Bisulfite pyrosequencing results of 10 selected genes in 6 bladder cancer cell lines and 26 primary bladder tumors are shown in . Eight genes (A2BP1, NPTX2, SOX11, PENK, NKX6-2, DBC1, MYO3A and CA10) were highly methylated in bladder tumors and had very low levels of methylation in normal leukocytes. Their methylation frequencies in 26 primary bladder cancer were 62%, 88%, 77%, 92%, 69%, 69%, 65% and 85%, respectively. Two genes, HSPB9 and NPY2R were highly methylated in both bladder tumors and normal leukocyte DNA.
Figure 1 Scatter plot of bisulfite pyrosequencing results of candidate genes in normal bladders (NB), normal leukocytes (NL), 6 bladder tumor cell lines (BTC) and 26 primary bladder tumors (BCa). We used a mixture of normal bladder DNA from 3 persons (2 males (more ...)
To apply this gene panel to early detection, we first analyzed DNA methylation by bisulfite pyrosequencing in urine sediments for two genes, SOX11 and HSPB9 as examples. SOX11 showed increased methylation in urine from bladder cancer patients compared to control but the differences were small, in part due to the relatively high background of pyrosequencing (5%). HSPB9 was highly methylated in the urine sediment of controls, and similarly methylated (though more variable) in the urine sediment of patients (Supplementary Figure 2
). These results are consistent with the fact that urine sediment DNA contains a high proportion of leukocyte-derived DNA (even in patients with cancer) and that detection of cancer would require more sensitive and clear cutoff point methods to detect a low frequency of tumor-derived DNA. We therefore applied the qMSP method to overcome these problems and analyzed the 8 genes (DBC1, MYO3A, SOX11, NPTX2, NKX6-2, A2BP1, PENK and CA10) which had low levels of methylation in normal leukocytes.
Overall, we studied urine sediments from 128 bladder cancer patients (median age 69) and 110 control subjects (median age 67) ( and Supplementary Table 3
). The bladder cancer patients consisted of 58 cases of non-muscle invasive tumors (30 cases of pTa, 5 cases of Tis and 23 cases of T1) and 70 cases of muscle invasive tumors (62 cases of T2, 6 cases of T3 and 2 cases of T4). Most (87%) of them were of TCC type. Control subjects consisted of 71 cases of benign urologic symptoms, 39 normal controls including 5 healthy volunteers (Supplementary Table 3
). The distribution of qMSP results of each gene in urine sediments is shown in . It is obvious that all 8 genes show substantially and significantly more methylation in tumor cases than controls.
Demographic and clinical characteristics of the bladder cancer patients (n=128)
Figure 2 Graph of qMSP results of each gene in urine sediments of bladder cancer patients (n=128) and controls (n=110). The relative level of methylated DNA is depicted as 40-dCt[Ct of specific gene – Ct of mC-LESS (internal control)]. A higher 40-dCt (more ...)
We evaluated the power of each methylation marker by calculating the area under curve (AUC) of receiver operating characteristic (ROC) using total data set of 128 tumors and 110 controls. A random marker unrelated to bladder cancer is expected to have an AUC value of 0.5. The AUC values for the eight methylation markers we selected in the order from high to low are MYO3A (AUC=0.841, P <0.0001), CA10 (AUC=0.835, P <0.0001), NKX6-2 (AUC=0.823, P <0.0001), PENK (AUC=0.802, P <0.0001), SOX11 (AUC=0.797, P <0.0001), DBC1 (AUC=0.774, P <0.0001), NPTX2 (AUC=0.747, P <0.0001) and A2BP1 (AUC=0.710, P <0.0001). We also performed a correlation analysis for all pairs of markers (). All pairs of methylation level of genes were correlated with statistical significance (P<0.0001).
Spearman correlation of methylation level of each gene of DNA in urine sediments
To develop a multi-gene predictive model, we used a combinatorial analysis of methylation of 8 biomarkers. In this analysis, a model including 4 genes, MYO3A + CA10 + NKX6-2 + DBC1 or MYO3A + CA10 + NKX6-2 + SOX11 yielded an AUC of 0.939 (95% CI = 0.901 to 0.966, P <0.0001) for the set [tumor patients urine (TU) = 128 and controls urine (NU) = 110]. The models including 5 genes, MYO3A + CA10 + NKX6-2 + DBC1+ SOX11 or MYO3A + CA10 + NKX6-2 + DBC1 + PENK yielded the same AUC of 0.939 (95% CI = 0.901 to 0.966, P <0.0001).
The performances of single and combined qMSP markers for detection of bladder cancer in urine sediments are shown in . Comparison of ROC curve of the panel of combined markers is shown in . In the panel of 3 genes (MYO3A + CA10 + NKX6-2), if a urine sample has 2 or 3 genes methylation, the sensitivity was 86 % (95% CI = 78.7 to 91.4, P <0.0001) and specificity 93% (95% CI = 86.2 to 96.8, P <0.0001) for detection of bladder tumors and an AUC of 0.933 (CI = 0.894 to 0.962, P <0.0001). In the models of 4 gene panel (MYO3A + CA10 + NKX6-2 + DBC1 or MYO3A + CA10 + NKX6-2 + SOX11), if a urine sample has 3 or 4 genes methylation, the sensitivity was 81% (95% CI = 73.4 to 87.6, P <0.0001) and specificity 97% (95% CI = 92.2 to 99.4, P <0.0001). In the models of 5 gene panel (MYO3A + CA10 + NKX6-2 + DBC1 + SOX11 or MYO3A + CA10 + NKX6-2 + DBC1 + PENK), if a sample has 3 or more than 3 gene methylation, the sensitivity was 85% (95% CI = 77.8 to 90.8, P <0.0001) and specificity 95% (95% CI = 88.5 to 98.0, P <0.0001). Panels of 4 or 5 selected methylation markers had the same AUC of 0.939 and showed the best accuracy of detection of bladder cancer in urine sediments ().
Diagnostic information of single or combined qMSP markers for detection of bladder cancer in urine sediments from 128 bladder tumor patients and 110 control subjects
Figure 3 Receiver operating characteristics (ROC) for bladder cancer detection of the combined dataset (TU = 128 and NU = 110). A. ROC curves of the biomarkers sets (2-5 markers) that showed the highest AUC. Detailed information of the best combined markers was (more ...)
Analyzing by stage of bladder cancer using the 5-gene panel, detection rate based on 3 gene methylation or greater was 5 of 5 (100 %) in Tis, 21 of 30 (70 %) in pTa, 21 of 23 (91 %) in T1, 54 of 62 (87 %) in T2 and 8 of 8 (100 %) in T3/T4 (). Thus, cancer could be detected at a sensitivity of 81 % and a specificity of 95 % and an AUC of 0.911 (95% CI = 0.857 to 0.949, P<0.0001) in non-muscle invasive stage bladder cancer (Tis-pTa-T1 stages) and a sensitivity of 90 % and a specificity of 95 % and an AUC of 0.962 (95% CI = 0.923 to 0.985, P<0.0001) in invasive stage bladder cancer (T2-T3-T4 stages) (). When classified by grade, detection rate by the 5-gene panel was 95 out of 111 (86 %) in grade 3, 8 out of 13 (61 %) in grade 2 and 1 out of 3 (33%) in grade 1. The 4-gene panel had a sensitivity/specificity of 76%/97% (AUC=0.913, P <0.0001) in Tis-pTa-T1 stages and 86%/97% (AUC=0.961, P <0.0001) in T2-T3-T4 stages. Analyzing the 2 control groups using the 5-gene panel, the benign urological group (n=71) had 3 false positive cases and the non urologic control group (n=39) had 3 false positive cases. There were no significant differences between the benign urological group and the normal control group. The detection rate of primary and recurrent cancers using the 5-gene panel was identical [75/88 (85%) and 34/40 (85%), respectively].