The genomic complexity of profound copy number aberrations has prevented effective molecular stratification of ovarian cancers. Here, to decode this complexity, we derived copy number signatures from shallow whole-genome sequencing of 117 high-grade serous ovarian cancer (HGSOC) cases, which were validated on 527 independent cases. We show that HGSOC comprises a continuum of genomes shaped by multiple mutational processes that result in known patterns of genomic aberration. Copy number signature exposures at diagnosis predict both overall survival and the probability of platinum-resistant relapse. Measurement of signature exposures provides a rational framework to choose combination treatments that target multiple mutational processes.

Background New approaches are urgently required to address increasing rates of gonorrhoea and the emergence and global spread of antibiotic-resistant Neisseria gonorrhoeae. Whole genome sequencing (WGS) can be applied to study transmission and track resistance. Methods We performed WGS on 1659 isolates from Brighton, UK, and 217 additional isolates from other UK locations. We included WGS data (n=196) from the USA. Estimated mutation rates, plus diversity observed within patients across anatomical sites and probable transmission pairs, were used to fit a coalescent model to determine the number of single nucleotide polymorphisms (SNPs) expected between sequences related by direct/indirect transmission, depending on the time between samples. Findings We detected extensive local transmission. 281/1061(26%) Brighton cases were indistinguishable (0 SNPs) to ≥1 previous case(s), and 786(74%) had evidence of a sampled direct or indirect Brighton source. There was evidence of sustained transmission of some lineages. We observed multiple related samples across geographic locations. Of 1273 infections in Brighton, 225(18%) were linked to another case from elsewhere in the UK, and 115(9%) to a case from the USA. Four lineages initially identified in Brighton could be linked to 70 USA sequences, including 61 from a lineage carrying the mosaic penA XXXIV associated with reduced cefixime susceptibility. Interpretation We present a WGS-based tool for genomic contact tracing of N. gonorrhoeae and demonstrate local, national and international transmission. WGS can be applied across geographical boundaries to investigate gonorrhoea transmission and to track antimicrobial resistance. Funding Oxford NIHR Health Protection Research Unit and Biomedical Research Centre.

BackgroundTracking the spread of antimicrobial-resistant Neisseria gonorrhoeae is a major priority for national surveillance programmes.ObjectivesWe investigate whether WGS and simultaneous analysis of multiple resistance determinants can be used to predict antimicrobial susceptibilities to the level of MICs in N. gonorrhoeae.MethodsWGS was used to identify previously reported potential resistance determinants in 681 N. gonorrhoeae isolates, from England, the USA and Canada, with phenotypes for cefixime, penicillin, azithromycin, ciprofloxacin and tetracycline determined as part of national surveillance programmes. Multivariate linear regression models were used to identify genetic predictors of MIC. Model performance was assessed using leave-one-out cross-validation.ResultsOverall 1785/3380 (53%) MIC values were predicted to the nearest doubling dilution and 3147 (93%) within ±1 doubling dilution and 3314 (98%) within ±2 doubling dilutions. MIC prediction performance was similar across the five antimicrobials tested. Prediction models included the majority of previously reported resistance determinants. Applying EUCAST breakpoints to MIC predictions, the overall very major error (VME; phenotypically resistant, WGS-prediction susceptible) rate was 21/1577 (1.3%, 95% CI 0.8%–2.0%) and the major error (ME; phenotypically susceptible, WGS-prediction resistant) rate was 20/1186 (1.7%, 1.0%–2.6%). VME rates met regulatory thresholds for all antimicrobials except cefixime and ME rates for all antimicrobials except tetracycline. Country of testing was a strongly significant predictor of MIC for all five antimicrobials.ConclusionsWe demonstrate a WGS-based MIC prediction approach that allows reliable MIC prediction for five gonorrhoea antimicrobials. Our approach should allow reasonably precise prediction of MICs for a range of bacterial species.