NOTICE: The 3DCR Training Program has received NCI funding as of September 1, 2024!

Despite decades of research effort directed towards understanding the etiology, prevention, and treatment of malignancy, cancer remains one of the most serious health problems for the US population and is increasingly a global problem. Addressing the burden of cancer in the US and worldwide depends on development of a cadre of population-oriented quantitative scientists with skills and knowledge to excel in an increasingly data-rich world.

The explosion of biomedical “big data” has dramatically and irrevocably changed the landscape of cancer research. From molecular investigations studying genomic drivers of cancer to population studies tracking health behaviors and utilization, new data resources are creating unprecedented opportunities to solve the many unanswered questions about cancer.

To prepare junior scientists to address the cancer research needs of a data-rich 21st century, we have established a training program focusing on Developing Data-Driven Cancer Researchers. Trainees in the program will learn to approach cancer research from the perspective of the strengths, weaknesses, value, and analytic features of different types of data including multi-omics, clinical, administrative, medical-record-based, survey, and mobile-health data. Features of the training program include:

  1. Mentoring by one or more program faculty mentors from the UW departments of Biostatistics, Epidemiology and Health Services in the School of Public Health, The CHOICE Institute in the School of Pharmacy, the Fred Hutchinson Cancer Research Center, Kaiser Permanente Washington Health Research Institute and the Institute for Health Metrics and Evaluation (IHME);
  2. Enrollment in program-relevant UW courses that will provide analytic and programming skills needed for data-focused research in cancer. Courses will include “Cancer: Epidemiology and Biology,” “Biological Basis of Neoplasia,” “Advanced Health Services Research Methods I: Large Public Databases: Big Data,” and “Machine Learning for Biomedical and Public Health Big Data”;
  3. Participation in a new, interactive “The Data of Cancer Research” seminar focused on the generation, processing and analysis of cancer data;
  4. Completion of a “Big Data practicum” research exercise that builds proficiency in working with cancer data; and
  5. Development of dissertation (pre-doctoral) or research (post-doctoral) projects around the data-driven focus of the training program.