Tissue microarray technology holds great potential for reducing the time and cost associated with conducting investigative research in cancer biology, oncology, and drug discovery. TMA's make it possible to construct a carefully planned array such that a 20-year survival analysis can be performed on a cohort of 600 or more patients using only a few micro-liters of antibody. However, capturing, organizing, updating, exchanging, and analyzing the data generated by this technology creates a number of significant challenges. The sheer volume of data, text, and images arising from even limited studies involving tissue microarrays can over time quickly approach those of a small clinical department. The central objective of this revised renewal application is to (1) build upon the progress made in the first phase of research by expanding the reference archive of imaged TMA specimens and correlated clinical data to include a wider scope of malignancies, tissues and biomarkers;(2) develop advanced imaging, computational and data management tools to support automated analysis of tissue microarrays in collaborative frameworks;and (3) increase dissemination of the query-enabled image archive and imaging and data management tools to the clinical and research communities for research, education and clinical decision support.
The aims of the proposed project will be achieved through the development and implementation of advanced computational, imaging, and pattern recognition tools and new technologies.

Public Health Relevance

Tissue microarray technology holds great promise for advancing investigative research in cancer biology, oncology and drug discovery. The overarching objective of the proposed project is to develop a suite of algorithms and software tools which facilitate automated imaging, analysis, and archiving of tissue microarrays. The key computational and imaging tools that were developed in the first phase of the project including a color decomposition algorithm for analyzing the staining characteristics of the histology;image analysis tools for automatically computing the integrated staining intensity, effective staining area and effective staining intensity of expression patterns;an intelligent image archiving system;caBIG compliant data management tools;a reference library of expression signatures for more than 120, 000 imaged tissue discs originating from a mixed set of cancer tissue microarrays;and a new texture descriptor based on region covariance which was shown to provide quick, reliable performance for identifying and delineating tumor regions and performing antigen localization at the tissue level. The central objective of this revised renewal application is to build upon the progress made in the first phase of our research by (1) expanding the reference archive of imaged specimens and correlated clinical data to include a wider range of tissues, cancer types and biomarkers;(2) building upon our prior work by integrating a vendor-independent interface to the TMA analysis and data management toolset to support a full range of commercially available virtual slide formats;(3) investigating the use of a new repulsion force term to be used in conjunction with the existing internal and external energy equations for improved accuracy in delineating boundaries in regions exhibiting dense, concentrations of cells;(4) integrating a variable channel module into the segmentation algorithm and evaluate its capacity to support multi-dimensional image data;(5) building upon our successful efforts to design, develop, and evaluate a quick, reliable approach for performing unsupervised, deformable co-registration of consecutive histological sections to facilitate analysis across multiple experiments and correlate image features across adjacent sections;(6) deploying the updated software suite, data management tools and query-enabled reference archive of imaged TMA specimens to the consortium of adopter sites and assess performance using quantitative imaging experiments and a newly developed man-machine comparative analysis software toolkit. Upon completion of the project the archive of imaged specimens, computational and data management tools will be made available to the clinical and research communities as shareable resources for collaborative research, education and clinical decision support. Project Narrative Tissue microarray technology holds great promise for advancing investigative research in cancer biology, oncology and drug discovery. The overarching objective of the proposed project is to develop a suite of algorithms and software tools which facilitate automated imaging, analysis, and archiving of tissue microarrays. The key computational and imaging tools that were developed in the first phase of the project including a color decomposition algorithm for analyzing the staining characteristics of the histology;image analysis tools for automatically computing the integrated staining intensity, effective staining area and effective staining intensity of expression patterns;an intelligent image archiving system;caBIG compliant data management tools;a reference library of expression signatures for more than 120, 000 imaged tissue discs originating from a mixed set of cancer tissue microarrays;and a new texture descriptor based on region covariance which was shown to provide quick, reliable performance for identifying and delineating tumor regions and performing antigen localization at the tissue level. The central objective of this revised renewal application is to build upon the progress made in the first phase of our research by (1) expanding the reference archive of imaged specimens and correlated clinical data to include a wider range of tissues, cancer types and biomarkers;(2) building upon our prior work by integrating a vendor-independent interface to the TMA analysis and data management toolset to support a full range of commercially available virtual slide formats;(3) investigating the use of a new repulsion force term to be used in conjunction with the existing internal and external energy equations for improved accuracy in delineating boundaries in regions exhibiting dense, concentrations of cells;(4) integrating a variable channel module into the segmentation algorithm and evaluate its capacity to support multi-dimensional image data;(5) building upon our successful efforts to design, develop, and evaluate a quick, reliable approach for performing unsupervised, deformable co-registration of consecutive histologic sections to facilitate analysis across multiple experiments and correlate image features across adjacent sections;(6) deploying the updated software suite, data management tools and query-enabled reference archive of imaged TMA specimens to the consortium of adopter sites and assess performance using quantitative imaging experiments and a newly developed man-machine comparative analysis software toolkit. Upon completion of the project the archive of imaged specimens, computational and data management tools will be made available to the clinical and research communities as shareable resources for collaborative research, education and clinical decision support.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Project (R01)
Project #
5R01CA156386-10
Application #
8716690
Study Section
Special Emphasis Panel (ZRG1-SBIB-Q (90))
Program Officer
Li, Jerry
Project Start
2004-04-01
Project End
2015-07-31
Budget Start
2014-08-01
Budget End
2015-07-31
Support Year
10
Fiscal Year
2014
Total Cost
$217,284
Indirect Cost
$59,301
Name
Rbhs-Robert Wood Johnson Medical School
Department
Pathology
Type
Schools of Medicine
DUNS #
078795875
City
Piscataway
State
NJ
Country
United States
Zip Code
08854
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 :
Pandya, Hardik J; Park, Kihan; Chen, Wenjin et al. (2015) Simultaneous MEMS-based electro-mechanical phenotyping of breast cancer. Lab Chip 15:3695-706
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
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
Wang, Daihou; Foran, David J; Ren, Jian et al. (2015) Exploring automatic prostate histopathology image Gleason grading via local structure modeling. Conf Proc IEEE Eng Med Biol Soc 2015:2649-52
Ren, Jian; Sadimin, Evita T; Wang, Daihou et al. (2015) Computer aided analysis of prostate histopathology images Gleason grading especially for Gleason score 7. Conf Proc IEEE Eng Med Biol Soc 2015:3013-6
Pandya, Hardik J; Kim, Hyun Tae; Roy, Rajarshi et al. (2014) Towards an Automated MEMS-based Characterization of Benign and Cancerous Breast Tissue using Bioimpedance Measurements. Sens Actuators B Chem 199:259-268

Showing the most recent 10 out of 28 publications