Sun, Zhifu

Abstract Purpose: Discriminant markers for pancreatic cancer detection are needed. We sought to identify and validate methylated DNA markers for pancreatic cancer using next-generation sequencing unbiased by known targets. Experimental Design: At a referral center, we conducted four sequential case–control studies: discovery, technical validation, biologic validation, and clinical piloting. Candidate markers were identified using variance-inflated logistic regression on reduced-representation bisulfite DNA sequencing results from matched pancreatic cancers, benign pancreas, and normal colon tissues. Markers were validated technically on replicate discovery study DNA and biologically on independent, matched, blinded tissues by methylation-specific PCR. Clinical testing of six methylation candidates and mutant KRAS was performed on secretin-stimulated pancreatic juice samples from 61 patients with pancreatic cancer, 22 with chronic pancreatitis, and 19 with normal pancreas on endoscopic ultrasound. Areas under receiver-operating characteristics curves (AUC) for markers were calculated. Results: Sequencing identified >500 differentially hyper-methylated regions. On independent tissues, AUC on 19 selected markers ranged between 0.73 and 0.97. Pancreatic juice AUC values for CD1D, KCNK12, CLEC11A, NDRG4, IKZF1, PKRCB, and KRAS were 0.92*, 0.88, 0.85, 0.85, 0.84, 0.83, and 0.75, respectively, for pancreatic cancer compared with normal pancreas and 0.92*, 0.73, 0.76, 0.85*, 0.73, 0.77, and 0.62 for pancreatic cancer compared with chronic pancreatitis (*, P = 0.001 vs. KRAS). Conclusions: We identified and validated novel DNA methylation markers strongly associated with pancreatic cancer. On pilot testing in pancreatic juice, best markers (especially CD1D) highly discriminated pancreatic cases from controls. Clin Cancer Res; 21(19); 4473–81. ©2015 AACR.

BNLF2a is an Epstein‐Barr virus (EBV) immune evasion gene. Its protein is located in the endoplasmic reticulum (ER) membrane, and can inhibit the antigen transporting function of TAP, thereby perturbing the immune response to EBV in lytic and prelatent phase. In order to explore whether the polymorphism of BNLF2a gene has a role in different types of EBV associated tumors, we conducted complete sequencing of the gene BNLF2a in 408 cases of EBV positive tumors (76 lymphomas, 45 gastric carcinomas, and 85 nasopharyngeal carcinomas in northern China and 27 lymphomas, 30 gastric carcinomas, and 57 nasopharyngeal carcinomas in southern China) and throat washings from healthy individuals (39 in northern China and 49 in southern China). Two main variant types of BNLF2a were identified. Type BNLF2a‐A, which was similar to B95‐8, was dominant in all sub‐populations (66.7–100%) in this study. Type BNLF2a‐B was characterized by the mutations at position 8 and 40. The variation patterns of BNLF2a were significantly different between samples from northern and southern China ( P < 0.05), and between the tumors and healthy donor samples from the northern China ( P < 0.0167). Type BNLF2a‐B was more frequent in healthy donors of northern China (33.3%), and the proportion of this type was higher in the northern than in the southern NPCs. These data demonstrate that the BNLF2a gene is highly conserved, and its polymorphism is geographically restricted. Type BNLF2a‐B is more prevalent in northern China and may be less tumor transformative. J. Med. Virol. 87:1946–1952, 2015 . © 2015 Wiley Periodicals, Inc.

Abstract Motivation: Reference genome assemblies are subject to change and refinement from time to time. Generally, researchers need to convert the results that have been analyzed according to old assemblies to newer versions, or vice versa, to facilitate meta-analysis, direct comparison, data integration and visualization. Several useful conversion tools can convert genome interval files in browser extensible data or general feature format, but none have the functionality to convert files in sequence alignment map or BigWig format. This is a significant gap in computational genomics tools, as these formats are the ones most widely used for representing high-throughput sequencing data, such as RNA-seq, chromatin immunoprecipitation sequencing, DNA-seq, etc. Results: Here we developed CrossMap, a versatile and efficient tool for converting genome coordinates between assemblies. CrossMap supports most of the commonly used file formats, including BAM, sequence alignment map, Wiggle, BigWig, browser extensible data, general feature format, gene transfer format and variant call format. Availability and implementation: CrossMap is written in Python and C. Source code and a comprehensive user’s manual are freely available at: http://crossmap.sourceforge.net/. Contact: Kocher.JeanPierre@mayo.edu or wang.liguo@mayo.edu Supplementary information: Supplementary data are available at Bioinformatics online.

Abstract Summary: Reduced representation bisulfite sequencing (RRBS) is a cost-effective approach for genome-wide methylation pattern profiling. Analyzing RRBS sequencing data is challenging and specialized alignment/mapping programs are needed. Although such programs have been developed, a comprehensive solution that provides researchers with good quality and analyzable data is still lacking. To address this need, we have developed a Streamlined Analysis and Annotation Pipeline for RRBS data (SAAP-RRBS) that integrates read quality assessment/clean-up, alignment, methylation data extraction, annotation, reporting and visualization. This package facilitates a rapid transition from sequencing reads to a fully annotated CpG methylation report to biological interpretation. Availability and implementation: SAAP-RRBS is freely available to non-commercial users at the web site http://ndc.mayo.edu/mayo/research/biostat/stand-alone-packages.cfm. Contact: baheti.saurabh@mayo.edu or sun.zhifu@mayo.edu Supplementary Information: Supplementary data are available at Bioinformatics online.

Abstract Purpose: The aim of this study was to investigate the promoter hypermethylation as diagnostic markers to detect malignant prostate cells and as prognostic markers to predict the clinical recurrence of prostate cancer. Experimental Design: DNA was isolated from prostate cancer and normal adjacent tissues. After bisulfite conversion, methylation of 14,495 genes was evaluated using the Methylation27 microarrays in 238 prostate tissues. We analyzed methylation profiles in four different groups: (i) tumor (n = 198) versus matched normal tissues (n = 40), (ii) recurrence (n = 123) versus nonrecurrence (n = 75), (iii) clinical recurrence (n = 80) versus biochemical recurrence (n = 43), and (iv) systemic recurrence (n = 36) versus local recurrence (n = 44). Group 1, 2, 3, and 4 genes signifying biomarkers for diagnosis, prediction of recurrence, clinical recurrence, and systemic progression were determined. Univariate and multivariate analyses were conducted to predict risk of recurrence. We validated the methylation of genes in 20 independent tissues representing each group by pyrosequencing. Results: Microarray analysis revealed significant methylation of genes in four different groups of prostate cancer tissues. The sensitivity and specificity of methylation for 25 genes from 1, 2, and 4 groups and 7 from group 3 were shown. Validation of genes by pyrosequencing from group 1 (GSTP1, HIF3A, HAAO, and RARβ), group 2 (CRIP1, FLNC, RASGRF2, RUNX3, and HS3ST2), group 3 (PHLDA3, RASGRF2, and TNFRSF10D), and group 4 (BCL11B, POU3F3, and RASGRF2) confirmed the microarray results. Conclusions: Our study provides a global assessment of DNA methylation in prostate cancer and identifies the significance of genes as diagnostic and progression biomarkers of prostate cancer. Clin Cancer Res; 18(10); 2882–95. ©2012 AACR.