Workshop to Facilitate Cancer Systems Epidemiology Research

Workshop to Facilitate Cancer Systems Epidemiology Research, February 28 – March 1, 2019, Bethesda, MD


NCI is seeking broad input from the scientific community to identify areas of research need and opportunities to facilitate cancer systems epidemiology research. Please share your ideas by April 30, 2019!

See NOT-CA-19-019 for more details


The availability of high throughput -omic technologies, novel devices for exposure assessment, and electronic medical records have the potential to facilitate a more comprehensive study of risk factors contributing to development of and outcomes from cancer.

Despite individual successes at identifying genetic, biological, and environmental risk factors for cancer, much of the etiology remains unexplained. This may be due in part to the limited focus of many studies on a single or small set of risk factors or data types (i.e. measures such as DNA sequence, methylation data, variables from questionnaires). Moreover, many studies fail to consider the complexities and interrelations among multiple risk factors and associated outcomes. For example, each individual risk factor, such as a single dietary component or genetic polymorphism, occurs in a broader biological (e.g. pathways) or societal (e.g. individual in social network) context which could modulate the effect of individual risk factors on disease. Further, many risk factors for disease can be highly correlated with possible interactive, synergistic, or attenuating effects. Importantly, risk factors can change over time.

A more comprehensive, systems modeling based type of approach, which accounts for multiple dimensions, integration of diverse data types, and changes over time, is needed to better understand contributors to disease and treatment outcomes and provide clues for improved intervention.

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The objective of this workshop was to facilitate interdisciplinary discussion about the application of systems modeling approaches for population-based cancer epidemiology research. By bringing together scientists from various fields that use systems modeling the workshop aimed to:

  • Identify ideas and strategies to improve understanding of systems modeling among population scientists and epidemiology amongst modelers;
  • Share lessons learned in the application of systems approaches from other fields (e.g. cancer biology)
  • Identify of potential high-impact use cases for systems modeling in population science;
  • Increase understanding of potential barriers and facilitators to taking a system modeling approach in population science (including dataset availabilities, data and methods needs)
  • Establish new collaborative interdisciplinary relationships between statisticians, mathematicians, computer scientists, bioinformaticians, epidemiologists, and clinicians.

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Draft Agenda

View agenda for February 28, 2019
Thursday, February 28 Topic
9:00 a.m.

Kathy J. Helzlsouer, M.D., M.H.S.
Division of Cancer Control and Population Sciences (DCCPS), NCI

9:15 a.m.

Introduction to the Meeting
Leah E. Mechanic, Ph.D., M.P.H.

9:35 a.m.

State of the Science

Systems Epidemiology: Definitions, Applications, and Common Misconceptions
Bruce Y. Lee, M.D.
Johns Hopkins University

Group Discussion
Leah Mechanic, Ph.D., M.P.H.

Elizabeth M. Gillanders, Ph.D.

10:40 a.m. Break
11:00 a.m.

Successes and Challenges in Systems Modeling (Session I)

What Can We Learn from the NIH Obesity Modeling Network – Envision?
Patricia Mabry, Ph.D.
Health Partners Institute

Systems Approach to Distinguish Aggressive Cancer vs. Benign Breast Lesions: Opportunities and Challenges
Victoria L. Seewaldt, M.D.
City of Hope

Successes and Challenges to Population Modeling of Breast Cancer in the Cancer Intervention and Surveillance Modeling Network (CISNET)
Amy Trentham-Dietz, Ph.D.
University of Wisconsin

Panel and Group Discussion
Jill Reedy, Ph.D., M.P.H., R.D.

Marissa Shams-White, Ph.D., L.Ac., M.P.H.

12:30 p.m. Lunch
1:30 p.m.

Successes and Challenges in Systems Modeling (Session II)

Using SEER Data to Develop Synthetic Cancer Trajectories that Enable Cancer Research
Sarah E. Michalak, Ph.D.
Los Alamos National Laboratory

Challenges and Considerations for Synthetic Data: A Study based on SEER Population
Ana Paula Sales, Ph.D.
Lawrence Livermore National Laboratory

Nucleotides to Neighborhoods – Integrating Complex Spatial, Behavioral, and Multi-omits Data
Marta Jankowska, Ph.D.
University of California at San Diego

Panel and Group Discussion
Gabriel Lai, Ph.D.

Brionna Hair, Ph.D., M.P.H.

3:00 p.m. Break
3:20 p.m.

Successes and Challenges in Systems Modeling (Session III)

Collaborative Science through Collaborative Software
Danielle Ciofani, B.S.
Broad Institute

Systems Approach to Risk Score Prediction in Epidemiology
John Witte, Ph.D.
University of California San Francisco

Learning to Impute Methylome Signatures and Environmental Measures into Biobanks
Nancy J. Cox, Ph.D.
Vanderbilt University Medical Center

Panel and Group Discussion
Melissa Rotunno, Ph.D.

Rolando Barajas, M.P.H.

4:50 p.m.

Dissemination and Implementation of Systems Modeling: How can these methods be applied? How can methods be interpreted and translated?

Stacy Lindau, M.D., M.A.
University of Chicago

Nico Pronk, Ph.D., M.A.
Health Partners Institute

Amy Trentham-Dietz, Ph.D.
University of Wisconsin

Panel and Group Discussion
Patricia Mabry, Ph.D.
Health Partners Institute

Marissa Shams-White, Ph.D., L.Ac., M.P.H.

5:40 p.m. Adjourn

View agenda for March 1, 2019
Friday, March 1 Topic
8:30 a.m.

Introduction and Charge for Day 2
Jill Reedy, Ph.D., M.P.H., R.D.

8:40 a.m.

Perspectives: What is the Ideal Future for Modeling in Epidemiological Studies (Panel Discussion)

Bruce Y. Lee, M.D.
Johns Hopkins University

Chirag Patel, Ph.D.
Harvard Medical School

Sylvia Plevritis, Ph.D.
Stanford Medicine

Marylyn D. Ritchie, Ph.D.
University of Pennsylvania

Panel and Group Discussion
Elizabeth M. Gillanders, Ph.D.

Leah E. Mechanic, Ph.D., M.P.H.

9:25 a.m. Break/Travel to Discussion Groups
9:40 a.m.

Discussion Sessions

Group 1: Facilitators and Barriers to Success
Robert A. Hiatt, M.D., Ph.D.
University of California San Francisco

Group 2: Opportunities for Systems Epidemiology
Frank Hu, M.D., Ph.D.
Harvard University

Group 3: Approaches and Methods
Lisa M. Klesges, Ph.D.
University of Memphis

Group 4: Data Availability
Chris Amos, Ph.D.
Baylor College of Medicine

Discussion Groups Summarize and Identify Priorities for Reporting

11:10 a.m. Break
11:30 a.m. Report Back and Group Discussion for Each Topic
12:50 p.m. Next Steps and Action Items
Elizabeth M. Gillanders, Ph.D.
1:00 p.m. Adjourn

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Systems Epidemiology Key References


Bennet BJ et al (2015) Nutrition and the science of disease prevention: a systems approach to support metabolic health. Ann N Y Acad Sci; 1352:1-12.

Burke T et al. (2017) Rethinking Environmental Protection: Meeting the Challenges of a Changing World. Environmental Health Perspectives; 125(3): A43-A49.

Cornelis MC and Hu FB (2013) Systems Epidemiology: A New Direction in Nutrition and Metabolic Disease Research. Curr Nutr Rep; 2(4).

Cronbach LJ and Meehl PE (1955) Construct Validity in Psychological Tests. Psychol Bull; 52(4):281-302.

Dammann O et al. (2014) Systems Epidemiology:  What’s in a Name? Online Journal of Public Health Informatics; 6(3):  e198.

Diez Roux AV (2011) Complex Systems Thinking and Current Impasses In Health Disparities Research. Am J Public Health; 101(9):1627-1634.

Hammond RA (2009) Complex Systems Modeling for Obesity Research. Preventing Chronic Disease; 6(3):A97.

Joffe M et al. (2012) Causal Diagrams in Systems Epidemiology. Emerg Themes Epidemiol; 9(1):1.

Krauth SJ et al. (2019) A Call for Systems Epidemiology to Tackle the Complexity of Schistosomiasis, Its Control, and Its Elimination. Trop Med Infect Dis; 4(1): 21.

Lee BY et al. (2017) A Systems Approach to Obesity. Nutr Rev; 75(suppl 1):94-106.

Lund E and Dumeaux V (2008) Systems Epidemiology in Cancer. Cancer Epidemiol Biomarkers Prev; 17(11).

Ritchie MD et al. (2015) Methods of Integrating Data to Uncover Genotype-Phenotype Interactions. Nat Rev Genet; 16(2):85-97.

Rothman KJ and Greenland S. (2005) Causation and Causal Inference in Epidemiology. American Journal of Public Health; 95.S1 (2005): S144-S150.

Rutter H et al. (2017) The Need for a Complex Systems Model of Evidence for Public Health. The Lancet; 390(10112):2602-2604.

Warnecke et al. (2008) Approaching Health Disparities from a Population Perspective: The National Institutes of Health Centers for Population Health and Health Disparities. Am J Public Health; 98(9): 1608–1615.

Research Studies

Curtis C et al. (2012) The Genomic and Transcriptomic Architecture of 2,000 Breast Tumours Reveals Novel Subgroups. Nature, 486(7403): 346–352.

Finucane HK et al. (2018) Heritability Enrichment of Specifically Expressed Genes Identifies Disease-Relevant Tissues and Cell Types. Nat Genet; 50(4): 621–629.

Hiatt RA et al. (2014) A Multilevel Model of Postmenopausal Breast Cancer Incidence. Cancer Epidemiol Biomarkers Prev; 23(10): 2078-2092.

Jones AP et al. (2006) Understanding Diabetes Population Dynamics Through Simulation Modeling and Experimentation. Am J Public Health; 96(3):488-94.

Kaligotla C et al. (2018) Modeling an Information-Based Community Health Intervention on the South Side of ChicagoExternal Web Site Policy. In: 2018 Winter Simulation Conference (WSC) 2018 Dec 9 (pp. 2600-2611). IEEE.

Kettunen J et al. (2016) Genome-wide study for circulating metabolites identifies 62 loci and reveals novel systemic effects of LPA. Nat Commun; 7:11122.

Price ND et al. (2017) A Wellness Study of 108 Individuals Using Personal, Dense, Dynamic Data Clouds. Nat Biotechnol; 35(8):  747-756.

Shaman J and Karspeck A (2012) Forecasting Seasonal Outbreaks of Influenza. Proc Natl Acad Sci USA; 109(50):  20425-20430.

Dissemination & Implementation Panel

Aarons G et al. (2014) Aligning leadership across systems and organizations to develop a strategic climate for evidence-based practice implementation.  Annu Rev Public Health; 34:255-74.

Lindau ST et al. (2016) CommunityRx: A Population Health Improvement Innovation that Connects Clinics to Communities. Health Affairs, 35(11):2020-2029.

Mandelblatt et al. (2016) Collaborative Modeling of the Benefits and Harms Associated With Different U.S. Breast Cancer Screening Strategies. Ann Intern Med. 2016;164:215-225.

Supplement on Breast Cancer Simulation Modeling in CISNETExternal Web Site Policy. Medical Decision Making; 39(2).

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Planning Committee

NCI Staff

External Members

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Contact for Questions

  • Rolando Barajas, M.P.H., Cancer Research Training Award Fellow, Genomic Epidemiology Branch, EGRP, DCCPS, NCI

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