NSAIDs Modulate Clonal Evolution in Barrett's Esophagus

Cancer is considered an outcome of decades-long clonal evolution fueled by acquisition of somatic genomic abnormalities (SGAs). Non-steroidal anti-inflammatory drugs (NSAIDs) have been shown to reduce cancer risk, including risk of progression from Barrett's esophagus (BE) to esophageal adenocarcinoma (EA). However, the cancer chemopreventive mechanisms of NSAIDs are not fully understood. We hypothesized that NSAIDs modulate clonal evolution by reducing SGA acquisition rate. We evaluated thirteen individuals with BE. Eleven had not used NSAIDs for 6.2±3.5 (mean±standard deviation) years and then began using NSAIDs for 5.6±2.7 years, whereas two had used NSAIDs for 3.3±1.4 years and then discontinued use for 7.9±0.7 years. 161 BE biopsies, collected at 5–8 time points over 6.4–19 years, were analyzed using 1Million-SNP arrays to detect SGAs. Even in the earliest biopsies there were many SGAs (284±246 in 10/13 and 1442±560 in 3/13 individuals) and in most individuals the number of SGAs changed little over time, with both increases and decreases in SGAs detected. The estimated SGA rate was 7.8 per genome per year (95% support interval [SI], 7.1–8.6) off-NSAIDs and 0.6 (95% SI 0.3–1.5) on-NSAIDs. Twelve individuals did not progress to EA. In ten we detected 279±86 SGAs affecting 53±30 Mb of the genome per biopsy per time point and in two we detected 1,463±375 SGAs affecting 180±100 Mb. In one individual who progressed to EA we detected a clone having 2,291±78 SGAs affecting 588±18 Mb of the genome at three time points in the last three of 11.4 years of follow-up. NSAIDs were associated with reduced rate of acquisition of SGAs in eleven of thirteen individuals. Barrett's cells maintained relative equilibrium level of SGAs over time with occasional punctuations by expansion of clones having massive amount of SGAs

Genomic alterations indicate tumor origin and varied metastatic potential of disseminated cells from prostate-cancer patients

Disseminated epithelial cells can be isolated from the bone marrow of a far greater fraction of prostate-cancer patients than the fraction of patients who progress to metastatic disease. To provide a better understanding of these cells, we have characterized their genomic alterations. We first present an array comparative genomic hybridization method capable of detecting genomic changes in the small number of disseminated cells (10-20) that can typically be obtained from bone-marrow aspirates of prostate-cancer patients. We show multiple regions of copy-number change, including alterations common in prostate cancer, such as 8p loss, 8q gain, and gain encompassing the androgen-receptor gene on Xq, in the disseminated cell pools from 11 metastatic patients. We found fewer and less striking genomic alterations in the 48 pools of disseminated cells from patients with organ-confined disease. However, we identify changes shared by these samples with their corresponding primary tumors and prostate-cancer alterations reported in the literature, evidence that these cells, like those in advanced disease, are disseminated tumor cells (DTCs). We also demonstrate that DTCs from patients with advanced and localized disease share several abnormalities, including losses containing cell-adhesion genes and alterations reported to associate with progressive disease. These shared alterations might confer the capability to disseminate or establish secondary disease. Overall, the spectrum of genomic deviations is evidence for metastatic capacity in advanced-disease DTCs and variation in that capacity in DTCs from localized disease. Our analysis lays the foundation for elucidation of the relationship between DTC genomic alterations and progressive prostate cancer

A genome-wide association study identifies new susceptibility loci for esophageal adenocarcinoma and Barrett's esophagus.

Esophageal adenocarcinoma is a cancer with rising incidence and poor survival. Most such cancers arise in a specialized intestinal metaplastic epithelium, which is diagnostic of Barrett's esophagus. In a genome-wide association study, we compared esophageal adenocarcinoma cases (n = 2,390) and individuals with precancerous Barrett's esophagus (n = 3,175) with 10,120 controls in 2 phases. For the combined case group, we identified three new associations. The first is at 19p13 (rs10419226: P = 3.6 × 10(-10)) in CRTC1 (encoding CREB-regulated transcription coactivator), whose aberrant activation has been associated with oncogenic activity. A second is at 9q22 (rs11789015: P = 1.0 × 10(-9)) in BARX1, which encodes a transcription factor important in esophageal specification. A third is at 3p14 (rs2687201: P = 5.5 × 10(-9)) near the transcription factor FOXP1, which regulates esophageal development. We also refine a previously reported association with Barrett's esophagus near the putative tumor suppressor gene FOXF1 at 16q24 and extend our findings to now include esophageal adenocarcinoma

Protocol optimization of a targeted sequencing panel for genomic profiling of bronchoalveolar lavage fluid in lung cancer

Abstract Introduction We investigated a commercially available sequencing panel to study the effect of sequencing depth, variant calling strategy, and targeted sequencing region on identifying tumor‐derived variants in cell‐free bronchoalveolar lavage (cfBAL) DNA compared with plasma cfDNA. Methods Sequencing was performed at low or high coverage using two filtering algorithms to identify tumor variants on two panels targeting 77 and 197 genes respectively. Results One hundred and four sequencing files from 40 matched DNA samples of cfBAL, plasma, germline leukocytes, and archival tumor specimens in 10 patients with early‐stage lung cancer were analyzed. By low‐coverage sequencing, tumor‐derived cfBAL variants were detected in 5/10 patients (50%) compared with 2/10 (20%) for plasma. High‐coverage sequencing did not affect the number of tumor‐derived variants detected in either biospecimen type. Accounting for germline mutations eliminated false‐positive plasma calls regardless of coverage (0/10 patients with tumor‐derived variants identified) and increased the number of cfBAL calls (5/10 patients with tumor‐derived variants identified). These results were not affected by the number of targeted genes

Nightshift work, chronotype, and genome-wide DNA methylation in blood

Molecular mechanisms underlying the negative health effects of shift work are poorly understood, which remains a barrier to developing intervention strategies to protect the long-term health of shift workers. We evaluated genome-wide differences in DNA methylation (measured in blood) between 111 actively employed female nightshift and 86 actively employed female dayshift workers from the Seattle metropolitan area. We also explored the effect of chronotype (i.e., measure of preference for activity earlier or later in the day) on DNA methylation among 110 of the female nightshift workers and an additional group of 131 male nightshift workers. Methylation data were generated using the Illumina Infinium HumanMethylation450 BeadChip (450K) Array. After applying the latest methylation data processing methods, we compared methylation levels at 361,210 CpG loci between the groups using linear regression models adjusted for potential confounders and applied the false-discovery rate (FDR) ≤ 0.05 to account for multiple comparisons. No statistically significant associations at the genome-wide level were observed with shift work or chronotype, though based on raw P values and absolute effect sizes, there were suggestive associations in genes that have been previously linked with cancer (e.g., BACH2, JRK, RPS6KA2) and type-2 diabetes (e.g., KCNQ1). Given that our study was underpowered to detect moderate effects, examining these suggestive results in well-powered independent studies or in pooled data sets may improve our understanding of the pathways underlying the negative health effects of shift work and the influence of personal factors such as chronotype. Such an approach may help identify potential interventions that can be used to protect the long-term health of shift workers

Nightshift work and genome-wide DNA methylation

<div><p>The negative health effects of shift work, including carcinogenesis, may be mediated by changes in DNA methylation, particularly in the circadian genes. Using the Infinium HumanMethylation450 Bead Array (Illumina, San Diego, CA), we compared genome-wide methylation between 65 actively working dayshift workers and 59 actively working nightshift workers in the healthcare industry. A total of 473 800 loci, including 391 loci across the 12 core circadian genes, were analyzed to identify methylation markers associated with shift work status using linear regression models adjusted for gender, age, body mass index, race, smoking status and leukocyte cell profile as measured by flow cytometry. Analyses at the level of gene, CpG island and gene region were also conducted. To account for multiple comparisons, we controlled the false discovery rate (FDR ≤0.05). Significant differences between nightshift and dayshift workers were found at 16 135 of 473 800 loci, across 3769 of 20 164 genes, across 7173 of 22 721 CpG islands and across 5508 of 51 843 gene regions. For each significant loci, gene, CpG island or gene region, average methylation was consistently found to be decreased among nightshift workers compared to dayshift workers. Twenty-one loci located in the circadian genes were also found to be significantly hypomethylated among nightshift workers. The largest differences were observed for three loci located in the gene body of <i>PER3</i>. A total of nine significant loci were found in the <i>CSNK1E</i> gene, most of which were located in a CpG island and near the transcription start site of the gene. Methylation changes in these circadian genes may lead to altered expression of these genes which has been associated with cancer in previous studies. Gene ontology enrichment analysis revealed that among the significantly hypomethylated genes, processes related to host defense and immunity were represented. Our results indicate that the health effects of shift work may be mediated by hypomethylation of a wide variety of genes, including those related to circadian rhythms. While these findings need to be followed-up among a considerably expanded group of shift workers, the data generated by this study supports the need for future targeted research into the potential impacts of shift work on specific carcinogenic mechanisms.</p></div

Dual-strain genital herpes simplex virus type 2 (HSV-2) infection in the US, Peru, and 8 countries in sub-Saharan Africa: A nested cross-sectional viral genotyping study

<div><p>Background</p><p>Quantitative estimation of the extent to which the immune system’s protective effect against one herpes simplex virus type 2 (HSV-2) infection protects against infection with additional HSV-2 strains is important for understanding the potential for HSV-2 vaccine development. Using viral genotyping, we estimated the prevalence of HSV-2 dual-strain infection and identified risk factors.</p><p>Methods and findings</p><p>People with and without HIV infection participating in HSV-2 natural history studies (University of Washington Virology Research Clinic) and HIV prevention trials (HIV Prevention Trials Network 039 and Partners in Prevention HSV/HIV Transmission Study) in the US, Africa, and Peru with 2 genital specimens each containing ≥10⁵ copies herpes simplex virus DNA/ml collected a median of 5 months apart (IQR: 2–11 months) were included. It is unlikely that 2 strains would be detected in the same sample simultaneously; therefore, 2 samples were required to detect dual-strain infection. We identified 85 HSV-2 SNPs that, in aggregate, could determine whether paired HSV-2 strains were the same or different with >90% probability. These SNPs were then used to create a customized high-throughput array-based genotyping assay. Participants were considered to be infected with more than 1 strain of HSV-2 if their samples differed by ≥5 SNPs between the paired samples, and dual-strain infection was confirmed using high-throughput sequencing (HTS). We genotyped pairs of genital specimens from 459 people; 213 (46%) were men, the median age was 34 years (IQR: 27–44), and 130 (28%) were HIV seropositive. Overall, 272 (59%) people were from the US, 59 (13%) were from Peru, and 128 (28%) were from 8 countries in Africa. Of the 459 people, 18 (3.9%) met the criteria for dual-strain infection. HTS and phylogenetic analysis of paired specimens confirmed shedding of 2 distinct HSV-2 strains collected at different times in 17 pairs, giving an estimated dual-strain infection prevalence of 3.7% (95% CI = 2.0%–5.4%). Paired samples with dual-strain infection differed by a median of 274 SNPs in the U_L_U_S region (range 129–413). Matching our observed dual-strain infection frequency to simulated data of varying prevalences and allowing only 2 samples per person, we inferred the true prevalence of dual-strain infection to be 7%. In multivariable analysis, controlling for HIV status and continent of origin, people from Africa had a higher risk for dual-strain infection (risk ratio [RR] = 9.20, 95% CI = 2.05–41.32), as did people who were HIV seropositive (RR = 4.06, 95% CI = 1.42–11.56).</p><p>Conclusions</p><p>HSV-2 dual-strain infection was detected in 3.7% of paired samples from individual participants, and was more frequent among people with HIV infection. Simulations suggest that the true prevalence of dual-strain infection is 7%. Our data indicate that naturally occurring immunity to HSV-2 may be protective against infection with a second strain. This study is limited by the inability to determine the timing of acquisition of the second strain.</p></div

Clonal evolution in individuals b and j.

<p>(Panel A) Solid lines connect the mean amount of SGA detected across biopsies at each time point; dots correspond to individual biopsies. In individual b (black line), we observed evolutionary stasis with mean SGA remaining at 119±79 Mb over more than a decade of follow-up. In individual j (red line), a massive burst of SGA was detected in year 8.5; three years later individual j progressed to esophageal adenocarcinoma. Individual b started NSAIDs after year 5, while individual j started NSAIDs only after year 10. (Panels B and C) Circos plots showing genome-wide views of SGA over time. Each ring, labeled with a biopsy number, represents whole-genome SGA data from a different biopsy, with earlier samples toward the center. Thin black line rings separate endoscopies (time points), white background shows time periods off-NSAIDs and gray background shows time periods on-NSAIDs. Within the rings, black segments designate homozygous deletion, red single copy loss, orange copy-neutral LOH, and green copy gain. (Panel B) Circos plot of individual b. Note the appearance of “new” whole chromosome LOH at chromosome 6 and 11 in biopsy 5, taken during the off-NSAIDs period, and the detection of a minimally mutated clone in biopsies 9 and 7, taken during the on-NSAIDs period. (Panel C) Circos plot of individual j. A massive burst of SGAs was detected first in biopsy 8, in year 8.5, before the individual began regular NSAID use. Biopsy 2 (second inner ring), taken at the baseline endoscopy 8.5 years prior to the burst, shared a subset of the SGAs seen in the massively altered clone (chromosomes 10, 12, 17 and 18), and thus is likely an early example of its lineage. (Panels D and G) Consensus phylogenetic trees estimated by BEAST reveal long-term co-existence of clones. Branch lengths are scaled according to time, the tips of the phylogeny are biopsies aligned on the x-axis according to their sampling time, and all internal nodes are estimated coalescence times assuming a logistic population growth. Dashed gray line represents the start of NSAID use. (Panels E, F, H, I) Maximum parsimony trees estimated by PAUP reveal the ancestral relationships among biopsies based on shared SGA characters. Differences between the topology of the trees estimated by PAUP and BEAST are typically due to poorly supported short branches and do not affect the analyses of SGA acquisition rates. Branch lengths are scaled according to estimated number of SGAs (Panels E, H) or the amount of genome affected by SGA (Panels F, I). Note that these trees appear very different from those estimated by BEAST as the BEAST branch lengths are scaled by inferred time depth, and the rate of SGA accumulation appears highly variable with time.</p