Prediction of outcome in breast cancer patients using gene expression profiling Public Deposited

Downloadable Content

Download PDF
Last Modified
  • March 21, 2019
Creator
  • Oh, Daniel S.
    • Affiliation: School of Medicine, Curriculum in Genetics and Molecular Biology
Abstract
  • Breast cancer, the most common cancer diagnosed in women, is a complex and heterogeneous disease. In order to make the best treatment decision for a breast cancer patient, it is important to accurately determine that patient's risk of recurrence and the therapy to which that patient's tumor is most likely to respond. The prognostic and/or predictive factors currently accepted for use in primary breast cancer decision making (i.e. lymph node status, tumor size, nuclear grade, etc.) are not enough to accurately identify those patients who may require therapy and gives little information about what therapy they might best benefit from. Recent discoveries using gene expression profiling have greatly improved our understanding of the molecular pathogenesis of breast cancer. We believe that gene expression profiling may also improve the prognostication and/or prediction of breast cancer outcomes, and thus, the main objective of this work has been to develop and test gene expression-based predictors of outcome in breast cancer patients. First, we developed an expression-based predictor of outcome for Estrogen Receptor (ER) and/or Progesterone Receptor (PR)-positive breast cancer patients using biological differences among these tumors. Second, we developed a predictor for objectively classifying tumors into one of five intrinsic subtypes and validated this using multiple test sets. Next, using a single patient dataset, we determined the concordance in outcome predictions made by several different gene expression profiles (developed on different platforms by different laboratories). Lastly, we developed gene expression-based predictors for response to neoadjuvant chemotherapy. In summary, this work shows that gene expression profiling holds great promise in being clinically useful in the treatment decision-making process for breast cancer patients.
Date of publication
DOI
Resource type
Rights statement
  • In Copyright
Advisor
  • Perou, Charles
Degree granting institution
  • University of North Carolina at Chapel Hill
Language
Access
  • Open access
Parents:

This work has no parents.

Items