Jung et al. BMC Cancer 2012, 12:382 http://www.biomedcentral.com/1471-2407/12/382 Daejon, Korea). Dye-labeled DNA was purified with BioPrime spin columns (Invitrogen, Carlsbad, CA) and precipitated with 100 μg of human Cot-1 DNA (ConnectaGen, Seoul, Korea). The labeled DNA pellet was dissolved in 50 μl of DIG hybridization buffer (Roche, Mannheim, Germany), to which 600 μg of yeast t-RNA (Invitrogen) was added. The labeled DNA solu- tion was applied on the array and incubated for 48 hours at 37°C in a MAUI hybridization machine (BioMicro, Salt Lake City, UT). After washing the slides, arrays were scanned using a GenePix 4000B scanner (Axon Ins- truments, Sunnyvale, CA) and feature extraction was performed using GenePix Pro 6.0. Normalization and re- alignment of raw array CGH data were performed using the web-based array CGH analysis interface, Array- CyGHt [21]. A print-tip Loess normalization method was used and each probe was mapped according to its genomic location in the UCSC genome browser (Human NCBI36/hg18). In total, 24,107 probes were processed out of initial 26,616 probes. Array-CGH data for all 48 cancers are available through GEO (accession no GSE37839). Detection of recurrent copy number alterations The rank-segmentation statistical algorithm in NEXUS software v3.1 (BioDiscovery Inc., El Segundo, CA) was used to define CNAs of each sample. To optimize the al- gorithmic parameters for calling CNAs, 11 independent normal-to-normal hybridizations were performed (10 self-to-self and 1 male-to-female hybridizations). The parameters for defining CNAs were as follows: signifi- cance threshold = 5.0E-4; maximum contiguous probe spacing (Kbp) = 1000; minimum number of contiguous probes per CNA segment = 5; threshold of signal inten- sity ratio >0.2 on log2 scale for gains and < −0.3 on log2 scale for losses. After defining CNAs, RAR was deter- mined to be the chromosomal segment covering over- lapping CNAs that appeared in at least 30% of the samples with P < 0.05 in the discovery set (NEXUS soft- ware v3.1). High-level amplification (amplification here- after) was defined as a probe signal intensity ratio of 1.5 or higher on the log2 scale. Likewise, a homozygous de- letion (HD) was defined as a ratio of −1.5 or lower on the log2 scale. Genomic quantitative PCR analysis qPCR validation of the significant RARs was performed using genomic DNA extracted from the FFPE samples of 97 EBCs. As a diploid internal control, a genomic region on chromosome13 (13q32.1) that showed no genomic alteration in the array-CGH data was used. Details in- cluding primer information for targets and the diploid control locus are available in Additional file 1. Genomic qPCR was performed using the Mx3000P qPCR system Page 3 of 11 (Stratagene, La Jolla, CA), as described elsewhere [22]. In brief, a 10-μl real-time qPCR mixture containing 10 ng of genomic DNA, SYBR Premix Ex Taq II™ (TaKaRa Bio, Japan), 1× ROX, and 5 pmole of each primer was prepared. Thermal cycling conditions consisted of one cycle of 30 sec at 95°C followed by 45 cycles of 5 sec at 95°C, 10 sec at 55–61°C, and 20 sec at 72°C. All qPCR experiments were repeated three times and relative quantification was performed by the ΔΔCT method. When mean genomic dosage ratios of the region be- tween the target sample and female control DNA (ΔΔCT of target and internal control) were above two, the region was defined as a copy number gain. Association rule mining The association rule mining is used for finding interest- ing relations among variables in a database. In bioinfor- matics, the information metric was commonly used to assess the degree of “surprise” when a pattern actually occurs [23]. We used CPAR (Classification based on Pre- dictive Association Rules) [24] algorithm adopting the information metric which was implemented by the LUCS-KDD research group (http://www.csc.liv.ac.uk/ ~frans/KDD/Software). In CPAR, Laplace accuracy is used to measure the accuracy of rules. Given a rule r, Laplace accuracy is defined as follows: Laplace accuracyðrÞ ¼ ðNc þ 1Þ ðNtotal þ mÞ where m is the number of classes and Ntotal is the total number of examples that satisfy the rule’s body, among which Nc examples belong to the predicted class, C of the rule. Through the CPAR algorithm, association rules were generated between RAR markers and the survival status in the discovery set. Each RAR marker was coded as 0 or 1 based on the copy number status; 0 indicates no copy number variation and 1 indicates copy number change in the marker region. Likewise, the survival sta- tus was coded as 0 (dead) or 1 (alive). Statistical analysis To examine the clinicopathologic implications of RARs, five clinical parameters were used as categorical variables: age at diagnosis (<50 vs. ≥50 years), stage, ER status, PR status, and HER2 status. Differential distributions of RARs in each category were tested by a two-sided Fisher’s exact test. The false discovery rate (FDR) was used for multiple comparison correction. In univariate survival analysis, cu- mulative overall survival was calculated according to the Kaplan-Meier method. Differences in survival curves were assessed with the log-rank test. Cox regression was per- formed to identify RARs associated with prognosis after