Bacterial Cancer Therapy in the Light of Quantitative Synthetic Biology

Although bacterial cancer therapy dates back nearly 160 years, and several synthetic live bacterial therapeutics engineered with genetic circuits have entered clinical trials, none has yet obtained regulatory approval. Identifying the barriers to such clinical translation is a prerequisite for breakthroughs in this field. Here we review the current state and remaining challenges across three core aspects, chassis strain selection, genetic circuit design, and administration routes, and highlight that the prevailing paradigm is critically limited by insufficient quantitative analysis of the in vivo behavior of these agents, which constrains precise design and effective control over their dynamic functions. Informed by recent quantitative studies on the antitumor immune mechanisms of engineered Salmonella (a representative chassis organism), we propose a new research framework for solid tumor therapy rooted in quantitative synthetic biology. This framework uses multi-scale quantitative tools to systematically characterize the dynamic interplay among synthetic bacteria, the tumor microenvironment, and the host immune system, and employs mathematical modeling to translate experimental observations into mechanistically predictive insights. These insights, in turn, guide the rational design of next-generation bacterial therapeutics, forming an iterative “build-measure-model-design” loop. Ultimately, this approach aims to shift the development of bacterial tumor therapy from empirical trial-and-error toward predictable, rational engineering.