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 Coloradocancercenter.org 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. .
News Article | March 21, 2016
At Monday's launch event, Apple announced CareKit, a new tool that lets patients share data with researchers and keep tabs on their medical conditions. CareKit, which comes out of Apple's medical-research platform, ResearchKit, is intended for a broad spectrum of patients, not just people taking part in medical studies. Any health-app maker can use it to make it easier for patients to share their health information with physicians, friends, or family members who take care of them. Users can also pull in other information about their health, such as exercise results. The software is free and open-source, meaning it could, in theory, even work on other platforms like Android, says Thomas Goetz, founder of Iodine, which is one of six CareKit launch partners. His company's app, Start, allows patients with depression to track how well their medications are working and report symptoms back to their doctors. Goetz says that CareKit has made it easier to design the app they wanted. "It solved a lot of that UX [user experience] design that we've been wresting with," he says. Apple announced that CareKit will launch in April. Along with Iodine, HealthKit is launching with five other partners (or groups of partners). Sage Bionetworks and the University of Rochester are updating their ResearchKit-based program on Parkinson's disease to provide insights to patients. Texas Medical Center has developed an app for cardiothoracic surgery patients to incorporate health data from Bluetooth devices such as blood pressure monitors and also to report symptoms back to doctors. Beth Israel Deaconess Medical Center will also incorporate info from health monitoring devices, though it hasn't provided many details yet. Diabetes-management app One Drop will integrate data like blood glucose measurements, insulin doses, carbohydrate intake, and physical activity. Glow, Inc. will incorporate CareKit modules into its pregnancy and child health management apps, Glow Nurture and Glow Baby. CareKit has four components. Care Card is intended to help patients track activities like taking medication and doing exercise. Symptom and Measurement Tracker is for recording symptoms and data like body temperature or blood pressure. Insight Dashboard links data from the first two components to see how well a treatment plan is working. And Connect allows patients to share that health info with others. Iodine uses Care Card to improve the app's function of allowing patients with depression to track their symptoms, in order to shorten the amount of time (typically six to nine months) it takes to find the antidepressant that works best. "It’s that kind of context that surrounds the condition, in our case the conditions of depression and mental health," says Goetz. The connect component makes it much easier for the app to share info with caretakers, says Goetz, calling the company's initial attempt at this "a half step." CareKit will "start to knit the patient information back to the clinician in a meaningful way," says Goetz. Current applications of ResearchKit hint at other ways that CareKit can be used. Apple highlighted several of them during its keynote presentation and in a video on its website. They include a program by Duke University and the University of Cape Town to diagnose autism. Their Autism & Beyond iPhone app shows videos to children and uses the front-facing camera to measure their facial expressions to judge what kinds of reactions they have, such as neutral, happy, or sad. EpiWatch is an Apple Watch app by Johns Hopkins University that lets epilepsy patients input their symptoms, info about seizures, medication, and events that they think may have triggered seizures. It combines that with heart rate, accelerometer, and gyroscope data from the Apple Watch or a connected iPhone. The goal is to learn enough about epilepsy to create an early warning app telling patients when they are likely to have a seizure. Apple isn't alone in developing a health research platform. Google Life Sciences (recently renamed Verily) developed an app called "Study Kit" as part of its effort to develop a baseline for what constitutes a healthy individual. But it's not nearly as far along as ResearchKit, and Google doesn't yet seem to have anything like CareKit for apps that directly serve consumers. That may not be necessary, though, since Apple's CareKit is an open-source platform that might even work with Android devices. "If somebody does that work in porting it into an Android context, that would be something we would use," says Goetz. UPDATE: This article has been corrected to clarify when CareKit will launch and include additional info provided by One Drop.
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.
News Article | July 25, 2016
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 go.nature.com/29fbvch). 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 go.nature.com/29aznyw). 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 go.nature.com/29kskg3). Stop the privatization of health data [John T. Wilbanks and Eric J. Topol/Nature]
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.
News Article | February 15, 2017
SEATTLE--(BUSINESS WIRE)--In the second paragraph, second sentence, the URL embedded in "publication" should read: https://doi.org/10.1038/sdata.2017.5 (instead of https://www.doi.org/10.1038/sdata.2017.5) 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 http://www.sagebase.org.
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 | March 22, 2016
It's been just under a year since Apple launched ResearchKit, its first service for medical researchers to develop iPhone-based studies. These apps are currently being used to study a variety of ailments, including diabetes, breast cancer, asthma, and heart disease. ResearchKit is an open-source framework that is primarily intended for use by developers at major academic hospitals and universities, such as Mount Sinai, Stanford Children's Hospital, Harvard University, and Ochsner Medical Center in New Orleans. The goal is to make it cheaper and easier for these researchers to recruit participants for their studies. "Researchers have been able to get infinitely richer data sets than before," Bud Tribble, vice president of software engineering at Apple, told Fortune magazine back in October of 2015. On the eve of its major press event, I spoke to developers, entrepreneurs, and researchers to find out whether Apple's much-hyped ResearchKit has lived up to its promise of revolutionizing medical research. At the six-month mark, Apple announced that 100,000 people had been involved in ResearchKit-enabled studies. That is an impressive figure, given the usual size and scope of research studies. Apple has not yet announced an updated figure, which is likely far higher. "Most medical researchers are used to physically approaching a patient, asking them to enroll, and doing in-person followups," says Brandon Ballinger, a developer with a startup called Cardiogram, who has used both Apple's HealthKit and Alphabet's rival service, Google Fit. For many researchers, he added, even recruiting 100 patients to a clinical study is a challenging feat. Why has ResearchKit been so successful in this regard? The mobile aspect does make it easier for people to sign up and share their health data, including step counts, mood, and heart rate. "There was rapid enrollment of many thousands of patients," says Stephen Friend of Sage Bionetworks, the company behind the mPower Parkinson's Disease study. Friend said the data collected via ResearchKit poured in "multiple times a day as opposed to monthly or yearly," which made it particularly useful to study. As ResearchKit moves into its second year, one potential stumbling block to its continued growth is the focus on iOS. That leaves out a huge chunk of the patient population that uses Android hardware, as well as those who lack smartphones. An at-risk Medicaid population of senior citizens might want to get involved with these studies, but lack the necessary high-tech gadgets. "It puts the burden on partners—the developers—to expand beyond Apple's ecosystem," says Colin Anawaty from Patient IO, a startup that develops patient-focused apps for hospitals. Moreover, some critics have pointed out that participants in these mobile-based studies tend to skew young and male. Some of the partners are aware of this, and are taking active steps to ensure a more balanced demographic. According to Nature, Stanford's ResearchKit app MyHeart Counts is partnering with the Women's Health Initiative to target female participants. Ballinger says UCSF has targeted female participants by approaching nonprofits and church groups. Apple is constantly adding new biometrics to its HealthKit and ResearchKit services. In response to public pressure from its female users, it recently added period-tracking. That will likely open up new opportunities for mobile-based reproductive health studies. But the major gap that remains is mental health. Psychiatrists told me back in January that it's increasingly possible (though not yet definitively proven) for patients and their caregivers to track the symptoms of depression and anxiety using a smartphone. Through ResearchKit, researchers could explore whether a variety of data sources—smartphone usage, self-reported mood, heart rate, and other biometrics—are indicative of a variety of mental health disorders. Going forward, I'm expecting to see Apple's ResearchKit team focus more of its efforts on mental health studies. Already, it is taking some tentative steps into the space. One of its CareKit partners, Iodine, is working to help patients understand if their antidepressants are working for them or not. Apple's ResearchKit counts 25 academic and research institutions among its partners (it started with just five). My prediction is that will continue to expand its base of partners in the coming year, but slowly. As Ballinger explains, one of the major problems is that few medical researchers can code. What that means is that the researchers need to have the budget and resources to hire an app development team. "You need a decent-sized budget and expertise to build an app" that leverages ResearchKit, he explained. Ballinger suggests that Apple's ResearchKit team could get involved in helping forge partnerships between medical research groups and startups. "I do think we'll need to see new types of hybrid organizations—research groups with software skills, or software startups with skill clinical research—to make the most of mobile data in medical research," he said.
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. .