Additional file 4: of Increased DNA methylation variability in rheumatoid arthritis-discordant monozygotic twins

Table S3. Pathways enriched in differentially variable positions identified in RA-discordant twins, restricted to sites which were hypervariable in healthy co-twins. Pathway analysis was performed using the gometh function in the MissMethyl package. Pathways are ranked by p value (p < 0.05). (PDF 87 KB) (PDF 84 kb

Additional file 2: of Increased DNA methylation variability in rheumatoid arthritis-discordant monozygotic twins

Table S1. Full list of differentially variable positions (n = 1171) between RA-affected and non-RA twins. Probe names are shown, along with t-statistic p value, Bartlett’s test for differential variability, which group was hypervariable, and probe annotation. (PDF 340 KB) (PDF 334 kb

Additional file 3: of Increased DNA methylation variability in rheumatoid arthritis-discordant monozygotic twins

Table S2. Pathways enriched in differentially variable positions identified in RA-discordant twins. Pathway analysis was performed using the gometh function in the MissMethyl package. Pathways are ranked by p value (p < 0.05). (PDF 112 KB) (PDF 109 kb

Additional file 1: of Increased DNA methylation variability in rheumatoid arthritis-discordant monozygotic twins

Figure S1. Multidimensional scaling plot of DMARD use in RA-discordant twins. Figure S2. Cell composition estimates for RA-discordant twins. Figure S3. Feature enrichment for differentially variable positions. Figure S4. Feature enrichment for DVPs identified in both RA and type 1 diabetes disease-discordant twins. Figure S5. Variance and range for DVPs which were hypervariable in healthy co-twins. (PDF 423 KB) (PDF 410 kb

Supplementary Figure 5 from Epigenetics Markers of Metastasis and HPV-Induced Tumorigenesis in Penile Cancer

Supplementary Figure 5. A) Receiver Operator Curve (ROC), for the accuracy of the epigenetic lymph node prediction signature in cross validation. B) Examples of FLI1 and IRX4 immunohistochemical staining of a PeCa TMA, in samples showing either methylation.</p

Supplementary Table 2 from Epigenetics Markers of Metastasis and HPV-Induced Tumorigenesis in Penile Cancer

Supplementary Table 2. Primers sequences for A) HPV primers, B)analysis of CDO1 and AR gene expression and C) MSP analysis of HMX3, PPP2R5C, IRF4, FLI1.</p

Supplementary Figure 2 from Epigenetics Markers of Metastasis and HPV-Induced Tumorigenesis in Penile Cancer

Supplementary Figure 2. Comparison of gene expression between PeCa and matched normal tissue for PRC2 complex members</p

Supplementary Figure 4 from Epigenetics Markers of Metastasis and HPV-Induced Tumorigenesis in Penile Cancer

Supplementary Figure 4. Comparison of DMR profiles across canonical features for PeCa (green) and normal squamous epithelium (red), for three candidate epigenetically regulated genes involved in the development of penile cancer (AR &CDO1).</p

Table1_Donor whole blood DNA methylation is not a strong predictor of acute graft versus host disease in unrelated donor allogeneic haematopoietic cell transplantation.DOCX

Allogeneic hematopoietic cell transplantation (HCT) is used to treat many blood-based disorders and malignancies, however it can also result in serious adverse events, such as the development of acute graft-versus-host disease (aGVHD). This study aimed to develop a donor-specific epigenetic classifier to reduce incidence of aGVHD by improving donor selection. Genome-wide DNA methylation was assessed in a discovery cohort of 288 HCT donors selected based on recipient aGVHD outcome; this cohort consisted of 144 cases with aGVHD grades III-IV and 144 controls with no aGVHD. We applied a machine learning algorithm to identify CpG sites predictive of aGVHD. Receiver operating characteristic (ROC) curve analysis of these sites resulted in a classifier with an encouraging area under the ROC curve (AUC) of 0.91. To test this classifier, we used an independent validation cohort (n = 288) selected using the same criteria as the discovery cohort. Attempts to validate the classifier failed with the AUC falling to 0.51. These results indicate that donor DNA methylation may not be a suitable predictor of aGVHD in an HCT setting involving unrelated donors, despite the initial promising results in the discovery cohort. Our work highlights the importance of independent validation of machine learning classifiers, particularly when developing classifiers intended for clinical use.</p

Supplementary Figure 6 from Epigenetics Markers of Metastasis and HPV-Induced Tumorigenesis in Penile Cancer

Supplementary Figure 6. Heatmap of 6 HNSCC cell lines (in duplicate) showing the methylation of the 30 probe set HPV classifier</p