Today, a breakthrough report in the international journal Lancet Oncology1 demonstrates how a collaborative effort to analyze broadly accessible clinical data led to novel insights and improvements in cancer treatment and management.
Participants in the Prostate Cancer DREAM (Dialogue for Reverse Engineering Assessments and Methods) Challenge – an effort initiated by Project Data Sphere, LLC (PDS) in collaboration with Sage Bionetworks using proven DREAM methodology – developed new risk factor models for metastatic castration-resistant prostate cancer (mCRPC). A total of 50 competing teams comprised of international data scientists created models using data hosted on the Project Data Sphere®Online Service – a broad-access research platform that collects and curates patient-level data from completed, phase III cancer clinical trials.
The winning model developed by a collaborative team from the Institute for Molecular Medicine Finland (FIMM) at the University of Helsinki and the University of Turku (UTU) accurately predicts patient outcomes that could lead to improved clinical trial design and treatment options. In addition to outperforming all other entries, the model was also more accurate than another newly published advanced prognostic model.
“Analyses of PDS-shared patient data using machine learning models led to the identification of biomarker combinations that accurately predict how a patient’s disease will progress,” said Prof. Tero Aittokallio, group leader at FIMM and professor in the Department of Mathematics and Statistics at UTU. “In addition to immune system biomarkers and renal and hepatic function, our algorithm identified an under-reported cancer biomarker, aspartate aminotransferase, as an important factor in making prognoses.”
These results validate data-mining as an effective way to reveal new insights about disease from patterns in patients’ clinical data.
“The fact that we were able to gain such deep insights from clinical studies that concluded years ago shows how important it is for scientists in industry, government and academia to share clinical trial data on an ongoing basis,” said Dr. Justin Guinney, Director of Computational Oncology at Sage Bionetworks and a co-director of the Challenge.
PDS provides data scientists with no-cost access to cancer trial data provided by leading research organizations, analytics software contributed by SAS, and a collaborative online research environment to facilitate analyses of tens of thousands of patients’ records in the database.
“The Prostate Cancer DREAM Challenge showcased the value and potential of data shared through PDS,” said Dr. Liz Zhou, Director of Global Health Outcomes Research at Sanofi and co-lead of the Challenge.
The PDS-shared datasets used in the Challenge included more than 150 clinical variables for use in the modeling, including patient demographics, lab values, medical history, lesion volume and prior therapies. Competitors accessed three of the clinical trial datasets as training data for their model and used a version of the fourth trial to test and validate their solution.
“The Project Data Sphere Online Serviceis an ideal platform for crowdsourced research projects,” said Dr. Martin J. Murphy, Chief Executive Officer, Project Data Sphere, LLC. “This may be part of the reason that the Prostate Cancer DREAM Challenge had the highest participation of any DREAM challenge at that time – a wonderful testament to the importance of user-friendly data sharing platforms in the study of complex medical issues.”
Organizers and Prostate Cancer DREAM Challenge winners include participants from: Sage Bionetworks, US; the University of Texas Southwestern Medical Center, US; the University of Turku, Finland; University of Helsinki, Finland; Helsinki University Hospital, Finland; the University of Colorado, US; AstraZeneca, US; IBM T.J. Watson Research Center, US; Prostate Cancer Foundation, US; Harvard Medical School, Boston, MA, US; the University of California, San Francisco, US; Memorial Sloan-Kettering Cancer Center and Weill Cornell Medical College, US; Tulane Cancer Center, US; University of Texas Southwestern Medical Center, US; and the University of Colorado Comprehensive Cancer Center, US.
About Project Data Sphere, LLC
Project Data Sphere, LLC, is an independent, not-for-profit initiative of the CEO Roundtable on Cancer Inc.’s Life Sciences Consortium. The CEO Roundtable on Cancer is a 501(c)(3) nonprofit organization founded by President George H.W. Bush to develop and implement initiatives that reduce the risk of cancer, enable early diagnosis, facilitate access to the best available treatments and hasten the discovery of novel and more effective anti-cancer therapies.
About Sage Bionetworks
Sage Bionetworks is a nonprofit biomedical research organization, founded in 2009, with a vision to promote innovations in personalized medicine by enabling a community-based approach to scientific inquiries and discoveries. Sage Bionetworks strives to activate patients and to incentivize scientists, funders and researchers to work in fundamentally new ways in order to shape research, accelerate access to knowledge and transform human health. The organization is located on the campus of the Fred Hutchinson Cancer Research Center in Seattle, Washington and is supported through a portfolio of philanthropic donations, competitive research grants, and commercial partnerships. More information is available at http://www.sagebase.org.
The Institute for Molecular Medicine Finland (FIMM) is an international research institute in Helsinki focusing on human genomics and personalized medicine. FIMM is hosted by the University of Helsinki and is part of the Nordic EMBL Partnership in Molecular Medicine and a member of the EU-LIFE alliance. FIMM integrates molecular medicine research, technology center and bio-banking infrastructures “under one roof” and thereby promotes translational research and adoption of personalized medicine in health care. For more information, visit www.fimm.fi/en/.
1 Guinney, J., Tao W., Teemu DL, et al. “Prediction of Overall Survival for Patients with Metastatic Castration-Resistant Prostate Cancer: Development of a Prognostic Model through a Crowdsourced Challenge with Open Clinical Trial Data.” The Lancet Oncology, 2016, doi:10.1016/s1470-2045(16)30560-5. Available at http://www.thelancet.com/journals/lancet/article/PIIS1470-2045(16)30560-5/fulltext.
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Last updated on: 16/11/2016
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