Bacterial cancer therapy (BCT) shows great promise for treatment of solid tumors, yet basic mechanisms of bacterial-induced tumor suppression remain undefined. Attenuated strains of Salmonella enterica serovar Typhimurium (STm) have commonly been used in mouse models of BCT in xenograft and orthotopic transplant cancer models. We aimed to better understand the tumor epithelium–targeted mechanisms of BCT by using autochthonous mouse models of intestinal cancer and tumor organoid cultures to assess the effectiveness and consequences of oral treatment with aromatase A–deficient STm (STmΔaroA). STmΔaroA delivered by oral gavage significantly reduced tumor burden and tumor load in both a colitis-associated colorectal cancer (CAC) model and in a spontaneous Apcmin/+ intestinal cancer model. STmΔaroA colonization of tumors caused alterations in transcription of mRNAs associated with tumor stemness, epithelial-mesenchymal transition, and cell cycle. Metabolomic analysis of tumors demonstrated alteration in the metabolic environment of STmΔaroA-treated tumors, suggesting that STmΔaroA imposes metabolic competition on the tumor. Use of tumor organoid cultures in vitro recapitulated effects seen on tumor stemness, mesenchymal markers, and altered metabolome. Furthermore, live STmΔaroA was required, demonstrating active mechanisms including metabolite usage. We have demonstrated that oral BCT is efficacious in autochthonous intestinal cancer models, that BCT imposes metabolic competition, and that BCT has direct effects on the tumor epithelium affecting tumor stem cells.
Gillian M. Mackie, Alastair Copland, Masumi Takahashi, Yumiko Nakanishi, Isabel Everard, Tamotsu Kato, Hirotsugu Oda, Takashi Kanaya, Hiroshi Ohno, Kendle M. Maslowski
Usage data is cumulative from November 2021 through November 2022.
Usage information is collected from two different sources: this site (JCI) and Pubmed Central (PMC). JCI information (compiled daily) shows human readership based on methods we employ to screen out robotic usage. PMC information (aggregated monthly) is also similarly screened of robotic usage.
Various methods are used to distinguish robotic usage. For example, Google automatically scans articles to add to its search index and identifies itself as robotic; other services might not clearly identify themselves as robotic, or they are new or unknown as robotic. Because this activity can be misinterpreted as human readership, data may be re-processed periodically to reflect an improved understanding of robotic activity. Because of these factors, readers should consider usage information illustrative but subject to change.