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: 1) 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, 2) 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 3) 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 (Emory University and University of Pennsylvania - see letters of support). To accomplish this, we have assembled an excellent team of engineers and clinicians from the University Of Maryland, College Park and The Cancer Institute of New Jersey for this extremely important NIH project.

Public Health Relevance

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. The central objective of this grant proposal is to design, develop and evaluate computational and imaging tools, which provide insight regarding the changes that occur in terms of biomarker and morphologic signatures starting with the onset of a malignancy and follow those changes throughout the course of disease progression using a representative ensemble of breast cancer tissue specimens.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Project (R01)
Project #
1R01CA161375-01A1
Application #
8256045
Study Section
Biomedical Computing and Health Informatics Study Section (BCHI)
Program Officer
Ossandon, Miguel
Project Start
2012-03-12
Project End
2017-02-28
Budget Start
2012-03-12
Budget End
2013-02-28
Support Year
1
Fiscal Year
2012
Total Cost
$330,542
Indirect Cost
$93,061
Name
University of Maryland College Park
Department
Engineering (All Types)
Type
Schools of Engineering
DUNS #
790934285
City
College Park
State
MD
Country
United States
Zip Code
20742
Boregowda, Rajeev K; Medina, Daniel J; Markert, Elke et al. (2016) The transcription factor RUNX2 regulates receptor tyrosine kinase expression in melanoma. Oncotarget 7:29689-707
Pandya, Hardik J; Park, Kihan; Chen, Wenjin et al. (2016) Toward a Portable Cancer Diagnostic Tool Using a Disposable MEMS-Based Biochip. IEEE Trans Biomed Eng 63:1347-53
Yildirim, Esma; Foran, David (2016) Parallel vs. Distributed Data Access for Giga-pixel-resolution Histology Images: Challenges and Opportunities. IEEE J Biomed Health Inform :
Park, Kihan; Desai, Jaydev P (2016) Micropositioning and Control of an Underactuated Platform for Microscopic Applications. IEEE ASME Trans Mechatron 21:2635-2646
Pandya, Hardik J; Park, Kihan; Chen, Wenjin et al. (2015) Simultaneous MEMS-based electro-mechanical phenotyping of breast cancer. Lab Chip 15:3695-706
Pandya, Hardik J; Roy, Rajarshi; Chen, Wenjin et al. (2015) Accurate characterization of benign and cancerous breast tissues: aspecific patient studies using piezoresistive microcantilevers. Biosens Bioelectron 63:414-24
Chen, Wenjin; Brandes, Zachary; Roy, Rajarshi et al. (2015) Robot-Guided Atomic Force Microscopy for Mechano-Visual Phenotyping of Cancer Specimens. Microsc Microanal 21:1224-35
Kurc, Tahsin; Qi, Xin; Wang, Daihou et al. (2015) Scalable analysis of Big pathology image data cohorts using efficient methods and high-performance computing strategies. BMC Bioinformatics 16:399
Pandya, Hardik J; Park, Kihan; Desai, Jaydev P (2015) Design and fabrication of a flexible MEMS-based electromechanical sensor array for breast cancer diagnosis. J Micromech Microeng 25:
Su, Hai; Shen, Yong; Xing, Fuyong et al. (2015) Robust automatic breast cancer staging using a combination of functional genomics and image-omics. Conf Proc IEEE Eng Med Biol Soc 2015:7226-9

Showing the most recent 10 out of 22 publications