e89 Nucleic Acids Research, 2009, Vol. 37, No. 12 PAGE 6 OF 11 Figure 3. The mean correlation for each sample with the normal sam- ples (mean of 12 correlations for each tumor and 11 correlations for each normal) is shown. The 12 samples to the left of the vertical line are the normals. The 40 to the right are tumors. The horizontal line is drawn at the minimum mean correlation for the normals with normals. It is apparent that the tumors are on the whole distinct from the normals in comparison with each other (P < 10E–017). (Supplementary Table 2, panel A). Of the 26, 17 regions were properly identiﬁed with methylation in PANC1 or both. Four regions identiﬁed as methylated by Sato and colleagues were not detected in our assay. There was dis- agreement in four regions. One gene RELN, which was reported as not methylated in either sample by Sato and colleagues, was found methylated in PANC1 cells. Interestingly, this gene is frequently methylated and silenced in pancreatic tumors (45), suggesting that our cell line may have genetically drifted over time apart from that used by Sato and colleagues. With our method we can obtain positional information for the region of the CpG island methylated and for all methyla- tion events the region is very close to the TSS, the region critical for suppression of transcription. To determine the accuracy of methylation detection as well as the positional prediction we performed sodium bisulﬁte sequencing on the fragment, which was detected as methylated in the RELN CpG island in the PANC1 cell line. Interestingly, we found that the CpG island fragment at position chr7:103223731–103223823 is methylated (Supplementary Table 2, panel B) demonstrating that the method can accurately identify methylation and that positional infor- mation is also available. In addition we have determined that our line of PANC1 cells has deviated from the line used by SATO and colleagues. The standard method to analyze tumor-methylation proﬁling is to utilize matched normal-tumor samples, and we have done this for 12 breast tumor and normal pairs. As with many tumor banks, ours contain many unmatched tumor samples. To determine if unmatched tumor-normal pairs could be analyzed ﬁrst we compared our results obtained with matched samples to those obtained with unmatched samples as a set and measured the degree of common alterations. We developed statisti- cal criteria for identifying CpG-islands most signiﬁcantly altered between tumor and normal (see ‘Methods for details’ section). Our analysis identiﬁed considerable over- lap (30%) of CpG-islands signiﬁcantly altered (Supplementary Table 3) between a matched tumor- normal set (12 normals, 12 tumors) compared to an unmatched set (12 normals, 28 tumors). Since all tumor samples are diﬀerent, similarity among a set could be a measure of tumor selection. We determined how diﬀerent all 40 tumors were from the normal samples. We com- puted pair-wise correlations between each normal sample and the other normals and each tumor sample to each normal. From this data we then computed the mean cor- relation between each sample and the normal samples. The mean correlation from normals to normals is 0.93, from tumors to normals is .88 (P < 10E–017). These data (plotted in Figure 3) show that tumors are diﬀerent from matched or unmatched normals. However, while some tumors are vastly diﬀerent from normal several are more normal like in their methylation proﬁle. In the future it will be interesting to have more detailed clinical infor- mation to determine if methylation proﬁle can determine speciﬁc clinical parameters. Thus, we moved on to the analysis of a larger set of unmatched tumors and normals. We then examined the set of 40 breast tumors, com- pared to 12 normal breast samples and 11 ovarian tumors compared to 7 normal ovarian samples (33). Using the statistical criteria developed (detailed in the ‘Methods’ section) we obtained a list of 916 signiﬁcant alterations in breast cancer and 151 in ovarian cancer and have listed these by their genomic location (see Supplementary Table 4 for entire list and Table 2 for a summary of whether the alterations were found with more methylation or less methylation than normal, based on speciﬁc classes, non-promoter associated or promoter associated as well as non-island regions). In Table 1 we highlight genes selected based on functional information, many of which have previously been documented to alter methylation in cancer again demonstrating the ability of this methodology to detect CpG methylation. Genes iden- tiﬁed hypermethylated in both ovarian and breast tumors include several HOX genes and protocadherins, known to be methylated in many tumor types (46–49). In addition to genes known to undergo methylation we have found new targets of methylation. For example, we detected for pro- moter methylation of a micro-RNA gene has-mir-9-3, occurring in more than half the breast tumors. This miRNA has been shown to be downregulated transcrip- tionally in thyroid cancers (50). Another gene identiﬁed as methylated in the breast tumors, MTSS1 (metastasis suppressor 1) (Table 1) is known to be preferentially methylated in several cancers including breast cancer (51–53). To determine if our ana- lytical methods identiﬁed this gene accurately, two pri- mary tumors were identiﬁed with methylation from the dataset for further validation. Being that they were not cell lines we only had 25 ng of material that we wanted to check multiple times. Although bisulﬁte sequencing is generally used for validation we wished to use an alterna- tive due to the small amount of genomic DNA remaining.