Notice Number: NOT-CA-19-019

Key Dates
Release Date: January 28, 2019
Response Date: April 30 , 2019

Issued by
National Cancer Institute (NCI)

Purpose

The National Cancer Institute (NCI) seeks broad input from members of the scientific community to identify areas of need and opportunities for interdisciplinary, systems science research focused on cancer risk and prognosis, particularly data analysis and interpretation when applying comprehensive analytical approaches, such as systems or computational modeling, to study of the etiology of cancer. This Request for Information (RFI) is part of a larger planning effort to identify current challenges and set priorities for analytical approaches and data needs for population-based epidemiology studies of cancer risk and prognosis.

Background

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

Despite individual successes at identifying genetic, biological, and environmental risk factors for cancer, much of the etiology remains unexplained. The unexplained etiology may be due, in part, to the limited focus of many studies on a small number of risk factors within specific domains (e.g., genetic, epigenetic, clinical, or questionnaire data) or measures (e.g., the genetics domain may include DNA sequence data, genotyping, epigenetic profiling, etc.). Moreover, many studies are not designed to evaluate the complexities and interrelations among multiple risk factors on each other and the study outcomes. For example, each individual risk factor, such as a single dietary component or genetic polymorphism, occurs in a broader biological (e.g., transcriptional pathways) or societal (e.g., social networks) context that may modulate the effect of individual risk factors on cancer. Furthermore, many risk factors for cancer are highly correlated with possible interactive, additive, synergistic, or attenuating effects. Importantly, risk factors can change over the life course and the timing of exposure (e.g., critical windows of susceptibility, cumulative exposure, or acute exposure) may modify cancer risk.

A more comprehensive, holistic modeling based-approach (e.g., systems science or computational modeling) - which accounts for multiple dimensions, integration of diverse data types, and changes over time may provide a better understanding of the contributors to cancer and treatment outcomes and provide clues for improved intervention.

Information Requested

This RFI targets researchers in population-based epidemiology, bioinformatics, biostatistics, statistics, and computer science fields who have interests in identifying interdisciplinary, systems science opportunities, including comprehensive analytical approaches, to improve the understanding of factors impacting cancer risk and prognosis.

The NCI is seeking information that includes any or all of the following topics:

• The impacts that better integration of genomic, environmental, or contextual data can have on furthering the understanding of cancer risk or outcomes -- describe what can be learned from studies that include multiple dimensions of genomic variation or environmental exposure.
• The critical levels of analysis that should be included (e.g., population level, external environment, individual person, internal environment, individual tissues or cells, etc.) in studies of multiple dimensions of genomic variation or environmental exposure.
• Examples of what can be learned from studies that incorporate or consider time variation (e.g., critical windows of susceptibility, cumulative or acute exposure) or dynamism of exposure.
• Data collection methods, analytical or simulation approaches that are needed for population-based cancer epidemiology studies using systems or computational modeling.
• Examples of what can be accomplished if these methods or approaches are applied to population-based questions related to cancer risk and prognosis, or the types of questions amenable to this approach. List any cancer research opportunities that currently have appropriate data available for a systems or computational modeling strategy.
• Available datasets, data linkages, or consortia that can be leveraged to address systems science types of epidemiology questions, including the dataset name(s), types of cancer that can be studied, database URLs, etc.
• Data sharing challenges or needs related to these types of systems or computational modeling approaches.
• Barriers to applying comprehensive modeling approaches, including systems or computational modeling, to population-based cancer epidemiology studies of cancer risk and prognosis.
• Critical factors or activities that are required for successful application of comprehensive modeling approaches, such as systems or computational modeling, to cancer epidemiology studies.

How to Submit a Response

Responses will be accepted through April 30, 2019.

Responses are entirely voluntary and can be anonymous. If willing, you may indicate the environment to which your perspective pertains to (e.g., academia, clinical research, etc.). No proprietary, classified, confidential, or sensitive information should be included in your response. Responses should be limited to one to two page(s). Responses in electronic formats are preferred and can be e-mailed to NCICancerSysEpi_RFI@mail.nih.gov. Please include the Notice number in the subject line. All individual responses will remain confidential. Any identifiers (e.g., names, institutions, e-mail addresses, etc.) will be removed when responses are compiled. Only the processed, anonymized results will be shared internally with NIH staff members and any member of scientific working groups convened by the NCI, as appropriate.

Every respondent will receive an automated e-mail confirmation acknowledging receipt of a successfully submitted response but will not receive any individualized feedback.

This RFI is for information and planning purposes only and should not be construed as a solicitation or as an obligation on the part of the Federal Government, the National Institutes of Health (NIH), and/or the NCI.

Inquiries

Please direct all inquiries to:

Leah E. Mechanic, Ph.D., M.P.H.
National Cancer Institute (NCI)
Telephone: 240-276-6847
Email: mechanil@mail.nih.gov