e89 Nucleic Acids Research, 2009, Vol. 37, No. 12 Table 2. Summary of signiﬁcant methylation changes listed in Supplementary Table 4 for breast cancera CGI (801) Non CGI (115) TSS associated (%) Methylated 166 (77.93) 3 (33.33) Demethylated 47 (22.07) 6 (66.64) Total 213 9 Non-TSS associated (%) Methylated 517 (87.92) 23 (21.70) Demethylated 71 (12.07) 83 (78.30) PAGE 8 OF 11 Total 588 106 aAll methylation and demethylation events from Supplemental Table 4 were broken down into categories of associated to a gene (TSS associated) or not (Non-TSS associated), and methylated or demethylated within each of these groups. Finally the probes are divided based on inclusiveness to a CpG island or the probes that are non-CGI probes. The total number is given for breast cancer comparison as well as the conversion to percentages in parentheses. compared to the normal, which was not digested by McrBC (no methylation). Fragment 5 that is far from the TSS is ampliﬁed in all samples including McrBC digested due to a lack of methylation and no cleavage by McrBC, demonstrating that the MTSS1 CpG island is methylated in primary tumors. DISCUSSION We have designed a methylation array and developed methods to detect CpG methylation. This methodology was performed on cell lines and measurements validated with more standard methods. Comparing the analysis of two cell lines by our methodology allowed us to test the accuracy by bisulﬁte sequencing fragments with diﬀeren- tial methylation between the two cell lines. We found no errors except for one fragment that was methylated but was not detected. This could have been a failure of the McrBC or the array detection. We suspect the oligonu- cleotide probe since as in all hybridization-based method, there are bound to be probes that do not report well. In comparison of our method with another array- based methods for the analysis of the same samples, we found good correspondence between the two methods. One gene we found particularly interesting, RELN, is fre- quently silenced in pancreatic cancer. Sato and colleagues demonstrate that it is not methylated in this particular pancreatic cancer cell line but our measurements suggest that it is methylated. Judging from its frequency of silenc- ing and our success in validation of our measurements it is likely that our version of the cell line has genetically, or in this case, epigenetically drifted from that used by Sato and colleagues. To determine if this was the case we validated our ﬁndings by bisulﬁte sequencing and determined that our version of the cell line had deviated and the array was correct in its measurement. In our analysis of tumors versus normal, as expected, the tumors had more variation than the normals. However, some tumors were remarkably similar while others very diﬀerent from their matched normal. More importantly, the unmatched tumors were for the most part similar in variation to normal as the matched tumors. Three breast tumors out of 40 and 2 ovarian tumors out of 11 were extremely diﬀerent from the nor- mals, which could be interesting in reference to clinical parameters or genome structure and we are investigating this further with sample sets of a larger size. We then developed statistical methods that could be used to identify those CpG islands that diﬀer in their methylation status in the tumors as compared to the normals. We have identiﬁed a number of CpG islands, both asso- ciated with gene TSSs or islands far from the TSS for any genes, which have altered methylation from both breast and ovarian tumors as compared to normal. However, we did not identify well-known tumor suppressors in the pri- mary tumor dataset such as p16. The lack of known tumor suppressors could be a fault in our analysis or our meth- ods. However, the frequency of methylation for many classically known tumor suppressors varies widely, some being as low as 15% (55–58). Using p16 as an example, analysis of the tumor samples with matched normals did not uncover tumor suppresors such as p16 and since we demonstrate the ability to detect p16 methylation in the cell line HuH7 (43) with known methylation it is likely that none of the tumors had methylation of p16. Therefore, although improvements are always possible, we feel that our technical and analytical methods are sound. Unfortunately, the tumor suppressor Rassf1a that is a common target of methylation in cancer is not measurable with the current array. The next generation array will include better design of CpG islands such as those proximal to the Rassf1A. One disadvantage of our method of analysis is that those genes whose CGIs are found with altered methyla- tion in few samples may not be identiﬁed. The MiR-196 loci on chromosome 17 is methylated in several of the breast cancers in our study as compared to matched nor- mals but overall is not signiﬁcant in the larger set. It has been previously shown that the miR-196 directs the cleav- age of HoxB8 mRNA (59), which appears to function in myeloid diﬀerentiation (60,61), however it is possible that it plays a role in the deregulation of breast cells in becom- ing cancer. Presently we are further developing our statis- tical methods to improve our level of detection. Of the islands associated with gene TSSs identiﬁed as methylated there are a number that have been found altered and/or have been shown by other to decrease in expression in cancer, such as MTSS1 (51–53). We found this island methylated in a number of the primary breast tumors and veriﬁed by McrBC PCR (54) that it was methylated in two of these (one such sample shown in Figure 4). Tumor suppressors that are methylated are often found in regions of loss of heterozygosity (LOH). It will be interesting to determine which of the genes found methylated are in regions of LOH. It is interesting that MTSS1 is found on the q arm of chromosome 8, which is