Somatic whole genome dynamics of precancer in Barrett's esophagus reveals features associated with disease progression.

While the genomes of normal tissues undergo dynamic changes over time, little is understood about the temporal-spatial dynamics of genomes in premalignant tissues that progress to cancer compared to those that remain cancer-free. Here we use whole genome sequencing to contrast genomic alterations in 427 longitudinal samples from 40 patients with stable Barrett's esophagus compared to 40 Barrett's patients who progressed to esophageal adenocarcinoma (ESAD). We show the same somatic mutational processes are active in Barrett's tissue regardless of outcome, with high levels of mutation, ESAD gene and focal chromosomal alterations, and similar mutational signatures. The critical distinction between stable Barrett's versus those who progress to cancer is acquisition and expansion of TP53-/- cell populations having complex structural variants and high-level amplifications, which are detectable up to six years prior to a cancer diagnosis. These findings reveal the timing of common somatic genome dynamics in stable Barrett's esophagus and define key genomic features specific to progression to esophageal adenocarcinoma, both of which are critical for cancer prevention and early detection strategies.

Elephant genotypes reveal the size and connectivity of transnational ivory traffickers.

Transnational ivory traffickers continue to smuggle large shipments of elephant ivory out of Africa, yet prosecutions and convictions remain few. We identify trafficking networks on the basis of genetic matching of tusks from the same individual or close relatives in separate shipments. Analyses are drawn from 4,320 savannah (Loxodonta africana) and forest (L. cyclotis) elephant tusks, sampled from 49 large ivory seizures totalling 111 t, shipped out of Africa between 2002 and 2019. Network analyses reveal a repeating pattern wherein tusks from the same individual or close relatives are found in separate seizures that were containerized in, and transited through, common African ports. Results suggest that individual traffickers are exporting dozens of shipments, with considerable connectivity between traffickers operating in different ports. These tools provide a framework to combine evidence from multiple investigations, strengthen prosecutions and support indictment and prosecution of transnational ivory traffickers for the totality of their crimes.

Within-patient phylogenetic reconstruction reveals early events in Barrett's Esophagus.

Barrett's Esophagus is a neoplastic condition which progresses to esophageal adenocarcinoma in 5% of cases. Key events affecting the outcome likely occur before diagnosis of Barrett's and cannot be directly observed; we use phylogenetic analysis to infer such past events. We performed whole-genome sequencing on 4-6 samples from 40 cancer outcome and 40 noncancer outcome patients with Barrett's Esophagus, and inferred within-patient phylogenies of deconvoluted clonal lineages. Spatially proximate lineages clustered in the phylogenies, but temporally proximate ones did not. Lineages with inferred loss-of-function mutations in both copies of TP53 and CDKN2A showed enhanced spatial spread, whereas lineages with loss-of-function mutations in other frequently mutated loci did not. We propose a two-phase model with expansions of TP53 and CKDN2A mutant lineages during initial growth of the segment, followed by relative stasis. Subsequent to initial expansion, mutations in these loci as well as ARID1A and SMARCA4 may show a local selective advantage but do not expand far: The spatial structure of the Barrett's segment remains stable during surveillance even in patients who go on to cancer. We conclude that the cancer/noncancer outcome is strongly affected by early steps in formation of the Barrett's segment.

Distinct Classes of Complex Structural Variation Uncovered across Thousands of Cancer Genome Graphs.

Cancer genomes often harbor hundreds of somatic DNA rearrangement junctions, many of which cannot be easily classified into simple (e.g., deletion) or complex (e.g., chromothripsis) structural variant classes. Applying a novel genome graph computational paradigm to analyze the topology of junction copy number (JCN) across 2,778 tumor whole-genome sequences, we uncovered three novel complex rearrangement phenomena: pyrgo, rigma, and tyfonas. Pyrgo are "towers" of low-JCN duplications associated with early-replicating regions, superenhancers, and breast or ovarian cancers. Rigma comprise "chasms" of low-JCN deletions enriched in late-replicating fragile sites and gastrointestinal carcinomas. Tyfonas are "typhoons" of high-JCN junctions and fold-back inversions associated with expressed protein-coding fusions, breakend hypermutation, and acral, but not cutaneous, melanomas. Clustering of tumors according to genome graph-derived features identified subgroups associated with DNA repair defects and poor prognosis.

Correction to: NSAID use and somatic exomic mutations in Barrett's esophagus.

It was highlighted that in the original article [1] the Availability of data and materials section was incorrect.

CNValidator: validating somatic copy-number inference.

MOTIVATION: CNValidator assesses the quality of somatic copy-number calls based on coherency of haplotypes across multiple samples from the same individual. It is applicable to any copy-number calling algorithm, which makes calls independently for each sample. This test is useful in assessing the accuracy of copy-number calls, as well as choosing among alternative copy-number algorithms or tuning parameter values. RESULTS: On a dataset of somatic samples from individuals with Barrett's Esophagus, CNValidator provided feedback on the correctness of sample ploidy calls and also detected data quality issues. AVAILABILITY AND IMPLEMENTATION: CNValidator is available on GitHub at https://github.com/kuhnerlab/CNValidator. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

NSAID use and somatic exomic mutations in Barrett's esophagus.

BACKGROUND: Use of aspirin and other non-steroidal anti-inflammatory drugs (NSAIDs) has been shown to protect against tetraploidy, aneuploidy, and chromosomal alterations in the metaplastic condition Barrett's esophagus (BE) and to lower the incidence and mortality of esophageal adenocarcinoma (EA). The esophagus is exposed to both intrinsic and extrinsic mutagens resulting from gastric reflux, chronic inflammation, and exposure to environmental carcinogens such as those found in cigarettes. Here we test the hypothesis that NSAID use inhibits accumulation of point mutations/indels during somatic genomic evolution in BE. METHODS: Whole exome sequences were generated from 82 purified epithelial biopsies and paired blood samples from a cross-sectional study of 41 NSAID users and 41 non-users matched by sex, age, smoking, and continuous time using or not using NSAIDs. RESULTS: NSAID use reduced overall frequency of point mutations across the spectrum of mutation types, lowered the frequency of mutations even when adjusted for both TP53 mutation and smoking status, and decreased the prevalence of clones with high variant allele frequency. Never smokers who consistently used NSAIDs had fewer point mutations in signature 17, which is commonly found in EA. NSAID users had, on average, a 50% reduction in functional gene mutations in nine cancer-associated pathways and also had less diversity in pathway mutational burden compared to non-users. CONCLUSIONS: These results indicate NSAID use functions to limit overall mutations on which selection can act and supports a model in which specific mutant cell populations survive or expand better in the absence of NSAIDs.

Evolution of Barrett's esophagus through space and time at single-crypt and whole-biopsy levels.

The low risk of progression of Barrett's esophagus (BE) to esophageal adenocarcinoma can lead to over-diagnosis and over-treatment of BE patients. This may be addressed through a better understanding of the dynamics surrounding BE malignant progression. Although genetic diversity has been characterized as a marker of malignant development, it is still unclear how BE arises and develops. Here we uncover the evolutionary dynamics of BE at crypt and biopsy levels in eight individuals, including four patients that experienced malignant progression. We assay eight individual crypts and the remaining epithelium by SNP array for each of 6-11 biopsies over 2 time points per patient (358 samples in total). Our results indicate that most Barrett's segments are clonal, with similar number and inferred rates of alterations observed for crypts and biopsies. Divergence correlates with geographical location, being higher near the gastro-esophageal junction. Relaxed clock analyses show that genomic instability precedes and is enhanced by genome doubling. These results shed light on the clinically relevant evolutionary dynamics of BE.

A Consensus Method for Ancestral Recombination Graphs.

We propose a consensus method for ancestral recombination graphs (ARGs) that generates a single ARG representing commonalities among a cloud of ARGs defined for the same genomic region and set of taxa. Our method, which we call "threshold consensus," treats a genomic location as a potential recombination breakpoint only if the number of ARGs in the cloud possessing a breakpoint at that location exceeds a chosen threshold. The estimate is further refined by ignoring recombinations that do not change the local tree topologies, as well as collapsing breakpoint locations separated only by invariant sites. We test the threshold consensus algorithm, using a range of threshold values, on simulated ARGs inferred by a genealogy sampling algorithm, and evaluate accuracy of breakpoints and local topologies in the resulting consensus ARGs.

Bulk Genotyping of Biopsies Can Create Spurious Evidence for Hetereogeneity in Mutation Content.

When multiple samples are taken from the neoplastic tissues of a single patient, it is natural to compare their mutation content. This is often done by bulk genotyping of whole biopsies, but the chance that a mutation will be detected in bulk genotyping depends on its local frequency in the sample. When the underlying mutation count per cell is equal, homogenous biopsies will have more high-frequency mutations, and thus more detectable mutations, than heterogeneous ones. Using simulations, we show that bulk genotyping of data simulated under a neutral model of somatic evolution generates strong spurious evidence for non-neutrality, because the pattern of tissue growth systematically generates differences in biopsy heterogeneity. Any experiment which compares mutation content across bulk-genotyped biopsies may therefore suggest mutation rate or selection intensity variation even when these forces are absent. We discuss computational and experimental approaches for resolving this problem.

Limitations of the Driver/Passenger Model in Cancer Prevention.

Mutations detected in cancers are often divided into "drivers" and "passengers." We suggest that this classification is potentially misleading for purposes of early detection and prevention. Specifically, some mutations are frequent in tumors and thus appear to be drivers, but are poor predictors of cancer; other mutations are individually rare and thus appear to be passengers, but may collectively explain a large proportion of risk. The assumptions bundled into the terms "driver" and "passenger" can lead to misunderstandings of neoplastic progression, with unintended consequences including overdiagnosis, overtreatment, and failure to identify the true sources of risk. We argue that samples from healthy, benign, or neoplastic tissues are critical for evaluating the risk of future cancer posed by mutations in a given gene. Cancer Prev Res; 9(5); 335-8. ©2016 AACR.

Assessment of Esophageal Adenocarcinoma Risk Using Somatic Chromosome Alterations in Longitudinal Samples in Barrett's Esophagus.

Cancers detected at a late stage are often refractory to treatments and ultimately lethal. Early detection can significantly increase survival probability, but attempts to reduce mortality by early detection have frequently increased overdiagnosis of indolent conditions that do not progress over a lifetime. Study designs that incorporate biomarker trajectories in time and space are needed to distinguish patients who progress to an early cancer from those who follow an indolent course. Esophageal adenocarcinoma is characterized by evolution of punctuated and catastrophic somatic chromosomal alterations and high levels of overall mutations but few recurrently mutated genes aside from TP53. Endoscopic surveillance of Barrett's esophagus for early cancer detection provides an opportunity for assessment of alterations for cancer risk in patients who progress to esophageal adenocarcinoma compared with nonprogressors. We investigated 1,272 longitudinally collected esophageal biopsies in a 248 Barrett's patient case-cohort study with 20,425 person-months of follow-up, including 79 who progressed to early-stage esophageal adenocarcinoma. Cancer progression risk was assessed for total chromosomal alterations, diversity, and chromosomal region-specific alterations measured with single-nucleotide polymorphism arrays in biopsies obtained over esophageal space and time. A model using 29 chromosomal features was developed for cancer risk prediction (area under receiver operator curve, 0.94). The model prediction performance was robust in two independent esophageal adenocarcinoma sets and outperformed TP53 mutation, flow cytometric DNA content, and histopathologic diagnosis of dysplasia. This study offers a strategy to reduce overdiagnosis in Barrett's esophagus and improve early detection of esophageal adenocarcinoma and potentially other cancers characterized by punctuated and catastrophic chromosomal evolution.

Assessing Differences Between Ancestral Recombination Graphs.

Ancestral recombination graphs (ARGs) represent the history of portions of a genome with recombination. Attempts to infer ARGs have been hampered by the lack of an ARG comparison metric which could be used to measure how well inference succeeded. We propose a simple ARG comparison framework based on averaging standard tree comparison measures across either all sites or variable sites only. Using simulated data, we show that this framework, instantiated with an appropriate tree comparison measure, can distinguish better from worse inferences of an ARG.

Practical performance of tree comparison metrics.

The phylogenetic literature contains numerous measures for assessing differences between two phylogenetic trees. Individual measures have been criticized on various grounds, but little is known about their comparative performance in typical applications. We evaluate the performance of nine tree distance measures on two tasks: 1) distinguishing trees separated by lesser versus greater numbers of recombinations, and 2) distinguishing trees inferred with lower versus higher quality data. We find that when the trees being compared are similar, measures that make use of branch lengths are superior, with the branch-length version of the Robinson-Foulds metric performing best. In contrast, for dissimilar trees topology-only measures are superior, with the Alignment metric of Nye et al. performing best. We also apply the measures to a mammalian dataset and observe that the best metric depends on whether branch-length information is of interest. We give practical recommendations for choosing a tree distance metric in different applications.

Correcting for sequencing error in maximum likelihood phylogeny inference.

Accurate phylogenies are critical to taxonomy as well as studies of speciation processes and other evolutionary patterns. Accurate branch lengths in phylogenies are critical for dating and rate measurements. Such accuracy may be jeopardized by unacknowledged sequencing error. We use simulated data to test a correction for DNA sequencing error in maximum likelihood phylogeny inference. Over a wide range of data polymorphism and true error rate, we found that correcting for sequencing error improves recovery of the branch lengths, even if the assumed error rate is up to twice the true error rate. Low error rates have little effect on recovery of the topology. When error is high, correction improves topological inference; however, when error is extremely high, using an assumed error rate greater than the true error rate leads to poor recovery of both topology and branch lengths. The error correction approach tested here was proposed in 2004 but has not been widely used, perhaps because researchers do not want to commit to an estimate of the error rate. This study shows that correction with an approximate error rate is generally preferable to ignoring the issue.

Bayesian inference of local trees along chromosomes by the sequential Markov coalescent.

We propose a genealogy-sampling algorithm, Sequential Markov Ancestral Recombination Tree (SMARTree), that provides an approach to estimation from SNP haplotype data of the patterns of coancestry across a genome segment among a set of homologous chromosomes. To enable analysis across longer segments of genome, the sequence of coalescent trees is modeled via the modified sequential Markov coalescent (Marjoram and Wall, Genetics 7:16, 2006). To assess performance in estimating these local trees, our SMARTree implementation is tested on simulated data. Our base data set is of the SNPs in 10 DNA sequences over 50 kb. We examine the effects of longer sequences and of more sequences, and of a recombination and/or mutational hotspot. The model underlying SMARTree is an approximation to the full recombinant-coalescent distribution. However, in a small trial on simulated data, recovery of local trees was similar to that of LAMARC (Kuhner et al. Genetics 156:1393-1401, 2000a), a sampler which uses the full model.

Joint inference of identity by descent along multiple chromosomes from population samples.

There has been much interest in detecting genomic identity by descent (IBD) segments from modern dense genetic marker data and in using them to identify human disease susceptibility loci. Here we present a novel Bayesian framework using Markov chain Monte Carlo (MCMC) realizations to jointly infer IBD states among multiple individuals not known to be related, together with the allelic typing error rate and the IBD process parameters. The data are phased single nucleotide polymorphism (SNP) haplotypes. We model changes in latent IBD state along homologous chromosomes by a continuous time Markov model having the Ewens sampling formula as its stationary distribution. We show by simulation that this model for the IBD process fits quite well with the coalescent predictions. Using simulation data sets of 40 haplotypes over regions of 1 and 10 million base pairs (Mbp), we show that the jointly estimated IBD states are very close to the true values, although the presence of linkage disequilibrium decreases the accuracy. We also present comparisons with the ibd_haplo program, which estimates IBD among sets of four haplotypes. Our new IBD detection method focuses on the scale between genome-wide methods using simple IBD models and complex coalescent-based methods that are limited to short genome segments. At the scale of a few Mbp, our approach offers potentially more power for fine-scale IBD association mapping.

Temporal and spatial evolution of somatic chromosomal alterations: a case-cohort study of Barrett's esophagus.

All cancers are believed to arise by dynamic, stochastic somatic genomic evolution with genome instability, generation of diversity, and selection of genomic alterations that underlie multistage progression to cancer. Advanced esophageal adenocarcinomas have high levels of somatic copy number alterations. Barrett's esophagus is a risk factor for developing esophageal adenocarcinoma, and somatic chromosomal alterations (SCA) are known to occur in Barrett's esophagus. The vast majority (∼95%) of individuals with Barrett's esophagus do not progress to esophageal adenocarcinoma during their lifetimes, but a small subset develop esophageal adenocarcinoma, many of which arise rapidly even in carefully monitored patients without visible endoscopic abnormalities at the index endoscopy. Using a well-designed, longitudinal case-cohort study, we characterized SCA as assessed by single-nucleotide polymorphism arrays over space and time in 79 "progressors" with Barrett's esophagus as they approach the diagnosis of cancer and 169 "nonprogressors" with Barrett's esophagus who did not progress to esophageal adenocarcinoma over more than 20,425 person-months of follow-up. The genomes of nonprogressors typically had small localized deletions involving fragile sites and 9p loss/copy neutral LOH that generate little genetic diversity and remained relatively stable over prolonged follow-up. As progressors approach the diagnosis of cancer, their genomes developed chromosome instability with initial gains and losses, genomic diversity, and selection of SCAs followed by catastrophic genome doublings. Our results support a model of differential disease dynamics in which nonprogressor genomes largely remain stable over prolonged periods, whereas progressor genomes evolve significantly increased SCA and diversity within four years of esophageal adenocarcinoma diagnosis, suggesting a window of opportunity for early detection.

GraphML specializations to codify ancestral recombinant graphs.

Software which simulates, infers, or analyzes ancestral recombination graphs (ARGs) faces the problem of communicating them. Existing formats omit information either about the location of recombinations along the chromosome or the position of recombinations relative to the branching topology. We present a specialization of GraphML, an XML-based standard for mathematical graphs, for communication of ARGs. The GraphML type is specialized to contain the node type, time, recombination location, and name. The GraphML type is specialized to contain the ancestral material passed along that edge. This approach, which we call ArgML, retains all information in the original ARG. Due to its use of established formats ArgML can be parsed, checked and displayed by existing software.

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.