Gene Expression Analysis of Breast Cancer Progression

Abstract

Breast cancer (BC) is a heterogeneous disease with varying clinical behavior, and response to therapy that cannot be predicted based on clinical and pathologic classifications. It is the primary goal of our research to identify and characterize biological pathways and individual molecular components that play a primary role in BC development and progression In order to identify genes, gene expression profiles and molecular pathways associated with metastatic BC we have performed genome-wide gene expression analysis of a large number of breast cancer samples. Both unsupervised and supervised analyses are being used to identify genes differentially expressed among samples. Hierarchical clustering showed that most samples grouped according to estrogen receptor status. In addition, matched primary carcinomas and lymph node metastases tended to pair demonstrating marked conservation of molecular phenotype within patients. Formal statistical testing is being used to identify genes with marked changes in expression during progression Lymph node metastases in particular showed significant decreases in the expression of many genes corresponding to extracellular matrix proteins and proteases when compared to matched primaries. Further expression changes in a variety of genes were associated with distant metastases. Immunohistochemistry and in situ hybridization are being used to validate and extend findings.

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Document Details

Document Type
Technical Report
Publication Date
Jul 01, 2004
Accession Number
ADA443987

Entities

People

  • Wiliam L. Gerald

Organizations

  • Memorial Sloan Kettering Cancer Center

Tags

DTIC Thesaurus Topics

  • Abstracts
  • Biology
  • Breast Cancer
  • Cell Physiological Processes
  • Clustering
  • Data Analysis
  • Diseases And Disorders
  • Estrogens
  • Gene Expression
  • Heterogeneous Conditions
  • Hybridization
  • Immunohistochemistry
  • Lymph Nodes
  • Lymphatic System
  • Neoplasms
  • Proteins
  • Transcription Factors

Fields of Study

  • Biology

Readers

  • Molecular and genetic basis of cancer.
  • Neural Network Machine Learning.
  • Oncology (Cancer Research).