CAREER: Uncovering the nature and impact of tumor cell clusters during metastatic migration through the circulatory system

Metastasis is the spread of tumor cells through blood or the lymph system to form new tumors in otherwise healthy organs. Circulating tumor cells (CTCs) in the bloodstream often form clusters of CTCs. Some evidence suggests that when CTCs form clusters, they become more resistant to the body’s immune responses. However, other evidence suggests that cluster formation does not significantly affect metastasis. This CAREER project will use computer modeling and artificial intelligence (AI) to examine CTC clusters in the circulatory system. Simulations will describe how CTC clusters move through the bloodstream, how they respond to challenges from the immune system, and how they differ in different types of cancer. AI will be used to analyze large data sets, find patterns, and predict the behavior of CTC clusters. The models will be integrated into user-friendly, open-source software. The project will develop interactive K-12 workshops that teach students about the structure of cells and, for advanced students, how to model them. Research findings will be integrated into courses at the University of Alabama in Huntsville. The project outcomes may lead to better ways to diagnose and treat cancer, especially by targeting CTC clusters. This CAREER project will provide insight into outstanding questions on CTC clusters from their entry to the circulatory system to their exit at a secondary location. The project will use a three-component computational modeling framework built on a comprehensive and continuously updated set of experimental data. The first component of the framework will be a new model of CTC cluster dynamics that combines mechanistic modeling of the clusters with environmental cues, interactions with other cells, intercellular communication, and the physiological state of clustered CTCs. The second component will result in a detailed kinetic model of CTC cluster metabolism that integrates multiple experimental datasets through advanced statistical methods, machine learning, and information theory. The third component will be a combination of the first two, leading to a new platform capable of executing realistic “what-if” scenarios. The framework will be used to examine CTC cluster formation and stability, understand how CTC clusters react to challenges encountered in the circulatory system, and compare the characteristics of CTC clusters from different primary cancers during that stage of their migration. Advanced machine learning techniques will be used to identify unknown parameters and missing components in kinetic models of cellular metabolism. Together, these efforts will direct experimental research, support the development of new therapeutic and diagnostic strategies, and pave the way toward computational predictions of metastasis and its targets from the state of primary cancer cells. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. NSF Award ID: 2543185 | Program: 01002627DB NSF RESEARCH & RELATED ACTIVIT | Principal Investigator: Agnieszka Truszkowska | Institution: University of Alabama in Huntsville, HUNTSVILLE, AL | Award Amount: $538,930 View on NSF Award Search: https://www.nsf.gov/awardsearch/show-award/?AWD_ID=2543185 View on Research.gov: https://www.research.gov/awardapi-service/v1/awards/2543185.html