Homogeneous datasets of triple negative breast cancers enable the identification of novel prognostic and predictive signatures.

BACKGROUND:Current prognostic gene signatures for breast cancer mainly reflect proliferation status and have limited value in triple-negative (TNBC) cancers. The identification of prognostic signatures from TNBC cohorts was limited in the past due to small sample sizes. METHODOLOGY/PRINCIPAL FINDING...

Full description

Bibliographic Details
Main Authors: Thomas Karn, Lajos Pusztai, Uwe Holtrich, Takayuki Iwamoto, Christine Y Shiang, Marcus Schmidt, Volkmar Müller, Christine Solbach, Regine Gaetje, Lars Hanker, Andre Ahr, Cornelia Liedtke, Eugen Ruckhäberle, Manfred Kaufmann, Achim Rody
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2011-01-01
Series:PLoS ONE
Online Access:https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/22220191/?tool=EBI
id doaj-11e2ff571171401c9ba3fb6bd4809bd3
record_format Article
spelling doaj-11e2ff571171401c9ba3fb6bd4809bd32021-03-03T20:30:48ZengPublic Library of Science (PLoS)PLoS ONE1932-62032011-01-01612e2840310.1371/journal.pone.0028403Homogeneous datasets of triple negative breast cancers enable the identification of novel prognostic and predictive signatures.Thomas KarnLajos PusztaiUwe HoltrichTakayuki IwamotoChristine Y ShiangMarcus SchmidtVolkmar MüllerChristine SolbachRegine GaetjeLars HankerAndre AhrCornelia LiedtkeEugen RuckhäberleManfred KaufmannAchim RodyBACKGROUND:Current prognostic gene signatures for breast cancer mainly reflect proliferation status and have limited value in triple-negative (TNBC) cancers. The identification of prognostic signatures from TNBC cohorts was limited in the past due to small sample sizes. METHODOLOGY/PRINCIPAL FINDINGS:We assembled all currently publically available TNBC gene expression datasets generated on Affymetrix gene chips. Inter-laboratory variation was minimized by filtering methods for both samples and genes. Supervised analysis was performed to identify prognostic signatures from 394 cases which were subsequently tested on an independent validation cohort (n = 261 cases). CONCLUSIONS/SIGNIFICANCE:Using two distinct false discovery rate thresholds, 25% and <3.5%, a larger (n = 264 probesets) and a smaller (n = 26 probesets) prognostic gene sets were identified and used as prognostic predictors. Most of these genes were positively associated with poor prognosis and correlated to metagenes for inflammation and angiogenesis. No correlation to other previously published prognostic signatures (recurrence score, genomic grade index, 70-gene signature, wound response signature, 7-gene immune response module, stroma derived prognostic predictor, and a medullary like signature) was observed. In multivariate analyses in the validation cohort the two signatures showed hazard ratios of 4.03 (95% confidence interval [CI] 1.71-9.48; P = 0.001) and 4.08 (95% CI 1.79-9.28; P = 0.001), respectively. The 10-year event-free survival was 70% for the good risk and 20% for the high risk group. The 26-gene signatures had modest predictive value (AUC = 0.588) to predict response to neoadjuvant chemotherapy, however, the combination of a B-cell metagene with the prognostic signatures increased its response predictive value. We identified a 264-gene prognostic signature for TNBC which is unrelated to previously known prognostic signatures.https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/22220191/?tool=EBI
collection DOAJ
language English
format Article
sources DOAJ
author Thomas Karn
Lajos Pusztai
Uwe Holtrich
Takayuki Iwamoto
Christine Y Shiang
Marcus Schmidt
Volkmar Müller
Christine Solbach
Regine Gaetje
Lars Hanker
Andre Ahr
Cornelia Liedtke
Eugen Ruckhäberle
Manfred Kaufmann
Achim Rody
spellingShingle Thomas Karn
Lajos Pusztai
Uwe Holtrich
Takayuki Iwamoto
Christine Y Shiang
Marcus Schmidt
Volkmar Müller
Christine Solbach
Regine Gaetje
Lars Hanker
Andre Ahr
Cornelia Liedtke
Eugen Ruckhäberle
Manfred Kaufmann
Achim Rody
Homogeneous datasets of triple negative breast cancers enable the identification of novel prognostic and predictive signatures.
PLoS ONE
author_facet Thomas Karn
Lajos Pusztai
Uwe Holtrich
Takayuki Iwamoto
Christine Y Shiang
Marcus Schmidt
Volkmar Müller
Christine Solbach
Regine Gaetje
Lars Hanker
Andre Ahr
Cornelia Liedtke
Eugen Ruckhäberle
Manfred Kaufmann
Achim Rody
author_sort Thomas Karn
title Homogeneous datasets of triple negative breast cancers enable the identification of novel prognostic and predictive signatures.
title_short Homogeneous datasets of triple negative breast cancers enable the identification of novel prognostic and predictive signatures.
title_full Homogeneous datasets of triple negative breast cancers enable the identification of novel prognostic and predictive signatures.
title_fullStr Homogeneous datasets of triple negative breast cancers enable the identification of novel prognostic and predictive signatures.
title_full_unstemmed Homogeneous datasets of triple negative breast cancers enable the identification of novel prognostic and predictive signatures.
title_sort homogeneous datasets of triple negative breast cancers enable the identification of novel prognostic and predictive signatures.
publisher Public Library of Science (PLoS)
series PLoS ONE
issn 1932-6203
publishDate 2011-01-01
description BACKGROUND:Current prognostic gene signatures for breast cancer mainly reflect proliferation status and have limited value in triple-negative (TNBC) cancers. The identification of prognostic signatures from TNBC cohorts was limited in the past due to small sample sizes. METHODOLOGY/PRINCIPAL FINDINGS:We assembled all currently publically available TNBC gene expression datasets generated on Affymetrix gene chips. Inter-laboratory variation was minimized by filtering methods for both samples and genes. Supervised analysis was performed to identify prognostic signatures from 394 cases which were subsequently tested on an independent validation cohort (n = 261 cases). CONCLUSIONS/SIGNIFICANCE:Using two distinct false discovery rate thresholds, 25% and <3.5%, a larger (n = 264 probesets) and a smaller (n = 26 probesets) prognostic gene sets were identified and used as prognostic predictors. Most of these genes were positively associated with poor prognosis and correlated to metagenes for inflammation and angiogenesis. No correlation to other previously published prognostic signatures (recurrence score, genomic grade index, 70-gene signature, wound response signature, 7-gene immune response module, stroma derived prognostic predictor, and a medullary like signature) was observed. In multivariate analyses in the validation cohort the two signatures showed hazard ratios of 4.03 (95% confidence interval [CI] 1.71-9.48; P = 0.001) and 4.08 (95% CI 1.79-9.28; P = 0.001), respectively. The 10-year event-free survival was 70% for the good risk and 20% for the high risk group. The 26-gene signatures had modest predictive value (AUC = 0.588) to predict response to neoadjuvant chemotherapy, however, the combination of a B-cell metagene with the prognostic signatures increased its response predictive value. We identified a 264-gene prognostic signature for TNBC which is unrelated to previously known prognostic signatures.
url https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/22220191/?tool=EBI
work_keys_str_mv AT thomaskarn homogeneousdatasetsoftriplenegativebreastcancersenabletheidentificationofnovelprognosticandpredictivesignatures
AT lajospusztai homogeneousdatasetsoftriplenegativebreastcancersenabletheidentificationofnovelprognosticandpredictivesignatures
AT uweholtrich homogeneousdatasetsoftriplenegativebreastcancersenabletheidentificationofnovelprognosticandpredictivesignatures
AT takayukiiwamoto homogeneousdatasetsoftriplenegativebreastcancersenabletheidentificationofnovelprognosticandpredictivesignatures
AT christineyshiang homogeneousdatasetsoftriplenegativebreastcancersenabletheidentificationofnovelprognosticandpredictivesignatures
AT marcusschmidt homogeneousdatasetsoftriplenegativebreastcancersenabletheidentificationofnovelprognosticandpredictivesignatures
AT volkmarmuller homogeneousdatasetsoftriplenegativebreastcancersenabletheidentificationofnovelprognosticandpredictivesignatures
AT christinesolbach homogeneousdatasetsoftriplenegativebreastcancersenabletheidentificationofnovelprognosticandpredictivesignatures
AT reginegaetje homogeneousdatasetsoftriplenegativebreastcancersenabletheidentificationofnovelprognosticandpredictivesignatures
AT larshanker homogeneousdatasetsoftriplenegativebreastcancersenabletheidentificationofnovelprognosticandpredictivesignatures
AT andreahr homogeneousdatasetsoftriplenegativebreastcancersenabletheidentificationofnovelprognosticandpredictivesignatures
AT cornelialiedtke homogeneousdatasetsoftriplenegativebreastcancersenabletheidentificationofnovelprognosticandpredictivesignatures
AT eugenruckhaberle homogeneousdatasetsoftriplenegativebreastcancersenabletheidentificationofnovelprognosticandpredictivesignatures
AT manfredkaufmann homogeneousdatasetsoftriplenegativebreastcancersenabletheidentificationofnovelprognosticandpredictivesignatures
AT achimrody homogeneousdatasetsoftriplenegativebreastcancersenabletheidentificationofnovelprognosticandpredictivesignatures
_version_ 1714822097475207168