Abstract
Abstract
Synthetic biology enables the engineering of bacteria to safely deliver potent payloads to tumors for effective anti-cancer therapies. However, a central challenge for translation is determining ideal bacterial therapy candidates for specific cancers and integrating them with other drug treatment strategies to maximize efficacy. To address this, we designed a screening and evaluation pipeline for characterization of bacterial therapies in lung cancer models. We screened 10 engineered bacterial toxins across 6 non-small cell lung cancer patient-derived cell lines and identified theta toxin as a promising therapeutic candidate. Using a bacteria-spheroid co-culture system (BSCC), analysis of differentially expressed transcripts and gene set enrichment revealed significant changes in at least 10 signaling pathways with bacteria-producing theta toxin. We assessed combinatorial treatment of small molecule pharmaceutical inhibitors targeting 5 signaling molecules and of 2 chemotherapy drugs along with bacterially-produced theta toxin and showed improved dose-dependent response. This combination strategy was further tested and confirmed, with AKT signaling as an example, in a mouse model of lung cancer. In summary, we developed a pipeline to rapidly characterize bacterial therapies and integrate them with current targeted therapies for lung cancer.
Publisher
Research Square Platform LLC
Reference30 articles.
1. The Treatment of Inoperable Sarcoma by Bacterial Toxins (the Mixed Toxins of the Streptococcus erysipelas and the Bacillus prodigiosus);Coley WB;Proc R Soc Med,1910
2. The toxins of William B. Coley and the treatment of bone and soft-tissue sarcomas;McCarthy EF;Iowa Orthop J,2006
3. Evaluation of the airway microbiome in nontuberculous mycobacteria disease;Sulaiman I;Eur Respir J,2018
4. Microbiome dysbiosis in lung cancer: from composition to therapy;Liu NN;NPJ Precis Oncol,2020
5. The Influence of Lung Microbiota on Lung Carcinogenesis, Immunity, and Immunotherapy;Ramirez-Labrada AG;Trends Cancer,2020