Physical and Biological Modulators of Space Radiation Carcinogenesis: Mechanistically- Based Model Development for Space Radiation Risk Assessment

The main goal of the current project is state of the art mechanistically-motivated modeling of experimental data from the NASA Specialized Center of Research (NSCOR) programs and the published literature. The ultimate purpose is to generate reliable estimates of heavy ion related cancer risks and uncertainties in astronauts on lengthy space exploration missions.

This task consists of four major components: The first component involves developing mechanistically-motivated mathematical models for heavy ion-induced carcinogenesis. It includes both targeted effects (TE), caused by DNA damage resulting from traversal of cells by ionizing tracks, and non-targeted effects (NTE), caused by radiation-induced perturbation of molecular signaling pathways between traversed and non-traversed cells. The second component involves estimating site-specific and consensus dose response functions for heavy ions produced by model-based analysis of NSCOR experimental data. The third component involves generating realistic uncertainty estimates for the functions from component two. Finally, in the fourth component, we will compare our results and uncertainties with current risk estimates and uncertainties from NASA.

To estimate heavy ion-induced cancer risks in astronauts engaged in long-distance space exploration such as a flight to Mars, we developed and are refining a mechanistically-motivated mathematical model of space radiation induced carcinogenesis. Our model (Shuryak et al., 2017) combines TE and NTE components. The TE component over the dose range of interest for space missions is reasonably described by a linear dependence. In contrast, the NTE component for heavy ions tends to be non-linear with a concave shape.

The recently updated mouse tumorigenesis data from our collaborators at Georgetown University show that not only overdispersion relative to the Poisson distribution (where variance/mean > 1), but also underdispersion (variance/mean < 1) are encountered, depending on radiation type and dose. Consequently, we generated a new detailed error distribution approach for the variability of tumor count data based on the weighted negative binomial (WNB) distribution. The motivation for using this more complex model is to reduce the errors on model-based radiation quality assessments and risk estimates by improved handling of the data variances.

Reference: Shuryak, I., Fornace, A.J., Datta, K., Suman, S., Kumar, S., Sachs, R.K., Brenner, D.J., 2017. Scaling Human Cancer Risks from Low LET to High LET when Dose-Effect Relationships are Complex. Radiat. Res. 187, 476–482.

Cancer is the second leading cause of death in the United States, exceeded only by heart disease ( https://www.cdc.gov/ ). It accounts for one of every four deaths in the United States. More than 1.8 million new cancer cases and over 606,500 cancer-related deaths are predicted to occur in the U.S. in 2020 ( https://www.cancer.org ). Considering this high frequency and lethality of cancer, even a small increase by space radiation would have a major impact on planning and design of future interplanetary manned space missions. Accurate estimation of space radiation-related cancer risks is, therefore, very important for NASA mission planning. Mathematical models of radiation carcinogenesis are important tools in this task.

This is early/mid-stage (TRL 5) — the most realistic path in is NASA SBIR/STTR, which funds small businesses and research institutions to develop technology aligned with NASA's needs (equity-free, phased funding). Check whether a current SBIR/STTR solicitation topic overlaps with this project's technology area, or contact the project directly (above) to ask.

None of these are guaranteed paths for this specific project — TechPort itself doesn't have an "apply" button. Reaching out to the contact(s) above with a specific question is usually the fastest way to find out what's actually open.