Notice Number: NOT-CA-19-010
Release Date: November 14, 2018
Response Date: January 21, 2019
National Cancer Institute (NCI)
Through this Request for Information (RFI) Notice, the National Cancer Institute (NCI) seeks public input and ideas on needs and possibilities for identifying, developing, advancing, implementing, and disseminating computational approaches in cancer prevention science.
Although computational approaches and methods are central to much of cancer research, they have been applied less in cancer prevention research than in other areas of cancer research; however, compelling new possibilities are now emerging. Cancer is a disease of somatic genomic instability and is a consequence of accumulating genomic damage during life. Therefore, methods to quantify and characterize that accumulating genomic damage before it culminates in cancer provide a solid scientific foundation for cancer prevention in general, and specifically for precision cancer prevention.
New data analysis challenges are arising from new technologies and their data streams. Recent research and clinical advances have made it technically feasible to non-invasively measure and monitor accumulating genomic damage in healthy people without cancer symptoms. Case-control studies found elevated chromosomal structural variation in cell-free DNA (cfDNA) from the blood of cancer-positive, versus cancer-free human subjects. Noninvasive blood tests can detect chromosomal structural abnormalities by analyzing the cfDNA released by mutated cells. Such tests on pregnant women, intended for prenatal diagnosis of fetal genetic abnormalities, inadvertently proved effective for detecting the abnormalities characteristic of pre-symptomatic cancer in tested women, resulting in multiple cases of presymptomatic cancer diagnosis. The NCI supports the development of such blood-based assays for tumor-associated DNA in patients with early stage disease or those at high risk (for example, as done through RFA-CA-17-029, which has expired). Demonstrating the potential to scale up this technology, such genetic analysis of cfDNA has already been validated as a companion diagnostic for detecting genomic biomarkers in liquid biopsies to guide clinical decisions during cancer treatment (of note, this assay has been approved and licensed as an available commercial product).
The increasing availability of data on accumulating genomic damage in healthy people, from cfDNA assays, will drive a need for new computational analysis techniques applicable to cancer prevention, as opposed to guiding treatment. Methods are widely available for characterizing and quantifying sequence changes, but these classical methods do not apply to the chromosomal structural variations that such tests can also detect, and that may be even more relevant to cancer risk. As one illustration, DNA copy number alterations are a hallmark of cancer cells, but do not typically involve any novel sequence variants. Therefore, analyzing and interpreting changes in copy number has required the development of novel computational techniques. The need becomes more acute when a single test sample of cfDNA may reveal multiple qualitatively different types of genetic changes, including base substitutions in addition to copy number changes, insertions, deletions, and other structural rearrangements, as well as microsatellite instability. The question of how an indicator of individual cancer risk might be generated by quantifying the total amount of genomic damage observed in the somatic cells of a healthy individual is important. Answers to such questions will be critically important in efforts to advance precision cancer prevention in humans.
Computational modeling and simulation are useful for formally modeling and evaluating mechanistic hypotheses using systems biology approaches. Some recent examples include simulation studies focused on: the dynamics of tumor growth; the critical molecular pathways of cancer cells; and the interactions between tumors and the immune system. In cancer prevention research, the relevant mechanistic hypotheses often concern the hidden dynamic processes of accumulating genomic damage, and of consequent oncogenesis through selection acting on the resulting cellular heterogeneity.
In contrast to chemical oncogenesis, which may act through direct and simple biochemical mechanisms, other important cancer risks involve lifestyle factors without a simple and clear molecular mechanism, such as energy balance and obesity, as influenced by diet and exercise. At current rates, lifestyle factors are expected to surpass smoking as the leading causes of cancer in the U.S. in the first half of this century. Effective cancer prevention will require addressing these more complex and indirect risk factors, which in turn will require understanding their mechanisms well enough to design effective interventions. Computational can play a central role in formally modeling and evaluating mechanistic hypotheses using systems biology.
The NCI is soliciting suggestions and opinions regarding the most promising research applications of computational methods to problems in the science of cancer prevention. Please note the specific focus on prevention versus other areas of cancer research.
Computational methods may include:
Areas of bioinformatic applications may include:
Submitting a Response
All responses must be submitted to email@example.com by January 21, 2019.
Please include the RFI Notice number in the subject line. Please be as specific as possible, provide examples or data to support your suggestions, prioritize comments, and include new ideas relevant to the question being asked. Please do not include any proprietary, classified, confidential, or sensitive information in your response. NIH will use all information submitted in response to this RFI Notice at its discretion and will not provide comments to any responder's submission. NIH may use information gathered by this RFI Notice to inform the development of future funding opportunity announcements and/or in any resultant solicitations.
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