Unravelling the mechanism behind the anti-tumor effect of commensal gut microbes in solid tumors

Cancer is the second leading cause of death globally with colorectal cancer (CRC) being the 3rd most common diagnosed cancer causing 9.2% of the cancer-related deaths worldwide (2018). Despite the recent advances in cancer research and therapy, including checkpoint blockade immunotherapy, the percentage of patients successfully responding to these treatments is still poor.

In the last years, the gut microbiome has emerged as one of the most influential factors in cancer development. Cancer patients harbour an altered microbiome, referred as dysbiosis, that eventually contribute to cancer pathogenesis and treatment resistance due to changes in the tumour microenvironment. Particularly in CRC pathogenesis, several studies showed a major structural imbalance of the intestinal microbiota towards a reduction of butyrate producing bacteria belonging to the Order Clostridiales.

In our laboratory, we previously identified bacteria that are highly efficient in the treatment of solid tumours, particularly CRC, in a broad number of mouse models. These specific Clostridiales were effective in a prophylactic as well as therapeutic supplementation setting. However, little is known about the underlying mechanism of the observed anticancerogenic effects.

With my project, I therefore aim to identify the specific molecular mechanisms behind the Clostridiales bacteria supplementation and the observed anti-tumor immune response leading to further tumor shrinkage. Additionally, I seek to investigate the potential of other Clostridiales species in cancer therapy, particularly focusing on CRC.

Aim 1: Define the molecular effects of Clostridales bacteria on epithelial and immune cells.

Aim 2: Investigate the molecular mode-of-action of specific Clostridiales bacteria in in vivo cancer models.

Aim 3: Dissect the cell type that mediates the bacteria-induced anti-tumor efficacy in vivo.

Aim 4: Test the efficacy in later CRC stages: Metastatic model.