Advanced

Classification of breast cancer using genetic algorithms and tissue microarrays

Dolled-Filhart, Marisa ; Rydén, Lisa LU ; Cregger, Melissa ; Jirström, Karin LU ; Harigopal, Malini ; Camp, Robert L and Rimm, David L (2006) In Clinical Cancer Research 12(21). p.6459-6468
Abstract
PURPOSE: A multitude of breast cancer mRNA profiling studies has stratified breast cancer and defined gene sets that correlate with outcome. However, the number of genes used to predict patient outcome or define tumor subtypes by RNA expression studies is variable, nonoverlapping, and generally requires specialized technologies that are beyond those used in the routine pathology laboratory. It would be ideal if the familiarity and streamlined nature of immunohistochemistry could be combined with the rigorously quantitative and highly specific properties of nucleic acid-based analysis to predict patient outcome. EXPERIMENTAL DESIGN: We have used AQUA-based objective quantitative analysis of tissue microarrays toward the goal of discovery of... (More)
PURPOSE: A multitude of breast cancer mRNA profiling studies has stratified breast cancer and defined gene sets that correlate with outcome. However, the number of genes used to predict patient outcome or define tumor subtypes by RNA expression studies is variable, nonoverlapping, and generally requires specialized technologies that are beyond those used in the routine pathology laboratory. It would be ideal if the familiarity and streamlined nature of immunohistochemistry could be combined with the rigorously quantitative and highly specific properties of nucleic acid-based analysis to predict patient outcome. EXPERIMENTAL DESIGN: We have used AQUA-based objective quantitative analysis of tissue microarrays toward the goal of discovery of a minimal number of markers with maximal prognostic or predictive value that can be applied to the conventional formalin-fixed, paraffin-embedded tissue section. RESULTS: The minimal discovered multiplexed set of tissue biomarkers was GATA3, NAT1, and estrogen receptor. Genetic algorithms were then applied after division of our cohort into a training set of 223 breast cancer patients to discover a prospectively applicable solution that can define a subset of patients with 5-year survival of 96%. This algorithm was then validated on an internal validation set (n=223, 5-year survival=95.8%) and further validated on an independent cohort from Sweden, which showed 5-year survival of 92.7% (n=149). CONCLUSIONS: With further validation, this test has both the familiarity and specificity for widespread use in management of breast cancer. More generally, this work illustrates the potential for multiplexed biomarker discovery on the tissue microarray platform. (Less)
Please use this url to cite or link to this publication:
author
; ; ; ; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
in
Clinical Cancer Research
volume
12
issue
21
pages
6459 - 6468
publisher
American Association for Cancer Research
external identifiers
  • pmid:17085660
  • scopus:33751290510
  • pmid:17085660
ISSN
1078-0432
DOI
10.1158/1078-0432.CCR-06-1383
language
English
LU publication?
yes
additional info
The information about affiliations in this record was updated in December 2015. The record was previously connected to the following departments: Pathology (Malmö) (013031000), Division V (013230900), Pathology, (Lund) (013030000), Oncology, MV (013035000), Surgery (Lund) (013009000)
id
2b314d85-7ec9-45bb-b8fd-ee307ffdcb12 (old id 1136732)
date added to LUP
2016-04-01 12:35:37
date last changed
2020-08-05 02:03:35
@article{2b314d85-7ec9-45bb-b8fd-ee307ffdcb12,
  abstract     = {PURPOSE: A multitude of breast cancer mRNA profiling studies has stratified breast cancer and defined gene sets that correlate with outcome. However, the number of genes used to predict patient outcome or define tumor subtypes by RNA expression studies is variable, nonoverlapping, and generally requires specialized technologies that are beyond those used in the routine pathology laboratory. It would be ideal if the familiarity and streamlined nature of immunohistochemistry could be combined with the rigorously quantitative and highly specific properties of nucleic acid-based analysis to predict patient outcome. EXPERIMENTAL DESIGN: We have used AQUA-based objective quantitative analysis of tissue microarrays toward the goal of discovery of a minimal number of markers with maximal prognostic or predictive value that can be applied to the conventional formalin-fixed, paraffin-embedded tissue section. RESULTS: The minimal discovered multiplexed set of tissue biomarkers was GATA3, NAT1, and estrogen receptor. Genetic algorithms were then applied after division of our cohort into a training set of 223 breast cancer patients to discover a prospectively applicable solution that can define a subset of patients with 5-year survival of 96%. This algorithm was then validated on an internal validation set (n=223, 5-year survival=95.8%) and further validated on an independent cohort from Sweden, which showed 5-year survival of 92.7% (n=149). CONCLUSIONS: With further validation, this test has both the familiarity and specificity for widespread use in management of breast cancer. More generally, this work illustrates the potential for multiplexed biomarker discovery on the tissue microarray platform.},
  author       = {Dolled-Filhart, Marisa and Rydén, Lisa and Cregger, Melissa and Jirström, Karin and Harigopal, Malini and Camp, Robert L and Rimm, David L},
  issn         = {1078-0432},
  language     = {eng},
  number       = {21},
  pages        = {6459--6468},
  publisher    = {American Association for Cancer Research},
  series       = {Clinical Cancer Research},
  title        = {Classification of breast cancer using genetic algorithms and tissue microarrays},
  url          = {http://dx.doi.org/10.1158/1078-0432.CCR-06-1383},
  doi          = {10.1158/1078-0432.CCR-06-1383},
  volume       = {12},
  year         = {2006},
}