Agent-based modeling predicts RAC1 is critical for ovarian cancer metastasis

Authors

Melanie Rivera, Department of Pathology, University of New Mexico Health Sciences Center, Albuquerque, NM 87131
Leslie Toledo-Jacobo, Department of Pathology, University of New Mexico Health Sciences Center, Albuquerque, NM 87131
Elsa Romero, Department of Pathology, University of New Mexico Health Sciences Center, Albuquerque, NM 87131
Tudor I. Oprea, Division of Translational Informatics, Department of Medicine, University of New Mexico Health Sciences Center, Albuquerque, NM 87131,Translational Informatics, Roivant Discovery, Boston, MA 02210,Translational Informatics, Roivant Discovery, Boston, MA 02210
Melanie E. Moses, Department of Computer Science, University of New Mexico, Albuquerque, NM 87131
Laurie G. Hudson, Cancer Research Facility, Comprehensive Cancer Center, University of New Mexico, Albuquerque, NM 87131,Department of Pharmaceutical Sciences, College of Pharmacy, University of New Mexico Health Sciences Center, Albuquerque, NM 87131
Angela Wandinger-Ness, Department of Pathology, University of New Mexico Health Sciences Center, Albuquerque, NM 87131,Cancer Research Facility, Comprehensive Cancer Center, University of New Mexico, Albuquerque, NM 87131
Martha M. Grimes, Cancer Research Facility, Comprehensive Cancer Center, University of New Mexico, Albuquerque, NM 87131,Department of Pharmaceutical Sciences, College of Pharmacy, University of New Mexico Health Sciences Center, Albuquerque, NM 87131

Document Type

Article

Publication Date

12-1-2022

Abstract

Experimental and computational studies pinpoint rate-limiting step(s) in metastasis governed by Rac1. Using ovarian cancer cell and animal models, Rac1 expression was manipulated, and quantitative measurements of cell-cell and cell-substrate adhesion, cell invasion, mesothelial clearance, and peritoneal tumor growth discriminated the tumor behaviors most highly influenced by Rac1. The experimental data were used to parameterize an agent-based computational model simulating peritoneal niche colonization, intravasation, and hematogenous metastasis to distant organs. Increased ovarian cancer cell survival afforded by the more rapid adhesion and intravasation upon Rac1 overexpression is predicted to increase the numbers of and the rates at which tumor cells are disseminated to distant sites. Surprisingly, crowding of cancer cells along the blood vessel was found to decrease the numbers of cells reaching a distant niche irrespective of Rac1 overexpression or knockdown, suggesting that sites for tumor cell intravasation are rate limiting and become accessible if cells intravasate rapidly or are displaced due to diminished viability. Modeling predictions were confirmed through animal studies of Rac1-dependent metastasis to the lung. Collectively, the experimental and modeling approaches identify cell adhesion, rapid intravasation, and survival in the blood as parameters in the ovarian metastatic cascade that are most critically dependent on Rac1.

Recommended Citation

Rivera M, Toledo-Jacobo L, Romero E, Oprea TI, Moses ME, Hudson LG, Wandinger-Ness A, Grimes MM. Agent-based modeling predicts RAC1 is critical for ovarian cancer metastasis. Mol Biol Cell. 2022 Dec 1;33(14):ar138. doi: 10.1091/mbc.E21-11-0540. Epub 2022 Oct 6. PMID: 36200848; PMCID: PMC9727804.