Translational Medicine Symposium

Susan is the executive leader of the 10-year $1 billion life sciences initiative enacted by the Massachusetts Legislature in June 2008. Her responsibilities include staffing the Center, developing policies & procedures, creating a brand and formulating the investment strategy. Under Susan’s leadership, in just three years the Center has invested $218 million, leveraged another $700 million in matching investment capital and created over 7,000 new life sciences jobs in the Commonwealth.

The Life Sciences Center is the hub for all sectors of the state’s life sciences community – biotechnology, pharmaceuticals, medical devices, medical diagnostics and bioinformatics. It is also promoting economic development, catalyzing innovation, strengthening Massachusetts’ global leadership position in the life sciences and accelerating the commercialization of promising treatments, therapies and cures.

Before assuming this role Susan was a founding partner of Abt Bio-Pharma Solutions, a boutique consulting firm serving life sciences companies where she managed the Commercial Strategy Group. In her 35-year consulting career she and has been instrumental in the successful launch of a number of well-known therapeutics, medical devices and novel biomarkers.

She has written numerous articles on competition in today’s health care marketplace and has co-authored two books: Competitive Strategy for Health Care Organizations, and Medicaid and Other Experiments in State Health Policy.

Susan holds a B.A. from Wellesley College, a doctorate in health policy & management from the Florence Heller School at Brandeis University and was a post-doctoral fellow at Harvard University’s John F. Kennedy School. She completed her doctoral work under a fellowship from the Ford Foundation.

Dr. Susan Windham-Bannister
President and CEO, Massachusetts Life Sciences Center

Massachusetts – Leading the Way in Personalized Medicine
Abstract: Susan Windham-Bannister, Ph.D., President & CEO of the Massachusetts Life Sciences Center, will discuss the state’s emerging leadership in personalized medicine, and the investments that Massachusetts is making to strengthen the state’s leadership in this field.

Since 2005, John has been Professor of Biostatistics & Computational Biology and Professor of Cancer Biology at the Dana-Farber Cancer Institute (DFCI) and Professor of Computational Biology & Bioinformatics at the Harvard School of Public Health. His work focuses on the analysis of human cancer using systems-based approaches to understanding and modeling biological problems. In 2009 he launched the Center for Cancer Computational Biology at the DFCI to provide broad-based bioinformatics support to the local research community using a collaborative consulting model.

John received his Ph.D. in theoretical physics in 1990 from UCLA working on string theory models. Following two years as a postdoctoral fellow in physics, he received a Special Emphasis Research Career Award from the National Center for Human Genome Research to work on the Human Genome Project. John spent two years at the Salk Institute developing physical maps of human chromosome 11, and two years at Stanford University working on new laboratory and computational strategies for sequencing the Human Genome.

In 1997 he joined the faculty of The Institute for Genomic Research (TIGR) where his focus began to shift to post-genomic applications with an emphasis on microarray analysis. Using a combination of laboratory and computational approaches, John’s group developed analytical methods based on data integration across domains to learn biological meaning from high-dimensional data.

John Quackenbush, Ph.D.
Professor of Computational Biology & Bioinformatics, Dana-Farber Cancer Institute

The Road to Personalized Medicine is Paved with Data and Information
The rapid advance of genomics and genomic technologies has generated tremendous interest in the potential for personalized medicine and the development of associated biomarkers. This presentation will explore the elements necessary to successfully develop an integrated program using genomics and medical data, together with other sources of information, to arrive at robust biomakers that can be reliably used in a clinical setting.

Martin Naley is the Head of the Genetic Care Interchange at Life Technologies, a global biotechnology company based in Carlsbad, California. The company enables basic research, drug discovery and drug development, and is actively advancing the field of personalized medicine.

Mr. Naley joined Invitrogen, a predecessor company to Life Technologies, in 1997. Prior to his current role, Mr. Naley held the position of Chief of Staff to the CEO, where he initiated the development of the company’s business strategy for medical use of sequencing technologies. He has held various sales, marketing, business development, and general management positions including Vice President of the Cell Culture Research business unit. Mr. Naley holds a B.A. in Biological Sciences from Cornell University and an M.B.A. from Yale School of Management.

Martin Naley,
Head of Genetic Care Interchange, Life Technologies

Genomic Medical Care: Envisioning the Workflow of the Future

Genomic analysis is likely to be too intimidating and complex for practical adoption in health care.  Without significant innovation in the way that data is delivered, a treating physician is unlikely to be able to make sense of genomic findings to guide disease management.
We believe that the future will usher forward a new workflow. A treating physician will order a test through a genomic medicine-enabled health care institution, where cutting-edge genetic analysis products are used to deliver sequence results to trained molecular pathologists.  Specially configured software applications will be used to review medical guidelines, explore available drug trials, and review the latest biomedical research to translate the patient’s biological findings into insightful treatment considerations for the patient’s physician.  Every patient’s test results, treatments, and medical outcomes will be stored in a secure, private database to be used for comparative analysis, in which prior patients’ genetic profiles (together with other relevant insights) can be used to illuminate new treatment considerations.  In this manner, every patient becomes a participant in cutting-edge genomic research.  Their treating physician will get a single, user-friendly report summarizing genetic findings and identifying potential treatment options to consider for their patient. 
In this session we will discuss a potential solution to enable this genomic medicine workflow.

Dr. Emanuele de Rinaldis is a Senior Research Fellow in Genome Bioinformatics in the Division of Cancer Studies at King's College, where he is leading the bioinformatics team of the Breakthrough Breast Cancer Research Unit.

The team works on the discovery of novel biomarkers, prognostic factors and therapeutic targets in basal-like breast cancer, using genomics and bioinformatics approaches.

Emanuele holds a PhD in biochemistry and has matured extensive knowledge in bioinformatics and cancer research, through several research activities carried out in biotech/pharma companies and academic environments.  In the course of his career Emanuele has directed the development of open source and commercial software for the analysis of protein structures,  microarray data, pathways and DNA polymorphisms and contributed to a number of drug and biomarker discovery programs at Merck MRL. In this areas he also authored numerous peer-reviewed publications.

His main topics of research are:

• Cancer bioinformatics, genomics, molecular profiling, molecular biomarkers
•  Genomic and clinical data integration and analysis for supporting multiple aspects of R&D pipeline (early discovery, diagnostics, clinical development)
• New target, diagnostic and biomarker discovery through integrative mining of large-scale data from clinical and pre-clinical studies
• Data analysis, algorithm development

Recent publications (2008-):

1. de Rinaldis E, Tutt A and Dontu G: Genomic approaches in breast cancer clinic: current status and future directions. In: Breast Pathology, 2nd Edition (2011). Publisher: Churchill Livingstone
2. Uva P., Lahm A., Sbardellati A., Grigoriadis A., Tutt A. and de Rinaldis E.: Comparative Membranome expression analysis in primary tumors and derived cell lines. 2010, PLOS One
3. Marra E, Uva P, Viti V, Simonelli V, Dogliotti E, de Rinaldis E, Lahm A, La Monica N, Nicosia A, Ciliberto G and Palombo F: Growth delay of human bladder cancer cells by Prostate Stem. 2010, BMC Cancer
4. Dontu G. and de Rinaldis E.: MicroRNAs-shortcuts in dealing with molecular complexity? 2010, Breast Cancer Research
5. Cadot B., Brunetti M., Coppari S., de Rinaldis E., Fedeli S., Dello Russo C., Gallinari P., De Francesco R., Steinkühler C. And Filocamo G.: Loss of histone deacetylase 4 causes segregation defects during mitosis of p53-deficient human tumor cells. 2009, Cancer Research
6. Peruzzi D., Mori F., Lazzaro D., de Rinaldis E, Ciliberto G., La Monica N. and Aurisicchio L.: MMP11: a novel target antigen for cancer immunotherapy. 2009, Clinical  Cancer Research
7. Uva P., Aurisicchio L., Watters J., Loboda A., Kulkarni A., Castle J., Palombo F., Viti V., Mesiti G., Zappulli V, Marconato L., Abramo F., Ciliberto G., Lahm A., La Monica N. and de Rinaldis E. Comparative expression pathway analysis of human and canine mammary tumors. 2009, BMC Genomics
8. de Rinaldis E., D'Errico M., [..] and Dogliotti E.: Identification of gene expression networks associated with genetic instability in gastric cancer and the role of DNA repair. European Journal of Cancer 2009, 45(3)
9. Uva P and de Rinaldis E: crosshyb: a Bioconductor package to detect probe cross- hybridization in microarray experiments. 2008, BMC Bioinformatics, 9:485
   

Emanuele de Rinaldis, Ph. D.,
Sr. Research Fellow & Bioinformatics Team Leader, Breakthrough Breast Cancer Research Unit - Division of Cancer Studies at King's College London

Systematic approaches for the identification of novel therapeutic targets in triple-negative breast cancer

The identification of novel therapeutic targets in triple-negative breast cancers (ER-, PR-, HER2-) is an elusive task. Towards this aim, different approaches can be undertaken based on the assessment of features considered as favourable for a "good target" gene.
We conceptualized this process by developing a semi-automated and systematic strategy based on the combined evaluation of a large set of molecular, clinical and pathological data, gathered from a curated collection of  triple-negative breast cancers and from public data sets.

To streamline the assessment of individual genes as potential therapeutic targets an automated Target ID Engine was devised which prioritizes genes according to the weighted evaluation of composite set of criteria.
The results obtained by using this platform in different analytical scenarios are presented.

As Director, Translational Medicine Solutions, Robin is responsible for the direction of the IDBS Translational Medicine Solution and Strategy. He has over 15 years of experience in Bioinformatics and extensive knowledge of the pharmaceutical and healthcare industries with in-depth knowledge of translational medicine, biomarker discovery and molecular analytics as well as the drug discovery process, clinical and healthcare information management systems. Before he joined IDBS he led teams to develop bioinformatics solutions for expression analysis, target tracking, protein and pathway interactions at companies such as LION bioscience and InforSense (now part of IDBS).

Robin holds a Computational Biochemistry Ph.D. from the MRC National Institute for Medical Research/University College London and a M.Sc. in Biological Computation from York University.

Robin Munro, PhD,
Director, Translational Medicine Solutions, IDBS

The Journey to Personalized Medicine Depends on Collaboration

To deliver personalized medicine requires the convergence of many complex factors that are challenging the health science industry – availability of high quality patient data, integration of data silos, combining clinical and omics data, the impact of Next Generation Sequencing and bridging the gap between research and clinical decision-making. Collaboration is at the heart of all these challenges because without it new discoveries and better understanding of disease can’t be made, let alone shared and implemented.

The entire health science ecosystem, including academic medical centers, pharmaceutical and diagnostic companies, hospitals and payers, need to work together to share translational medicine data in multiple ways to support drug discovery, diagnostic development and comparative effectiveness research. This presentation will look at these challenges and provide real world examples of how research and clinical teams are working together to make personalized medicine a reality.

Hai Hu, PhD is the Deputy Chief Scientific Officer and Senior Director of Biomedical Informatics of the Windber Research Institute with over 12 years of experiences in bioinformatics and biomedical informatics. He has led the development and deployment of a biomedical informatics infrastructure for translational research, including a data warehouse, a data tracking system, and a set of QA programs for clinical and molecular study data. He has also led a number of research projects, with experience in experimental design and data analysis support to other research projects. His current research and development support the Breast Cancer Center of Excellence and Gynecological Cancer Center of Excellence funded by United States Army Medical Research and Materiel Command via Henry Jackson Foundation in collaboration with the Walter Reed National Military Medical Center. He is a co-PI and site PI of a Susan Komen Promise Grant in a breast cancer consortium headed by the Thomas Jefferson University. He is also a member of the Breast Analysis Working Group of The Cancer Genome Atlas project.

Dr. Hu has published more than 30 peer-reviewed papers and book chapters on subjects including biomedical informatics, bioinformatics, proteomics, and genomics, and co-edited two books including one entitled ‘Biomedical Informatics in Translation Research’. He serves as a peer-reviewer for a number of journals including Bioinformatics, Proteomics, and Circulation Research. In addition, he has presented at numerous national and international scientific and business conferences, serving as bioinformatics program chair or session chair in several of them.

Dr. Hu’s educational background includes physics (BS), speech signal processing (MS), biophysics (PhD), molecular and cellular cardiology (post-doc), applied mathematics and statistics, and computer engineering.

Hai Hu, Ph. D.,
Deputy Chief Scientific Officer Sr. Director of Biomedical Informatics, Windber Research Institute

Challenges in data warehousing for translational research

In the last few years I have led a project team composed of clinicians, lab scientists, and biomedical informaticians from the Windber Research Institute and its clinical and research partners, in collaboration with IT developers from InforSense/IDBS, to develop a Data Warehouse for Translational Research, which lead to the commercialization of the product ClinicalSense. The system is based on its unique ontology including a patient-centric clinical data model, a specimen-centric molecular data model, a temporal data model, and an image data model. The system is also characterized by two intuitive interfaces targeting non-informatics specialists, for aggregated biomedical information browsing and individual subject information viewing. Currently, translational research is in rapid development in multi-facets. Take The Cancer Genome Atlas project as an example; more than 20 cancer types have been identified for study. For each cancer types, multiple platforms of data have been generated, including DNA sequencing, DNA copy number, DNA methylation, gene expression, RNA-sequencing, etc. Analysis of each platform of the data, and integrative analysis of these data, imposes challenges not only in data integration but also application integration. In this presentation, I will use concrete examples to lay out example challenges we are facing in data warehousing for translational research, and the solutions we have developed or conceived.

Dr. Xinwei is a senior research scientist of Biological and Translational Informatics group in Merck. He is also the project manager of Merck's Cancer Biomarker Development (CBD) data management/integration with IDBS Biomolecular Hub. His areas of expertise include genomics, next generation sequence analysis, development and integration of computational pipelines and bioinformatics support for drug/vaccine development. Xinwei joined Merck in 2006.

Xinwei received his Ph.D. in Immunology and M.S. in Computer Science from State University of New York at Buffalo in 2003. He was trained as a postdoctoral fellow in Case Western Research University and University of Washington at Seattle, where he focused his research on genomic segmental duplication and copy number variation. Xinwei has authored numerous peer-reviewed publications during his scientific career.

Xinwei She, PhD,
Senior Research Scientist, Biological and Translational Informatics, Merck

From Cell Line Data to Biomarkers: A Use Case of Translational Informatics Approach

There has been a lot of effort from the industry and academia to systematically characterize the genetics/genomics and pharmacological features of large collections of cancer cell lines, in order to identify cancer biomarkers and their association with drug response, which can be translated into patient stratification strategy. The complexity and the amount of data from the cell line studies, including cell line metadata, drug response, sequence variation, gene expression and copy number variation, post a great challenge to effective data integration and data analysis for biomarkers. Using IDBS’ translational medicine platform, we integrated public and proprietary cell line data and implemented most commonly used analytical services, which allows analysts and biologists to have easy access to the data and provides them a useful tool for data mining and analysis.