Image taken from microscope in the Castro Cancer Research and Cellular Therapies Lab

Research Projects

Research in the Cancer Research and Cellular Therapies Laboratory includes the development of potential cellular therapies that target primary and metastatic tumors using advanced technologies and improvements in care for patients undergoing cancer treatment.

Our lab has several ongoing research projects led by Dr. Castro, including these projects:

  • Cellular therapy for hematological cancers. Our lab has extensive experience conducting leading-edge research and clinical trials in cellular therapy. Our goal is to develop better, more patient-friendly therapies for different cancers, including acute and chronic leukemias and lymphomas.
  • Cellular therapy for solid tumors. In collaboration with Mitesh J. Borad, M.D., and Karen S. Anderson, M.D., Ph.D., both physicians at Mayo Clinic in Phoenix, Arizona, we're working to develop new therapeutic targets for pancreatic cancer and breast cancer using chimeric antigen receptor (CAR)-T cells and exploring new ways to improve the efficacy of CAR-T cell therapy.
  • Immune system response. Our lab is identifying novel markers at different time points during whole-cell therapy for hematological malignancies and solid tumors to help fight cancer with new mechanisms.
  • Early detection of side effects. We're developing new biotechnologies to better monitor patients undergoing cellular therapies, with the goal of improving early detection and management of side effects to reduce hospital admissions.
  • Artificial intelligence and machine learning models applied to immuno-oncology. Clinical prediction models are usually based on multiple variables, and sometimes it's not feasible to identify which characteristics discriminate better between two sets of patients. Machine learning is a subset of artificial intelligence techniques that can be defined as the scientific study that makes computers learn from data in order to discover knowledge from them. Machine learning also has the particular advantage of recognizing subtle patterns in complex data sets by creating a decision boundary known as a hyperplane.