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. 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:
Model Analysis Applications for Large Cancer Genomics Datasets:
Projects that address important questions in cancer research with targeted analysis of information in the Genomic Data Commons (GDC) and other sources. For example, if a drug targets a specific mutation in colon cancer, researchers might be searching for examples and frequencies of other cancers with the same mutation. The goal would be to create both a demonstration and a methodology and tool that could be used to replicate the analytic approach for other mutations.
Creation and Analysis of Large Data Resources:
Research that leads to the creation and analysis of large databases of genetic and clinical data from cancer patients to increase understanding of comparative effectiveness of approved treatments and to develop hypotheses on genetic markers that impact treatment effectiveness.
Bridging Cancer Genomics Data Resources:
Projects in which investigators address important questions in cancer research that can be solved by integrating information from multiple sources. 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 analysis of cancer genomics datasets held at a cancer center in the context of larger datasets available publicly or via the NCI Genomic Data Commons, thus increasing the statistical power of analyses.
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 the NCI Genomic Data Commons or accrued in cancer centers. Examples might include using Sync for Science (S4S) to enrich clinical data or to integrate patient-contributed genomic and associated clinical data into a cancer genomic database to facilitate the power of analyses linking genotype to phenotype in cancer research.
Functional Annotation of Genetic Variants in Cancer:
Projects aiming to substantially increase the corpus of expert 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 or functional genomics datasets; or computational tools that facilitate crowd sourcing of expert annotation by the biological research community with built-in reliability metrics and quality control.
Mobile Apps for Cancer Patients Undergoing Cancer 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, patients would record how they are responding to treatment and information about their state of health on a smart phone 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 and share their data with their oncologist and, via an opt-in mechanism, provide it for aggregated use in cancer research using advanced data sciences and machine learning methods.
Accelerating the Development and Validation of Liquid Biopsy Assays:
Projects that advance the exploration, implementation, and assessment of potential clinical validity and utility of liquid biopsies to understand the temporal evolution of a patient's disease. Examples might include developing tools or computational methods to aggregate and harmonize data from sources such as a) CTC, ctDNA, proteins including tumor associated autoantibodies, and exosome assays, b) associated clinical data such as clinical diagnosis, treatment history, and outcomes, and c) sample collection, preparation, and handling protocols. (Note: applicants in this topic area should describe the data that they will use and confirm their access to the data.)