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News Article | May 16, 2017

A team led by researchers at the University of Illinois at Chicago, along with collaborators at the University of Michigan and Sage Bionetworks, has won the Mood Challenge for ResearchKit, a contest that called on researchers to come up with new ways to study mood disorders using Apple's ResearchKit, an open-source platform for creating iOS apps. BiAffect, which unobtrusively monitors mobile device usage, including keyboard dynamics such as typing speed to predict manic and depressive episodes in people with bipolar disorder, was developed by a team led by Dr. Alex Leow, associate professor of psychiatry in the UIC College of Medicine and professor of bioengineering and computer science, and Peter Nelson, professor of computer science and dean of the UIC College of Engineering. The BiAffect team will receive the $200,000 grand prize to continue to refine and launch their app in the App Store. The Mood Challenge is a New Venture Fund program funded by the Robert Wood Johnson Foundation. "The vision for BiAffect is for it to serve as a kind of 'fitness tracker' for the brain," said Leow. "The Mood Challenge helped us to realize this vision, and the finished app will be a first-of-its kind tool for researchers to study mood disorders and even cognitive disorders such as Parkinson's and Alzheimer's disease." BiAffect would also help researchers determine the efficacy of different treatments for bipolar and other mood disorders. The project has been a personal endeavor for Nelson, whose adult son was diagnosed with bipolar disorder as a freshman in college. "I began working on this idea many years ago as a way to help my son, and to see it come to fruition with this kind of recognition, and to know that the app will be out there to help people get a better understanding of this disorder is thrilling," Nelson said. Bipolar disorder, which causes extreme mood swings between the emotional highs of manic episodes and low periods of depression, affects approximately 5.7 million, or 2.6 percent, of adult Americans, according to the National Institute of Mental Health. Diagnosis relies on careful history-taking and examination. In previous research, Leow and Nelson, in collaboration with Kelly Ryan, clinical assistant professor of psychiatry at the University of Michigan, completed a pilot study of 30 participants that showed altered keystroke dynamics correlated with depressive and manic episodes in people with bipolar disorder. "During a manic episode, people with bipolar disorder exhibit some common behaviors, such as talking really, really fast, with diminished self-control and flight of ideas," Leow said. "It is thus natural that they also exhibit similar abnormalities in non-verbal communications that are typed on their phones." Spell-check requires the smartphone user to pause and determine whether to edit or accept suggestions made by auto-correct rather than to simply keep typing. "People in the midst of a manic episode commonly have reduced impulse control, so it is not surprising that our pilot data supported that they tend to blow through the spell-check alerts," Leow said. During depressive episodes, typing a long message may become laborious, and messages tend to be shorter, she explained. "Unobtrusively monitoring health from an iPhone combines low-cost scalability with far-reaching impact to potentially improve the lives of millions of people," said Nelson. The Mood Challenge attracted more than 70 applications after it launched just over a year ago. BiAffect and four other semi-finalist teams were recommended by a panel of judges to receive $20,000 and expert mentorship at a two-day app design boot camp. BiAffect was selected as one of two finalists last October and received an additional $100,000 to develop their ResearchKit design into a prototype to pilot. Other senior personnel of BiAffect include Dr. Olusola Ajilore, Scott Langenecker, Dr. Neil Smalheiser, Dr. Philip Yu and Dr. Jennifer Duffecy of UIC, and Dr. Melvin McInnis and Kelly Ryan of the University of Michigan. The lead iOS developers are Andrea Piscitello and Faraz Hussain. Dr. John Zulueta, and Bokai Cao of UIC and the Motus Design Group are also members of the BiAffect team.

The OHSU Knight Cancer Institute's project aims to develop strategies for improving treatment-resistant triple negative breast cancer, an aggressive form of breast cancer that lacks key receptors known to fuel most breast cancers: estrogen receptors, progesterone receptors and human epidermal growth factor receptor 2 (HER2). Using advanced microscopy, the team will leverage tools for quantitative analysis and visualization of images generated, together with computational approaches for integrating diverse molecular data types. Through analysis of core cell lines, patient-derived cultures and primary tumors, the team aims to uncover molecular networks that underlie disease progression and therapeutic response. Joe Gray, Ph.D., director of the OHSU Center for Spatial Systems Biomedicine (OCSSB) and the OHSU Knight Cancer Institute associate director for biophysical oncology will lead the investigative team as a principal investigator. "Triple negative breast cancer is a particularly difficult form of the disease to treat," said Gray. "Our goals in the CSBC Research Center are to identify the mechanisms by which these cancers evolve and adapt to become resistant to treatment, and to develop new strategies to counter these mechanisms. Our multidisciplinary approach treats these cancers as adaptive systems that can be controlled using multiple drug combinations." Co-principal investigators on the project include: Rosalie Sears, Ph.D., professor of molecular and medical genetics in the OHSU School of Medicine and a senior member of the Knight Cancer Institute; Claire Tomlin, Ph.D., the Charles A. Desoer Professor of Engineering in the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley; Adam Margolin, Ph.D., associate professor of biomedical engineering and director of computational biology in the OHSU School of Medicine and the Knight Cancer Institute. Overall research themes of the consortium's Research Centers address important questions in basic cancer research, including the emergence of drug resistance, the mechanisms underlying cancer metastasis, and the role of the immune system in cancer progression and treatment. The interdisciplinary investigators of the CSBC will integrate experimental biology with mathematical and computational modeling to gain insight into processes relevant to cancer initiation, progression and treatment options. The consortium brings together clinical and basic science cancer researchers with physician-scientists, engineers, mathematicians and computer scientists to tackle key questions in cancer biology from a novel point of view. "Cancer is a complex disease and it challenges our traditional approaches, making it hard to predict tumor growth and drug response," said Daniel Gallahan, Ph.D., deputy director of NCI's Division of Cancer Biology. "Cancer systems biologists embrace that complexity and use many different types of data to build mathematical models that allow us to make predictions about whether a tumor will metastasize or what drug combinations will be effective." In addition to applying systems biology approaches to gain important insight into cancer, each consortium Research Center supports an outreach program to promote training in interdisciplinary science, disseminate important research findings to the community, and to engage the public in cancer systems biology research. Sage Bionetworks in Seattle serves as the consortium's Coordinating Center, facilitating data and resource sharing and collaborative scientific activities across the nine Research Centers as well as two new Research Projects. More information can be found on the project website. The Knight Cancer Institute at Oregon Health & Science University is a pioneer in the field of precision cancer medicine. The institute's director, Brian Druker, M.D., helped prove it was possible to shut down just the cells that enable cancer to grow. This breakthrough has made once-fatal forms of the disease manageable and transformed how cancer is treated. The OHSU Knight Cancer Institute is the only National Cancer Institute-designated Cancer Center between Sacramento and Seattle – an honor earned only by the nation's top cancer centers. It is headquarters for one of the National Cancer Institute's largest research collaboratives, SWOG, in addition to offering the latest treatments and technologies as well as hundreds of research studies and clinical trials. For additional information on the OHSU Knight Cancer Institute visit or follow us on Facebook and Twitter. To view the original version on PR Newswire, visit:

News Article | November 15, 2016

Knowing the likely course of cancer can influence treatment decisions. Now a new prediction model published today in Lancet Oncology offers a more accurate prognosis for a patient's metastatic castration-resistant prostate cancer. The approach was as novel as the result - while researchers commonly work in small groups, intentionally isolating their data, the current study embraces the call in Joe Biden's "Cancer Moonshot" to open their question and their data, collecting previously published clinical trial data and calling for worldwide collaboration to evaluate its predictive power. That is, researchers crowdsourced the question of prostate cancer prognosis, eventually involving over 550 international researchers and resulting in 50 computational models from 50 different teams. The approach was intentionally controversial. "Scientists like me who mine open data have been called 'research parasites'. While not the most flattering name, the idea of leveraging existing data to gain new insights is a very important part of modern biomedical research. This project shows the power of the parasites," says James Costello, PhD, senior author of the paper, investigator at the University of Colorado Cancer Center, assistant professor in the Department of Pharmacology at the CU School of Medicine, and director of Computational and Systems Biology Challenges within the Sage Bionetworks/DREAM organization. The project was overseen as a collaborative effort between 16 institutions, led by academic research institutions including CU Cancer Center, open-data initiatives including Project Data Sphere, Sage Bionetworks, and the National Cancer Institute's DREAM Challenges, and industry and research partners including Sanofi, AstraZeneca, and the Prostate Cancer Foundation. Challenge organizers made available the results from five completed clinical trials. Teams were challenged to connect a deep set of clinical measurements to overall patient survival, organizing their insights into novel computational models to better predict patient survival based on clinical data. "The idea is that if a patient comes into the clinic and has these measurements and test results, can we put this data in a model to say if this patient will progress slowly or quickly. If we know the features of patients at the greatest risk, we can know who should receive standard treatment and who might benefit more from a clinical trial," Costello says. The most successful of the 50 models was submitted by a team led by Tero Aittokallio, PhD, from the Institute for Molecular Medicine Finland, FIMM, at University of Helsinki, and professor in the Department of Mathematics and Statistics at University of Turku, Finland. "My group has a long-term expertise in developing multivariate machine learning models for various biomedical applications, but this Challenge provided the unique opportunity to work on clinical trial data, with the eventual aim to help patients with metastatic castration-resistant prostate cancer," Aittokallio says. Basically, the model depended on not only groups of single patient measurements to predict outcomes, but on exploring which interactions between measurements were most predictive - for example, data describing a patient's blood system composition and immune function were only weakly predictive of survival on their own, but when combined became an important part of the winning model. The model used a computational learning strategy technically referred to as an ensemble of penalized Cox regression models, hence the model's name ePCR. This model then competed with 49 other entries, submitted by other teams working independently around the world. "Having 50 independent models allowed us to do two very important things. First when a single clinical feature known to be predictive of patient survival is picked out by 40 of the 50 teams, this greatly strengthens our overall confidence. Second, we were able to discover important clinical features we hadn't fully appreciated before," Costello says. In this case, many models found that in addition to factors like prostate-specific antigen (PSA) and lactate dehydrogenase (LDH) that have long been known to predict prostate cancer performance, blood levels of an enzyme called asparate aminotransferease (AST) is an important predictor of patient survival. This AST is an indirect measure of liver function and the fact that disturbed levels of AST are associated with poor patient performance implies that studies could evaluate the role of AST in prostate cancer. "The benefits of a DREAM Challenge are the ability to attract talented individuals and teams from around the world, and a rigorous framework for the assessment of methods. These two ingredients came together for our Challenge, leading to a new benchmark in metastatic prostate cancer," says paper first author, Justin Guinney, PhD, director of Computational Oncology for Sage Bionetworks located at Fred Hutchinson Cancer Research Center. "A goal of the Project Data Sphere initiative is to spark innovation - to unlock the potential of valuable data by generating new insights and opening up a new world of research possibilities. Prostate Cancer DREAM Challenge did just that. To witness cancer clinical trial data from Project Data Sphere be used in research collaboration and ultimately help improve patient care in the future is extremely rewarding!" says Liz Zhou, MD, MS, director of Global Health Outcome Research at Sanofi. The goal now is to make the ePCR model publicly accessible through an online tool with an eye towards clinical application. In fact, the National Cancer Institute (NCI) has contracted the winning team to do exactly this. Soon, when patients face difficult decisions about the best treatment for metastatic castration-resistant prostate cancer, ePCR tool could be an important piece of the decision-making process. Challenge winners and results can be found on the Prostate Cancer DREAM Challenge homepage. The clinical trial data can be found at Project Data Sphere. The research article describing this work can be found at The Lancet Oncology. Additional papers that describe individual team methods can be found in the DREAM Channel at F1000Research. The University of Colorado Cancer Center, located at the Anschutz Medical Campus, is Colorado's only National Cancer Institute-designated comprehensive cancer center, a distinction recognizing its outstanding contributions to research, clinical trials, prevention and cancer control. CU Cancer Center's clinical partner University of Colorado Hospital is ranked 15th by US News and World Report for Cancer and the CU Cancer Center is a member of the prestigious National Comprehensive Cancer Network®, an alliance of the nation's leading cancer centers working to establish and deliver the gold standard in cancer clinical guidelines. CU Cancer Center is a consortium of more than 400 researchers and physicians at three state universities and three institutions, all working toward one goal: Translating science into life. For more information visit and follow CU Cancer Center on Facebook and Twitter. Founded in 2006 by A. Califano (Columbia University) and Gustavo Stolovitzky (IBM Research) the Dialogue on Reverse Engineering Assessment and Methods (DREAM) Challenges Initiative poses fundamental questions about systems biology and translational medicine. Designed and run by a community of researchers from a variety of organizations, the DREAM challenges invite participants to propose solutions -- fostering collaboration and building communities in the process. Expertise and institutional support are provided by Sage Bionetworks, along with the infrastructure to host challenges via their Synapse platform. Together, the leaders of the DREAM Challenges Initiative share a vision allowing individuals and groups to collaborate openly so that the "wisdom of the crowd" provides the greatest impact on science and human health. More information is available at: http://dreamchallenges. . Project Data Sphere, LLC, an independent, not-for-profit initiative of the CEO Roundtable on Cancer's Life Sciences Consortium (LSC), operates the Project Data Sphere® platform. Launched in April 2014, the Project Data Sphere platform provides one place where the cancer community can broadly share, integrate, analyze and discuss historical patient-level comparator arm data sets (historical patient-level cancer phase III) from multiple providers, with the goal of advancing research. With its broad-access approach, the initiative brings diverse minds and technology together to help unleash the full potential of existing clinical trial data and speed innovation by generating collective insights that may lead to improved trial design, disease modeling and beyond. The platform currently contains 27,600 patient lives of data; 9,400 of those are across a wide spectrum of prostate cancer populations. In order to ensure that researchers can realize the full potential of this data, PDS teamed with CEO Roundtable on Cancer Member, SAS Institute Inc. SAS, a leader in data and health analytics, developed and hosts the site and provides free state-of-the-art analytic tools to authorized users within the Project Data Sphere environment. 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. It 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. .

Our public health data is being ingested into Silicon Valley's gaping, proprietary maw In a lead editorial in the current Nature, John Wilbanks (formerly head of Science Commons, now "Chief Commons Officer" for Sage Bionetworks) and Eric Topol (professor of genomics at the Scripps Institute) decry the mass privatization of health data by tech startups, who're using a combination of side-deals with health authorities/insurers and technological lockups to amass huge databases of vital health information that is not copyrighted or copyrightable, but is nevertheless walled off from open research, investigation and replication. The key to their critique isn't just this enclosure of something that rightfully belongs to all of us: it's that this data and its analysis will be used to make decisions that profoundly affect the lives of billions of people; without public access to this, it could be used to magnify existing inequities and injustice (see also Weapons of Math Destruction). Even when corporations do give customers access to their own aggregate data, built-in blocks on sharing make it hard for users to donate them to science. 23andMe, holder of the largest repository of human genomic data in the world, allows users to view and download their own single-letter DNA variants and share their data with certain listed institutions. But for such data to truly empower patients, customers must be able to easily send the information to their health provider, genetic counsellor or any analyst they want. Pharmaceutical firms have long sequestered limited types of hard-to-obtain data, for instance on how specific chemicals affect certain blood measurements in clinical trials. But they generally lack longitudinal health data about individuals outside the studies that they run, and often cannot connect a participant in one trial to the same participant in another. Many of the new entrants to health, unbound by fragmented electronic health-record platforms, are poised to amass war chests of data and enter them into systems that are already optimized (primarily for advertising) to make predictions about individuals. The companies jostling to get into health face some major obstacles, not least the difficulties of gaining regulatory approval for returning actionable information to patients. Yet the market value of Internet-enabled devices that collect and analyse health and fitness data, connect medical devices and streamline patient care and medical research is estimated to exceed US$163 billion by 2020, as a January report from eMarketer notes (see 'The digital health rush' and Such a tsunami of growth does not lend itself to ethically minded decision-making focused on maximizing the long-term benefits to citizens. It is already clear that proprietary algorithms can replicate and exacerbate societal biases and structural problems. Despite the best efforts of Google's coders, the job postings that its advertising algorithm serves to female users are less well-paying than are those displayed to male users2. A ProPublica investigation in May demonstrated that algorithms being used by US law-enforcement agencies are likely to wrongly predict that black defendants will commit a crime (see And thanks to 'demographically blind' algorithms, in several US cities, black people are about half as likely as white people to live in neighbourhoods that have access to Amazon's one-day delivery service (see Stop the privatization of health data [John T. Wilbanks and Eric J. Topol/Nature]

News Article | April 11, 2016

Clues to novel treatments could be gleaned from people who aren’t sick, but should be. The hunt is on for people who are healthy—even though their genes say they shouldn’t be. A massive search through genetic databases has found evidence for more than a dozen “genetic superheroes,” people whose genomes contain serious DNA errors that cause devastating childhood illnesses but who say they aren’t sick. The new study is part of a trend toward studying the DNA of unusually healthy people to determine if there’s something about them that can be discovered and bottled up as a treatment for everyone else. There’s already evidence from large families afflicted by genetic disease that some members are affected differently—or not at all. The current study took a different approach, scouring DNA data collected on 589,306 mostly unrelated individuals, and is the “the largest genome study to date,” according to Mount Sinai’s Icahn School of Medicine in New York. “There hasn’t been nearly enough attention paid to looking at healthy people’s genomes,” says Eric Topol, a cardiologist and gene scientist at the Scripps Institute. “This confirms that there are many people out there that should be manifesting disease but aren’t. It’s a lesson from nature.” The researchers, led by Stephen Friend, president of Sage Bionetworks, a nonprofit based in Seattle, and genome scientist Eric Schadt of Mount Sinai, reported today in Nature Biotechnology how they looked for people with mutations in any of 874 genes that should doom them to a childhood of pain or misery, but whose medical records or self-reports didn’t indicate any problem. In the end, they found 13 people who qualify as genetic “superheroes” but, under medical privacy agreements, were unable to contact them. That meant the scientists weren’t able to learn what’s actually different about them. “It’s like you got the box and couldn’t take the wrapping off,” Friend said during a media teleconference last week. The team consulted DNA data from nearly 400,000 people provided by 23andMe, the direct-to-consumer testing company. The team also used more detailed genome information contributed by BGI, a large genome center in China, and the Ontario Institute for Cancer Research. “The best approach to discovering large numbers of resilient individuals will involve data sharing on a global scale, involving many sequencing projects,” says Daniel MacArthur, who developed a pooled DNA database at the Broad Institute in Cambridge, Massachusetts, which he says also holds evidence of resilient individuals. Some companies, including the biotechnology company Regeneron (see “The Search for Exceptional Genomes”), have already started large searches for people whose genes seem to protect them against disease. Regeneron's focus is on common illnesses like heart disease and diabetes. Mayana Zatz, a geneticist in Sao Paulo, Brazil, who studies large families affected by inherited disease, says she’s found instances where people seem to dodge genetic destiny. For example, she located two Brazilian half-brothers with the same mutation that causes muscular dystrophy, but while one was in a wheelchair at age nine, the other is 16 and has no symptoms. Zatz says the reason could be some other gene that “rescues” the patient, or perhaps environmental factors. She says women are more often found to be resilient than men, though the reason isn’t clear. Friend says his “extraordinarily large pilot” study is meant to determine if the same sort of discoveries made by looking at affected families could be made by dredging large DNA databases. “The purpose was to see if the technology is ready, and worth the effort, and we think the answer is yes,“ he says.

Brown C.D.,University of Pennsylvania | Mangravite L.M.,Sage Bionetworks | Engelhardt B.E.,Duke University
PLoS Genetics | Year: 2013

Genetic variants in cis-regulatory elements or trans-acting regulators frequently influence the quantity and spatiotemporal distribution of gene transcription. Recent interest in expression quantitative trait locus (eQTL) mapping has paralleled the adoption of genome-wide association studies (GWAS) for the analysis of complex traits and disease in humans. Under the hypothesis that many GWAS associations tag non-coding SNPs with small effects, and that these SNPs exert phenotypic control by modifying gene expression, it has become common to interpret GWAS associations using eQTL data. To fully exploit the mechanistic interpretability of eQTL-GWAS comparisons, an improved understanding of the genetic architecture and causal mechanisms of cell type specificity of eQTLs is required. We address this need by performing an eQTL analysis in three parts: first we identified eQTLs from eleven studies on seven cell types; then we integrated eQTL data with cis-regulatory element (CRE) data from the ENCODE project; finally we built a set of classifiers to predict the cell type specificity of eQTLs. The cell type specificity of eQTLs is associated with eQTL SNP overlap with hundreds of cell type specific CRE classes, including enhancer, promoter, and repressive chromatin marks, regions of open chromatin, and many classes of DNA binding proteins. These associations provide insight into the molecular mechanisms generating the cell type specificity of eQTLs and the mode of regulation of corresponding eQTLs. Using a random forest classifier with cell specific CRE-SNP overlap as features, we demonstrate the feasibility of predicting the cell type specificity of eQTLs. We then demonstrate that CREs from a trait-associated cell type can be used to annotate GWAS associations in the absence of eQTL data for that cell type. We anticipate that such integrative, predictive modeling of cell specificity will improve our ability to understand the mechanistic basis of human complex phenotypic variation. © 2013 Brown et al.

SEATTLE--(BUSINESS WIRE)--In the second paragraph, second sentence, the URL embedded in "publication" should read: (instead of Mole photos, measurements, and melanoma risk factor data contributed by over 2,500 participants are made available by Sage Bionetworks and OHSU to accelerate skin cancer research. Sage Bionetworks and Oregon Health & Science University (OHSU) today publicly released data contributed by 2,798 participants in the Mole Mapper melanoma study. The app-based research study uses Apple’s ResearchKit to enroll participants who use the phone camera to map and measure their moles over time. Abnormal or changing moles can be an indicator of the skin cancer melanoma, so remote monitoring with the possibility of early detection holds great promise for cancer prevention. Whereas most research data are generated in a clinical or laboratory setting, Mole Mapper is crowd-sourced by individuals contributing data to the study from their own phones. Curated Mole Mapper data, consisting of mole photos and measurements together with melanoma risk factors, have been made available to qualified researchers on Sage Bionetworks’ collaborative science platform Synapse and accompanied by a publication in Nature Scientific Data. This is the second such mobile health study that has been made broadly available to qualified researchers around the world. “In designing the study, we first wanted to know if research run remotely and entirely through an app could find the same melanoma risks as years of rigorous epidemiology and genetics research,” said lead author Dan Webster, Research Fellow at the National Cancer Institute. “We show, for instance, that Mole Mapper participants with red hair were significantly more likely to be diagnosed with melanoma. This is in alignment with previously published data showing that people with red hair caused by mutations in the MC1R gene have a higher risk for melanoma.” The study data also touches on a frequently asked question about moles: “Is this normal?” Stanford University researchers recently demonstrated that algorithms can accurately diagnose skin conditions by training on a large database of high-quality medical skin images. The Mole Mapper team aims to create a similarly foundational database from participant-contributed data. While clinical resources will undoubtedly be important in answering this question, most moles that are measured and monitored in a clinical setting are already suspect and may already be abnormal. “With Mole Mapper, we have a unique ability to collect thousands of measurements from ‘pre-clinical’ moles that people measure themselves at home,” said Webster. “Over time, this can provide a basis for mole size and shape distributions to serve as a new benchmark for future studies.” The release of the Mole Mapper study data is a part of the larger mobile health ecosystem that Sage Bionetworks is cultivating. Developing open-source modules for integration into mobile applications and enabling the broad sharing of the resulting data are cornerstones of this effort. “In the promising space of mobile health, too often data is controlled by private interests,” said study coauthor Brian Bot, Principal Scientist, Sage Bionetworks. “Shared data resources such as these will help enable the scientific community to more quickly determine what can and cannot be gleaned from these types of remote measurements.” Sage Bionetworks is a 501(c) (3) 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. It 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

Hood L.,Institute for Systems Biology | Friend S.H.,Sage Bionetworks
Nature Reviews Clinical Oncology | Year: 2011

Medicine will move from a reactive to a proactive discipline over the next decade-a discipline that is predictive, personalized, preventive and participatory (P4). P4 medicine will be fueled by systems approaches to disease, emerging technologies and analytical tools. There will be two major challenges to achieving P4 medicine-technical and societal barriers-and the societal barriers will prove the most challenging. How do we bring patients, physicians and members of the health-care community into alignment with the enormous opportunities of P4 medicine? In part, this will be done by the creation of new types of strategic partnerships-between patients, large clinical centers, consortia of clinical centers and patient-advocate groups. For some clinical trials it will necessary to recruit very large numbers of patients-and one powerful approach to this challenge is the crowd-sourced recruitment of patients by bringing large clinical centers together with patient-advocate groups. © 2011 Macmillan Publishers Limited. All rights reserved.

News Article | April 12, 2016

As part of a global collaboration, scientists from the Icahn School of Medicine at Mount Sinai and Sage Bionetworks conducted the largest genome study to date and reported the first systematic search across hundreds of Mendelian disorders in hundreds of thousands of individuRead more about Analysis of Nearly 600K Genomes for Resilience ProjectComments

News Article | February 18, 2017

BOSTON, USA -- The Global Alliance for Genomics and Health (GA4GH) is an international coalition of academic, industry, and patient groups that aims to foster a culture of data-sharing between researchers and clinicians. On 18 February 2017 at 1:00pm, GA4GH will host a symposium in the Medical Sciences and Public Health track of the 2017 Annual Meeting of the American Association for the Advancement of Science (AAAS). The session, "Genomic and Health Data: Global Sharing and Local Governance," will consider how funding agencies, journals, regulators, health payers, and patient groups are moving to influence data-sharing policy, while simultaneously calibrating the notion of who "owns" genomic data. The theme of the 2017 AAAS meeting is "serving society through science policy." This means, in part, addressing ways that policy can be used to advance the practice of science. In 2017, genomic and health-related data from millions of individuals stand to improve human health and medicine considerably, especially as health care systems around the globe engage in ever more ambitious sequencing initiatives. But in many cases, the data produced in research and clinical settings around the globe are locked in silos due to incompatible formats and challenging jurisdictional barriers. Only by developing forward-looking international sharing policies can the community benefit from promise of these data. The GA4GH session will host lectures from three leading researchers in the genomic policy field: Bartha Knoppers (McGill University) will discuss the human rights foundation for global data sharing, Robert Cook-Deegan (Arizona State University) will discuss policies to promote data sharing 21 years after the Bermuda principles, and Meg Doerr (Sage Bionetworks) will discuss honesty, choice, and accountability in data sharing. One means by which GA4GH is working to advance data sharing policy is by offering practicalframeworks that can be tailored and implemented by institutions around the globe. For instance, the Regulatory and Ethics Working Group developed the Framework for Responsible Sharing of Genomic and Health-Related Data, which balances individual privacy, recognition for researchers, and the right of citizens to benefit from the progress of science in order to promote both open and tiered consent to sharing. "The Framework goes against the traditional presumption that biomedical research is somehow harmful and instead focuses on the human right to benefit from scientific progress as outlined in the Universal Declaration of Human Rights of 1948" said Knoppers. "We now need to work out the practical applications of that foundation, and begin to mobilize the policies, tools, and political will to inspire governments to respect this right and thereby foster and facilitate international data sharing." The hope, Knoppers said, is that by taking this approach, GA4GH will be able to overcome the policy and legal roadblocks to sharing, which present perhaps a greater hurdle than technology. Since 1996, genomic data sharing has been loosely governed by the principles set forth at the International Strategy Meeting on Human Genome Sequencing in Bermuda. "But the landscape has become significantly more complex since Bermuda," said Cook-Deegan. "Data sources are numerous and varied, ranging from the commercial sector to the clinic and from institutions around the world. Data users aren't just scientists anymore, but also consumers and clinicians. This all means we that we now need to expand on the Bermuda principles to promote a data sharing ecosystem that can account for all this diversity." "It also requires a rethinking of participant consent and researcher accountability," said Doerr. "At Sage, we're using our experiences in mobile health research to think of new processes for enabling participant choice and for granting researchers access to data they wish to analyze. This is just one answer to the many pressing policy questions that remain as we work to build this new ecosystem." Doerr, Cook-Deegan, and Knoppers will present in Room 309 of the Hynes Convention Center in Boston, Mass. from 1:00pm to 2:30pm on Saturday, 18 February 2017. The Global Alliance for Genomics and Health is an international, non-profit alliance formed to accelerate the potential of genomic medicine to advance human health. Bringing together over 450 leading organizations working in healthcare, research, disease and patient advocacy, life science, and information technology, GA4GH Members are working together to create a common framework of tools, methods, and harmonized approaches and supporting demonstration projects to enable the responsible, voluntary, and secure sharing of genomic and clinical data. Learn more at: http://genomicsandhealth. .

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