ICI grants are restricted awards that support investigator-initiated projects for up to 2 years. Awards may be up to $100,000 per year (direct costs), plus 12% allowable indirect costs. (Note: ICI adheres to NIH’s guidelines as it relates to salary cap. The 12% cap on directs applies to each year). Two-year grant submissions should include mid-grant milestones (at approximately 12-months) for the purpose of evaluating progress toward overall project goals. These milestones must be achieved in order to qualify for the second year of funding. In addition, all projects are evaluated for their potential clinical impact.
Topics of Special Interest:
The Creation and Analysis of Data Resources to Further Cancer Research:
Of interest are both research that leads to the creation and analysis of new data resources of genomic, clinical, imaging, microbiome, and other data from cancer patients and the analysis of current data resources. Topics include data and analysis to increase understanding of cancer, the comparative effectiveness of approved treatments, targets for new treatments, and to develop hypotheses about somatic or germline genetic markers predictive of response to therapy. The focus should be on data resources that are likely to have near or intermediate-term clinical impact. Of particular interest are the creation of AI-ready data resources.
Bridging Cancer Genomics Data Resources:
Projects that address important questions in cancer research that can be solved by integrating information from multiple sources of cancer-related data. Examples of sources from which data might be integrated include the Genomic Data Commons, the American Association for Cancer Research's Project GENIE, the American Society of Clinical Oncology's CancerLinQ, or any of these plus genomic and/or patient data at cancer centers. Another example would be tools that facilitate the analysis of cancer datasets across multiple cancer centers or cancer data repositories, thus increasing the statistical power of analyses. All data types are of interest, including genomic, clinical, imaging, and other data types.
Analysis of Clinical Trials Data:
Projects using such tools as natural language processing (NLP) of journal articles in order to identify patterns in clinical trial results and other characteristics of clinical trials. For example, an NLP tool could enrich the records in clinicaltrials.gov with detailed information about the outcome of the clinical trials, including those that failed. Such data could accelerate the discovery process, facilitate the design of clinical trials and the assignment of patients to trials, and help prioritize resource allocation.
Integration of Cancer Genomics Data with Electronic Medical or Health Record Systems:
Projects such as extracting information from electronic medical or health systems to enrich the clinical data submitted to national systems, such as the NCI Genomic Data Commons, accrued in data repositories managed by cancer centers, or contributed to other data sharing projects, such as patient partnered research projects.
Functional Annotation of Genetic Variants in Cancer:
Projects aiming to substantially enhance annotation of the functional and clinical implications of genetic, epigenetic, gene expression, and other changes available in large cancer genomics datasets. For example, computational tools that extract functional annotation from textual sources, functional genomics or large-scale human genetics datasets; or computational tools that facilitate crowd-sourcing of expert annotation by the biological and clinical research communities with built-in reliability metrics and quality control. The primary focus should be on clinically actionable functional annotation with the aim to support data-driven decision support in clinical practice.
Mobile Apps for Cancer Patients Undergoing Treatment to Record Their Health Data:
Projects that develop applications by which patients can record detailed health information and provide it to trusted partners, such as their physician, to improve their own treatment. For example, cancer patients would record how they are responding to treatment and information about their state of health on a smartphone on a daily or weekly basis and the encrypted information would be shared via a trusted repository. Patients would own their data, remain in complete control of its use, share their data with their oncologist, and, via an opt-in mechanism, provide it for aggregated use in cancer research using advanced data science and machine learning methods.
Accelerating the Development and Validation of Novel or Advanced Biomarker Assays:
Projects that advance the exploration, implementation, and assessment of potential clinical utility of liquid biopsies to track the temporal evolution of a patient's disease in support of more flexible adjustment of treatment options. Examples might include developing tools or computational methods to aggregate and harmonize data about a) circulating tumor cells, cell-free tumor DNA, tumor-specific proteins, tumor-associated autoantibodies, or exosome assays, b) clinical diagnosis, treatment history, and outcomes, and c) sample collection, preparation, and handling protocols.
Decision Support and Software Tools
Projects that focus on the development of software tools or data infrastructure that can improve data collection, data processing, data sharing, or facilitate decision-making in the treatment of cancer patients. Examples might include tools that can improve tumor board expertise, capture and annotate tumor board discussions and outcomes, share treatment and outcomes data, or provide resources for physicians at non-cancer centers to access the expertise and informed treatment decisions of larger, specialized treatment centers.
Data Analysis to Facilitate Prevention, Early Detection Or Improved Treatment:
Projects that focus on data analysis of electronic medical records or other health systems records (including genetic, molecular, and imaging profiles of early-stage cancerous and pre-cancerous tissue) to improve early detection, create personalized prevention programs, facilitate anti-cancer vaccines, or improve treatment. Examples might include the use of electronic health records to better target screening programs to the optimal patient population, identify obstacles to treatment compliance for patients, and identify early non-invasive biomarkers from easily accessible clinical data for early detection.