NIH/NCI – Modular R01s in Cancer Control and Population Sciences (R01 Clinical Trial Optional)

August 1, 2018 by School of Medicine Webmaster

This funding opportunity announcement (FOA) calls for research on a broad range of scientific areas within NCI’s Division of Cancer Control and Populations Sciences’ (DCCPS) mission and portfolio, including but not limited to research in statistical and analytic methods, epidemiology and genomics, cancer survivorship, cancer-related behaviors, health care delivery, and implementation science.


Competition for research funding has grown increasingly more challenging, and the stakes are particularly high within NCI and NIH. In addition to an increasing number of R01 applications, another issue that the NCI is grappling with is the increasing average cost of grants, and the resulting increased competition for limited funds.

Additionally, as the cost of grants continues to increase, scientists and policy makers are concerned about the challenges early-stage investigators (ESIs) face, including the length of time it takes to achieve their first R01 award. Chief among those challenges is the unprecedented number of applicants competing for funding pools. This has become a focal point of NIH with the Next Generation Researchers Initiative, and in the call to action in the 21st Century Cures Act, “to promote opportunities for new researchers and earlier research independence, such as policies to increase opportunities for new researchers to receive funding, enhance training and mentorship programs for researchers, and enhance workforce diversity.”

This FOA will promote a diversity of research topics and scientific challenges in the population sciences that lend themselves to a shorter time span and reduced budget. This FOA encourages and supports ESIs and grow the ESI applicant pool and portfolio.


The mission of NCI’s Division of Cancer Control and Population Sciences (DCCPS) is to reduce risk, incidence, and deaths from cancer, as well as enhance the quality of life for cancer survivors. The division conducts and supports an integrated program of the highest quality genetic, epidemiologic, behavioral, social, applied, and surveillance cancer research. DCCPS-funded research aims to understand the causes and distribution of cancer in populations, support the development and delivery of effective interventions, and monitor and explain cancer trends in all segments of the population.

Areas of scientific emphasis for this FOA reflect those of high priority for DCCPS.  Through this FOA, NCI encourages applications that address a variety of topics that are a high priority for DCCPS, including, but not limited to the following:

Statistical and Analytic Methods

  • Decision modeling using simulation or other methods to determine efficient or cost-effective strategies for the prevention, early detection, or treatment of cancer;
  • Statistical methods for cancer-related research involving complex survey data, including the possible area of small-area estimation;
  • Behavioral, genetic, molecular, environmental, and epidemiological features underlying the risk of cancer development, such as statistical methods for incorporating multiple -omics resources; statistical genetics simulation models;
  • New methods and tools for determining geographic and temporal scales that are most relevant for research of different cancers;
  • Spatial and temporal methodology that detects new patterns and trends in cancer burden, disparity, and cancer care delivery;
  • Statistical methods for comparative effectiveness research using observational data;
  • Statistical methods for analyzing linked administrative and/or survey data;
  • Innovative tools and approaches for the analysis, reporting, visualization, and interpretation of cancer surveillance data.

Applied Informatics Methods for Cancer Surveillance

  • Planning, developing, evaluating, testing, validating, and analyzing a single data linkage (traditional cancer registry data [SEER] and novel data sources) to expand and enhance the scope of cancer surveillance research across the cancer continuum;
  • Methods and systems for natural language processing (NLP) and deep learning to automate manual information processing, and to maximize the value of free text documents (e.g., electronic pathology reports and radiologic dictation);
  • Developing and testing semantic tools (e.g., ontologies, metadata repositories) to facilitate mapping and querying of cancer registry data with other linked data sources (e.g., claims, pharmacy data, genomic data, EMR data);
  • Evaluation of health disparities related to technology dispersion/saturation, focusing on technology that assists patients in making health care decisions, by geographic location (e.g., urban/rural areas).

Using Trends in Cancer Survival Estimates to Inform Cancer Control

  • Time series analysis/forecasting techniques, using relative survival estimates as data points, for projecting cancer death burden.

Cancer Survivorship

  • Risk stratification and management of late-effects of cancer and cancer treatment;
  • Identification of aging phenotypes in cancer survivors, mechanisms underlying the emergent phenomena in long-term survivors; and consequences of aging on cancer and cancer treatment;
  • Research on the changes in the hallmarks of aging, including aging biomarkers, and relevance to cancer survivorship outcomes;
  • Observational or interventional studies of clinical, genomic and lifestyle factors that influence cancer outcomes among those living with cancer and their families, particularly among those affected by rare cancers;
  • Research to uncover trends in adolescent and young adult survivors as related to introduction of treatments for specific cancers (outside of clinical trials), especially those with greater mortality.

Environmental Epidemiology

  • Assessing cancer risk associated with multiple exposures combined and/or exposures over time;
  • Studies of geographic factors in multilevel analyses of cancer, such as neighborhood and social environments, as determinants of cancer risk;
  • Assessing cancer risk associated with exogenous (e.g., chemical agents, infectious agents, radiation, medication nutrition, tobacco, and drug use) and endogenous (e.g., metabolome, microbiome, adductome) factors, mixtures and interaction of these factors, and effects during early life and critical periods across the life course;
  • Evaluation of epigenetic changes during different developmental stages in response to social adversities; investigate interaction between socio-economic factors, environmental exposure, and epigenetic modifications;
  • Studies examining variation in profiling of epigenetic components with exposure to social-contextual factors, as well as epigenomic profiling to evaluate early-life experiences that contribute to social inadequacies in cancer risk.

Genomic Epidemiology

  • Studies investigating the role of both common and rare genetic variation in cancer susceptibility particularly among those affected by rare cancers, cancer in high-risk families and across diverse and understudied populations, or those with cancer health disparities;
  • Use of existing data sets to develop new methods and models for genetic epidemiologic research, especially methods to examine gene-gene and gene-environment interactions, integration with other –omics data (metabolomics, proteomics, transcriptomics, epigenomics, etc.), and interplay between the inherited and somatic genomes;
  • Studies to address the ethical, legal, and social issues (ELSI) of genomics research and cancer-related bioethics in general are of interest;
  • Investigating risk factors for cancers of unknown primary and metastasis.

Systems Modeling in Cancer Epidemiology

  • Studies examining the complex interplay of genetic, environmental, host and societal factors operating over a prolonged time;
  • Longitudinal measures and development of more sophisticated analytical methods that support comprehensive (e.g., systems or computational modeling) approaches to address combined contribution of risk factors to disease in populations.

Behavioral Research

  • Studies examining cancer-related behaviors and biobehavioral risk factors such as, behavioral genetics; diet, energy balance, and obesity; physical activity and sedentary behavior; sun safety; alcohol use; tobacco use; sleep and circadian functions; and adherence to cancer-related medical and behavioral regimens in the general population and among cancer survivors;
  • Examine the effects of built, sociocultural, communication, and policy environments on cancer risk and behavioral risk factors;
  • Identify the role of perceptual, cognitive, or psychological factors in cancer detection and diagnosis;
  • Development and/or evaluation of multi-level interventions that aim to influence combinations of biological, psychological, behavioral, social, environmental, and/or policy targets on behavioral and cancer-related outcomes;
  • Examine biological pathways through which psychosocial stressors influence cancer outcomes;
  • Assess the cognitive effects following treatment for non-central nervous system malignancies;
  • Use of conventional, social, and mobile media for cancer control and prevention observational studies and interventions;
  • Impact of health literacy, technology literacy, genomic literacy, and science literacy on cancer outcomes;
  • Investigate patterns, trends, and determinants of tobacco use behaviors, including novel product use and poly-tobacco product use behaviors, among youth and adults;
  • Evaluation of methods, measurements and/or modeling of tobacco-related behaviors to better understand the effects of diverse tobacco control interventions, including those to improve public knowledge; interfere with tobacco industry efforts to promote tobacco use; prevent youth and young adult smoking initiation; and promote cessation among tobacco product users;
  • Health disparities in behavioral risk factors for cancer patient-centered palliative care and end-of-life decision making;
  • Evaluation/validation of novel tools, biomarkers, or statistical methods related to diet or physical activity data (including environmental measures, patterns, and measurement error).

Health Care Delivery Research                                                                                

  • Access to and receipt of high-quality care for underserved populations, including those in low-resource settings;
  • Overuse and underuse of guideline-recommended care across the cancer continuum;
  • Adoption and effective delivery of cancer-related health services, including precision medicine, by community-based health care providers and health care delivery organizations;
  • Financial hardship, including impact on employment outcomes;
  • Use of IT in low-resource settings to improve cancer care delivery and patient outcomes;
  • Effects of cancer and its treatment on patient-centered outcomes;
  • Development of novel tools (e.g., wearable technologies and mobile apps) to capture clinically relevant patient-generated data;
  • Clinical application of patient-generated data;
  • Strategies to enhance patient-centered care, shared decision making, and patient-provider communication;
  • Patient-, clinician-, health care system-, and community-level factors to improve access to and appropriate utilization of health care services across the cancer continuum.
  • Strategies for increasing guideline concordant use of precision medicine.

Implementation Science

  • Development of valid, reliable, and pragmatic measures to assess the implementation of interventions, including adaptation, feasibility, fidelity, maintenance, penetration, sustainability, and scale-up of a given intervention or set of implementation strategies, as well as implementation context and climate;
  • Evaluate the effectiveness and implementation of existing guidelines and best practices that are based on expert opinion or case studies only (and not based on experimental, quasi-experimental, or large observational studies), to help identify strategies that may reduce or modify the use of these guidelines, as needed;
  • Developing and testing patient-provider communication strategies to encourage use of appropriate care in cancer delivery settings;
  • Evaluating natural experiments of implementation of evidence-based interventions in cancer care delivery settings.

Deadlines:  November 7, 2018; March 6, 2019; November 7, 2019; March 6, 2020; November 6, 2020; March 8, 2021


Filed Under: Funding Opportunities