher O, Oestermeier U. . User-Driven Development of a Novel Molecular Tumor Board Support Tool. Accessed February 11, 2022 at: https://events.tib.eu/fileadmin/data/dils2018/paper/paper_6.pdf
- 33 Fegeler C, Zsebedits D, Bochum S, Finkeisen D, Martens UM. Implementierung eines IT-gestützten molekularen Tumorboards in der Regelversorgung. Forum 2018; 33: 322-328
- 34 Melzer G, Maiwald T, Prokosch H-U, Ganslandt T. Leveraging real-world data for the selection of relevant eligibility criteria for the implementation of electronic recruitment support in clinical trials. Appl Clin Inform 2021; 12 (01) 17-26
- 35 Sun Y, Butler A, Diallo I. et al. A framework for systematic assessment of clinical trial population representativeness using electronic health records data. Appl Clin Inform 2021; 12 (04) 816-825
- 36 O'Leary T, Weiss J, Toll B, Brandt C, Bernstein SL. Automated generation of CONSORT diagrams using relational database software. Appl Clin Inform 2019; 10 (01) 60-65
- 37 Dharod A, Bellinger C, Foley K, Case LD, Miller D. The reach and feasibility of an interactive lung cancer screening decision aid delivered by patient portal. Appl Clin Inform 2019; 10 (01) 19-27
- 38 Naceanceno KS, House SL, Asaro PV. Shared-task worklists improve clinical trial recruitment workflow in an academic emergency department. Appl Clin Inform 2021; 12 (02) 293-300
- 39 Xu J, Lee H-J, Zeng J. et al. Extracting genetic alteration information for personalized cancer therapy from ClinicalTrials.gov. J Am Med Inform Assoc 2016; 23 (04) 750-757
- 40 Zeng J, Shufean MA, Khotskaya Y. et al. OCTANE: oncology clinical trial annotation engine. JCO Clin Cancer Inform 2019; 3: 1-11
- 41 Saleh RR, Chan O, Kuld S. et al. A novel electronic platform to improve clinical trial workflow and screening. Presented at AACR Special Conference on Advancing Precision Medicine Drug Development: Incorporation of Real-World Data and Other Novel Strategies; January 9–12: San Diego, CA; 2020
- 42 Lipscomb CE. Medical subject headings (MeSH). Bull Med Libr Assoc 2000; 88 (03) 265-266
- 43 Consortium GO. Gene Ontology Consortium. Creating the gene ontology resource: design and implementation. Genome Res 2001; 11 (08) 1425-1433
- 44 Wain HM, Bruford EA, Lovering RC, Lush MJ, Wright MW, Povey S. Guidelines for human gene nomenclature. Genomics 2002; 79 (04) 464-470
- 45 Abras C, Maloney-Krichmar D, Preece J. User-centered design. In: Bainbridge W, ed. Encyclopedia of Human-Computer Interaction. Thousand Oaks, CA: Sage Publications.; 2004: 445-456
- 46 Virzi RA. Refining the test phase of usability evaluation: how many subjects is enough?. Hum Factors 1992; 34 (04) 457-468