Doctoral thesis

I

Immunotherapies are revolutionizing cancer treatment, allowing the treatment of tumors with poor prognosis. Some of the most innovative immunotherapies are cell therapies based on tumor-infiltrating lymphocytes (TILs). Lymphocytes are isolated from patients' tumors, expanded ex vivo, and reinfused as therapy. The low number of TILs that actually recognize tumor antigens results in only a small percentage of patients responding to treatment. The lack of permanent responses, frequent relapses, and side effects underline the urgent need for more effective innovative therapeutic approaches.

We propose an innovative strategy to overcome the challenges of cellular therapies by harnessing the power of bacteria-instructed lymphocytes (expressing tumor antigens; BacT). This cutting-edge technology, currently under development by our research group, has the potential to revolutionize cancer treatment, minimizing side effects and increasing response rates.

BacT-activated CD8+ T cells, unlike DC-mediated or polyclonal (anti-CD3/CD28) activation, differentiate into central memory cells, highly resistant to tumor-induced exhaustion, with minimal PD-1 expression. In addition, BacTs repolarize the tumor microenvironment, promoting a higher proportion of M1 macrophages, which is associated with a better prognosis.

In proof-of-concept experiments, BacT cells prevented tumor implantation in a mouse melanoma model. Furthermore, treatment with bacteria-instructed lymphocytes resulted in significant protection against tumor growth in different mouse tumor models.

We aim to shape the next generation of BacT-based immunotherapies to improve survival in cancers with poor prognosis. If successful, our project will represent an advance over the state of the art as it will represent the first effective cellular immunotherapy against solid tumors while reducing the side effects of current ineffective therapies.

Objectives:

1. Engineer bacteria to enhance BacT-mediated antitumor responses.
2. To identify tumor neoantigens (TNAs) and express them in bacteria. We have developed a machine-learning-based platform to identify tumor-specific antigens.
3. To evaluate the antitumor impact of ex vivo expanded BacT-activated tumor-specific CD8+ T cells.
4. Dissect the molecular mechanisms of bacterial instruction.