Placental somatic mutation in human stillbirth and live birth: A pilot case-control study of paired placental, fetal, and maternal whole genomes.

INTRODUCTION: A high frequency of single nucleotide somatic mutations in the placenta has been recently described, but its relationship to placental dysfunction is unknown. METHODS: We performed a pilot case-control study using paired fetal, maternal, and placental samples collected from healthy live birth controls (n = 10), live births with fetal growth restriction (FGR) due to placental insufficiency (n = 7), and stillbirths with FGR and placental insufficiency (n = 11). We quantified single nucleotide and structural somatic variants using bulk whole genome sequencing (30-60X coverage) in four biopsies from each placenta. We also assessed their association with clinical and histological evidence of placental dysfunction. RESULTS: Seventeen pregnancies had sufficiently high-quality placental, fetal, and maternal DNA for analysis. Each placenta had a median of 473 variants (range 111-870), with 95 % arising in just one biopsy within each placenta. In controls, live births with FGR, and stillbirths, the median variant counts per placenta were 514 (IQR 381-779), 582 (450-735), and 338 (245-441), respectively. After adjusting for depth of sequencing coverage and gestational age at birth, the somatic mutation burden was similar between groups (FGR live births vs. controls, adjusted diff. 59, 95 % CI -218 to +336; stillbirths vs controls, adjusted diff. -34, -351 to +419), and with no association with placental dysfunction (p = 0.7). DISCUSSION: We confirmed the high prevalence of somatic mutation in the human placenta and conclude that the placenta is highly clonal. We were not able to identify any relationship between somatic mutation burden and clinical or histologic placental insufficiency.

Random allelic expression in the adult human body.

Genes are typically assumed to express both parental alleles similarly, yet cell lines show random allelic expression (RAE) for many autosomal genes that could shape genetic effects. Thus, understanding RAE in human tissues could improve our understanding of phenotypic variation. Here, we develop a methodology to perform genome-wide profiling of RAE and biallelic expression in GTEx datasets for 832 people and 54 tissues. We report 2,762 autosomal genes with some RAE properties similar to randomly inactivated X-linked genes. We found that RAE is associated with rapidly evolving regions in the human genome, adaptive signaling processes, and genes linked to age-related diseases such as neurodegeneration and cancer. We define putative mechanistic subtypes of RAE distinguished by gene overlaps on sense and antisense DNA strands, aggregation in clusters near telomeres, and increased regulatory complexity and inputs compared with biallelic genes. We provide foundations to study RAE in human phenotypes, evolution, and disease.

Searching thousands of genomes to classify somatic and novel structural variants using STIX.

Structural variants are associated with cancers and developmental disorders, but challenges with estimating population frequency remain a barrier to prioritizing mutations over inherited variants. In particular, variability in variant calling heuristics and filtering limits the use of current structural variant catalogs. We present STIX, a method that, instead of relying on variant calls, indexes and searches the raw alignments from thousands of samples to enable more comprehensive allele frequency estimation.

CaBagE: A Cas9-based Background Elimination strategy for targeted, long-read DNA sequencing.

A substantial fraction of the human genome is difficult to interrogate with short-read DNA sequencing technologies due to paralogy, complex haplotype structures, or tandem repeats. Long-read sequencing technologies, such as Oxford Nanopore's MinION, enable direct measurement of complex loci without introducing many of the biases inherent to short-read methods, though they suffer from relatively lower throughput. This limitation has motivated recent efforts to develop amplification-free strategies to target and enrich loci of interest for subsequent sequencing with long reads. Here, we present CaBagE, a method for target enrichment that is efficient and useful for sequencing large, structurally complex targets. The CaBagE method leverages the stable binding of Cas9 to its DNA target to protect desired fragments from digestion with exonuclease. Enriched DNA fragments are then sequenced with Oxford Nanopore's MinION long-read sequencing technology. Enrichment with CaBagE resulted in a median of 116X coverage (range 39-416) of target loci when tested on five genomic targets ranging from 4-20kb in length using healthy donor DNA. Four cancer gene targets were enriched in a single reaction and multiplexed on a single MinION flow cell. We further demonstrate the utility of CaBagE in two ALS patients with C9orf72 short tandem repeat expansions to produce genotype estimates commensurate with genotypes derived from repeat-primed PCR for each individual. With CaBagE there is a physical enrichment of on-target DNA in a given sample prior to sequencing. This feature allows adaptability across sequencing platforms and potential use as an enrichment strategy for applications beyond sequencing. CaBagE is a rapid enrichment method that can illuminate regions of the 'hidden genome' underlying human disease.

OncoGEMINI: software for investigating tumor variants from multiple biopsies with integrated cancer annotations.

BACKGROUND: DNA sequencing has unveiled extensive tumor heterogeneity in several different cancer types, with many exhibiting diverse subclonal populations. Identifying and tracing mutations throughout the expansion and progression of a tumor represents a significant challenge. Furthermore, prioritizing the subset of such mutations most likely to contribute to tumor evolution or that could serve as potential therapeutic targets represents an ongoing problem. RESULTS: Here, we describe OncoGEMINI, a new tool designed for exploring the complex patterns and trajectory of somatic and inherited variation observed in heterogeneous tumors biopsied over the course of treatment. This is accomplished by creating a searchable database of variants that includes tumor sampling time points and allows for filtering methods that reflect specific changes in variant allele frequencies over time. Additionally, by incorporating existing annotations and resources that facilitate the interpretation of cancer mutations (e.g., CIViC, DGIdb), OncoGEMINI enables rapid searches for, and potential identification of, mutations that may be driving subclonal evolution. CONCLUSIONS: By combining relevant genomic annotations alongside specific filtering tools, OncoGEMINI provides powerful and customizable approaches that enable the quick identification of individual tumor variants that meet specified criteria. It can be applied to a wide range of tumor-derived sequence data, but is especially designed for studies with multiple samples, including longitudinal datasets. It is available under an MIT license at github.com/fakedrtom/oncogemini .

Somalier: rapid relatedness estimation for cancer and germline studies using efficient genome sketches.

BACKGROUND: When interpreting sequencing data from multiple spatial or longitudinal biopsies, detecting sample mix-ups is essential, yet more difficult than in studies of germline variation. In most genomic studies of tumors, genetic variation is detected through pairwise comparisons of the tumor and a matched normal tissue from the sample donor. In many cases, only somatic variants are reported, which hinders the use of existing tools that detect sample swaps solely based on genotypes of inherited variants. To address this problem, we have developed Somalier, a tool that operates directly on alignments and does not require jointly called germline variants. Instead, Somalier extracts a small sketch of informative genetic variation for each sample. Sketches from hundreds of germline or somatic samples can then be compared in under a second, making Somalier a useful tool for measuring relatedness in large cohorts. Somalier produces both text output and an interactive visual report that facilitates the detection and correction of sample swaps using multiple relatedness metrics. RESULTS: We introduce the tool and demonstrate its utility on a cohort of five glioma samples each with a normal, tumor, and cell-free DNA sample. Applying Somalier to high-coverage sequence data from the 1000 Genomes Project also identifies several related samples. We also demonstrate that it can distinguish pairs of whole-genome and RNA-seq samples from the same individuals in the Genotype-Tissue Expression (GTEx) project. CONCLUSIONS: Somalier is a tool that can rapidly evaluate relatedness from sequencing data. It can be applied to diverse sequencing data types and genome builds and is available under an MIT license at github.com/brentp/somalier .

Regulatory sharing between estrogen receptor α bound enhancers.

The human genome encodes an order of magnitude more gene expression enhancers than promoters, suggesting that most genes are regulated by the combined action of multiple enhancers. We have previously shown that neighboring estrogen-responsive enhancers exhibit complex synergistic contributions to the production of an estrogenic transcriptional response. Here we sought to determine the molecular underpinnings of this enhancer cooperativity. We generated genetic deletions of four estrogen receptor α (ER) bound enhancers that regulate two genes and found that enhancers containing full estrogen response element (ERE) motifs control ER binding at neighboring sites, while enhancers with pre-existing histone acetylation/accessibility confer a permissible chromatin environment to the neighboring enhancers. Genome engineering revealed that two enhancers with half EREs could not compensate for the lack of a full ERE site within the cluster. In contrast, two enhancers with full EREs produced a transcriptional response greater than the wild-type locus. By swapping genomic sequences, we found that the genomic location of a full ERE strongly influences enhancer activity. Our results lead to a model in which a full ERE is required for ER recruitment, but the presence of a pre-existing permissible chromatin environment can also be needed for estrogen-driven gene regulation to occur.

Germline mutation rates in young adults predict longevity and reproductive lifespan.

Ageing may be due to mutation accumulation across the lifespan, leading to tissue dysfunction, disease, and death. We tested whether germline autosomal mutation rates in young adults predict their remaining survival, and, for women, their reproductive lifespans. Age-adjusted mutation rates (AAMRs) in 61 women and 61 men from the Utah CEPH (Centre d'Etude du Polymorphisme Humain) families were determined. Age at death, cause of death, all-site cancer incidence, and reproductive histories were provided by the Utah Population Database, Utah Cancer Registry, and Utah Genetic Reference Project. Higher AAMRs were significantly associated with higher all-cause mortality in both sexes combined. Subjects in the top quartile of AAMRs experienced more than twice the mortality of bottom quartile subjects (hazard ratio [HR], 2.07; 95% confidence interval [CI], 1.21-3.56; p = 0.008; median survival difference = 4.7 years). Fertility analyses were restricted to women whose age at last birth (ALB) was ≥ 30 years, the age when fertility begins to decline. Women with higher AAMRs had significantly fewer live births and a younger ALB. Adult germline mutation accumulation rates are established in adolescence, and later menarche in women is associated with delayed mutation accumulation. We conclude that germline mutation rates in healthy young adults may provide a measure of both reproductive and systemic ageing. Puberty may induce the establishment of adult mutation accumulation rates, just when DNA repair systems begin their lifelong decline.

Coexpression patterns define epigenetic regulators associated with neurological dysfunction.

Coding variants in epigenetic regulators are emerging as causes of neurological dysfunction and cancer. However, a comprehensive effort to identify disease candidates within the human epigenetic machinery (EM) has not been performed; it is unclear whether features exist that distinguish between variation-intolerant and variation-tolerant EM genes, and between EM genes associated with neurological dysfunction versus cancer. Here, we rigorously define 295 genes with a direct role in epigenetic regulation (writers, erasers, remodelers, readers). Systematic exploration of these genes reveals that although individual enzymatic functions are always mutually exclusive, readers often also exhibit enzymatic activity (dual-function EM genes). We find that the majority of EM genes are very intolerant to loss-of-function variation, even when compared to the dosage sensitive transcription factors, and we identify 102 novel EM disease candidates. We show that this variation intolerance is driven by the protein domains encoding the epigenetic function, suggesting that disease is caused by a perturbed chromatin state. We then describe a large subset of EM genes that are coexpressed within multiple tissues. This subset is almost exclusively populated by extremely variation-intolerant genes and shows enrichment for dual-function EM genes. It is also highly enriched for genes associated with neurological dysfunction, even when accounting for dosage sensitivity, but not for cancer-associated EM genes. Finally, we show that regulatory regions near epigenetic regulators are genetically important for common neurological traits. These findings prioritize novel disease candidate EM genes and suggest that this coexpression plays a functional role in normal neurological homeostasis.

Publisher Correction: Combating subclonal evolution of resistant cancer phenotypes.

The originally published version of this Article contained an error in Figure 4. In panel a, grey boxes surrounding the subclones associated with patients #2 and #4 obscured adjacent portions of the heatmap. This error has now been corrected in both the PDF and HTML versions of the Article.

GIGGLE: a search engine for large-scale integrated genome analysis.

GIGGLE is a genomics search engine that identifies and ranks the significance of genomic loci shared between query features and thousands of genome interval files. GIGGLE (https://github.com/ryanlayer/giggle) scales to billions of intervals and is over three orders of magnitude faster than existing methods. Its speed extends the accessibility and utility of resources such as ENCODE, Roadmap Epigenomics, and GTEx by facilitating data integration and hypothesis generation.

Identification of ATIC as a Novel Target for Chemoradiosensitization.

PURPOSE: Mutations in the gene encoding 5-aminoimidazole-4-carboxamide ribonucleotide formyltransferase/IMP cyclohydrolase (ATIC), a bifunctional enzyme that catalyzes the final 2 steps of the purine de novo biosynthetic pathway, were identified in a subject referred for radiation sensitivity testing. Functional studies were performed to determine whether ATIC inhibition was radiosensitizing and, if so, to elucidate the mechanism of this effect and determine whether small molecule inhibitors of ATIC could act as effective radiosensitizing agents. METHODS AND MATERIALS: Both small interfering RNA knockdown and small molecule inhibitors were used to inactivate ATIC in cell culture. Clonogenic survival assays, the neutral comet assay, and γH2AX staining were used to assess the effects of ATIC inhibition or depletion on cellular DNA damage responses. RESULTS: Depletion of ATIC or inhibition of its transformylase activity significantly reduced the surviving fraction of cells in clonogenic survival assays in multiple cancer cell lines. In the absence of ionizing radiation exposure, ATIC knockdown or chemical inhibition activated cell cycle checkpoints, shifting cells to the more radiosensitive G2/M phase of the cell cycle, and depleted cellular adenosine triphosphate but did not result in detectable DNA damage. Cells in which ATIC was knocked down or inhibited and then treated with ionizing radiation displayed increased numbers of DNA double-strand breaks and a delay in the repair of those breaks relative to irradiated, but otherwise untreated, controls. Supplementation of culture media with exogenous adenosine triphosphate ameliorated the DNA repair phenotypes. CONCLUSIONS: These findings implicate ATIC as an effective, and previously unrecognized, target for chemoradiosensitization and, more broadly, suggest that purine levels in cells might have an underappreciated role in modulating the efficiency of DNA damage responses that could be exploited in radiosensitizing strategies.

Combating subclonal evolution of resistant cancer phenotypes.

Metastatic breast cancer remains challenging to treat, and most patients ultimately progress on therapy. This acquired drug resistance is largely due to drug-refractory sub-populations (subclones) within heterogeneous tumors. Here, we track the genetic and phenotypic subclonal evolution of four breast cancers through years of treatment to better understand how breast cancers become drug-resistant. Recurrently appearing post-chemotherapy mutations are rare. However, bulk and single-cell RNA sequencing reveal acquisition of malignant phenotypes after treatment, including enhanced mesenchymal and growth factor signaling, which may promote drug resistance, and decreased antigen presentation and TNF-α signaling, which may enable immune system avoidance. Some of these phenotypes pre-exist in pre-treatment subclones that become dominant after chemotherapy, indicating selection for resistance phenotypes. Post-chemotherapy cancer cells are effectively treated with drugs targeting acquired phenotypes. These findings highlight cancer's ability to evolve phenotypically and suggest a phenotype-targeted treatment strategy that adapts to cancer as it evolves.

SpeedSeq: ultra-fast personal genome analysis and interpretation.

SpeedSeq is an open-source genome analysis platform that accomplishes alignment, variant detection and functional annotation of a 50× human genome in 13 h on a low-cost server and alleviates a bioinformatics bottleneck that typically demands weeks of computation with extensive hands-on expert involvement. SpeedSeq offers performance competitive with or superior to current methods for detecting germline and somatic single-nucleotide variants, structural variants, insertions and deletions, and it includes novel functionality for streamlined interpretation.

Population-based structural variation discovery with Hydra-Multi.

UNLABELLED: Current strategies for SNP and INDEL discovery incorporate sequence alignments from multiple individuals to maximize sensitivity and specificity. It is widely accepted that this approach also improves structural variant (SV) detection. However, multisample SV analysis has been stymied by the fundamental difficulties of SV calling, e.g. library insert size variability, SV alignment signal integration and detecting long-range genomic rearrangements involving disjoint loci. Extant tools suffer from poor scalability, which limits the number of genomes that can be co-analyzed and complicates analysis workflows. We have developed an approach that enables multisample SV analysis in hundreds to thousands of human genomes using commodity hardware. Here, we describe Hydra-Multi and measure its accuracy, speed and scalability using publicly available datasets provided by The 1000 Genomes Project and by The Cancer Genome Atlas (TCGA). AVAILABILITY AND IMPLEMENTATION: Hydra-Multi is written in C++ and is freely available at https://github.com/arq5x/Hydra. CONTACT: aaronquinlan@gmail.com or ihall@genome.wustl.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

SubcloneSeeker: a computational framework for reconstructing tumor clone structure for cancer variant interpretation and prioritization.

Many tumors are composed of genetically divergent cell subpopulations. We report SubcloneSeeker, a package capable of exhaustive identification of subclone structures and evolutionary histories with bulk somatic variant allele frequency measurements from tumor biopsies. We present a statistical framework to elucidate whether specific sets of mutations are present within the same subclones, and the order in which they occur. We demonstrate how subclone reconstruction provides crucial information about tumorigenesis and relapse mechanisms; guides functional study by variant prioritization, and has the potential as a rational basis for informed therapeutic strategies for the patient. SubcloneSeeker is available at: https://github.com/yiq/SubcloneSeeker.

LUMPY: a probabilistic framework for structural variant discovery.

Comprehensive discovery of structural variation (SV) from whole genome sequencing data requires multiple detection signals including read-pair, split-read, read-depth and prior knowledge. Owing to technical challenges, extant SV discovery algorithms either use one signal in isolation, or at best use two sequentially. We present LUMPY, a novel SV discovery framework that naturally integrates multiple SV signals jointly across multiple samples. We show that LUMPY yields improved sensitivity, especially when SV signal is reduced owing to either low coverage data or low intra-sample variant allele frequency. We also report a set of 4,564 validated breakpoints from the NA12878 human genome. https://github.com/arq5x/lumpy-sv.

Breakpoint profiling of 64 cancer genomes reveals numerous complex rearrangements spawned by homology-independent mechanisms.

Tumor genomes are generally thought to evolve through a gradual accumulation of mutations, but the observation that extraordinarily complex rearrangements can arise through single mutational events suggests that evolution may be accelerated by punctuated changes in genome architecture. To assess the prevalence and origins of complex genomic rearrangements (CGRs), we mapped 6179 somatic structural variation breakpoints in 64 cancer genomes from seven tumor types and screened for clusters of three or more interconnected breakpoints. We find that complex breakpoint clusters are extremely common: 154 clusters comprise 25% of all somatic breakpoints, and 75% of tumors exhibit at least one complex cluster. Based on copy number state profiling, 63% of breakpoint clusters are consistent with being CGRs that arose through a single mutational event. CGRs have diverse architectures including focal breakpoint clusters, large-scale rearrangements joining clusters from one or more chromosomes, and staggeringly complex chromothripsis events. Notably, chromothripsis has a significantly higher incidence in glioblastoma samples (39%) relative to other tumor types (9%). Chromothripsis breakpoints also show significantly elevated intra-tumor allele frequencies relative to simple SVs, which indicates that they arise early during tumorigenesis or confer selective advantage. Finally, assembly and analysis of 4002 somatic and 6982 germline breakpoint sequences reveal that somatic breakpoints show significantly less microhomology and fewer templated insertions than germline breakpoints, and this effect is stronger at CGRs than at simple variants. These results are inconsistent with replication-based models of CGR genesis and strongly argue that nonhomologous repair of concurrently arising DNA double-strand breaks is the predominant mechanism underlying complex cancer genome rearrangements.

Characterizing complex structural variation in germline and somatic genomes.

Genome structural variation (SV) is a major source of genetic diversity in mammals and a hallmark of cancer. Although SV is typically defined by its canonical forms (duplication, deletion, insertion, inversion and translocation), recent breakpoint mapping studies have revealed a surprising number of 'complex' variants that evade simple classification. Complex SVs are defined by clustered breakpoints that arose through a single mutation but cannot be explained by one simple end-joining or recombination event. Some complex variants exhibit profoundly complicated rearrangements between distinct loci from multiple chromosomes, whereas others involve more subtle alterations at a single locus. These diverse and unpredictable features present a challenge for SV mapping experiments. Here, we review current knowledge of complex SV in mammals, and outline techniques for identifying and characterizing complex variants using next-generation DNA sequencing.