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Series GSE12071 Query DataSets for GSE12071
Status Public on Aug 29, 2008
Title Gene expression variation to predict 10-year survival in lymph-node-negative breast cancer
Organism Homo sapiens
Experiment type Expression profiling by array
Summary Expression profiling of breast cancer tumours, comparing 10 year survivors to deceased patients

Background
It is of great significance to find better markers to correctly distinguish between high-risk and low-risk breast cancer patients since the majority of breast cancer cases are at present being overtreated.

Methods
46 tumours from node-negative breast cancer patients were studied with gene expression microarrays. A t-test was carried out in order to find a set of genes where the expression might predict clinical outcome. Two classifiers were used to evaluate the gene lists on the different data sets, a correlation-based classifier and a VFI (Voting Features Interval) classifier. We then evaluated the predictive accuracy of this expression signature on tumour sets from two similar studies on lymph-node negative patients which had developed gene expression signatures superior to current methods in classifying node-negative breast tumours. These two signatures were also tested on our material.

Results
A list of 51 genes whose expression profiles could predict clinical outcome with high accuracy in our material (96% or 89% accuracy in cross-validation, depending on type of classifier) was developed. When tested on two independent data sets, the expression signature based on the 51 identified genes had good predictive qualities in one of the data sets (74% accuracy), whereas their predictive value on the other data set were poor, presumably due to the fact that only 23 of the 51 genes were found in that material. We also found that previously developed expression signatures could predict clinical outcome well to moderately well in our material (72% and 61%, respectively).

Conclusion
The list of 51 genes derived in this study might have potential for clinical utility as a prognostic gene set, and may include candidate genes of potential relevance for clinical outcome in breast cancer. According to the predictions by this expression signature, 30 of the 46 patients should have had different adjuvant treatment than they did.

Keywords: Expression Microarray, Lymph-node-negative Breast Cancer, Clinical Outcome, Classification
 
Overall design 23 fresh frozen primary node negative breast cancer tumours from 10 year survivors were compared to 23 tumours from deceased patients in order to find genes where the expression differed between the two groups

Microarrays were produced at the Swegene DNA Microarray Resource Center, Department of Oncology, Lund University, Sweden (http://swegene.onk.lu.se).

Frozen tumours were homogenized together with TRIzol Reagent (Invitrogen, Carlsbad, CA, USA). From the cell-suspension total RNA was extracted using RNeasy mini kit (Qiagen, Valencia, CA, USA) according to the manufacturer’s protocol. The quality of the RNA was evaluated with the Agilent 2100 BioAnalyser (Agilent Technologies, Palo Alto, CA, USA). Specimens where the 28S/16S ratio was lower than 1.0 or the RIN-value was lower than 6.7 were excluded from the study. For each sample, Cy3-dCTP (Amersham Biosciences, Buckinghamshire, UK) labelled probes were synthesized from 5 µg of the total tumour RNA and Cy5-dCTP (Amersham Biosciences) labelled reference were synthesized from 5 µg of commercial reference RNA (Universal Human Reference RNA, Stratagene, La Jolla, CA, USA) by reverse transcription. The probes were purified using ChipShot™ labelling cleanup system (Promega, Madison, WI, USA). The hybridizations were carried out using Pronto! Micro Array reagent systems (Corning Inc., Corning, NY, USA). For each sample, labelled tumour cDNA and reference cDNA were co-precipitated and hybridised to the microarray slide. The microarray slides were scanned with Agilent microarray scanner G2565AA (Agilent Technologies) and image analysis was performed using the Genepix 6.0.0.45 software (Axon Instruments, Union City, CA, USA).
 
Contributor(s) Karlsson E, Delle U, Helou K
Citation(s) 18778486
Submission date Jul 10, 2008
Last update date Mar 20, 2012
Contact name Elin Karlsson
E-mail(s) elin.karlsson@gu.se
Organization name Göteborg University
Department Oncology
Street address Blå stråket 2
City Göteborg
ZIP/Postal code SE-41345
Country Sweden
 
Platforms (1)
GPL5186 SWEGENE H_v3.0.1 35K
Samples (46)
GSM304824 10 year survivor, 4194
GSM304825 10 year survivor, 5550
GSM304826 10 year survivor, 6061
Relations
BioProject PRJNA113323

Download family Format
SOFT formatted family file(s) SOFTHelp
MINiML formatted family file(s) MINiMLHelp
Series Matrix File(s) TXTHelp

Supplementary file Size Download File type/resource
GSE12071_RAW.tar 150.6 Mb (http)(custom) TAR (of GPR)
Processed data included within Sample table

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