A highthroughput production of composite breast tumoroids: a tool for investigation of cellular heterogeneity and drug delivery

Poster presented at Biomedical Technology Showcase 2006, Philadelphia, PA. Retrieved 18 Aug 2006 from http://www.biomed.drexel.edu/new04/Content/Biomed_Tech_Showcase/Poster_Presentations/Tozeren.pdf.Breast tumors are typically heterogeneous and contain diverse subpopulations of tumor cells with differing phenotypic properties. This study has developed an in vitro co-culture-based three-dimensional breast tumor model that studies the effects of mixing heterogeneous tumor cell populations. Breast cancer cell lines of different phenotypes (MDAMB231, MCF7 and ZR751) were co-cultured in a rotating wall vessel (RWV) bioreactor to form a large number of heterogeneous tumoroids. Prior to each experiment, cells were labeled with cell tracker dyes to allow for time-course fluorescence microscopy to monitor cell aggregation. Histological sections of the tumor spheroids were stained with hematoxylin and eosin (HE), progesterone receptor (PR), E-cadherin (E-cad) and proliferation marker, ki67. Results showed that heterogeneous tumoroids reflecting the composition of the growth rate, invasion potential, and spatial distributions of heterogeneous tumor spheroids were highly dependent on cell composition. A suitable in vitro model for studying tumor-cell heterogeneity and reciprocal interactions will accelerate understanding of tumor cell phenotype population dynamics

Large-scale computations on histology images reveal grade-differentiating parameters for breast cancer

BACKGROUND: Tumor classification is inexact and largely dependent on the qualitative pathological examination of the images of the tumor tissue slides. In this study, our aim was to develop an automated computational method to classify Hematoxylin and Eosin (H&E) stained tissue sections based on cancer tissue texture features. METHODS: Image processing of histology slide images was used to detect and identify adipose tissue, extracellular matrix, morphologically distinct cell nuclei types, and the tubular architecture. The texture parameters derived from image analysis were then applied to classify images in a supervised classification scheme using histologic grade of a testing set as guidance. RESULTS: The histologic grade assigned by pathologists to invasive breast carcinoma images strongly correlated with both the presence and extent of cell nuclei with dispersed chromatin and the architecture, specifically the extent of presence of tubular cross sections. The two parameters that differentiated tumor grade found in this study were (1) the number density of cell nuclei with dispersed chromatin and (2) the number density of tubular cross sections identified through image processing as white blobs that were surrounded by a continuous string of cell nuclei. Classification based on subdivisions of a whole slide image containing a high concentration of cancer cell nuclei consistently agreed with the grade classification of the entire slide. CONCLUSION: The automated image analysis and classification presented in this study demonstrate the feasibility of developing clinically relevant classification of histology images based on micro- texture. This method provides pathologists an invaluable quantitative tool for evaluation of the components of the Nottingham system for breast tumor grading and avoid intra-observer variability thus increasing the consistency of the decision-making process

Tozeren A. Automated identification of microstructures on histology slides

Grading of breast cancer and the subsequent treatment options are largely dependent on the pathological examination of the histology slides from the tumor tissue. Tumor grading is currently based on the spatial organization of the tissue, including the distribution of cancer cells, the morphological properties of their nuclei and the presence/absence of cancerassociated surface receptors these cells express. In this study, we have developed an automated image processing method to detect and identify clinically relevant microscopic structures on histology slides. The tissue components identified with our program are as follows: fat cells, stroma, and three morphologically distinct cell nuclei types used in grading cancer on the Haematoxylin and Eosin (H&E) stained slides. The image processing is based on gray-scale segmentation, feature extraction, supervised learning, subsequent training and clustering. Our automated processing system has an accuracy of 89% ±0.8 in correctly identifying the three different nuclei types observed in H & E stained histology slides. 1

A Novel Cross-Disciplinary Multi-Institute Approach to Translational Cancer Research: Lessons Learned from Pennsylvania Cancer Alliance Bioinformatics Consortium (PCABC)

Background The Pennsylvania Cancer Alliance Bioinformatics Consortium (PCABC, http://www.pcabc.upmc.edu ) is one of the first major project-based initiatives stemming from the Pennsylvania Cancer Alliance that was funded for four years by the Department of Health of the Commonwealth of Pennsylvania. The objective of this was to initiate a prototype biorepository and bioinformatics infrastructure with a robust data warehouse by developing a statewide data model (1) for bioinformatics and a repository of serum and tissue samples; (2) a data model for biomarker data storage; and (3) a public access website for disseminating research results and bioinformatics tools. The members of the Consortium cooperate closely, exploring the opportunity for sharing clinical, genomic and other bioinformatics data on patient samples in oncology, for the purpose of developing collaborative research programs across cancer research institutions in Pennsylvania. The Consortium's intention was to establish a virtual repository of many clinical specimens residing in various centers across the state, in order to make them available for research. One of our primary goals was to facilitate the identification of cancer-specific biomarkers and encourage collaborative research efforts among the participating centers. Methods The PCABC has developed unique partnerships so that every region of the state can effectively contribute and participate. It includes over 80 individuals from 14 organizations, and plans to expand to partners outside the State. This has created a network of researchers, clinicians, bioinformaticians, cancer registrars, program directors, and executives from academic and community health systems, as well as external corporate partners - all working together to accomplish a common mission. The various sub-committees have developed a common IRB protocol template, common data elements for standardizing data collections for three organ sites, intellectual property/tech transfer agreements, and material transfer agreements that have been approved by each of the member institutions. This was the foundational work that has led to the development of a centralized data warehouse that has met each of the institutions’ IRB/HIPAA standards. Results Currently, this “virtual biorepository” has over 58,000 annotated samples from 11,467 cancer patients available for research purposes. The clinical annotation of tissue samples is either done manually over the internet or semi-automated batch modes through mapping of local data elements with PCABC common data elements. The database currently holds information on 7188 cases (associated with 9278 specimens and 46,666 annotated blocks and blood samples) of prostate cancer, 2736 cases (associated with 3796 specimens and 9336 annotated blocks and blood samples) of breast cancer and 1543 cases (including 1334 specimens and 2671 annotated blocks and blood samples) of melanoma. These numbers continue to grow, and plans to integrate new tumor sites are in progress. Furthermore, the group has also developed a central web-based tool that allows investigators to share their translational (genomics/proteomics) experiment data on research evaluating potential biomarkers via a central location on the Consortium's web site. Conclusions The technological achievements and the statewide informatics infrastructure that have been established by the Consortium will enable robust and efficient studies of biomarkers and their relevance to the clinical course of cancer. </jats:sec