Endocrine Therapy of Breast Cancer

Abstract

A controversy in the treatment of estrogen receptor positive (ER+) breast cancers is whether an aromatase inhibitor, e.g., letrozole (LET) or TAM should be given as first line endocrine therapy. Unfortunately, response rates are lower, and response durations are shorter, on crossover than when these agents are given as first line therapies, e.g., ~40% of tumors show cross resistance to TAM or an aromatase inhibitor on crossover. Only 50% of ER+ tumors respond to endocrine therapy. Currently, we fail to predict endocrine responsiveness in about 66% of ER+/PgR- (progesterone receptor), 55% of ER-/PgR+, and 25% of ER+/PgR+ tumors. In this Clinical Translational Research Award, we hypothesize that our analytical methods (including new methods we will develop/test) can extract expression profiles of breast tumors that define their responsiveness (sensitive vs. resistant) to endocrine therapies. These profiles will support neural network and biostatistical classifiers or committee machines that predict each tumor's endocrine responsiveness. Our objectives are to array breast cancer cases, build classifiers of endocrine responsiveness (using microarray data), and validate these classifiers in independent data sets. Genes will be further studied using cellular and molecular methods, and their role as therapeutic targets explored. In the long term, we will design custom arrays for use in clinical practice.

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Document Details

Document Type
Technical Report
Publication Date
Jun 01, 2009
Accession Number
ADA511433

Entities

People

  • Robert Clarke

Organizations

  • Georgetown University

Tags

Communities of Interest

  • Advanced Electronics

DTIC Thesaurus Topics

  • Antineoplastic Agents
  • Breast Cancer
  • Cell Physiological Processes
  • Cells
  • Chemistry
  • Computational Science
  • Data Mining
  • Health Services
  • Information Science
  • Medical Personnel
  • Network Science

Fields of Study

  • Medicine

Readers

  • Breast cancer cell signaling and growth regulation.
  • Neural Network Machine Learning.
  • Oncology and Biomarker-Based Cancer Detection.

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks