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Robles JTO Supplemental Digital Content 1 Supplementary Materials An Integrated Prognostic Classifier for Stage I Lung Adenocarcinoma based on mRNA, microRNA and DNA Methylation Biomarkers Ana I. Robles1, Eri Arai2, Ewy A. Mathé1, Hirokazu Okayama1, Aaron Schetter1, Derek Brown1, David Petersen3, Elise D. Bowman1, Rintaro Noro1, Judith A. Welsh1, Daniel C. Edelman3, Holly S. Stevenson3, Yonghong Wang3, Naoto Tsuchiya4, Takashi Kohno4, Vidar Skaug5, Steen Mollerup5, Aage Haugen5, Paul S. Meltzer3, Jun Yokota6, Yae Kanai2 and Curtis C. Harris1 Affiliations: 1Laboratory of Human Carcinogenesis, NCI-CCR, National Institutes of Health, Bethesda, MD 20892, USA. 2Division of Molecular Pathology, National Cancer Center Research Institute, Tokyo 104-0045, Japan. 3Genetics Branch, NCI-CCR, National Institutes of Health, Bethesda, MD 20892, USA. 4Division of Genome Biology, National Cancer Center Research Institute, Tokyo 104-0045, Japan. 5Department of Chemical and Biological Working Environment, National Institute of Occupational Health, NO-0033 Oslo, Norway. 6Genomics and Epigenomics of Cancer Prediction Program, Institute of Predictive and Personalized Medicine of Cancer (IMPPC), 08916 Badalona (Barcelona), Spain. List of Supplementary Materials Supplementary Materials and Methods Fig. S1. Hierarchical clustering of based on CpG sites differentially-methylated in Stage I ADC compared to non-tumor adjacent tissues. Fig. S2. Confirmatory pyrosequencing analysis of DNA methylation at the HOXA9 locus in Stage I ADC from a subset of the NCI microarray cohort. 1 Fig. S3. Methylation Beta-values for HOXA9 probe cg26521404 in Stage I ADC samples from Japan. Fig. S4. Kaplan-Meier analysis of HOXA9 promoter methylation in a published cohort of Stage I lung ADC (J Clin Oncol 2013;31(32):4140-7). Fig. S5. Kaplan-Meier analysis of a combined prognostic biomarker in Stage I lung ADC. Table S1. CpG sites differentially-methylated in Stage I ADC compared to non-tumor adjacent tissues. Table S2. Functional characterization of genes hypermethylated in tumors using Gene Ontology and INTERPRO. Table S3. Functional characterization of genes hypomethylated in tumors using Gene Ontology and INTERPRO. Table S4. Summary of Ingenuity Pathway Analysis of genes differentially methylated in tumors. Table S5. Gene Set Enrichment Analysis of genes differentially methylated in tumors from the NCI microarray cohort. Table S6. Gene Set Enrichment Analysis of genes differentially methylated in tumors from the Japan microarray cohort. Table S7. Hypermethylated probe sets corresponding to genes marked by H3K27me3 in ESC. Table S8. Association between methylation cluster and clinical-demographic variables in the NCI microarray cohort. Table S9. Gene expression differences between high and low methylation clusters in the NCI microarray cohort. Table S10. Gene Set Enrichment Analysis of genes differentially expressed between high and low methylation clusters in the NCI microarray cohort. Table S12. miRNA expression differences between high and low methylation clusters in the NCI microarray cohort. Table S13. Univariable and Multivariable Cox Regression of HOXA9 promoter methylation in two cohorts. Table S14. Univariable and Multivariable Cox Regression of 4-protein-coding gene classifier, miR-21 expression and HOXA9 promoter methylation in two cohorts and their overall combination. Table S15. Univariable and Multivariable Cox Regression of High combined 4-gene classifier, miR-21 expression and HOXA9 methylation in the combined NCI/Norway and Japan cohorts. 2 Supplementary Materials and Methods DNA methylation array data preprocessing Fluorescent signals were collated via Bead Studio software and converted into β-values (β = ratio of signal from methylated probe relative to the sum of both methylated and unmethylated probes), which, ranging from 0 to 1, reflect the methylation level at each single CpG site. IlluminaBead Studio software output data were normalized using SSN (simple scaling normalization), to correct for dye bias and transformed into M-values (M = log2 ratio of signal from methylated probe signal relative to unmethylated probe), using the R package lumi. Partek Genomics Suite 6.6 was used for visualization and analysis. As an initial quality control, the accurate discrimination of males and females based on X chromosome methylation markers was confirmed. Probe sets corresponding to CpG loci on Xchr and Ychr (1093 probe sets) were excluded from further analysis. Additionally, 616 CpG loci with associated p-values > 0.05, indicative of poor hybridization quality, in > 10% samples, were also excluded, as were 4050 probes containing a known single nucleotide polymorphism, with MAF > 0.05, leaving 22,008 autosomal probes for analysis of differential methylation. Initial exploratory visualization by Principal Component Analysis (PCA) identified a large effect of experimental batch on data distribution. Therefore, visualizations and statistical tests were performed on batch-adjusted data using the “Batch remove” function in Partek. Tumor/non-tumor was the most important source of variation in the data, after batch-adjustment. Differential methylation was assessed by FDR-adjusted paired t-test. mRNA array data preprocessing Raw Data was preprocessed using Bioconductor’s “lumi” package in R. Data from samples with good overall signal intensity were uploaded to BRB-ArrayTools for normalization by robust spline normalization (RSN). BRB-ArrayTools is an Excel package developed by Dr. Richard Simon and the BRB-ArrayTools Development Team. miRNA array data preprocessing nCounter RCC files were imported into nSolver (Nanostring). Samples with good overall signal were normalized to the geometric mean of the top 100 expressed miRNAs within each sample. Normalized probes were imported into Partek Genomics Suite. Further, miRNAs were deemed absent if intensity < 10, and excluded from the analysis if they were not present in at least 40% of samples, leaving 424 miRNAs for further analysis. Initial exploratory visualization by PCA identified a large effect of experimental batch on data distribution. For analysis of differential miRNA expression and visualization, the effect of batch was eliminated using the “Batch Remove” function in Partek. Tumor/non-tumor tissue was the most important source of variation in the data, after batch-adjustment. 3 T CG N non-CGI -2.0 0 Fig. S1. Hierarchical clustering of based on CpG sites differentially methylated in Stage I ADC compared to non-tumor adjacent tissues. Each row represents an individual patient and each column an individual CpG probe. T: Tumor tissue; NT: non-tumor adjacent tissue; CGI: CpG Island; non-CGI: non-CpG Island. 4 cohort. microarray Fig. S2. HOXA9 Mean Methylation (%) 40 60 80 20 0 A Confirmatory pyrosequencing analysis of DNA methylation at the HOXA9 locus in Stage IADC locus fro of the HOXA9 analysis DNA at Confirmatory pyrosequencing methylation Tumor p = 0 . Non-tumor 0 0 0 4 B miR-196b Mean Methylation (%) 20 40 60 80 0 Tumor p = 0 . Non-tumor 0 0 0 1 m a subsetof the NCI 5 p < 0.0001 0.8 0.6 e u l a v 0.4 - a t e B 0.2 0.0 umor umor T Non-T Fig. S3. Methylation Beta-values for HOXA9 probe cg26521404 in Stage I ADC samples from Japan. 6 cg16104915 1.0 Unmethylated (n=107) ) n o i Methylated (n=10) t r o 0.8 p o r p ( l a 0.6 v i v r u S 0.4 e e r F - e s 0.2 p a l e R Log-rank P = 0.0002 0.0 0 5 10 15 20 Time to Recurrence (years) Fig. S4. Kaplan-Meier analysis of HOXA9 promoter methylation in a published cohort of Stage I lung ADC (J Clin Oncol 2013;31(32):4140-7). 7 Stage I Stage IA Stage IB 100 100 100 ) ) ) % % % ( ( ( l l l a a 80 80 a 80 v v v i i i v v v r r r u u u s s 60 60 s 60 c c c i i i f f f i i i c c c e e 40 40 e 40 p p p s s s - - - r r 0 (n=8) 0 (n=24) 0 (n=16) r e e 20 20 e 1 (n=12) c c 20 1 (n=30) 1 (n=16) c n n 2 (n=30) 2 (n=18) n 2 (n=9) a NCI/Norway a trend P = 0.003 trend P = 0.004 a 3 (n=2) trend P = 0.01 C C 3 (n=7) 3 (n=4) C 0 0 0 0 20 40 60 0 20 40 60 0 20 40 60 Time after surgery (months) Time after surgery (months) !"#$%&$'()*$ Time after surgery (months) !"#$%&$'()*$ !"#$%&$'()*$ +$$$$$$$$$$$$$$0 $$$$$$$$$$$$$$$$$$$$$$$$$$3 $$$$$$$$$$$$$$2 $$$$$$$$$$$$$$$$$$$$$$$$$$-$ +$$$$$$$$$$$$$$$$.2 $$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$.2 $$$$$$$$$$$$$$$$$$.2 $$$$$$$$$$$$$$$$$$$$$$$$$$$$$$.+$ + $,- $$$$$$$$$$$$,, $$$$$$$$$$$$$$$$$$$$$$$$$,. $$$$$$$$$$$$$.-$ .$$$$$$$$$$$$$., $$$$$$$$$$$$$$$$$$$$$$$$$.. $$$$$$$$$$$$$$0 $$$$$$$$$$$$$$$$$$$$$$$$$$1$ . $/+ $$$$$$$$$$$$,/ $$$$$$$$$$$$$$$$$$$$$$$$$.0 $$$$$$$$$$$$$.1$ .$$$$$$$$$$$$$$$$.2 $$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$., $$$$$$$$$$$$$$$$$$.+ $$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$4$ ,$$$$$$$$$$$$$$4 $$$$$$$$$$$$$$$$$$$$$$$$$$3 $$$$$$$$$$$$$$1 $$$$$$$$$$$$$$$$$$$$$$$$$$-$ ,$$$$$$$$$$$$$$$$.0 $$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$.1 $$$$$$$$$$$$$$$$$$.. $$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$0$ , $/+ $$$$$$$$$$$$,/ $$$$$$$$$$$$$$$$$$$$$$$$$.2 $$$$$$$$$$$$$./$ /$$$$$$$$$$$$$$, $$$$$$$$$$$$$$$$$$$$$$$$$. $$$$$$$$$$$$$$. $$$$$$$$$$$$$$$$$$$$$$$$$$+$ / $$$3 $$$$$$$$$$$$$$/ $$$$$$$$$$$$$$$$$$$$$$$$$$$, $$$$$$$$$$$$$$$+$ /$$$$$$$$$$$$$$$$$$- $$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$, $$$$$$$$$$$$$$$$$$$$. $$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$+$ 100 100 100 ) ) ) % % % ( ( ( l l 80 80 l 80 a a a v v v i i i v v v r r r u 60 u 60 u 60 s s s e e e e e e r r r f f 40 40 f 40 - - - e e e Japan s s 0 (n=26) 0 (n=17) s 0 (n=9) p p p a a 1 (n=36) a 1 (n=6) l 1 (n=30) l 20 20 l 20 e e 2 (n=28) 2 (n=22) e 2 (n=6) R R 3 (n=23) trend P < 0.0001 3 (n=12) trend P = 0.003 R 3 (n=11) trend P = 0.005 0 0 0 0 20 40 60 0 20 40 60 0 20 40 60 Time after surgery
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