Over the last 2 years we have developed a set of algorithms (called AQUA) that allow the rapid, automated, continuous and quantitative analysis of tissue microarrays, including the separation of tumor from stromal elements and the sub-cellular localization of signals. We validated the technology and have used it to discover new biologically based disease sub-classifications. Validation studies using estrogen receptor in breast carcinoma show that automated analysis matches or exceeds the results of conventional pathologist-based scoring. We then used the automated analysis to show 2 examples of disease classifications not discernable by traditional pathologist based analysis. First, by measuring membrane levels of HER2 in 350 node positive breast cancers, we were able to detect two groups of patients with poor outcome, over expressers, as has been seen by pathologists, and a second group of very low level expressers, never seen by pathologists, but previously described in a work where breast tissues were quantitatively analyzed by enzyme-linked immunoassay. Thus the accuracy of this new technology appears to be as sensitive as """"""""grind and measure"""""""" type assays but with the added critical advantage of in situ subcellular localization. Subcellular localization is crucial in some situations. Our second study showed that quantification of beta-catenin expression within defined sub-cellular compartments allowed fractionation of colon cancer patients into two novel, prognostically significant groups that would be impossible to discover by traditional pathologist-based scoring. Although the small size of tissue microarray spots facilitated the development of AQUA, that technology could now be translated to examination of biopsy tissue to translate the advantages of digital, continuous pathology to patient specimens. Here we propose translation of the tissue microarray based AQUA algorithms to whole tissue sections, toward the goal of digitally defining expression levels to optimally match biospecific therapies to their targets. Our model system will be breast cancer since there are already 3 biospecific therapies and more are in the pipeline and since core needle biopsy is the most common current method for assessment of breast masses.
Our aims i nclude 1) Translation of the AQUA score to an absolute protein concentration which will require optimization of the image acquisition and construction of a standard curve; and 2) proof of the concept by comparing AQUA analyzed biopsies to current standards and patient outcomes.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Exploratory/Developmental Grants (R21)
Project #
1R21CA100825-01
Application #
6596576
Study Section
Special Emphasis Panel (ZRG1-CAMP (01))
Program Officer
Song, Min-Kyung H
Project Start
2003-03-01
Project End
2005-02-28
Budget Start
2003-03-01
Budget End
2004-02-29
Support Year
1
Fiscal Year
2003
Total Cost
$163,500
Indirect Cost
Name
Yale University
Department
Pathology
Type
Schools of Medicine
DUNS #
043207562
City
New Haven
State
CT
Country
United States
Zip Code
06520
Sullivan, C A W; Ghosh, S; Ocal, I T et al. (2009) Microvessel area using automated image analysis is reproducible and is associated with prognosis in breast cancer. Hum Pathol 40:156-65
Chung, Gina G; Zerkowski, Maciej P; Ghosh, Sriparna et al. (2007) Quantitative analysis of estrogen receptor heterogeneity in breast cancer. Lab Invest 87:662-9
Dolled-Filhart, Marisa; McCabe, Anthony; Giltnane, Jennifer et al. (2006) Quantitative in situ analysis of beta-catenin expression in breast cancer shows decreased expression is associated with poor outcome. Cancer Res 66:5487-94
McCarthy, Mary M; DiVito, Kyle A; Sznol, Mario et al. (2006) Expression of tumor necrosis factor--related apoptosis-inducing ligand receptors 1 and 2 in melanoma. Clin Cancer Res 12:3856-63
Dolled-Filhart, Marisa; Ryden, Lisa; Cregger, Melissa et al. (2006) Classification of breast cancer using genetic algorithms and tissue microarrays. Clin Cancer Res 12:6459-68
Cowan, James D; Rimm, David L; Tuck, David P (2006) Cruella: developing a scalable tissue microarray data management system. Arch Pathol Lab Med 130:817-22
Handerson, Tamara; Camp, Robert; Harigopal, Malini et al. (2005) Beta1,6-branched oligosaccharides are increased in lymph node metastases and predict poor outcome in breast carcinoma. Clin Cancer Res 11:2969-73
Berger, Aaron J; Davis, Darren W; Tellez, Carmen et al. (2005) Automated quantitative analysis of activator protein-2alpha subcellular expression in melanoma tissue microarrays correlates with survival prediction. Cancer Res 65:11185-92
Kluger, Harriet M; Chelouche Lev, Dina; Kluger, Yuval et al. (2005) Using a xenograft model of human breast cancer metastasis to find genes associated with clinically aggressive disease. Cancer Res 65:5578-87
Yu, Ziwei; Weinberger, Paul M; Provost, Elayne et al. (2005) beta-Catenin functions mainly as an adhesion molecule in patients with squamous cell cancer of the head and neck. Clin Cancer Res 11:2471-7

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