Joint Research Chair

Medical Date Science

Molecular profiling of tumors to develop innovative personalized medicine and novel therapeutics
  • Effects of nucleic acid-based anticancer drugs (e.g. TAS 102) on cancer stem cells
  • Drug discovery for digestive system cancer stem cells, cancer metabolism, one carbon metabolism
  • Study of the nucleus-mitochondria interaction for drug discovery
  • RNA modomics for new biomarkers and drugs
  • Use of transomics for better medical care

 

 

Next generation, interdisciplinary approach to the study of cancer stem cells, cancer metabolism and cancer drug development

The focus of our lab includes cancer stem cells. We are developing state-of-the-art techniques that study how these cells regulate cancer development and researching new drugs that can inhibit their effects. Our research covers the basic to clinical, but we seek to build new strategic approaches that take advantage of the academic environment. One major goal is to create an accurate human cancer model. This work involves rigorous study of a number of cancer topics, including the cancer metabolism, cancer stem cells, transomics, and new biomarkers and drugs. Of special interest is TAS 102, an anticancer drug being used in ongoing clinical trials, and its effect on cancer stem cells, and we extend the research to other broadly related reagents. This drug is classified as a nucleic acid drug, as it targets specific genes that are thought to regulate the cancer. Other research on drugs and biomarkers involves the investigation of miRNA and lncRNA. We are also studying the cancer metabolism to understand how cancer develops and progresses and ways to counter these events. One carbon metabolism, because of its role in DNA synthesis and DNA methylation, is thought to have an important role in cancer stem cells. We are looking at drugs and biomarkers that target one-carbon metabolism by studying interactions between the nucleus and mitochondria, RNA modomics, and transomics. Finally, we are attempting to bring machine learning into our studies. Overall, our research depends on strong industry-university collaborations.

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