Our Research
Welcome to the Warren Lab. Our research lies within the field of cancer immunology, and we have a particular interest in the cellular and molecular mechanisms that mediate cancer regression after immune-based therapy. Manipulating the immune system to eliminate cancer is now one of the four pillars of cancer treatment, along with surgery, chemotherapy, and radiation. Our lab is dedicated to developing cancer therapy that takes advantage of the ability of T lymphocytes to recognize cancer cells with exquisite specificity and to eliminate them with remarkable potency.
Studies focused on the immunobiology of kidney cancer as well as developing T-cell therapy for advanced kidney cancer are a major focus of the lab’s current effort. We are evaluating the protein products of human endogenous retroviruses (hERVs) as suitable targets for T-cell therapy of renal cell carcinoma (RCC), the most common type of kidney cancer. We are also investigating whether T cells expressing a chimeric antigen receptor recognizing FOLR1, the folate receptor alpha, which is commonly expressed at high levels on RCC cells as well as other epithelial cancers, might have activity against RCC. We have developed a 3D microphysiological system – “RCC-on-a-chip” – to permit rigorous in vitro studies of T-cell trafficking into the RCC tumor microenvironment, and we are also characterizing the endothelial cells and T cells in kidney tumors to identify potential approaches to targeting the tumor vasculature for kidney cancer therapy. We have recently launched a comprehensive study to identify and isolate the T cells that infiltrate into RCC tumors (called “TIL,” for tumor-infiltrating lymphocytes) and recognize antigens selectively expressed on RCC cells, and to optimize the techniques for expanding these T cells in the laboratory. We anticipate that the completion of these studies will provide the foundation for developing tumor-specific T-cell therapy for RCC.
Photomicrograph of renal call carcinoma (RCC) illustrating the CD4+ T-cells (yellow), CD8+ T cells (red), RCC tumor cells expressing CAIX (light blue), and background stromal cells (blue).
Kaposi sarcoma (KS) – a virally driven cancer that develops primarily in individuals with T-cell deficiency or dysfunction – comprises a second major focus of current research in the Warren Lab. KS is a leading cause of cancer morbidity and mortality in East and Southern Africa, where infection with Kaposi sarcoma-associated herpesvirus (KSHV), the virus that causes KS, is endemic. We are conducting a systematic study of the T-cell response to KSHV, and are identifying the viral antigens that are targeted by KSHV-specific T cells. Defining the defects in this response that allow KSHV to initiate the cellular transformation that leads to KS will enable us to design strategies that preserve or restore the T-cell response to KSHV in those who are at risk. We feel that this approach holds great promise for the prevention or treatment of this lethal cancer.
A third general area of active investigation in the Warren Lab is the development of computational approaches using machine learning to enable prediction of the antigenic specificity of T cells. T cells have remarkable potential for recognizing and eliminating malignant cells, but they can also mediate immunopathology. Determining the antigens recognized by T cells via their T-cell antigen receptor is key to both successful exploitation of T cells for cancer therapy and to prevention or mitigation of T-cell-mediated immunopathology. Determining the antigenic specificity of T cells, however, is experimentally arduous, painstaking, and time-consuming. The development of a computational approach to accurate prediction of T-cell antigenic specificity would profoundly accelerate the development of T-cell-based immunotherapeutics for cancer as well as mechanistic dissection of pathogenic T-cell responses involved in autoimmune disease. We are developing a computational approach to prediction of T-cell antigenic specificity that is based on fine-tuning existing large language models to address this challenge.