Big Data. & Life Sciences

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1 Big Data & Life Sciences

2 Acknowledgments The Mass Technology Leadership Council is grateful for the leadership and support that Governor Deval Patrick has provided to the region s big data ecosystem with his Big Data Initiative. We look forward to working with Administration, and the Massachusetts Life Sciences Center, the Massachusetts ehealth Institute at the Massachusetts Technology Collaborative, along with industry representatives and other government agencies to implement the key recommendations made in this report. The MassTLC Big Data initiatives, including this report, would not be possible without the commitment and engagement of many talented leaders and volunteers in the Mass Technology Leadership Council s Big Data Cluster. Cluster community leaders include Co-Chairs: Leo Hermacinski, CEO, dside Technologies; Marilyn Matz, Co-Founder and CEO, Paradigm4; and Bob Zurek, Senior Vice President of Product, Epsilon. We would also like to acknowledge the people responsible for the overall strategy and thought process behind this big data and life sciences initiative: Richard Dale, COO, Optum Labs; Dmitri Gunn, MIT Media Lab; Leo Hermacinski, CEO, dside Technologies; Patrick Larkin, Director, The Innovation Institute at the Massachusetts Technology Collaborative; Marilyn Matz, Co-Founder and CEO, Paradigm4; Steven O Leary, Managing Director, Aeris Partners; David Power, President, Power Strategy; Susan-Windham Bannister, Ph.D., President and CEO, the Massachusetts Life Sciences Center. And to Janice McCallum, Managing Director, Health Content Advisors for her work in writing this report. Thank you to Optum Labs for hosting the big data and Life Sciences roundtable at their Cambridge office. This report was funded by a grant from the Massachusetts Life Sciences Center. About the Mass Technology Leadership Council With more than 550 member companies, the Mass Technology Leadership Council (MassTLC) is the region s leading technology association and the premier network for tech executives, entrepreneurs, investors and policy leaders. MassTLC s mission is to accelerate innovation by connecting people from across the technology landscape, providing access to industry-leading content and ideas, and offering a platform for member companies to collectively advance their interests. For more information on MassTLC visit 1

3 Table of Contents 03 PREFACE 04 introduction 05 Executive Summary 06 Framing the Problem: From Data Scarcity to Abundance Challenge: Managing the Growth in Data Sources Constraints: Access, Interoperability, and Metadata Constraints: Consent 12 DATA SCIENCE OPPORTUNITIES WHO IS DOING IT RIGHT? Case Study I: Accelerated Cure Project CONCLUSION & NEXT STEPS 2

4 Preface This big data and life sciences report is part of the Mass Technology Leadership Council s (MassTLC) work with the Mass Life Sciences Center in support of Massachusetts Governor Deval Patrick s ongoing initiatives to identify and exploit key economic and leadership opportunities for the Commonwealth. We engaged in this initiative to foster greater collaboration between sectors; to spotlight and enhance the leadership capacity of our region; to attract talent, investment, and new business; and to position our region at the forefront of the big data revolution. We have employed a Delphi methodology to gather the leading minds within key industries to lend their expertise to help identify both short- and long-term opportunities. Our work builds upon our 2012 report, Big Data and Analytics: A Major Market Opportunity for Massachusetts, and subsequent big data Delphi roundtable report, finding, among other themes, that leadership opportunities exist for Massachusetts in capitalizing on its strengths and focusing on actionable business insights. With this, we set out in 2013 to bump and connect the region s big data thought leaders with those in other key strength areas, including life sciences, healthcare, and urban infrastructure. We leveraged our four-decade legacy of data and technology leadership to explore some of the most vexing challenges and opportunities of our times. Our Delphi process involved four steps: (1) interview thought leaders representing both big data and each of the target industries, (2) survey participants to identify areas of consensus as well as differences of opinion, (3) provide survey findings back to the Delphi participants, and (4) explore the opportunities, barriers, and possible public/private interventions through an interactive roundtable. On November 7, 2013, MassTLC, with the support of the Massachusetts Life Sciences Center (MLSC), convened a roundtable of big data and life sciences thought leaders which included representatives from pharma, biotech, personalized medicine, venture capital, patient centered disease research groups, academia and government to explore opportunities, barriers, and solutions. The big data and life sciences report highlights a real use case study in Massachusetts that has developed collaborative research frameworks and provides early models for success in applying big data technology in life sciences. It is instructive to read both the life sciences and healthcare reports together as there is increasing convergence between data sources produced and used in these industries and both reports touch on common themes of data quality, metadata, and access to data. 3

5 Introduction The Life Sciences Delphi report is part of MassTLC s series on big data in key industries in Massachusetts. Almost two years ago, we released a report on big data in the region and have been focused on how to identify economic development opportunities for big data within the commonwealth. The objective of the Big Data and Life Sciences Delphi process is to build on the technology legacy and unique collection of assets in the life sciences and healthcare sectors in Massachusetts to target areas that can advance technologydriven data science initiatives in life sciences. As part of the Delphi process, MassTLC convened a Big Data Life Sciences roundtable, held on November 7, 2013, which included representatives from pharma, biotech, personalized medicine, venture capital, patient-centered disease research groups, academics and government. This report summarizes the fundamental challenges that were discussed, points to possible solutions, and highlights a real use case study in Massachusetts that has developed collaborative research frameworks and provides early models for success in applying big data technology in life sciences. Robert McBurney The Accelerated Cure Project Tom Hopcroft MassTLC Brad Margus Genome Bridge Steve Papa Infinio Martin Leach Biogen Idec Susan Windham- Bannister MLSC Eric Neumann Foundation Medicine Mark Schreiber Novartis Andy Palmer Data-Tamer Matthew Trunnell Broad Institute Eric Perakslis Harvard Marilyn Matz Paradigm4 Joshua Boger Vertex The Delphi Panel Michael McManus Knome David de Graaf Selventa Deniz Kural Seven Bridges Genomics Anthony Philippakis Genome Bridge John Keilty Infinity Pharmaceuticals John Quackenbush Harvard Dmitri Gunn MIT Media Lab Richard Dale Optum Labs Dave Dickinson Optum Labs Winston Hide Harvard Thomas Tsang Merck Vitalii Doban Pfizer 4

6 Executive Summary Massachusetts has a rich history of technology innovation and is well known for its vibrant life sciences community, which benefits from proximity to world-class academic institutions and leading medical centers. By bringing together leaders from life sciences, academics, personalized medicine, technology, venture capital, and government, MassTLC and the Massachusetts Life Sciences Center (MLSC) set out to explore opportunities for big data initiatives that could catalyze major breakthroughs in life sciences research and development. Despite success with big data technologies in other industries, such as ecommerce and financial services, Life Sciences has lagged in leveraging new technologies that enable analysis of a richer mix of data sources. (see Figure 1 technology diffusion curve below) Our goal is to help researchers cut through the existing barriers to progress by identifying target segments that are more open to data sharing and to incentivize the development of new data science technology that will lead to breakthroughs in life sciences research. There was uniform agreement among the participants in the Delphi process that existing computer platforms and tools in use in biomedical research are insufficient to manage and analyze the highly complex and fast-growing data sources. The data management problems associated with the growth in data sources was discussed at length, especially issues related to metadata standards and interoperability. Cultural issues that impede data sharing and the lack of a framework that facilitates patient consent were also acknowledged as constraints to researchers who want to combine multiple types of data in their research. Despite the current data access constraints, the group recognized that progress is being made on the access and interoperability fronts. Instead of waiting for more ideal conditions for access, attention was focused on specific areas where progress has already been made on patient consent and data interoperability issues. The target project we selected for a more in-depth look is The Accelerated Cure Project, a non-profit which makes available data and biospecimens to researchers that are seeking better care for people with Multiple Sclerosis. We expect that the approaches being tested by the Accelerated Cure Project will offer lessons for the next generation of data scientists at the intersection of life sciences that would help this industry move faster and more efficiently, thereby shortening the long time line from research to development and drastically reducing costs. Figure 1: Adoption of Big Data Technology by Industry:Life Sciences vs. ecommerce & Financial Services Financial Services 100 Life Sciences ecommerce 50 Adoption 5 Innovators Early Adopters Early Majority Late Majority Laggards 0

7 Framing the Problem: From Data Scarcity to Abundance John Quackenbush: [W]e don t have the analytical tools to be able to take data science advances [from other fields] and directly apply them to the complexity of the data that we re looking at [in life sciences]. Quackenbush sums up the core problem that we want to help solve. Advances in computer processing and data analytics have made great strides in other scientific fields, including physics. Other industries, such as financial services and e-commerce, have advanced to the stage where predictive analytics are producing valuable results. But life science research lags in developing new methods for analyzing complex research data from multiple sources and remains stalled due to limited data integration and data sharing capabilities. Genomics represents one field where Massachusetts entrepreneurs and companies have developed breakthrough scientific discoveries, but have not yet leveraged Big Data technologies. We were fortunate to have many of these entrepreneurs and researchers participate in our Delphi process to talk about data and analytical challenges. John Quackenbush, who earned his Ph.D. in theoretical particle physics, described how the big data environment in the field of physics differs from life sciences. In physics, there are massive data sets, but limited metadata; sample sizes are huge, but the number of variables is small. In life sciences, the opposite conditions exist: the metadata are far more complex and the number of variables is huge, but the number of data points per variable has traditionally been small. With the rapid increases in the data sources available to biomedical researchers comes the need for new data science tools to 1 Nurses Health Study began in 1976 (see: and the Framingham Heart Study began in 1948 (see: ). process and analyze this rich and massive data. Challenge: Managing the Growth in Data Sources In Life Sciences, the predominant source for evidence-based data remains data from randomized controlled clinical trials (RCT). Access to clinical trial data has improved in recent years, with the availability of the ClinicalTrials.gov registry and results database, a major advance in open access to data that had previously required costly subscriptions to commercial databases. Longitudinal studies, such as the Nurses Health Study and the Framingham Heart Study, have provided useful data for analyzing the etiology of disease. However, when these studies originated in mid 20th century, 1 the process for collecting data for longitudinal studies was costly and it took years before enough data was collected to allow for meaningful analysis. Claims data, the data that providers submit to payers to get reimbursed for their services, have been used in life sciences research for some time. Claims data were automated well ahead of patient history data that are recorded in electronic health records (EHRs), mostly by third parties that charged providers 6

8 to manage their revenue cycle management (RCM), the term used to describe the billing and reimbursement process in healthcare. Although the data recorded in billing records can serve as a proxy for diagnosis and treatment data, there are multiple limitations to using claims data for analysis. The limitations include the mismatch between services that are billed on behalf of the patient and the patient s actual medical condition, the inadequate coding system used in claims processing (ICD codes for diagnoses and services), and the lack of a persistent patient ID across providers and insurers. These limitations make it difficult to aggregate and analyze claims data in meaningful ways. It is important to note that there are data brokers in the life sciences field that have experience managing the data aggregation problems. Some of these life science data aggregators have traditionally focused on drug related information (e.g., IMS Health) and some on payer data (e.g., Truven Health Analytics). But, with the emerging availability of clinical data from EMR systems, new aggregators, such as Optum Labs, are broadening the scope of data sources they aggregate to include EMR data. The traditional aggregators are also acting quickly to incorporate EMR data, but the challenges addressed in this report continue to limit access and use. The data brokers use a traditional economic model of charging fees for use of their data and compensating the parties that contribute data. Note, however, that for these aggregators, the issue of patient consent is sidestepped via deidentification, which inhibits integrating data from disparate sources at the patient level. Today many more sources of information are produced and recorded digitally: patient data, diagnoses, and treatment plans are being recorded within EHRs; lab results and image results are recorded electronically; genome sequencing is fast and more affordable; medical devices generate Figure 2: Growth in Data Sources for Life Sciences Research OTC Purchases Product Registries Medical Device Data Patient recorded/ reported data Rx Purchases/ adherence Data Claims Data EHR Data Disease Registries Genomic Data Clinical Trial Data Longitudinal/ Observational Studies Sensor Data/activity tracking/cell phone data Patient Registries 7 Emerging Data Sources traditional Data Sources

9 and communicate data, and consumer devices and cell phones track an escalating amount of biological, physical activity, location, and other data. In addition, patients and healthy consumers are contributing directly to the amount of data about themselves and their environment via social media. The foregoing diagram (Figure 2) offers a partial depiction of the variety of traditional and emerging data sources that are relevant to biomedical research. Clearly, the dearth of digital data sources is no longer the primary constraint for life sciences researchers. However, even though more data are being produced, the ability to use and extract value from the emerging data sources is limited by insufficient metadata, and the lack of bioinformatics tools that are scalable to the extent needed for integrating large diverse data sets. The limitations related to data access are explored in more depth in the following section. Constraints: Access, Interoperability, and Metadata Dissatisfaction with the current state of useful metadata to enable integrating different data sources provided the most heated discussion during the roundtable session. Vitalii Doban: EMR systems don t talk among themselves. There are data silos within types of data, so we on the Pharma side cannot really connect claims data to EMR data to genomic data. When you talk about the higher quality of data and richer data, I think being able to link those different types of data sets will help us tremendously. Furthermore, life sciences research brings its own set of data management challenges to the table: David DeGraaf: The messiness of biological data poses a set of different challenges that need to be solved. Just mapping metadata from one set to another is impossible. And on top of that, we re dealing with medical descriptions. In autoimmune diseases where we work, the concordance between two physicians primary diagnoses is 35%, which means 65% of the time they disagree on what the patient has. Even internal R&D groups have difficulty interpreting their own results in large part due to a lack of metadata standards in life sciences research. In fact, Mark Schreiber, Associate Director of Knowledge Engineering at Novartis, went as far as to say that the releasing their internal data to a competitor would be tantamount to a poison pill, since their IT group would be stuck for 10 years trying to figure it out. Mark Schrieber: [I]t s challenging for us to make use of it, and we have access to the people who produced it. So you absolutely need metadata; data quality is essentially metadata. It doesn t matter if your data came off a noisy microarray, if you ve got metadata, someone can decide whether it s of good or bad quality. And so our data is not accessible. Most of it would come to you right now as an Oracle dump, which you cannot easily integrate. Relational databases, ironically, are almost impossible to integrate, by design, because they enforce schema. So use RDF (Resource Description Framework, a semantic metadata framework). If you want to distribute data to other people, put it in RDF, and that means you can actually put the metadata in, as well, if you choose to. Steve Papa, founder and former CEO of Endeca, recounted the difficulties that they faced in the days of trying to aggregate product data for e-commerce applications: Steve Papa: When we [Endeca] started out, we couldn t get IBM to give us their product information. They literally didn t have it. They would 8

10 9 be scrambling and building a spreadsheet so that Anthony Philippakis: Let s step back for a they could help sell their products online. Fast second and think about what has happened in the forward to today, now 13 years later, there are entire last decade in other fields. When you think about companies that help people manage the process the fact of dealing with noisy data, there are very of annotating the data. So in some ways, some good solutions. RDF is a wonderful approach to of the predecessor systems that we all work with dealing with this data. can be designed in ways that help foster improving But it s not just data structure and metadata some of the messiness. But it s hard, like you said, if standards that constrain the sharing of data sets. the actual humans are disagreeing. The longstanding culture of guarding research Matthew Trunnell, CIO of the data and the lack of a system that Broad Institute, contends that data science expertise is not sufficient to tackle the current metadata and interoperability challenges in life sciences, due to the complexity of the data. In his view, applying and interpreting metadata requires scientific expertise. Matthew Trunnell: Part of the real challenge here is that these data require real scientific expertise to provide color to the metadata. The challenge of bringing together even marginally disparate data sets is that it requires scientific expertise from two different areas. The ability to use and extract value from the emerging data sources is limited by insufficient metadata and the lack of bioinformatics tools that are scalable to the extend needed for integrating large diverse data sets rewards the contributions of those who add value to research data in a collaborative environment present a greater challenge than the technical knowledge management challenges. Anthony Philippakis: [T]he number of challenges that one sees at the intersection of the life sciences and technology is quite vast. I think the number of opportunities is even greater, and I think that one of the things that especially needs to be addressed is that the culture of technology and the culture of life sciences are often That need to bring together, to collaborate at that quite different. I think as we think about how life level of thinking about data instead of thinking about sciences approaches data and how the technology science in the context of the data industry approaches data, you could not pick two is challenging. industries that are further apart from one another.to However, using a lot of physicians, and I m a cardiologist at Brigham RDF, a semantic and Women s hospital, data should never be shared metadata due to concerns of patient privacy. It should always framework, live behind a firewall. helps to Philippakis sums up the challenge related to understand the history of guarding data in life sciences and the data: healthcare, but goes on to say that technology could help by developing a framework for enhancing discovery of existing data sources and permitting limited access based on use case.

11 Anthony Philippakis: I think that there s a need and a demand for technologies that enable graded sharing of data. Making the data discoverable and providing access depending on permitted use cases are important elements of a potential data platform, but there s an economic component needed as well. In the current academic and research model, researchers are rewarded for publishing and patenting new discoveries. There needs to be some incentive for adding value to data. Winston Hide: You have to incentivize a group to make that data stable in some way, to annotate the data in some way that s consistent so that another group can compare it with them. And there s no real incentive to do that unless there s value to the group who generate the data, and the only way you re going to Constraints: Consent Regulations related to patient privacy and consent in healthcare constrain data sharing in life sciences research. In clinical trial environments, patients give consent to specific uses of their data, but obtaining consent for extended uses creates a barrier to sharing research data with others. There are also business reasons to avoid sharing data without receiving compensation of some kind for the effort expended to collect, organize, apply metadata, and I think that one of the things that especially needs to be addressed is that the culture of technology and the culture of life sciences are often quite different. analyze patient-level data and other primary data sources. Our roundtable discussion made it clear that a framework that facilitates portable consent is essential to enabling the level of data sharing that leads to higher quality and reproducible results. Michael McManus sums up the encourage them to either share anonymously or to technology implications of a culture of privacy and share in a way where they can provide new meaning fear of change from his experience at Knome to their data, so the incentive is to join a commons as follows: or to join a system that allows you to better share Michael McManus: At Knome we have a your data. software-hardware appliance called knosys that goes into clinics; it s a grid-computing system. Raw sequence data gets loaded on, genomes get processed, and at the end, the knosys generates a short-list of variants for the lab staff to review. The reason for an installed system versus a cloud solution are a) security concerns; b) slow upload speed to a cloud; and c) control over the software feature set. Of course, this environment can be difficult to scale at an institution with installed systems. Expansion requires further capital investment and the data are not easily shared between institutions. 10

12 Unfortunately, these are the preferences in these institutions. Knome s response to the constraints of the current environment involve planning for the future by using a software and hardware architecture that can be replicated in a cloud environment: Michael McManus: We ve tried to hedge against the possibility that these installed customers might move to the cloud. We ve designed our system architecture to be easily replicable inside of a cloud environment; for example, either a private cloud or Amazon Web Services. When the transition occurs, we will move our architecture in a cloud environment. The roundtable participants discussed various solutions to privacy concerns and potential models for encouraging portable patient consent, including a patient donor card. Martin Leach: I like the idea of the data being portable with the patient, and maybe they can run an algorithm or something on the back of their card that totally hashes it up and it s non-decryptable. There s ways that you could actually destroy your data if you wanted to. While recognizing the importance of privacy and security concerns, we determined that in order to stay focused on data science technology, we should focus attention on a segment with highly motivated patients who place high value on contributing to research that might lead to a cure. Brad Margus: But there are different types of people in the public. From the whole rare disease community, we re all in favor of giving you every single thing. But we re not typical. We ve had a wakeup call that says forget about worrying about your privacy and stuff, we need some answers, and we don t want to impede research. Figure 3 illustrates the importance of metadata and consent to improve access to data sources. But improved access is an interim, not the ultimate, goal. Ultimately, we want to identify technology solutions that can process and analyze the full range of emerging data sources and lead to new discoveries. Therefore, our primary focus is centered on the second green box in the diagram: Data Science Tools for Life Sciences. The next section reviews the roundtable discussion on these data science opportunities. Figure 3: Overcoming Constraints to Big Data-Driven BioMedical Discoveries Step 1: Consent framework and metadata enable life science researchers access to collections of new, potentially valuable, data inputs. EHR Data + + Clinical Trial Data Genomic Data Consent framework for researchers Rich metadata + + More efficient discovery process 11 Interoperable access to rich data repository Big data platform Life science modeling and analytic tools Better diagnostic tools Step 2: Scalable big data processing platforms and life science analytic tools are needed for researchers to create models based on richer sets of data inputs. Personalized drugs

13 Data Science Opportunities Eric Neumann: One of the challenges with data, but especially in big data, is being able to get it together and to process it. There are different sets of questions that you can ask, and there are different things that you will find, even by surprise. How are we going to make this a continuous process so we can mine the new stuff and compare it to the old in some sort of meta-analytical way that works? Neumann sums up the greater challenge we want to tackle: how do we construct a continuous learning environment that encourages collaboration and allows for new, more complex problems to be modeled? As Quackenbush stated earlier, we don t yet have the tools to adequately analyze the complex sets of data that are currently being compiled and aggregated; the early generation of big data technology tools developed for other industries The overarching question we want to address is: How can we build on our resources and expertise to develop innovative platforms to facilitate data access and introduce the next generation of tools for analyzing diverse sets of data? analysis level. New tools, like SciDB from Paradigm4 are required to analyze large diverse sources of scientific data. Yet, the history of innovation in information technology and life sciences research runs deep in Massachusetts. The overarching question we want to address is: How can we build on our resources and expertise to develop innovative platforms to facilitate data access and introduce the next generation of tools for analyzing diverse sets of data? Andy Palmer: The Hadoop community and those tool sets were a recognition that the technologies that existed mostly in relational databases were inadequate to serve the needs of the community of internet companies that were evolving at the time. We have an opportunity, as the people that operated at the intersection of the life science and the computer science, to lead is not sufficient for achieving the level of life science in this area, and to make those statements that research breakthroughs envisioned by this group. nothing that exists is good enough; we need to Denis Kural: Hadoop is a Yahoo clone of a build some infrastructure from scratch, we need Google technology that Google stopped using new sets of tools. five years ago. Hadoop is not the right way to [W]e as a state and as a region here in Boston, process this data. You can t really run an aligner can show the rest of the world how to do this. We re on Hadoop, so I feel like many issues get mixed uniquely positioned to be able to do that. up. I think that, at the same time, there s a great The roundtable meeting closed with a discussion lack of understanding from the IT side of the kind of how to focus on what the Massachusetts Life of subtleties of the algorithms and tools involved in Sciences Center and MassTLC can do to support what you need to do. big data innovations in life sciences. Furthermore, Hadoop doesn t help with the data 12

14 Susan Windham-Bannister: [W]e re here to really be a catalyzer and help invest and create some of these solutions. I ve heard about some policy solutions; I ve heard about the need to create or fund entities that will compile the data, create the tools, etcetera. I m really listening for what you think would be key and catalyzing activities. These are perhaps things that the state could fund, or maybe develop some public-private partnerships around, because we bring both a funding opportunity, as well as a convening opportunity. about consent and how we do this come down to how we re going to actually use the data. Let s start before we actually design it by asking how we re going to use it. And then once we figure out how we re going to use it, then we can build it to address those questions. We should come out of this with a plan for how to get working groups together to start to address different issues. Richard Dale: I think the challenge is for this room is to come up with a well framed question, which can have a well framed, well researched answer and collaboration. It s not what s going on today, but what are you going to do to help us tomorrow, right? John Quackenbush: One place to start is to bring together a group to develop use cases, because all the different questions that we have 13

15 Who Is Doing It Right? The following case study recaps the discussionthat recognizes limitations in the general research environment and is responding to the challenges with projects that target highly motivated patient populations. Case Study: Accelerated Cure Project Eric Perakslis, Exec. Director, Center for Biomedical Information, Harvard Medical School: The patient voice and the active community are transforming healthcare; they re transforming regulation; and, they re transforming medical research. The genius of ACP was to focus not on directly funding research but on removing a key barrier that limits medical research - access to high-quality biological samples and data. With no barrier of access to crucial research materials and open sharing of results, MS research has been accelerated by ACP s innovations. ACP is an example of an initiative that is overcoming the challenges that hinder the pace of medical research by focusing on a highly motivated patient segment and providing a framework for collaboration. Robert McBurney, CEO of ACP, participated in the roundtable discussion and we followed up with him to gain further insight into the structure and incentives within ACP that are breaking through previous barriers and creating an environment that is designed to promote data-driven discoveries. ACP is a patient-founded, non-profit that promotes scientific collaboration and aims to accelerate research by rapidly and cost-effectively providing researchers with data and biospecimens they need to explore novel research ideas that can lead to better care for people with MS. The repository of biological samples not only provides much-needed samples and data to researchers studying MS and related neurological diseases, but also aggregates the results from all of these studies so that they can be analyzed collectively leading to new findings and breakthroughs [1]. What factors were necessary to make ACP s innovation possible? Robert McBurney: ACP is focused on multiple sclerosis. But, as I ve been asked many times, Couldn t there be an Accelerated Cure Project for every disease?. And, the answer is clearly Yes!. It s only a matter of money and organization. Patient involvement has been incredibly important in the efforts to collect the biological samples and data that make up the ACP Repository. Perhaps ACP s most important early decision was to pay for a study coordinator (research nurse) at every one of the 10 clinical sites around the US that contributes biological samples and initial data to the Repository. The study coordinators at each MS clinic identify people with MS, talk to them about ACP and the way that the ACP Repository is accelerating MS research, discuss the informed consent, and, once 14

16 a person agrees to participate, draw the blood samples and make sure that the 50+ page case report form of data is completed. This case report form goes way beyond the traditional electronic medical record and contains a great deal of personal characteristics, family and medical history, and exposures. To date, the Repository contains biological samples and data from over 3200 individuals - people with MS or related diseases and control subjects. In an open access way, we ve made those biological samples available to almost 80 research groups around the world, with one proviso - on a date certain after they receive the samples, they return the raw measurement data to our central database for the benefit of all researchers. This arrangement allows for intellectual property protection because, frankly, life science business depends critically on intellectual property in a way that IT business does not. Without IP protection, great discoveries might never make it onto the expensive path to products that will transform the lives of people with MS. next challenge. My point in all of this is that the patients were really important in getting this innovative approach going. There are new models for accelerating research to impact patients, ACP s is just one. Patients are actually impatient for new discoveries and their translation to products that will improve diagnosis, optimize treatment and cure diseases. These new research models must be pursued even if that means breaking the traditional academic medical research model, because that model is likely outdated. And, I say this as someone who benefitted from that model for 15 years. So far, over 20 of those raw data sets have been returned - comprising many different data types: genomics, proteomics, metabolomics, antibodies, etcetera. In the 20-plus data sets that have been returned, some particular patient IDs are associated with as many as 10 of those data sets. I don t know what it would have taken to get those 10 organizations, some of which are for-profit organizations and some of which are academic groups, to collaborate de novo on generating those combined data sets but, without complicated multi-party collaboration agreements, those rich data sets now exist. In fact, for certain patients, the Repository database probably contains more information about them than has ever been known before about any MS patients. These growing, rich sets of Big Data are ripe for mining. That s the 15

17 Conclusion & Next Steps Pervasive connectivity, data analytics, and other information technologies will transform the way we conduct research, clinical trials, and drug development. Massachusetts is a global leader in biomedical research, drug development, medical devices, big data analytics, mobile communications, cloud computing, intelligent automation/robotics, and information security. Taken together with its forward-looking public sector, academic research institutions, and investment community, Massachusetts possesses a unique capacity to be a leader in creating a future with lifesaving therapies, enabling people around the globe to live happier, healthier, more productive and independent lives. This report illustrates the state s potential for global leadership by highlighting early positive signals of data-driven innovation in healthcare. Despite this leadership position, the state possesses significant capacity to further accelerate innovation. As follow-up from the Delphi roundtable, MassTLC convened a subset of roundtable participants to further explore use case groups in which a patient data portability project could be piloted. The outcomes of that subsequent discussion include the following recommendations: 1) That MassTLC, the Massachusetts Life Sciences Center, the Massachusetts ehealth Institute at the Massachusetts Tech Collaborative, and MassBio, work together to identify a patient-centered disease community and seek to establish a rich set of longitudinal patient data to be stored in an open data repository. This repository would provide access to support better patient stratification and identification for predictive biomarkers for biomedical research, clinical trials, diagnostic test development, drug discovery, and drug development. 2) That MassTLC, the Massachusetts Life Sciences Center, the Massachusetts ehealth Institute at the Massachusetts Tech Collaborative, and MassBio, work together with the appropriate technology leaders and organizations to launch a challenge to develop new mobile apps and devices for collecting data, as well as new data management, data security, data mining, data interpretation, and data visualization tools for both researchers and patients. Together, the Massachusetts IT and life science communities have the collective expertise and opportunity to re-imagine and enable more complex life science research models. 16

18 Appendix A Delphi Participants Joshua Boger, Ph.D. Founder and Chairman of the Board, Vertex Joshua Boger, Ph.D., founded Vertex in 1989 and was the company s Chief Executive Officer from 1992 until May He served in the additional role of Chairman of the Board from 1997 until Dr. Boger began his industry career at Merck in Immediately before he founded Vertex, he was Senior Director of Basic Chemistry at Merck Sharp & Dohme Research Laboratories. Dr. Boger holds a B.A. in chemistry and philosophy from Wesleyan University and M.S. and Ph.D. degrees in chemistry from Harvard University. Richard Dale COO, Optum Labs Richard Dale is COO of Optum Labs, the health care industry s first open research and innovation facility, designed solely around the goal of improving patient care. Previously, Richard was a Principal at Sigma Partners where he was a member of the deal team and also worked on special projects for the benefit of the entire portfolio. Richard came to the VC world after a long period as an entrepreneur and startup executive. He co-founded and had various leadership roles at Phase Forward, a provider of software services for pharmaceutical clinical trials which went public and later was sold to Oracle. Prior to that, Richard was VP Operations for Vermeer Technologies, creators of FrontPage which was acquired by Microsoft in Prior to Vermeer, Richard held roles in professional services management, senior product management and IS management at some of industry s best technology companies including SQL Solutions, Sybase, Epoch Systems (acquired by EMC) and MicroTouch Systems. Richard also provided early stage advice and consulting to FirstSense, OnDisplay, Vignette and QXL.com. Richard s career began as a software engineer and database expert, and he has maintained a strong interest in data and application technologies. David de Graaf, Ph.D. President and CEO, Selventa Dr. David de Graaf joined Selventa in 2010 to define the company s scientific strategy, direction and general operations. Additionally, he is responsible for developing key partnerships that will enhance the company s significance. Previously, Dr. de Graaf served as the Vice President of Biotherapeutics and Integrative Biology at Boehringer-Ingelheim, where he built a successful portfolio of clinical candidates, initiated key collaborations as well as managed scientific leadership and site operations. He also held roles with increasing responsibilities at Pfizer, AstraZeneca and the Whitehead/MIT Center for Genome Research with a focus on bringing innovative systems biology approaches to the early pipeline. Dr. de Graaf completed his postdoctoral fellowship at the Weizmann Institute of Science in Rehovot, Israel in pharmacogenomics of human olfactory systems. He also earned a Ph.D. in mammalian genetics from The University of Illinois 17

19 at Chicago and a Masters of Science degree in evolutionary genetics from the University of Utrecht in the Netherlands. He was awarded a Feinberg Fellowship and is invited frequently to speak at scientific conferences. Dave Dickinson CIO, Optum Labs Prior to joining Optum Labs as Chief Innovation Officer and Senior Vice President of Business Development, Dave was the CEO of Zeo, Inc. a digital health startup. He also served as the COO of StemCyte Inc, a cord blood stem cell banking pioneer. Dave s exierience also includes Global New Business Development, Marketing, and Sales roles within Fortune 500 companies: Bristol-Myers Squibb/Mead Johnson, Church & Dwight/Arm & Hammer, Johnson & Johnson and Procter & Gamble. Dave has deep consumer healthcare, startup and global business development experience. He was an early pioneer of the Quantified Self social movement. Dave is an experienced entrepreneur, intrapreneur, innovator, and marketer. Vitalii Doban Director, Real World Data and Analytics, Pfizer As Director of Real World Data and Analytics at Pfizer, Vitalii manages the consolidated cross-enterprise Real World Data and Analytics environment used to perform multidisciplinary studies that demonstrate product value, inform drug development, advise product launches, as well as to support other evidencebased data driven studies that improve health outcomes and quality of care. Vitalii has extensive experience partnering with business and technology leaders to define and implement strategies that drive business and technology alignment. Vitalii has delivered numerous Business Intelligence, Data Warehousing, CRM/SFA and Digital Marketing solutions. While at Pfizer, Vitalii has led a number of efforts including: their Business Intelligence and Data Warehouse integration for the Biopharma division; their technology solution to meet U.S. and state transparency laws; the implementation of CDI (Customer Data Integration) and MDM (Master Data Management) solutions for different data entities (customers, products); and large onshore and offshore development teams and multi-million technology projects. Vitalii has a strong technical background. He started his career as a software developer (MCSD. NET), database developer and administrator (MCDBA & OCP) and systems engineer (MCSE). Dmitri Gunn MIT Media Lab Dmitri is an entrepreneur with over a decade of expertise founding and accelerating the growth of early-stage companies. His unique blend of strategic perspective and tactical execution suited to environments where uncertainty is the rule rather than the exception. Dmitri is adept at conceptualizing and implementing partnerships and assembling technology coalitions to launch new growth initiatives. He is an active member of the Boston startup and TEDx community, who enjoys bringing individuals together to make great things happen. Dmitri is currently Executive Director of TEDxCambridge, Founder of Entrepreneur and Investors - a quarterly event bringing together 18

20 leaders of the Boston startup ecosystem, and an advisor at the MIT Media Lab. Winston Hide Associate Professor of Bioinformatics and Computational Biology, Department of Biostatistics, Harvard School of Public Health Winston Hide graduated in Zoology at the University of Wales in 1981, gained a Ph.D. at Temple University in Philadelphia in 1992, and was a post-doctoral fellow in molecular evolution under Wen-Hsuing Li at the the University of Texas in Houston. In addition he performed post doctoral study at Baylor College of Medicine at the Human Genome Centre with Richard Gibbs and also at the Smithsonian Museum of Natural History in Washington DC. He returned to South Africa to found and direct the South African National Bioinformatics Institute (SANBI) at the University of Western Cape in He established the first Ph.D. programme in Bioinformatics in Africa and is a founder of the African Society of Bioinformatics and Computational Biology and was the first African on the board of the International Society of Computational Biology. At SANBI he founded the Medical Research Council Unit for Bioinformatics Capacity Development and established the WHO regional Training Center for bioinformatics in Africa. Hide is now Associate Professor of Bioinformatics and Computational Biology in the Department of Biostatistics at Harvard School of Public Health where he leads development of bioinformatics addressing the genomic approaches to public health in the developed and developing worlds and directs the HSPH Bioinformatics Core. His research addresses integration across large scale genomics datasets to deliver clinical translation. He uses standardized approaches to discovery in stem cells and cancer stem cells, host response to pathogens, and complex diseases. An associate of the Ludwig Institute for Cancer Research, Hide has recently been appointed to direct the bioinformatics strategy for the Harvard Stem Cell Institute and is Director of the Center for Stem Cell Bioinformatics at the HSCI. Hide is also a keen contributor to the development of Africa s peoples and is a founder and member of the steering committee of the NIH-Wellcome Trust funded H3 Africa Initiative. Tom Hopcroft President and CEO, MassTLC Tom Hopcroft is President & CEO of the Mass Technology Leadership Council, Inc. (MassTLC), a business association that addresses the critical leadership issues of innovative technology and technologyenabled companies. Drawing on the rich legacies of Massachusetts Network Communications Council, the Massachusetts Software Council, and the New England Business and Technology Association, Inc., MassTLC accelerates innovation by connecting people from across the technology landscape, providing access to industry-leading content, and offering a collaborative platform for driving ideas and people forward. A Massachusetts attorney and former adjunct professor at Northeastern University s College of Business Administration, Mr. Hopcroft formerly led and founded the New England Business and Technology Association (NEBATA) which merged with the Mass Software Council in After the merger he led the Council s cluster development activities, growing the cluster portfolio from three to ten active groups. Prior to founding NEBATA, 19

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