Radiation Oncology

The Frock laboratory is interested in elucidating mechanisms of DNA double-stranded break (DSB) repair and chromosome translocations.  We employ a high-throughput sequencing technology that identifies and maps cellular DSBs.  We are interested in further developing this technology to more fully quantify the DSB repair fates from targeted DSBs.  Our research disciplines are broad and cover aspects of molecular and cancer biology, bioinformatics. immunology, genome editing, and radiation biology.

An emerging hallmark of cancer is the modulation of metabolic pathways by malignant cells to promote cancer development. Dr. Jiangbin Ye’s professional interest is to investigate the causes and consequences of the abnormal metabolic phenotypes of tumor cells, with the prospect that therapeutic approaches might be developed to target these metabolic pathways to improve cancer treatment. The lab’s current goal is to explore the complex role of metabolic reprogramming in epigenetic regulations, and how cell fate and differentiation process are controlled by these epigenetic regulations.

The Physical Oncology Lab develops instruments and algorithms at the interface between medical physics and biophysics, for applications in cancer research and cancer care. We use unconventional physical mechanisms to non-invasively interrogate biological processes in living organisms and physically enhance the efficacy of radiation treatments.

My laboratory is focused on development and application of molecular imaging techniques towards understanding radiation and cancer biology and improving treatment of human disease. Using modalities including positron emission tomography (PET), computed tomography (CT), fluorescence imaging, bioluminescence imaging, and small animal conformal radiotherapy, we are investigating the molecular and physiologic factors that determine tumor response to therapy.

My lab is focused on the development of imaging and molecular biomarkers for precision cancer medicine. We are interested in a broad range of clinical applications, including early cancer detection, diagnosis, prognostication, and prediction of treatment response. To achieve this goal, we integrate and analyze large-scale patient data sets with clinical annotations, including both imaging (radiologic, histopathologic) and molecular (genomic, epigenomic, transcriptomic) data. In addition, we develop and apply novel statistical and machine learning methods.