Enabling Translational Research Efficiency by Mapping Data Flow

Enabling Translational Research Efficiency by Mapping Data Flow and Linking Data Management Platforms

Seattle Translational Tumor Research Eric Holland, MD, Ph. D Sr. VP & Director Rachel Galbraith, MPH Operations Director

What is Translational Research? CLINICAL RESEARCH BASIC SCIENCES TRANSLATIONA L RESEARCH POPULATION RESEARCH STTR © Fred Hutchinson Cancer Research Center 4

STTR Mission & Vision Mission Create an environment which enables researchers and clinicians to accelerate scientific discovery and translate it into cures for patients Vision Our vision is an environment tailored to researchers and clinicians which allows them to accelerate scientific discovery and translate it into cures and research advances that will significantly improve patient quality of life and survival.

STTR Overview 15 tumor-specific translational research programs - Anogenital, bladder, brain, breast, colorectal, head & neck, leukemia, lung, lymphoma, MDS/MPN, myeloma, ovary, pancreas, prostate, sarcoma 500+ members Trans-institutional network - Fred Hutch, UWM, University of Washington, Seattle Children’s Hospital, Seattle Children’s Research Institute, Institute for Systems Biology, PATH, Gates Foundation, Puget Sound VA Health System © Fred Hutchinson Cancer Research Center 6

STTR as a Central Resource • Fielding questions from researchers • Facilitate connections across our network to • People • Expertise • Resources • Funding opportunities • Elevate needs of the research community to senior leadership • Advise on research strategy & organization-wide projects involving or impacting this space © Fred Hutchinson Cancer Research Center 7

Translational Research Pipeline Participant Consent • Current state landscape assessment • Roll-out prioritization Clinical Data Abstraction • Data dictionary development & abstraction into Caisis • Regular auditing to ensure data quality Biospecimen Access • STTR-funded collection • Decreased NWBT specimen costs • Biospecimen repository inventory • Swedish Cancer Institute partnership Cutting-Edge Data Analysis & Visualization © Fred Hutchinson Cancer Research Center 8

Common Questions Relating to the Pipeline • Do you have recommendations for protocols/consents for clinical research? • Where can I access specimens for my research? • Do you have a recommendation for a database to manage specimen/clinical/genomic data? • How do I store and work with specimen-associated data? • Is there a way to merge data across multiple databases? • How do I access and manage large-scale molecular data? © Fred Hutchinson Cancer Research Center 9

Get Curious • Learn about existing needs: how do people source & manage their data now • Think about how things would look in an “ideal state” • Map everything you know • Identify key bottlenecks, gaps or pressure points in the process • Seek out new information to better understand these areas • Target efforts to the bottlenecks, gaps and pressure points © Fred Hutchinson Cancer Research Center 10

Translational Research Data Flow © Fred Hutchinson Cancer Research Center 11

Translational Research Data Flow © Fred Hutchinson Cancer Research Center 12

Translational Research Data Flow © Fred Hutchinson Cancer Research Center 13

Translational Research Data Flow © Fred Hutchinson Cancer Research Center 14

Translational Research Data Flow © Fred Hutchinson Cancer Research Center 15

Translational Research Data Flow © Fred Hutchinson Cancer Research Center 16

Translational Research Data Flow © Fred Hutchinson Cancer Research Center 17

Translational Research Data Flow © Fred Hutchinson Cancer Research Center 18

Translational Research Data Flow © Fred Hutchinson Cancer Research Center 19

Translational Research Data Flow © Fred Hutchinson Cancer Research Center 20

Translational Research Data Flow © Fred Hutchinson Cancer Research Center 21

Program-Specific Mapping: Lung © Fred Hutchinson Cancer Research Center 22

Impact & Outcomes • Clear visual of infrastructure or data gaps in program resources • Recommendations exist for various data types - plug and play for new investigators/teams • Efficient, standardized data management across programs • Streamlined data access and sharing © Fred Hutchinson Cancer Research Center 23

Summary • Start with a vision of where you want to be • Begin with what you know • Find out what others are doing – someone already does this • Why does it work well? • what could work better? • Work with the experts • Data generators; data analysts; lab personnel; PIs; techies • Always be curious © Fred Hutchinson Cancer Research Center 24

Thank you, questions? Rachel Galbraith Operations Director, Seattle Translational Tumor Research rgalbrai@fredhutch. org