Research Focus and Interests
Interdisciplinary Oncology Lab
Study of Cancer Growth and Treatment from a Physical Sciences Perspective
Cancer is an abnormal growth of tissue caused by uncontrolled cell division or decreased cell death. This growth can spread to other parts of the body. Death usually results from obstruction and failure of major organs. There are more than one and a half million new cases of cancer diagnosed annually in the U.S., and over a half million deaths per year from the disease.
At its most fundamental level, cancer involves unrepaired genetic damage to cells. This damage can be caused by radiation, smoke, poisons, viruses, genetic inheritance, cellular malfunction, and perhaps the body’s own stem cells. There are many types of cancer based on the type of cell or organ where it starts. Carcinomas begin in the skin or in tissues that cover internal organs. Sarcomas involve connective or supportive tissue such as bone, cartilage, or fat. Leukemias start in the blood-forming tissue, typically in the bone marrow. Lymphoma and myelomas affect the immune system cells.
Yet molecular and cellular events cannot completely account for cancer behavior. Cancers are much more complex than a group of cells “going crazy”; they involve multifaceted interactions of multiple cell and tissue types within a diverse environment with effects across a wide range of time (seconds to years) and length (nano- to centimeter) scales. Many factors contribute to the complexity of this system, including the micro-structure of the tissue, inter- and intra-cellular signaling and interactions of different cell types, angiogenesis and vascularization, and the immune system response. Tumors behave as complex living entities for which growth, vascularization, and invasion are “system-level” behaviors. This behavior is tightly coupled to the genes and the interaction of the tumor tissue with its surrounding environment.
In order to study cancer at a system-level, engineering and physical sciences approaches tightly integrated with experimental data and clinical observations are necessary. The aim of this work is to predict tumor growth and invasion from the molecular and cellular scale events, with the ultimate goal to help analyze tumors of specific patients. The work begins with model development that describes tumor behavior in the language of mathematics and physics. Model parameter values are calibrated from experimental data. The experiments include culturing tumor cells in 2-D monolayers and as 3-D spheroids, analysis of histopathology of tumor biopsy specimens, and measurements and observations from previous work in the literature. The effects of varying the model parameters are then studied to predict the tumor behavior and to design optimal therapy. This process is iterative, with the findings used to refine the underlying model as well as to guide the experiments. This bioengineering work provides interdisciplinary exposure to the latest research and technologies in the exciting fields of cancer biology, scientific computing, data visualization, mathematical biology, and physical oncology.
There are big challenges with this research. A major difficulty is that the biological processes that need to be modeled may not be well understood. Cells are complex, and tissues magnify this complexity in unexpected ways. Furthermore, it may be challenging to determine values for the model parameters from the biological information, and many of the relevant parameters may not even be known before a model is developed. Another challenge is that the modeling work is iterative in nature. Hypothesis formulation enables simulation; comparison to experimental data may then lead to reformulation of the hypothesis. In this case, the model may have to undergo refinement or perhaps be completely reformulated. The biggest challenge of all, however, is to properly translate this work so that it can be applied to help patients who suffer from cancer.