The genetic engineering of cancer patients' own T cells to fight cancers has become a promising cancer treatment strategy, especially against blood tumors. While promising, improvement is needed to enable these engineered T cells to effectively treat other cancers. Some subsets of T cells are better than others at treating tumors. A robust manufacturing process to isolate pure anti-cancer T cell subsets will ultimately lead to a more effective and uniform clinical product. However, the standard approach for purifying T cell subsets, which requires highly complex machines, is difficult and expensive to scale up. Therefore, a manufacturing process that does not require these machines to yield purified T cell subsets would greatly improve the efficiency and reduce the cost of tumor-targeting T cell production. The goal of this project is to develop novel genetic classifiers that select desired T cell subsets that will not require machine-assisted purification steps. The researchers will build a series of increasingly sophisticated genetic classifiers for selecting potent anti-cancer T cell subsets and develop a gene delivery platform that contains a tumor targeting receptor and genetic classifier. Educational activities will include training and hosting a team of students to compete in the International Genetically Engineered Machines competition, developing a project on T cell engineering and manufacturing, and developing course materials on T cell biomanufacturing for undergraduate and postgraduate students.
Genetically engineered T cells expressing tumor-targeting chimeric antigen receptors (CAR) have demonstrated surprising anti-tumor efficacy against B cell malignancies. Further improvement is needed to enable CAR T cells to effectively treat other cancers. CAR T cells with the central memory phenotype are one of the most potent tumor-eradicating T cell subsets. A robust manufacturing process to generate homogeneous central memory CAR T cells will ultimately lead to a more effective and uniform clinical product. However, the standard approach for purifying T cell subsets require clinical cell sorters and magnetic separators operating under GMP conditions, which is difficult and expensive to scale up. Therefore, a manufacturing process that can yield purified T cell subsets without machine-assisted cell separation will greatly improve the efficiency and reduce the cost of the CAR T cells production. To enrich T cell subsets without cell sorting, a novel microRNA (miRNA)-based genetic classifiers will be developed to select for central memory T cells (TCM). The classifiers will express antibiotic resistance gene dependent on the TCM miRNA signature. The addition of antibiotics will eliminate all cells except for the T cells of interest. These classifiers will be introduced into the T cells along with CARs in the same gene delivery vehicle. A miRNA-based classifier is ideally suited for this application because miRNA binding sites are small, which allows compact circuit design that facilitates efficient gene delivery. In addition, miRNA signatures have been profiled for many different human T cell subsets, thus greatly simplifying the circuit design efforts. The project will build a series of miRNA classifiers for selecting central memory CAR T cells, and will develop a lentivirus platform that contains a constitutive CAR expression cassette and a miRNA classifier controlling the expression of an antibiotic resistance gene.
