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Mutational signatures are patterns of mutations that arise during tumorigenesis. We present an enhanced, practical framework for mutational signature analyses. Applying these methods on 3,107 whole genome sequenced (WGS) primary cancers of 21 organs reveals known signatures and nine previously undescribed rearrangement signatures. We highlight inter-organ variability of signatures and present a way of visualizing that diversity, reinforcing our findings in an independent analysis of 3,096 WGS metastatic cancers. Signatures with a high level of genomic instability are dependent on TP53 dysregulation. We illustrate how uncertainty in mutational signature identification and assignment to samples affects tumor classification, reinforcing that using multiple orthogonal mutational signature data is not only beneficial, it is essential for accurate tumor stratification. Finally, we present a reference web-based tool for cancer and experimentally-generated mutational signatures, called Signal (https://signal.mutationalsignatures.com), that also supports performing mutational signature analyses.

Preeclampsia is a serious complication of pregnancy, affecting both maternal and fetal health. In genome-wide association meta-analysis of European and Central Asian mothers, we identify sequence variants that associate with preeclampsia in the maternal genome at ZNF831/20q13 and FTO/16q12. These are previously established variants for blood pressure (BP) and the FTO variant has also been associated with body mass index (BMI). Further analysis of BP variants establishes that variants at MECOM/3q26, FGF5/4q21 and SH2B3/12q24 also associate with preeclampsia through the maternal genome. We further show that a polygenic risk score for hypertension associates with preeclampsia. However, comparison with gestational hypertension indicates that additional factors modify the risk of preeclampsia., Studies to identify maternal variants associated with preeclampsia have been limited by sample size. Here, the authors meta-analyze eight GWAS of 9,515 preeclamptic women, identifying five variants associated with preeclampsia and showing that genetic predisposition to hypertension is a major risk factor for preeclampsia.

Background The somatic mutations found through sequencing a whole tumour genome are the aggregate outcome of one or multiple mutational processes. Each of these processes leaves a characteristic imprint, or mutational signature, on the genome. The final mutational profile is determined by the pattern of exposure of each of these mutational processes. Deconstructing the signature composition of a tumour’s mutational profile provides insights into the biological processes that drive it. The initial research in this field extracted 5 substitution signatures from 21 breast cancer samples, but research since has extracted signatures from ever expanding datasets of whole tumour genomes and experimental mutagenesis systems. Signatures have also been developed for different mutation types. The field has great potential to aid in the diagnosis and treatment of cancer patients. Methods We developed a website using the ReactJS framework supported by a distributed compute cluster in OpenStack. Results Here we present Signal, a new website enabling exploration of the latest research in the field of mutational signatures. With Signal we intend to inform the scientific community of the power of mutational signatures and to provide the results of our research in a dynamic, easy-to-use interface. This includes tissue-specific substitution and rearrangement signatures extracted from the PanCan dataset, in addition to signatures extracted from cell-lines exposed to environmental mutagens and gene-knockouts. Users can upload their own variant calls for analysis by our pipeline, which will filter out kataegis regions, construct a substitution profile and detect the presence of any of our gold-standard substitution signatures. This signature fitting algorithm leverages the organ-specificity of our new signature extraction framework, leading to more accurate signature predictions than previously possible. Conclusions We intend for Signal to be the home page of mutational signatures which will be kept at the forefront of research with the most recent published data and analysis methodologies. Legal entity responsible for the study The authors. Funding CRUK. Disclosure All authors have declared no conflicts of interest.

Ralph McGinnis, Valgerdur Steinthorsdottir, Linda Morgan and colleagues perform a genome-wide association study in the offspring of preeclampsia pregnancies and identify variants in the fetal genome near FLT1 that are associated with risk of preeclampsia. FLT1 is known to encode an isoform of placental origin implicated in the pathology of preeclampsia, providing biological support for the association of this locus with preeclampsia risk. Preeclampsia, which affects approximately 5% of pregnancies, is a leading cause of maternal and perinatal death1. The causes of preeclampsia remain unclear, but there is evidence for inherited susceptibility2. Genome-wide association studies (GWAS) have not identified maternal sequence variants of genome-wide significance that replicate in independent data sets3,4. We report the first GWAS of offspring from preeclamptic pregnancies and discovery of the first genome-wide significant susceptibility locus (rs4769613; P = 5.4 × 10−11) in 4,380 cases and 310,238 controls. This locus is near the FLT1 gene encoding Fms-like tyrosine kinase 1, providing biological support, as a placental isoform of this protein (sFlt-1) is implicated in the pathology of preeclampsia5. The association was strongest in offspring from pregnancies in which preeclampsia developed during late gestation and offspring birth weights exceeded the tenth centile. An additional nearby variant, rs12050029, associated with preeclampsia independently of rs4769613. The newly discovered locus may enhance understanding of the pathophysiology of preeclampsia and its subtypes.