Cargando…

A robust prognostic signature for hormone-positive node-negative breast cancer

BACKGROUND: Systemic chemotherapy in the adjuvant setting can cure breast cancer in some patients that would otherwise recur with incurable, metastatic disease. However, since only a fraction of patients would have recurrence after surgery alone, the challenge is to stratify high-risk patients (who...

Descripción completa

Detalles Bibliográficos
Autores principales: Griffith, Obi L, Pepin, François, Enache, Oana M, Heiser, Laura M, Collisson, Eric A, Spellman, Paul T, Gray, Joe W
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3961800/
https://www.ncbi.nlm.nih.gov/pubmed/24112773
http://dx.doi.org/10.1186/gm496
_version_ 1782308341290106880
author Griffith, Obi L
Pepin, François
Enache, Oana M
Heiser, Laura M
Collisson, Eric A
Spellman, Paul T
Gray, Joe W
author_facet Griffith, Obi L
Pepin, François
Enache, Oana M
Heiser, Laura M
Collisson, Eric A
Spellman, Paul T
Gray, Joe W
author_sort Griffith, Obi L
collection PubMed
description BACKGROUND: Systemic chemotherapy in the adjuvant setting can cure breast cancer in some patients that would otherwise recur with incurable, metastatic disease. However, since only a fraction of patients would have recurrence after surgery alone, the challenge is to stratify high-risk patients (who stand to benefit from systemic chemotherapy) from low-risk patients (who can safely be spared treatment related toxicities and costs). METHODS: We focus here on risk stratification in node-negative, ER-positive, HER2-negative breast cancer. We use a large database of publicly available microarray datasets to build a random forests classifier and develop a robust multi-gene mRNA transcription-based predictor of relapse free survival at 10 years, which we call the Random Forests Relapse Score (RFRS). Performance was assessed by internal cross-validation, multiple independent data sets, and comparison to existing algorithms using receiver-operating characteristic and Kaplan-Meier survival analysis. Internal redundancy of features was determined using k-means clustering to define optimal signatures with smaller numbers of primary genes, each with multiple alternates. RESULTS: Internal OOB cross-validation for the initial (full-gene-set) model on training data reported an ROC AUC of 0.704, which was comparable to or better than those reported previously or obtained by applying existing methods to our dataset. Three risk groups with probability cutoffs for low, intermediate, and high-risk were defined. Survival analysis determined a highly significant difference in relapse rate between these risk groups. Validation of the models against independent test datasets showed highly similar results. Smaller 17-gene and 8-gene optimized models were also developed with minimal reduction in performance. Furthermore, the signature was shown to be almost equally effective on both hormone-treated and untreated patients. CONCLUSIONS: RFRS allows flexibility in both the number and identity of genes utilized from thousands to as few as 17 or eight genes, each with multiple alternatives. The RFRS reports a probability score strongly correlated with risk of relapse. This score could therefore be used to assign systemic chemotherapy specifically to those high-risk patients most likely to benefit from further treatment.
format Online
Article
Text
id pubmed-3961800
institution National Center for Biotechnology Information
language English
publishDate 2013
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-39618002014-03-22 A robust prognostic signature for hormone-positive node-negative breast cancer Griffith, Obi L Pepin, François Enache, Oana M Heiser, Laura M Collisson, Eric A Spellman, Paul T Gray, Joe W Genome Med Research BACKGROUND: Systemic chemotherapy in the adjuvant setting can cure breast cancer in some patients that would otherwise recur with incurable, metastatic disease. However, since only a fraction of patients would have recurrence after surgery alone, the challenge is to stratify high-risk patients (who stand to benefit from systemic chemotherapy) from low-risk patients (who can safely be spared treatment related toxicities and costs). METHODS: We focus here on risk stratification in node-negative, ER-positive, HER2-negative breast cancer. We use a large database of publicly available microarray datasets to build a random forests classifier and develop a robust multi-gene mRNA transcription-based predictor of relapse free survival at 10 years, which we call the Random Forests Relapse Score (RFRS). Performance was assessed by internal cross-validation, multiple independent data sets, and comparison to existing algorithms using receiver-operating characteristic and Kaplan-Meier survival analysis. Internal redundancy of features was determined using k-means clustering to define optimal signatures with smaller numbers of primary genes, each with multiple alternates. RESULTS: Internal OOB cross-validation for the initial (full-gene-set) model on training data reported an ROC AUC of 0.704, which was comparable to or better than those reported previously or obtained by applying existing methods to our dataset. Three risk groups with probability cutoffs for low, intermediate, and high-risk were defined. Survival analysis determined a highly significant difference in relapse rate between these risk groups. Validation of the models against independent test datasets showed highly similar results. Smaller 17-gene and 8-gene optimized models were also developed with minimal reduction in performance. Furthermore, the signature was shown to be almost equally effective on both hormone-treated and untreated patients. CONCLUSIONS: RFRS allows flexibility in both the number and identity of genes utilized from thousands to as few as 17 or eight genes, each with multiple alternatives. The RFRS reports a probability score strongly correlated with risk of relapse. This score could therefore be used to assign systemic chemotherapy specifically to those high-risk patients most likely to benefit from further treatment. BioMed Central 2013-10-11 /pmc/articles/PMC3961800/ /pubmed/24112773 http://dx.doi.org/10.1186/gm496 Text en Copyright © 2013 Griffith et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research
Griffith, Obi L
Pepin, François
Enache, Oana M
Heiser, Laura M
Collisson, Eric A
Spellman, Paul T
Gray, Joe W
A robust prognostic signature for hormone-positive node-negative breast cancer
title A robust prognostic signature for hormone-positive node-negative breast cancer
title_full A robust prognostic signature for hormone-positive node-negative breast cancer
title_fullStr A robust prognostic signature for hormone-positive node-negative breast cancer
title_full_unstemmed A robust prognostic signature for hormone-positive node-negative breast cancer
title_short A robust prognostic signature for hormone-positive node-negative breast cancer
title_sort robust prognostic signature for hormone-positive node-negative breast cancer
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3961800/
https://www.ncbi.nlm.nih.gov/pubmed/24112773
http://dx.doi.org/10.1186/gm496
work_keys_str_mv AT griffithobil arobustprognosticsignatureforhormonepositivenodenegativebreastcancer
AT pepinfrancois arobustprognosticsignatureforhormonepositivenodenegativebreastcancer
AT enacheoanam arobustprognosticsignatureforhormonepositivenodenegativebreastcancer
AT heiserlauram arobustprognosticsignatureforhormonepositivenodenegativebreastcancer
AT collissonerica arobustprognosticsignatureforhormonepositivenodenegativebreastcancer
AT spellmanpault arobustprognosticsignatureforhormonepositivenodenegativebreastcancer
AT grayjoew arobustprognosticsignatureforhormonepositivenodenegativebreastcancer
AT griffithobil robustprognosticsignatureforhormonepositivenodenegativebreastcancer
AT pepinfrancois robustprognosticsignatureforhormonepositivenodenegativebreastcancer
AT enacheoanam robustprognosticsignatureforhormonepositivenodenegativebreastcancer
AT heiserlauram robustprognosticsignatureforhormonepositivenodenegativebreastcancer
AT collissonerica robustprognosticsignatureforhormonepositivenodenegativebreastcancer
AT spellmanpault robustprognosticsignatureforhormonepositivenodenegativebreastcancer
AT grayjoew robustprognosticsignatureforhormonepositivenodenegativebreastcancer