Mechano-Visual Phenotying of Cancer: From Onset Through Disease Progression
The American Cancer Society estimated that 207,090 women were diagnosed with breast cancer in 2010 and that 39,840 women died of this disease in the United States alone during that year. This translates to about 1560 deaths per day attributed to cancer – overall, in the US, 1 in every 4 deaths is attributed to cancer. In the US, cancer is the second most common cause of death only next to deaths due to heart disease. Future progress in several key areas of cancer research and drug discovery will rely upon the capacity of investigators to reliably detect, characterize and track subtle changes that occur in the tumor environment during the transformation from the benign to cancerous state.
The central objective of this grant proposal is to design, develop and evaluate computational and imaging tools, which provide insight regarding the mechanical and morphological changes that occur starting with the onset of a malignancy and follow those changes throughout the course of disease progression using a representative ensemble of cancer tissue specimens from breast cancer cases. These new technologies will facilitate the discovery of novel diagnostic and prognostic clues, which are not apparent using traditional methods of assessment. The overarching objectives of the proposed project are:
- To investigate changes in the mechanical characteristics of sampled tissues through accurate non-linear finite element modeling based on the experimentally captured atomic force microscopy (AFM) data,
- To increase the sampling throughput to allow automated assessment of multiple regions of interest, simultaneously, using an array of micro force sensors based on micro-electro-mechanical systems (MEMS) technology, and
- To compare the mechanical changes, expression signatures, and spatial distribution of biomarkers in the normal tissue samples with those collected at the onset of malignancy and throughout the primary stages of disease progression for breast cancer cases.
Based on successful completion of these aims, we will design, develop and evaluate a reliable means for providing multimodal decision support for performing automated, higher-throughput characterization of specimens. Finally, our team will deploy, test and optimize the updated suite of computational and modeling tools at strategic adopter sites. To accomplish this, we have assembled an excellent team of engineers and clinicians from the The Robotics, Automation, and Medical Systems (RAMS) Laboratory, University of Maryland and Rutgers Cancer Institute of New Jersey for this extremely important NIH project.